From 2f06cbc16eb730ae94de59700acb34fa801de746 Mon Sep 17 00:00:00 2001
From: Michael Mayer <mayermichael79@gmail.com>
Date: Tue, 6 Feb 2024 21:56:59 +0100
Subject: [PATCH 01/66] Add Friedman's H-squared of pairwise interaction
 statistics

---
 sklearn/inspection/_friedmans_h.py | 167 +++++++++++++++++++++++++++++
 1 file changed, 167 insertions(+)
 create mode 100644 sklearn/inspection/_friedmans_h.py

diff --git a/sklearn/inspection/_friedmans_h.py b/sklearn/inspection/_friedmans_h.py
new file mode 100644
index 0000000000000..dad8e90a4f95a
--- /dev/null
+++ b/sklearn/inspection/_friedmans_h.py
@@ -0,0 +1,167 @@
+import itertools
+
+import numpy as np
+from sklearn.utils import _safe_indexing, _safe_assign, _get_column_indices
+from sklearn.ensemble._bagging import _generate_indices
+
+
+def _calculate_pd_over_data(estimator, X, feature_indices, sample_weight=None):
+    """Calculates partial dependence over the data distribution.
+    
+    It returns a 1D or 2D numpy array of the same length as X.
+    """
+
+    # Select grid columns and remove duplicates (will compensate below)
+    grid = _safe_indexing(X, feature_indices, axis=1)
+    grid, ix_reconstruct = np.unique(grid, return_inverse=True, axis=0)
+    n_grid = grid.shape[0]
+
+    # X is stacked n_grid times, and grid columns are replaced by replicated grid
+    n = X.shape[0]
+    X_stacked = _safe_indexing(X, np.tile(np.arange(n), n_grid), axis=0)
+    _safe_assign(
+        X_stacked, values = np.repeat(grid, n, axis=0), column_indexer=feature_indices
+    )
+
+    # Predict on stacked data
+    if hasattr(estimator, "predict_proba"):
+        preds = estimator.predict_proba(X_stacked)
+    else: 
+        preds = estimator.predict(X_stacked)
+    
+    # Predictions are averaged by grid value, mapped to original row order, and centered
+    averaged_predictions = np.array(
+        [np.average(Z, axis=0, weights=sample_weight) for Z in np.split(preds, n_grid)]
+    )
+    averaged_predictions = averaged_predictions[ix_reconstruct]
+    column_means = np.average(averaged_predictions, axis=0, weights=sample_weight)
+    return averaged_predictions - column_means
+
+
+def hstatistics(
+        model, 
+        X,
+        *,
+        features=None, 
+        n_max=500, 
+        random_state=None, 
+        sample_weight=None
+    ):
+    """Friedman and Popescu's H-statistics of pairwise interaction strength.
+
+    For each feature pair, Friedman and Popescu's H-squared statistic [FRI]_ of 
+    interaction is calculated. It equals the proportion of effect variability
+    of the two features unexplained by the main effects. Besides the "official"
+    H-squared statistic, also the unnormalized statistic is returned. Its root
+    is on the scale of the predictions and can directly compared between feature
+    pairs.
+
+    The complexity of the function is of O(n^2 p^2), where n is the number of 
+    data rows and p is the number of features considered. 
+    The size of n is automatically controlled via `n_max=500`, while it is 
+    your responsibility to pass only 2-5 *important* features or features of
+    special interest.
+
+    Parameters
+    ----------
+    estimator : object
+        An estimator that has already been :term:`fitted`.
+
+    X : ndarray or DataFrame, shape (n_observations, n_features)
+        Data for which :term:`estimator` is able to calculate predictions.
+
+    features : array-like of {int, str}, default=None
+        List of feature names or column indices used to calculate pairwise statistics.
+        The default, None, will use all column indices of X.
+    
+    n_max : int, default=500
+        The number of rows to draw without replacement from X (and `sample_weight`).
+
+    random_state : int, RandomState instance, default=None
+        Pseudo-random number generator used for subsampling via `n_max`.
+        See :term:`Glossary <random_state>`.
+
+    sample_weight : array-like of shape (n_samples,), default=None
+        Sample weights used in calculating partial dependences.
+
+    Returns
+    -------
+    result : A list with a tuple per feature pair. Each element contains the two
+       feature names/indices, the unnormalized H-squared statistic, and the usual
+       (normalized) H-squared statistic.
+
+    References
+    ----------
+    .. [FRI] :doi:`J. H. Friedman and B. E. Popescu, 
+            "Predictive Learning via Rule Ensembles", 
+            The Annals of Applied Statistics, 2(3), 916-954, 
+            2008. <10.1214/07-AOAS148>`
+
+    Examples
+    ----------
+
+    >>> from sklearn.ensemble import HistGradientBoostingRegressor
+    >>> from sklearn.inspection import permutation_importance
+    >>> from sklearn.datasets import load_diabetes
+
+    >>> X, y = load_diabetes(return_X_y=True)
+    >>> est = HistGradientBoostingRegressor().fit(X, y)
+
+    >>> # Get Friedman's H-squared for top three predictors
+    >>> imp = permutation_importance(est, X, y, n_repeats=10, random_state=0)
+    >>> top_3 = np.argsort(imp.importances_mean)[-3:]
+    >>> hstatistics(est, X=X, features=top_3, random_state=4)
+
+    >>> For features 3 and 2, ~4% of joint effect variability comes from interaction:
+    >>> [(3, 2, 50.42733968603663, 0.043352605995576346),
+    >>> (3, 8, 20.34764277579143, 0.015648706966531766),
+    >>> (2, 8, 56.778288912326026, 0.025823266523399196)]
+    
+    """
+    # Usually, the data is too large and we need subsampling
+    if X.shape[0] > n_max:
+        row_indices = _generate_indices(
+            random_state=random_state,
+            bootstrap=False,
+            n_population=X.shape[0],
+            n_samples=n_max,
+        )
+        X = _safe_indexing(X, row_indices, axis=0)
+        if sample_weight is not None:
+            sample_weight = _safe_indexing(sample_weight, row_indices, axis=0)
+    else:
+        X = X.copy()
+
+    if features is None:
+        features = feature_indices = np.arange(X.shape[1])
+    else:
+        feature_indices = np.asarray(
+            _get_column_indices(X, features), dtype="int32", order="C"
+        ).ravel()
+
+    # CALCULATIONS
+    pd_univariate = []
+    for ind in feature_indices:
+        pd_univariate.append(
+            _calculate_pd_over_data(
+                model, X=X, feature_indices=[ind], sample_weight=sample_weight
+            )
+        )
+    
+    stats = []
+    for i, j in itertools.combinations(range(len(feature_indices)), 2):
+        pd_bivariate = _calculate_pd_over_data(
+            model, 
+            X=X, 
+            feature_indices=feature_indices[[i, j]], 
+            sample_weight=sample_weight
+        )
+        numerator = np.average(
+            (pd_bivariate - pd_univariate[i] - pd_univariate[j])**2, 
+            axis=0, 
+            weights=sample_weight
+        )
+        denominator = np.average(pd_bivariate**2, axis=0, weights=sample_weight)
+        stats.append((features[i], features[j], numerator, numerator / denominator))
+
+    return stats
\ No newline at end of file

From 38f3d6c9f5cc726a04b384aefb5c76b548ff0599 Mon Sep 17 00:00:00 2001
From: Michael Mayer <mayermichael79@gmail.com>
Date: Tue, 6 Feb 2024 22:10:42 +0100
Subject: [PATCH 02/66] run black

---
 sklearn/inspection/_friedmans_h.py | 54 +++++++++++++-----------------
 1 file changed, 24 insertions(+), 30 deletions(-)

diff --git a/sklearn/inspection/_friedmans_h.py b/sklearn/inspection/_friedmans_h.py
index dad8e90a4f95a..1ccdce378ae0e 100644
--- a/sklearn/inspection/_friedmans_h.py
+++ b/sklearn/inspection/_friedmans_h.py
@@ -7,7 +7,7 @@
 
 def _calculate_pd_over_data(estimator, X, feature_indices, sample_weight=None):
     """Calculates partial dependence over the data distribution.
-    
+
     It returns a 1D or 2D numpy array of the same length as X.
     """
 
@@ -20,15 +20,15 @@ def _calculate_pd_over_data(estimator, X, feature_indices, sample_weight=None):
     n = X.shape[0]
     X_stacked = _safe_indexing(X, np.tile(np.arange(n), n_grid), axis=0)
     _safe_assign(
-        X_stacked, values = np.repeat(grid, n, axis=0), column_indexer=feature_indices
+        X_stacked, values=np.repeat(grid, n, axis=0), column_indexer=feature_indices
     )
 
     # Predict on stacked data
     if hasattr(estimator, "predict_proba"):
         preds = estimator.predict_proba(X_stacked)
-    else: 
+    else:
         preds = estimator.predict(X_stacked)
-    
+
     # Predictions are averaged by grid value, mapped to original row order, and centered
     averaged_predictions = np.array(
         [np.average(Z, axis=0, weights=sample_weight) for Z in np.split(preds, n_grid)]
@@ -39,26 +39,20 @@ def _calculate_pd_over_data(estimator, X, feature_indices, sample_weight=None):
 
 
 def hstatistics(
-        model, 
-        X,
-        *,
-        features=None, 
-        n_max=500, 
-        random_state=None, 
-        sample_weight=None
-    ):
+    model, X, *, features=None, n_max=500, random_state=None, sample_weight=None
+):
     """Friedman and Popescu's H-statistics of pairwise interaction strength.
 
-    For each feature pair, Friedman and Popescu's H-squared statistic [FRI]_ of 
+    For each feature pair, Friedman and Popescu's H-squared statistic [FRI]_ of
     interaction is calculated. It equals the proportion of effect variability
     of the two features unexplained by the main effects. Besides the "official"
     H-squared statistic, also the unnormalized statistic is returned. Its root
     is on the scale of the predictions and can directly compared between feature
     pairs.
 
-    The complexity of the function is of O(n^2 p^2), where n is the number of 
-    data rows and p is the number of features considered. 
-    The size of n is automatically controlled via `n_max=500`, while it is 
+    The complexity of the function is of O(n^2 p^2), where n is the number of
+    data rows and p is the number of features considered.
+    The size of n is automatically controlled via `n_max=500`, while it is
     your responsibility to pass only 2-5 *important* features or features of
     special interest.
 
@@ -73,7 +67,7 @@ def hstatistics(
     features : array-like of {int, str}, default=None
         List of feature names or column indices used to calculate pairwise statistics.
         The default, None, will use all column indices of X.
-    
+
     n_max : int, default=500
         The number of rows to draw without replacement from X (and `sample_weight`).
 
@@ -92,9 +86,9 @@ def hstatistics(
 
     References
     ----------
-    .. [FRI] :doi:`J. H. Friedman and B. E. Popescu, 
-            "Predictive Learning via Rule Ensembles", 
-            The Annals of Applied Statistics, 2(3), 916-954, 
+    .. [FRI] :doi:`J. H. Friedman and B. E. Popescu,
+            "Predictive Learning via Rule Ensembles",
+            The Annals of Applied Statistics, 2(3), 916-954,
             2008. <10.1214/07-AOAS148>`
 
     Examples
@@ -116,7 +110,7 @@ def hstatistics(
     >>> [(3, 2, 50.42733968603663, 0.043352605995576346),
     >>> (3, 8, 20.34764277579143, 0.015648706966531766),
     >>> (2, 8, 56.778288912326026, 0.025823266523399196)]
-    
+
     """
     # Usually, the data is too large and we need subsampling
     if X.shape[0] > n_max:
@@ -147,21 +141,21 @@ def hstatistics(
                 model, X=X, feature_indices=[ind], sample_weight=sample_weight
             )
         )
-    
+
     stats = []
     for i, j in itertools.combinations(range(len(feature_indices)), 2):
         pd_bivariate = _calculate_pd_over_data(
-            model, 
-            X=X, 
-            feature_indices=feature_indices[[i, j]], 
-            sample_weight=sample_weight
+            model,
+            X=X,
+            feature_indices=feature_indices[[i, j]],
+            sample_weight=sample_weight,
         )
         numerator = np.average(
-            (pd_bivariate - pd_univariate[i] - pd_univariate[j])**2, 
-            axis=0, 
-            weights=sample_weight
+            (pd_bivariate - pd_univariate[i] - pd_univariate[j]) ** 2,
+            axis=0,
+            weights=sample_weight,
         )
         denominator = np.average(pd_bivariate**2, axis=0, weights=sample_weight)
         stats.append((features[i], features[j], numerator, numerator / denominator))
 
-    return stats
\ No newline at end of file
+    return stats

From f173c5185e06bc3efb433b290751ed51f87b2a31 Mon Sep 17 00:00:00 2001
From: Michael Mayer <mayermichael79@gmail.com>
Date: Wed, 7 Feb 2024 22:08:24 +0100
Subject: [PATCH 03/66] np.unique() does not work well for non-standard values

---
 sklearn/inspection/_friedmans_h.py | 9 ++++++++-
 1 file changed, 8 insertions(+), 1 deletion(-)

diff --git a/sklearn/inspection/_friedmans_h.py b/sklearn/inspection/_friedmans_h.py
index 1ccdce378ae0e..d8090642ebe03 100644
--- a/sklearn/inspection/_friedmans_h.py
+++ b/sklearn/inspection/_friedmans_h.py
@@ -13,7 +13,14 @@ def _calculate_pd_over_data(estimator, X, feature_indices, sample_weight=None):
 
     # Select grid columns and remove duplicates (will compensate below)
     grid = _safe_indexing(X, feature_indices, axis=1)
-    grid, ix_reconstruct = np.unique(grid, return_inverse=True, axis=0)
+
+    # Unfortunately, the next line does not work in all cases. How to replace
+    # the next uncommented lines? We need unique grid rows and reconstruction index.
+    # grid, ix_reconstruct = np.unique(grid, return_inverse=True, axis=0)
+    _, ix, ix_reconstruct = np.unique(
+        [str(z) for z in grid.to_numpy()], return_index=True, return_inverse=True
+    )
+    grid = _safe_indexing(grid, ix, axis=0)
     n_grid = grid.shape[0]
 
     # X is stacked n_grid times, and grid columns are replaced by replicated grid

From 2e7b73141ee53d3e373650003a46c025d22c49d0 Mon Sep 17 00:00:00 2001
From: Michael Mayer <mayermichael79@gmail.com>
Date: Wed, 7 Feb 2024 22:36:30 +0100
Subject: [PATCH 04/66] Reorganize imports

---
 sklearn/inspection/_friedmans_h.py | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/sklearn/inspection/_friedmans_h.py b/sklearn/inspection/_friedmans_h.py
index d8090642ebe03..09ee76d80967a 100644
--- a/sklearn/inspection/_friedmans_h.py
+++ b/sklearn/inspection/_friedmans_h.py
@@ -1,8 +1,11 @@
+"""Friedman and Popescu's H-squared statistics"""
+
 import itertools
 
 import numpy as np
-from sklearn.utils import _safe_indexing, _safe_assign, _get_column_indices
-from sklearn.ensemble._bagging import _generate_indices
+
+from ..utils import _safe_indexing, _safe_assign, _get_column_indices
+from ..ensemble._bagging import _generate_indices
 
 
 def _calculate_pd_over_data(estimator, X, feature_indices, sample_weight=None):

From 64581e22dc46f08e9a861690e9d1b11a12689640 Mon Sep 17 00:00:00 2001
From: Michael Mayer <mayermichael79@gmail.com>
Date: Wed, 7 Feb 2024 22:41:13 +0100
Subject: [PATCH 05/66] run ruff

---
 sklearn/inspection/_friedmans_h.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/sklearn/inspection/_friedmans_h.py b/sklearn/inspection/_friedmans_h.py
index 09ee76d80967a..a9e4bfed612f6 100644
--- a/sklearn/inspection/_friedmans_h.py
+++ b/sklearn/inspection/_friedmans_h.py
@@ -4,8 +4,8 @@
 
 import numpy as np
 
-from ..utils import _safe_indexing, _safe_assign, _get_column_indices
 from ..ensemble._bagging import _generate_indices
+from ..utils import _get_column_indices, _safe_assign, _safe_indexing
 
 
 def _calculate_pd_over_data(estimator, X, feature_indices, sample_weight=None):

From 8e506378f369eac6e367f5000906f82de0e92d18 Mon Sep 17 00:00:00 2001
From: mayer79 <U118378@mobi.ch>
Date: Thu, 8 Feb 2024 09:19:59 +0100
Subject: [PATCH 06/66] check weights and is fitted

---
 sklearn/inspection/_friedmans_h.py | 13 ++++++++++---
 1 file changed, 10 insertions(+), 3 deletions(-)

diff --git a/sklearn/inspection/_friedmans_h.py b/sklearn/inspection/_friedmans_h.py
index a9e4bfed612f6..e157c3246fdbb 100644
--- a/sklearn/inspection/_friedmans_h.py
+++ b/sklearn/inspection/_friedmans_h.py
@@ -6,6 +6,7 @@
 
 from ..ensemble._bagging import _generate_indices
 from ..utils import _get_column_indices, _safe_assign, _safe_indexing
+from ..utils.validation import _check_sample_weight, check_is_fitted
 
 
 def _calculate_pd_over_data(estimator, X, feature_indices, sample_weight=None):
@@ -49,7 +50,7 @@ def _calculate_pd_over_data(estimator, X, feature_indices, sample_weight=None):
 
 
 def hstatistics(
-    model, X, *, features=None, n_max=500, random_state=None, sample_weight=None
+    estimator, X, *, features=None, n_max=500, random_state=None, sample_weight=None
 ):
     """Friedman and Popescu's H-statistics of pairwise interaction strength.
 
@@ -122,6 +123,11 @@ def hstatistics(
     >>> (2, 8, 56.778288912326026, 0.025823266523399196)]
 
     """
+    check_is_fitted(estimator)
+
+    if sample_weight is not None:
+        sample_weight = _check_sample_weight(sample_weight, X)
+
     # Usually, the data is too large and we need subsampling
     if X.shape[0] > n_max:
         row_indices = _generate_indices(
@@ -136,6 +142,7 @@ def hstatistics(
     else:
         X = X.copy()
 
+    # TODO: Improve logic, e.g., use column names if there are some
     if features is None:
         features = feature_indices = np.arange(X.shape[1])
     else:
@@ -148,14 +155,14 @@ def hstatistics(
     for ind in feature_indices:
         pd_univariate.append(
             _calculate_pd_over_data(
-                model, X=X, feature_indices=[ind], sample_weight=sample_weight
+                estimator, X=X, feature_indices=[ind], sample_weight=sample_weight
             )
         )
 
     stats = []
     for i, j in itertools.combinations(range(len(feature_indices)), 2):
         pd_bivariate = _calculate_pd_over_data(
-            model,
+            estimator,
             X=X,
             feature_indices=feature_indices[[i, j]],
             sample_weight=sample_weight,

From efa4071bb1f990c6c45ffdb0a31427cde99277db Mon Sep 17 00:00:00 2001
From: mayer79 <U118378@mobi.ch>
Date: Thu, 8 Feb 2024 09:41:58 +0100
Subject: [PATCH 07/66] Replace compression logic by try except

---
 sklearn/inspection/_friedmans_h.py | 17 +++++++++--------
 1 file changed, 9 insertions(+), 8 deletions(-)

diff --git a/sklearn/inspection/_friedmans_h.py b/sklearn/inspection/_friedmans_h.py
index e157c3246fdbb..7f646640b1fea 100644
--- a/sklearn/inspection/_friedmans_h.py
+++ b/sklearn/inspection/_friedmans_h.py
@@ -18,13 +18,13 @@ def _calculate_pd_over_data(estimator, X, feature_indices, sample_weight=None):
     # Select grid columns and remove duplicates (will compensate below)
     grid = _safe_indexing(X, feature_indices, axis=1)
 
-    # Unfortunately, the next line does not work in all cases. How to replace
-    # the next uncommented lines? We need unique grid rows and reconstruction index.
-    # grid, ix_reconstruct = np.unique(grid, return_inverse=True, axis=0)
-    _, ix, ix_reconstruct = np.unique(
-        [str(z) for z in grid.to_numpy()], return_index=True, return_inverse=True
-    )
-    grid = _safe_indexing(grid, ix, axis=0)
+    # Unfortunately, the next line does not work in all cases, especially not in
+    # the important case of discrete pandas Dataframes.
+    try:
+        compressed = True
+        grid, ix_reconstruct = np.unique(grid, return_inverse=True, axis=0)
+    except:
+        compressed = False
     n_grid = grid.shape[0]
 
     # X is stacked n_grid times, and grid columns are replaced by replicated grid
@@ -44,7 +44,8 @@ def _calculate_pd_over_data(estimator, X, feature_indices, sample_weight=None):
     averaged_predictions = np.array(
         [np.average(Z, axis=0, weights=sample_weight) for Z in np.split(preds, n_grid)]
     )
-    averaged_predictions = averaged_predictions[ix_reconstruct]
+    if compressed:
+        averaged_predictions = averaged_predictions[ix_reconstruct]
     column_means = np.average(averaged_predictions, axis=0, weights=sample_weight)
     return averaged_predictions - column_means
 

From f53b838fe2e9044670056aa78c9e8cc6a8896189 Mon Sep 17 00:00:00 2001
From: mayer79 <U118378@mobi.ch>
Date: Thu, 8 Feb 2024 10:26:50 +0100
Subject: [PATCH 08/66] Switch to Bunch output

---
 sklearn/inspection/_friedmans_h.py | 60 +++++++++++++++++++++---------
 1 file changed, 42 insertions(+), 18 deletions(-)

diff --git a/sklearn/inspection/_friedmans_h.py b/sklearn/inspection/_friedmans_h.py
index 7f646640b1fea..5ecd275d654d7 100644
--- a/sklearn/inspection/_friedmans_h.py
+++ b/sklearn/inspection/_friedmans_h.py
@@ -5,7 +5,7 @@
 import numpy as np
 
 from ..ensemble._bagging import _generate_indices
-from ..utils import _get_column_indices, _safe_assign, _safe_indexing
+from ..utils import _get_column_indices, _safe_assign, _safe_indexing, Bunch
 from ..utils.validation import _check_sample_weight, check_is_fitted
 
 
@@ -92,9 +92,21 @@ def hstatistics(
 
     Returns
     -------
-    result : A list with a tuple per feature pair. Each element contains the two
-       feature names/indices, the unnormalized H-squared statistic, and the usual
-       (normalized) H-squared statistic.
+    result : :class:`~sklearn.utils.Bunch`
+        Dictionary-like object, with the following attributes.
+
+        feature_pair : ndarray of shape (n_feature_pairs, )
+            Contains feature pair tuples in the same order as pairwise statistics.
+        numerator_pairwise : ndarray of shape (n_pairs, ) or (n_pairs, output_dim)
+            Numerator of pairwise H-squared statistic.
+            Useful to see which feature pair has strongest absolute interaction.
+            Take square-root to get values on the scale of the predictions.
+        denominator_pairwise : ndarray of shape (n_pairs, ) or (n_pairs, output_dim)
+            Denominator of pairwise H-squared statistic.
+        hsquared_pairwise : ndarray of shape (n_pairs, ) or (n_pairs, output_dim)
+            Pairwise H-squared statistic. Useful to see which feature pair has
+            strongest relative interation (relative with respect to joint effect).
+            Calculated as numerator_pairwise / denominator_pairwise.
 
     References
     ----------
@@ -104,8 +116,7 @@ def hstatistics(
             2008. <10.1214/07-AOAS148>`
 
     Examples
-    ----------
-
+    --------
     >>> from sklearn.ensemble import HistGradientBoostingRegressor
     >>> from sklearn.inspection import permutation_importance
     >>> from sklearn.datasets import load_diabetes
@@ -118,10 +129,12 @@ def hstatistics(
     >>> top_3 = np.argsort(imp.importances_mean)[-3:]
     >>> hstatistics(est, X=X, features=top_3, random_state=4)
 
-    >>> For features 3 and 2, ~4% of joint effect variability comes from interaction:
-    >>> [(3, 2, 50.42733968603663, 0.043352605995576346),
-    >>> (3, 8, 20.34764277579143, 0.015648706966531766),
-    >>> (2, 8, 56.778288912326026, 0.025823266523399196)]
+    >>> # For feature pair (3, 2), about 4% of joint effect variability comes from 
+    >>> # their interaction. Unnormalized statistics are highest for pair (2, 8):
+    >>> {'feature_pair': [(3, 2), (3, 8), (2, 8)],
+    >>> 'numerator_pairwise': array([50.42733969, 20.34764278, 56.77828891]),
+    >>> 'denominator_pairwise': array([1163.19050558, 1300.27629882, 2198.72605431]),
+    >>> 'hsquared_pairwise': array([0.04335261, 0.01564871, 0.02582327])}
 
     """
     check_is_fitted(estimator)
@@ -160,7 +173,11 @@ def hstatistics(
             )
         )
 
-    stats = []
+    hstats_results = Bunch()
+    hstats_results["feature_pair"] = list(itertools.combinations(features, 2))
+    num = []
+    denom = []
+
     for i, j in itertools.combinations(range(len(feature_indices)), 2):
         pd_bivariate = _calculate_pd_over_data(
             estimator,
@@ -168,12 +185,19 @@ def hstatistics(
             feature_indices=feature_indices[[i, j]],
             sample_weight=sample_weight,
         )
-        numerator = np.average(
-            (pd_bivariate - pd_univariate[i] - pd_univariate[j]) ** 2,
-            axis=0,
-            weights=sample_weight,
+        num.append(
+            np.average(
+                (pd_bivariate - pd_univariate[i] - pd_univariate[j]) ** 2,
+                axis=0,
+                weights=sample_weight,
+            )
         )
-        denominator = np.average(pd_bivariate**2, axis=0, weights=sample_weight)
-        stats.append((features[i], features[j], numerator, numerator / denominator))
+        denom.append(np.average(pd_bivariate**2, axis=0, weights=sample_weight))
+    
+    hstats_results["numerator_pairwise"] = np.array(num)
+    hstats_results["denominator_pairwise"] = np.array(denom)
+    hstats_results["hsquared_pairwise"] = (
+        hstats_results["numerator_pairwise"] / hstats_results["denominator_pairwise"]
+    )
 
-    return stats
+    return hstats_results

From 032e6615d33f38f2791e3074681ca5e26a74d5e0 Mon Sep 17 00:00:00 2001
From: mayer79 <U118378@mobi.ch>
Date: Thu, 8 Feb 2024 10:52:07 +0100
Subject: [PATCH 09/66] Clip small numerators

---
 sklearn/inspection/_friedmans_h.py | 18 ++++++++++++++++--
 1 file changed, 16 insertions(+), 2 deletions(-)

diff --git a/sklearn/inspection/_friedmans_h.py b/sklearn/inspection/_friedmans_h.py
index 5ecd275d654d7..abb9d2a2d9be1 100644
--- a/sklearn/inspection/_friedmans_h.py
+++ b/sklearn/inspection/_friedmans_h.py
@@ -51,7 +51,14 @@ def _calculate_pd_over_data(estimator, X, feature_indices, sample_weight=None):
 
 
 def hstatistics(
-    estimator, X, *, features=None, n_max=500, random_state=None, sample_weight=None
+    estimator,
+    X,
+    *,
+    features=None,
+    n_max=500,
+    random_state=None,
+    sample_weight=None,
+    eps=1e-10
 ):
     """Friedman and Popescu's H-statistics of pairwise interaction strength.
 
@@ -90,6 +97,9 @@ def hstatistics(
     sample_weight : array-like of shape (n_samples,), default=None
         Sample weights used in calculating partial dependences.
 
+    eps : float, default=1e-10
+        Threshold below which numerator values are set to 0.
+
     Returns
     -------
     result : :class:`~sklearn.utils.Bunch`
@@ -194,7 +204,11 @@ def hstatistics(
         )
         denom.append(np.average(pd_bivariate**2, axis=0, weights=sample_weight))
     
-    hstats_results["numerator_pairwise"] = np.array(num)
+    # Round small numerators to 0
+    num = np.array(num)
+    num[np.abs(num) < eps] = 0
+
+    hstats_results["numerator_pairwise"] = num
     hstats_results["denominator_pairwise"] = np.array(denom)
     hstats_results["hsquared_pairwise"] = (
         hstats_results["numerator_pairwise"] / hstats_results["denominator_pairwise"]

From 1335f0569654fc25eae697006a1d055f91f72703 Mon Sep 17 00:00:00 2001
From: Michael Mayer <mayermichael79@gmail.com>
Date: Fri, 9 Feb 2024 11:35:00 +0100
Subject: [PATCH 10/66] Apply suggestions from code review

Co-authored-by: Christian Lorentzen <lorentzen.ch@gmail.com>
---
 sklearn/inspection/_friedmans_h.py | 18 +++++++++---------
 1 file changed, 9 insertions(+), 9 deletions(-)

diff --git a/sklearn/inspection/_friedmans_h.py b/sklearn/inspection/_friedmans_h.py
index abb9d2a2d9be1..2e36bec9b8d54 100644
--- a/sklearn/inspection/_friedmans_h.py
+++ b/sklearn/inspection/_friedmans_h.py
@@ -50,7 +50,7 @@ def _calculate_pd_over_data(estimator, X, feature_indices, sample_weight=None):
     return averaged_predictions - column_means
 
 
-def hstatistics(
+def h_statistics(
     estimator,
     X,
     *,
@@ -58,7 +58,7 @@ def hstatistics(
     n_max=500,
     random_state=None,
     sample_weight=None,
-    eps=1e-10
+    eps=1e-10,
 ):
     """Friedman and Popescu's H-statistics of pairwise interaction strength.
 
@@ -69,10 +69,10 @@ def hstatistics(
     is on the scale of the predictions and can directly compared between feature
     pairs.
 
-    The complexity of the function is of O(n^2 p^2), where n is the number of
-    data rows and p is the number of features considered.
-    The size of n is automatically controlled via `n_max=500`, while it is
-    your responsibility to pass only 2-5 *important* features or features of
+    The complexity of the function is :math:`O(n^2 n^2)`, where :math:`n` is the number of
+    data rows and :math:`p` is the number of features considered.
+    The size of `n` is automatically controlled via `n_max=500`, while it is
+    the user's responsibility to pass only 2-5 *important* features or features of
     special interest.
 
     Parameters
@@ -105,8 +105,8 @@ def hstatistics(
     result : :class:`~sklearn.utils.Bunch`
         Dictionary-like object, with the following attributes.
 
-        feature_pair : ndarray of shape (n_feature_pairs, )
-            Contains feature pair tuples in the same order as pairwise statistics.
+        feature_pair : list of length n_feature_pairs
+            The list contains tuples of feature pairs (indices) in the same order as pairwise statistics.
         numerator_pairwise : ndarray of shape (n_pairs, ) or (n_pairs, output_dim)
             Numerator of pairwise H-squared statistic.
             Useful to see which feature pair has strongest absolute interaction.
@@ -171,7 +171,7 @@ def hstatistics(
         features = feature_indices = np.arange(X.shape[1])
     else:
         feature_indices = np.asarray(
-            _get_column_indices(X, features), dtype="int32", order="C"
+            _get_column_indices(X, features), dtype=np.int32, order="C"
         ).ravel()
 
     # CALCULATIONS

From a28e8e00878b5be1ea14eb5387a212b30874b077 Mon Sep 17 00:00:00 2001
From: Michael Mayer <mayermichael79@gmail.com>
Date: Fri, 9 Feb 2024 12:03:17 +0100
Subject: [PATCH 11/66] use sample_without_replacement, plus some docstring
 improvements

---
 sklearn/inspection/_friedmans_h.py | 38 +++++++++++++++---------------
 1 file changed, 19 insertions(+), 19 deletions(-)

diff --git a/sklearn/inspection/_friedmans_h.py b/sklearn/inspection/_friedmans_h.py
index 2e36bec9b8d54..4c09afa55c147 100644
--- a/sklearn/inspection/_friedmans_h.py
+++ b/sklearn/inspection/_friedmans_h.py
@@ -4,8 +4,8 @@
 
 import numpy as np
 
-from ..ensemble._bagging import _generate_indices
-from ..utils import _get_column_indices, _safe_assign, _safe_indexing, Bunch
+from ..utils import Bunch, _get_column_indices, _safe_assign, _safe_indexing
+from ..utils.random import sample_without_replacement
 from ..utils.validation import _check_sample_weight, check_is_fitted
 
 
@@ -69,11 +69,10 @@ def h_statistics(
     is on the scale of the predictions and can directly compared between feature
     pairs.
 
-    The complexity of the function is :math:`O(n^2 n^2)`, where :math:`n` is the number of
-    data rows and :math:`p` is the number of features considered.
+    The complexity of the function is :math:`O(n^2 n^2)`, where :math:`n` is
+    the number of observations and :math:`p` is the number of features considered.
     The size of `n` is automatically controlled via `n_max=500`, while it is
-    the user's responsibility to pass only 2-5 *important* features or features of
-    special interest.
+    the user's responsibility to select some *important* features.
 
     Parameters
     ----------
@@ -106,13 +105,17 @@ def h_statistics(
         Dictionary-like object, with the following attributes.
 
         feature_pair : list of length n_feature_pairs
-            The list contains tuples of feature pairs (indices) in the same order as pairwise statistics.
+            The list contains tuples of feature pairs (indices) in the same order
+            as pairwise statistics.
+
         numerator_pairwise : ndarray of shape (n_pairs, ) or (n_pairs, output_dim)
             Numerator of pairwise H-squared statistic.
             Useful to see which feature pair has strongest absolute interaction.
             Take square-root to get values on the scale of the predictions.
+
         denominator_pairwise : ndarray of shape (n_pairs, ) or (n_pairs, output_dim)
             Denominator of pairwise H-squared statistic.
+
         hsquared_pairwise : ndarray of shape (n_pairs, ) or (n_pairs, output_dim)
             Pairwise H-squared statistic. Useful to see which feature pair has
             strongest relative interation (relative with respect to joint effect).
@@ -137,14 +140,14 @@ def h_statistics(
     >>> # Get Friedman's H-squared for top three predictors
     >>> imp = permutation_importance(est, X, y, n_repeats=10, random_state=0)
     >>> top_3 = np.argsort(imp.importances_mean)[-3:]
-    >>> hstatistics(est, X=X, features=top_3, random_state=4)
+    >>> h_statistics(est, X=X, features=top_3, random_state=4)
 
-    >>> # For feature pair (3, 2), about 4% of joint effect variability comes from 
+    >>> # For feature pair (3, 2), about 4% of joint effect variability comes from
     >>> # their interaction. Unnormalized statistics are highest for pair (2, 8):
-    >>> {'feature_pair': [(3, 2), (3, 8), (2, 8)],
-    >>> 'numerator_pairwise': array([50.42733969, 20.34764278, 56.77828891]),
-    >>> 'denominator_pairwise': array([1163.19050558, 1300.27629882, 2198.72605431]),
-    >>> 'hsquared_pairwise': array([0.04335261, 0.01564871, 0.02582327])}
+    >>> # {'feature_pair': [(3, 2), (3, 8), (2, 8)],
+    >>> # 'numerator_pairwise': array([50.42733969, 20.34764278, 56.77828891]),
+    >>> # 'denominator_pairwise': array([1163.19050558, 1300.27629882, 2198.72605431]),
+    >>> # 'hsquared_pairwise': array([0.04335261, 0.01564871, 0.02582327])}
 
     """
     check_is_fitted(estimator)
@@ -154,11 +157,8 @@ def h_statistics(
 
     # Usually, the data is too large and we need subsampling
     if X.shape[0] > n_max:
-        row_indices = _generate_indices(
-            random_state=random_state,
-            bootstrap=False,
-            n_population=X.shape[0],
-            n_samples=n_max,
+        row_indices = sample_without_replacement(
+            n_population=X.shape[0], n_samples=n_max, random_state=random_state
         )
         X = _safe_indexing(X, row_indices, axis=0)
         if sample_weight is not None:
@@ -203,7 +203,7 @@ def h_statistics(
             )
         )
         denom.append(np.average(pd_bivariate**2, axis=0, weights=sample_weight))
-    
+
     # Round small numerators to 0
     num = np.array(num)
     num[np.abs(num) < eps] = 0

From 66f7bdd87dc8d9073ab109c82e596327b7d514fb Mon Sep 17 00:00:00 2001
From: Michael Mayer <mayermichael79@gmail.com>
Date: Fri, 9 Feb 2024 12:53:36 +0100
Subject: [PATCH 12/66] fix existing problems

---
 sklearn/inspection/_friedmans_h.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/sklearn/inspection/_friedmans_h.py b/sklearn/inspection/_friedmans_h.py
index 4c09afa55c147..2b0c750a2d5f0 100644
--- a/sklearn/inspection/_friedmans_h.py
+++ b/sklearn/inspection/_friedmans_h.py
@@ -23,7 +23,7 @@ def _calculate_pd_over_data(estimator, X, feature_indices, sample_weight=None):
     try:
         compressed = True
         grid, ix_reconstruct = np.unique(grid, return_inverse=True, axis=0)
-    except:
+    except TypeError:  # TODO Better solution
         compressed = False
     n_grid = grid.shape[0]
 
@@ -130,6 +130,7 @@ def h_statistics(
 
     Examples
     --------
+    >>> import numpy as np
     >>> from sklearn.ensemble import HistGradientBoostingRegressor
     >>> from sklearn.inspection import permutation_importance
     >>> from sklearn.datasets import load_diabetes

From 5beb941a3fd43647fd5299b75d854a51c76c07e1 Mon Sep 17 00:00:00 2001
From: Michael Mayer <mayermichael79@gmail.com>
Date: Fri, 9 Feb 2024 15:24:14 +0100
Subject: [PATCH 13/66] Rename things and fix imports

---
 sklearn/inspection/__init__.py                |  3 +-
 .../{_friedmans_h.py => _h_statistic.py}      | 40 +++++++++----------
 2 files changed, 22 insertions(+), 21 deletions(-)
 rename sklearn/inspection/{_friedmans_h.py => _h_statistic.py} (85%)

diff --git a/sklearn/inspection/__init__.py b/sklearn/inspection/__init__.py
index f8e08785e8358..1af20d6f22b25 100644
--- a/sklearn/inspection/__init__.py
+++ b/sklearn/inspection/__init__.py
@@ -1,12 +1,13 @@
 """The :mod:`sklearn.inspection` module includes tools for model inspection."""
 
-
+from ._h_statistic import h_statistic
 from ._partial_dependence import partial_dependence
 from ._permutation_importance import permutation_importance
 from ._plot.decision_boundary import DecisionBoundaryDisplay
 from ._plot.partial_dependence import PartialDependenceDisplay
 
 __all__ = [
+    "h_statistic",
     "partial_dependence",
     "permutation_importance",
     "PartialDependenceDisplay",
diff --git a/sklearn/inspection/_friedmans_h.py b/sklearn/inspection/_h_statistic.py
similarity index 85%
rename from sklearn/inspection/_friedmans_h.py
rename to sklearn/inspection/_h_statistic.py
index 2b0c750a2d5f0..f9af5acbefe67 100644
--- a/sklearn/inspection/_friedmans_h.py
+++ b/sklearn/inspection/_h_statistic.py
@@ -1,4 +1,4 @@
-"""Friedman and Popescu's H-squared statistics"""
+"""Friedman and Popescu's H-Statistic"""
 
 import itertools
 
@@ -50,7 +50,7 @@ def _calculate_pd_over_data(estimator, X, feature_indices, sample_weight=None):
     return averaged_predictions - column_means
 
 
-def h_statistics(
+def h_statistic(
     estimator,
     X,
     *,
@@ -60,14 +60,14 @@ def h_statistics(
     sample_weight=None,
     eps=1e-10,
 ):
-    """Friedman and Popescu's H-statistics of pairwise interaction strength.
+    """Friedman and Popescu's H-statistic of pairwise interaction strength.
 
-    For each feature pair, Friedman and Popescu's H-squared statistic [FRI]_ of
-    interaction is calculated. It equals the proportion of effect variability
-    of the two features unexplained by the main effects. Besides the "official"
-    H-squared statistic, also the unnormalized statistic is returned. Its root
-    is on the scale of the predictions and can directly compared between feature
-    pairs.
+    For each feature pair, Friedman and Popescu's H-statistic of
+    interaction strength [FRI]_ is calculated. It equals the proportion of
+    effect variability of the two features unexplained by their main effects.
+    Besides the (normalized) H-squared statistic, also the unnormalized statistic
+    is returned. Its root is on the scale of the predictions, and can directly
+    be compared between feature pairs.
 
     The complexity of the function is :math:`O(n^2 n^2)`, where :math:`n` is
     the number of observations and :math:`p` is the number of features considered.
@@ -106,7 +106,7 @@ def h_statistics(
 
         feature_pair : list of length n_feature_pairs
             The list contains tuples of feature pairs (indices) in the same order
-            as pairwise statistics.
+            as all pairwise statistics.
 
         numerator_pairwise : ndarray of shape (n_pairs, ) or (n_pairs, output_dim)
             Numerator of pairwise H-squared statistic.
@@ -132,7 +132,7 @@ def h_statistics(
     --------
     >>> import numpy as np
     >>> from sklearn.ensemble import HistGradientBoostingRegressor
-    >>> from sklearn.inspection import permutation_importance
+    >>> from sklearn.inspection import permutation_importance, h_statistic
     >>> from sklearn.datasets import load_diabetes
 
     >>> X, y = load_diabetes(return_X_y=True)
@@ -141,14 +141,14 @@ def h_statistics(
     >>> # Get Friedman's H-squared for top three predictors
     >>> imp = permutation_importance(est, X, y, n_repeats=10, random_state=0)
     >>> top_3 = np.argsort(imp.importances_mean)[-3:]
-    >>> h_statistics(est, X=X, features=top_3, random_state=4)
+    >>> h_statistic(est, X=X, features=top_3, random_state=4)
 
     >>> # For feature pair (3, 2), about 4% of joint effect variability comes from
     >>> # their interaction. Unnormalized statistics are highest for pair (2, 8):
     >>> # {'feature_pair': [(3, 2), (3, 8), (2, 8)],
     >>> # 'numerator_pairwise': array([50.42733969, 20.34764278, 56.77828891]),
     >>> # 'denominator_pairwise': array([1163.19050558, 1300.27629882, 2198.72605431]),
-    >>> # 'hsquared_pairwise': array([0.04335261, 0.01564871, 0.02582327])}
+    >>> # 'h_squared_pairwise': array([0.04335261, 0.01564871, 0.02582327])}
 
     """
     check_is_fitted(estimator)
@@ -184,8 +184,8 @@ def h_statistics(
             )
         )
 
-    hstats_results = Bunch()
-    hstats_results["feature_pair"] = list(itertools.combinations(features, 2))
+    hstat_results = Bunch()
+    hstat_results["feature_pair"] = list(itertools.combinations(features, 2))
     num = []
     denom = []
 
@@ -209,10 +209,10 @@ def h_statistics(
     num = np.array(num)
     num[np.abs(num) < eps] = 0
 
-    hstats_results["numerator_pairwise"] = num
-    hstats_results["denominator_pairwise"] = np.array(denom)
-    hstats_results["hsquared_pairwise"] = (
-        hstats_results["numerator_pairwise"] / hstats_results["denominator_pairwise"]
+    hstat_results["numerator_pairwise"] = num
+    hstat_results["denominator_pairwise"] = np.array(denom)
+    hstat_results["h_squared_pairwise"] = (
+        hstat_results["numerator_pairwise"] / hstat_results["denominator_pairwise"]
     )
 
-    return hstats_results
+    return hstat_results

From 9823fe547e61a7ffc5befe97409b946d5aab107c Mon Sep 17 00:00:00 2001
From: Michael Mayer <mayermichael79@gmail.com>
Date: Sat, 10 Feb 2024 11:16:58 +0100
Subject: [PATCH 14/66] More compact output organization, faster example

---
 sklearn/inspection/_h_statistic.py | 54 ++++++++++++++----------------
 1 file changed, 26 insertions(+), 28 deletions(-)

diff --git a/sklearn/inspection/_h_statistic.py b/sklearn/inspection/_h_statistic.py
index f9af5acbefe67..b719213874c98 100644
--- a/sklearn/inspection/_h_statistic.py
+++ b/sklearn/inspection/_h_statistic.py
@@ -134,21 +134,23 @@ def h_statistic(
     >>> from sklearn.ensemble import HistGradientBoostingRegressor
     >>> from sklearn.inspection import permutation_importance, h_statistic
     >>> from sklearn.datasets import load_diabetes
-
+    >>>
     >>> X, y = load_diabetes(return_X_y=True)
-    >>> est = HistGradientBoostingRegressor().fit(X, y)
-
-    >>> # Get Friedman's H-squared for top three predictors
-    >>> imp = permutation_importance(est, X, y, n_repeats=10, random_state=0)
-    >>> top_3 = np.argsort(imp.importances_mean)[-3:]
-    >>> h_statistic(est, X=X, features=top_3, random_state=4)
-
-    >>> # For feature pair (3, 2), about 4% of joint effect variability comes from
-    >>> # their interaction. Unnormalized statistics are highest for pair (2, 8):
-    >>> # {'feature_pair': [(3, 2), (3, 8), (2, 8)],
-    >>> # 'numerator_pairwise': array([50.42733969, 20.34764278, 56.77828891]),
-    >>> # 'denominator_pairwise': array([1163.19050558, 1300.27629882, 2198.72605431]),
-    >>> # 'h_squared_pairwise': array([0.04335261, 0.01564871, 0.02582327])}
+    >>> est = HistGradientBoostingRegressor(max_iter=5, max_depth=2).fit(X, y)
+    >>>
+    >>> # Get Friedman's H-squared for top m=3 predictors
+    >>> m = 3
+    >>> imp = permutation_importance(est, X, y, random_state=0)
+    >>> top_m = np.argsort(imp.importances_mean)[-m:]
+    >>> h_statistic(est, X=X, features=top_m, random_state=4)
+
+    >>> # For features (8, 2), 3.4% of the joint effect variability comes from
+    >>> # their interaction. These two features also have strongest absolute
+    >>> # interaction, see "numerator_pairwise":
+    >>> # {'feature_pair': [(3, 8), (3, 2), (8, 2)],
+    >>> # 'numerator_pairwise': array([ 1.2955532 ,  1.2419687 , 11.13358385]),
+    >>> # 'denominator_pairwise': array([131.39690331, 133.96210997, 323.6576595 ]),
+    >>> # 'h_squared_pairwise': array([0.00985985, 0.00927104, 0.03439926])}
 
     """
     check_is_fitted(estimator)
@@ -172,7 +174,7 @@ def h_statistic(
         features = feature_indices = np.arange(X.shape[1])
     else:
         feature_indices = np.asarray(
-            _get_column_indices(X, features), dtype=np.int32, order="C"
+            _get_column_indices(X, features), dtype=np.intp, order="C"
         ).ravel()
 
     # CALCULATIONS
@@ -184,10 +186,7 @@ def h_statistic(
             )
         )
 
-    hstat_results = Bunch()
-    hstat_results["feature_pair"] = list(itertools.combinations(features, 2))
-    num = []
-    denom = []
+    num, denom = [], []
 
     for i, j in itertools.combinations(range(len(feature_indices)), 2):
         pd_bivariate = _calculate_pd_over_data(
@@ -205,14 +204,13 @@ def h_statistic(
         )
         denom.append(np.average(pd_bivariate**2, axis=0, weights=sample_weight))
 
-    # Round small numerators to 0
     num = np.array(num)
-    num[np.abs(num) < eps] = 0
-
-    hstat_results["numerator_pairwise"] = num
-    hstat_results["denominator_pairwise"] = np.array(denom)
-    hstat_results["h_squared_pairwise"] = (
-        hstat_results["numerator_pairwise"] / hstat_results["denominator_pairwise"]
+    num[np.abs(num) < eps] = 0  # Round small numerators to 0
+    denom = np.array(denom)
+
+    return Bunch(
+        feature_pair=list(itertools.combinations(features, 2)),
+        numerator_pairwise=num,
+        denominator_pairwise=denom,
+        h_squared_pairwise=num / denom,
     )
-
-    return hstat_results

From 5aebb4c435c166ca881a3b651f88d44d5c90a0a1 Mon Sep 17 00:00:00 2001
From: Michael Mayer <mayermichael79@gmail.com>
Date: Sat, 10 Feb 2024 12:03:54 +0100
Subject: [PATCH 15/66] Add formula to docstring

---
 sklearn/inspection/_h_statistic.py | 33 ++++++++++++++++++++----------
 1 file changed, 22 insertions(+), 11 deletions(-)

diff --git a/sklearn/inspection/_h_statistic.py b/sklearn/inspection/_h_statistic.py
index b719213874c98..b32814a86e41a 100644
--- a/sklearn/inspection/_h_statistic.py
+++ b/sklearn/inspection/_h_statistic.py
@@ -62,17 +62,28 @@ def h_statistic(
 ):
     """Friedman and Popescu's H-statistic of pairwise interaction strength.
 
-    For each feature pair, Friedman and Popescu's H-statistic of
-    interaction strength [FRI]_ is calculated. It equals the proportion of
-    effect variability of the two features unexplained by their main effects.
-    Besides the (normalized) H-squared statistic, also the unnormalized statistic
-    is returned. Its root is on the scale of the predictions, and can directly
-    be compared between feature pairs.
-
-    The complexity of the function is :math:`O(n^2 n^2)`, where :math:`n` is
-    the number of observations and :math:`p` is the number of features considered.
-    The size of `n` is automatically controlled via `n_max=500`, while it is
-    the user's responsibility to select some *important* features.
+    Calculates Friedman and Popescu's H-statistic of interaction strength
+    for each feature pair j, k, see [FRI]_. The statistic is defined as::
+
+        H_jk^2 = Numerator_jk / Denominator_jk, where
+
+        - Numerator_jk = 1/n * sum(PD_{jk}(x_ij, x_ik) - PD_j(x_ij) - PD_k(x_ik)^2,
+        - Denominator_jk = 1/n * sum(PD_{jk}(x_ij, x_ik)^2),
+        - PD_j and PD_jk are the one- and two-dimensional partial dependence
+          functions centered to mean 0,
+        - and the sums run over 1 <= i <= n, where n is the sample size.
+
+    It equals the proportion of effect variability between two features that cannot
+    be explained by their main effects. When there is no interaction, the value is
+    precisely 0. The numerator (or its square root) provides an absolute measure
+    of interaction strength, enabling direct comparison across feature pairs.
+
+    The computational complexity of :term:`h_statistic()` is :math:`O(p^2 n^2)`,
+    where :math:`p` denotes the number of features considered. The size of `n` is
+    automatically controlled via `n_max=500`, while it is the user's responsibility
+    to select only a subset of *important* features. It is crucial to focus on important
+    features because for weak predictors, the denominator might be small, and
+    even a weak interaction could result in a high Friedman's H, sometimes exceeding 1.
 
     Parameters
     ----------

From 36ed7a8cbc3973bf28ccd70a7e80b2408c960286 Mon Sep 17 00:00:00 2001
From: Michael Mayer <mayermichael79@gmail.com>
Date: Sat, 10 Feb 2024 22:14:22 +0100
Subject: [PATCH 16/66] Add preliminary unit tests

---
 sklearn/inspection/tests/test_h_statistic.py | 132 +++++++++++++++++++
 1 file changed, 132 insertions(+)
 create mode 100644 sklearn/inspection/tests/test_h_statistic.py

diff --git a/sklearn/inspection/tests/test_h_statistic.py b/sklearn/inspection/tests/test_h_statistic.py
new file mode 100644
index 0000000000000..0ee8837e2afb1
--- /dev/null
+++ b/sklearn/inspection/tests/test_h_statistic.py
@@ -0,0 +1,132 @@
+import numpy as np
+import pytest
+from numpy.testing import assert_allclose
+
+from sklearn.base import clone
+from sklearn.datasets import make_classification, make_regression
+from sklearn.ensemble import (
+    HistGradientBoostingRegressor,
+    RandomForestClassifier,
+    RandomForestRegressor,
+)
+from sklearn.inspection import h_statistic
+from sklearn.linear_model import LinearRegression
+
+
+@pytest.mark.parametrize(
+    "est",
+    (
+        LinearRegression(),
+        HistGradientBoostingRegressor(random_state=0, max_depth=1, max_iter=20),
+    ),
+)
+@pytest.mark.parametrize("features", [None, [0, 1, 2]])
+@pytest.mark.parametrize("n_max", [500, 50])
+@pytest.mark.parametrize("sample_weight", [None, "ones"])
+def test_h_statistic_additive_regression(est, features, n_max, sample_weight):
+    # Checks that additive regressions get statistics of 0.
+    M_FEAT = 4
+    N = 200
+    X, y = make_regression(n_samples=N, n_features=M_FEAT, random_state=0)
+    w = np.ones(N) if sample_weight == "ones" else None
+
+    model = est.fit(X, y, sample_weight=w)
+    result = h_statistic(
+        model, X, features=features, random_state=1, n_max=n_max, sample_weight=w
+    )
+
+    m = M_FEAT if features is None else len(features)
+    expected_length = m * (m - 1) / 2
+
+    assert result.h_squared_pairwise.shape == (expected_length,)
+    assert_allclose(result.h_squared_pairwise, 0)
+
+
+def test_h_statistic_additive_classification():
+    # Checks that additive classification gets statistics of 0. The presence
+    # of link functions (especially for GradientBoosting with more than 2 classes)
+    # would make further tests tricky.
+    M_FEAT = 4
+    N_CLASSES = 4
+    X, y = make_classification(
+        n_samples=200,
+        n_features=M_FEAT,
+        n_informative=M_FEAT,
+        n_redundant=0,
+        n_classes=N_CLASSES,
+        random_state=0,
+    )
+    model = RandomForestClassifier(n_estimators=20, random_state=0, max_depth=1)
+    model.fit(X, y)
+    result = h_statistic(model, X, random_state=1)
+
+    expected_length = M_FEAT * (M_FEAT - 1) / 2
+
+    assert result.h_squared_pairwise.shape == (expected_length, N_CLASSES)
+    print(result)
+    assert_allclose(result.h_squared_pairwise, 0)
+
+
+@pytest.mark.parametrize(
+    "est",
+    (
+        RandomForestRegressor(random_state=0, max_depth=4, n_estimators=20),
+        HistGradientBoostingRegressor(random_state=0, max_iter=20),
+    ),
+)
+@pytest.mark.parametrize("features", [None, [0, 1, 2]])
+@pytest.mark.parametrize("n_max", [500, 50])
+@pytest.mark.parametrize("sample_weight", [None, "ones"])
+def test_h_statistic_regression(est, features, n_max, sample_weight):
+    # Tests that models with interactions will produce (some) positive values.
+    M_FEAT = 4
+    N = 200
+    X, y = make_regression(n_samples=N, n_features=M_FEAT, random_state=0)
+    w = np.ones(N) if sample_weight == "ones" else None
+
+    model = est.fit(X, y, sample_weight=w)
+    result = h_statistic(
+        model, X, features=features, random_state=1, n_max=n_max, sample_weight=w
+    )
+
+    assert any(result.h_squared_pairwise > 1e-5)
+
+
+@pytest.mark.parametrize("sample_weight", [None, "ones"])
+@pytest.mark.parametrize("n_max", [500, 50])
+def test_h_statistic_equivalence_array_dataframe(n_max, sample_weight):
+    # Checks that index operations give the same for numpy arrays or dataframes.
+    pd = pytest.importorskip("pandas")
+
+    N = 200
+    X, y = make_regression(n_samples=N, n_features=4, random_state=0)
+    X_df = pd.DataFrame(X)
+    w = np.ones(N) if sample_weight == "ones" else None
+
+    # Numpy
+    model_np = RandomForestRegressor(random_state=0, max_depth=4, n_estimators=20)
+    model_np.fit(X, y, sample_weight=w)
+    result_np = h_statistic(model_np, X, random_state=1, n_max=n_max, sample_weight=w)
+
+    # Pandas
+    model_pd = clone(model_np)
+    model_pd.fit(X_df, y, sample_weight=w)
+    result_pd = h_statistic(
+        model_pd, X_df, random_state=1, n_max=n_max, sample_weight=w
+    )
+
+    assert_allclose(result_np.h_squared_pairwise, result_pd.h_squared_pairwise)
+
+
+def test_h_statistic_reflect_interaction_constraints():
+    # Checks that interaction constraints are reflected.
+    X, y = make_regression(n_samples=200, n_features=4, random_state=0)
+
+    model = HistGradientBoostingRegressor(
+        random_state=0, max_iter=20, interaction_cst=[[0, 1], [2], [3]]
+    )
+    model.fit(X, y)
+    result = h_statistic(model, X, random_state=1)
+
+    assert result.h_squared_pairwise[0] > 1e-5
+    assert_allclose(result.h_squared_pairwise[1:], 0)

From 27e35407c2e65e0284df9a557b1e096f00198637 Mon Sep 17 00:00:00 2001
From: Michael Mayer <mayermichael79@gmail.com>
Date: Sun, 11 Feb 2024 18:29:13 +0100
Subject: [PATCH 17/66] Compare against two R packages

---
 sklearn/inspection/tests/test_h_statistic.py | 41 +++++++++++++++++++-
 1 file changed, 40 insertions(+), 1 deletion(-)

diff --git a/sklearn/inspection/tests/test_h_statistic.py b/sklearn/inspection/tests/test_h_statistic.py
index 0ee8837e2afb1..5d8a5b74631ca 100644
--- a/sklearn/inspection/tests/test_h_statistic.py
+++ b/sklearn/inspection/tests/test_h_statistic.py
@@ -2,7 +2,7 @@
 import pytest
 from numpy.testing import assert_allclose
 
-from sklearn.base import clone
+from sklearn.base import RegressorMixin, clone
 from sklearn.datasets import make_classification, make_regression
 from sklearn.ensemble import (
     HistGradientBoostingRegressor,
@@ -130,3 +130,42 @@ def test_h_statistic_reflect_interaction_constraints():
 
     assert result.h_squared_pairwise[0] > 1e-5
     assert_allclose(result.h_squared_pairwise[1:], 0)
+
+
+def test_h_statistic_matches_other_implementations():
+    # Checks for one specific example that the result matches the one from two
+    # independent R packages ({iml} of Christoph Molnar, {hstats} of Michael Mayer).
+    # The latter also allows for sample weights.
+    # X <- data.frame(x1 = c(1, 1, 1, 2), x2 = c(0, 0, 2, 1), x3 = c(2, 2, 1, 4))
+    # pred_fun <- function(model, newdata)
+    #   newdata[, 1] + newdata[, 2] * sqrt(newdata[, 3])
+    #
+    # library(iml)     # 0.11.1
+    # mod <- Predictor$new(data = X, predict.function = pred_fun)
+    # Interaction$new(mod, grid.size = 5, feature = "x3")$results$.interaction^2
+    # -> x2:x3 -> 0.078125
+    #
+    # library(hstats)  # 1.1.2
+    # h2_pairwise(hstats(X = X, pred_fun = pred_fun))           # x2:x3 -> 0.078125
+    # h2_pairwise(hstats(X = X, pred_fun = pred_fun, w = 0:3))  # weights -> 0.1010093
+    class Deterministic(RegressorMixin):
+        def __init__(self, pred_fun):
+            self._pred_fun = pred_fun
+            self.is_fitted_ = True
+
+        def fit(self, *args, **kwargs):
+            return self
+
+        def predict(self, X):
+            return self._pred_fun(X)
+
+    X = np.array([[1, 1, 1, 2], [0, 0, 2, 1], [2, 2, 1, 4]]).T
+
+    # Model with interaction in last feature pair
+    model = Deterministic(lambda X: X[:, 0] + X[:, 1] * np.sqrt(X[:, 2]))
+
+    assert_allclose(h_statistic(model, X=X).h_squared_pairwise[2], 0.078125)
+    assert_allclose(
+        h_statistic(model, X=X, sample_weight=range(4)).h_squared_pairwise[2],
+        0.1010093,
+    )

From e5f6e53e6cb9bc0278ba927cab60ab4855ebae94 Mon Sep 17 00:00:00 2001
From: Michael Mayer <mayermichael79@gmail.com>
Date: Mon, 12 Feb 2024 17:42:26 +0100
Subject: [PATCH 18/66] Split calculate_pd_over_data into two plus some
 optimizations

---
 sklearn/inspection/_h_statistic.py | 101 +++++++++++++++++++----------
 1 file changed, 67 insertions(+), 34 deletions(-)

diff --git a/sklearn/inspection/_h_statistic.py b/sklearn/inspection/_h_statistic.py
index b32814a86e41a..dbec153a71e68 100644
--- a/sklearn/inspection/_h_statistic.py
+++ b/sklearn/inspection/_h_statistic.py
@@ -4,50 +4,82 @@
 
 import numpy as np
 
+from ..base import is_classifier, is_regressor
 from ..utils import Bunch, _get_column_indices, _safe_assign, _safe_indexing
 from ..utils.random import sample_without_replacement
 from ..utils.validation import _check_sample_weight, check_is_fitted
 
 
+def _calculate_pd_brute_fast(estimator, X, feature_indices, grid, sample_weight=None):
+    """Fast version of _calculate_partial_dependence_brute()
+
+    Returns np.array of size (n_grid, ) or (n_ngrid, output_dimension).
+    """
+
+    if is_regressor(estimator):
+        pred_fun = estimator.predict
+    elif is_classifier(estimator) and hasattr(estimator, "predict_proba"):
+        pred_fun = estimator.predict_proba
+    else:
+        raise ValueError("The estimator has no predict or predict_proba method.")
+
+    # X is stacked n_grid times, and grid columns are replaced by replicated grid
+    n = X.shape[0]
+    n_grid = grid.shape[0]
+    X_eval = X.copy()
+
+    X_stacked = _safe_indexing(X_eval, np.tile(np.arange(n), n_grid), axis=0)
+    grid_stacked = _safe_indexing(grid, np.repeat(np.arange(n_grid), n), axis=0)
+    _safe_assign(X_stacked, values=grid_stacked, column_indexer=feature_indices)
+
+    # Predict on stacked data. Pick positive class probs for binary classification
+    preds = pred_fun(X_stacked)
+    if is_classifier(estimator) and preds.shape[1] == 2:
+        preds = preds[:, 1]
+
+    # Partial dependences are averages per grid block
+    pd_values = [
+        np.average(Z, axis=0, weights=sample_weight) for Z in np.split(preds, n_grid)
+    ]
+
+    return np.array(pd_values)
+
+
 def _calculate_pd_over_data(estimator, X, feature_indices, sample_weight=None):
-    """Calculates partial dependence over the data distribution.
+    """Calculates centered partial dependence over the data distribution.
 
-    It returns a 1D or 2D numpy array of the same length as X.
+    It returns a numpy array of size (n, ) or (n, output_dimension).
     """
 
     # Select grid columns and remove duplicates (will compensate below)
     grid = _safe_indexing(X, feature_indices, axis=1)
 
-    # Unfortunately, the next line does not work in all cases, especially not in
-    # the important case of discrete pandas Dataframes.
+    # np.unique() fails for mixed type and sparse objects
     try:
-        compressed = True
-        grid, ix_reconstruct = np.unique(grid, return_inverse=True, axis=0)
-    except TypeError:  # TODO Better solution
-        compressed = False
-    n_grid = grid.shape[0]
-
-    # X is stacked n_grid times, and grid columns are replaced by replicated grid
-    n = X.shape[0]
-    X_stacked = _safe_indexing(X, np.tile(np.arange(n), n_grid), axis=0)
-    _safe_assign(
-        X_stacked, values=np.repeat(grid, n, axis=0), column_indexer=feature_indices
+        ax = 0 if grid.shape[1] > 1 else None  # np.unique works better in 1 dim
+        _, ix, ix_reconstruct = np.unique(
+            grid, return_index=True, return_inverse=True, axis=ax
+        )
+        grid = _safe_indexing(grid, ix, axis=0)
+        compressed_grid = True
+    except (TypeError, np.AxisError):
+        compressed_grid = False
+
+    pd_values = _calculate_pd_brute_fast(
+        estimator,
+        X=X,
+        feature_indices=feature_indices,
+        grid=grid,
+        sample_weight=sample_weight,
     )
 
-    # Predict on stacked data
-    if hasattr(estimator, "predict_proba"):
-        preds = estimator.predict_proba(X_stacked)
-    else:
-        preds = estimator.predict(X_stacked)
+    if compressed_grid:
+        pd_values = pd_values[ix_reconstruct]
 
-    # Predictions are averaged by grid value, mapped to original row order, and centered
-    averaged_predictions = np.array(
-        [np.average(Z, axis=0, weights=sample_weight) for Z in np.split(preds, n_grid)]
-    )
-    if compressed:
-        averaged_predictions = averaged_predictions[ix_reconstruct]
-    column_means = np.average(averaged_predictions, axis=0, weights=sample_weight)
-    return averaged_predictions - column_means
+    # H-statistics are based on *centered* partial dependences
+    column_means = np.average(pd_values, axis=0, weights=sample_weight)
+
+    return pd_values - column_means
 
 
 def h_statistic(
@@ -180,7 +212,6 @@ def h_statistic(
     else:
         X = X.copy()
 
-    # TODO: Improve logic, e.g., use column names if there are some
     if features is None:
         features = feature_indices = np.arange(X.shape[1])
     else:
@@ -197,18 +228,19 @@ def h_statistic(
             )
         )
 
-    num, denom = [], []
+    num = []
+    denom = []
 
-    for i, j in itertools.combinations(range(len(feature_indices)), 2):
+    for j, k in itertools.combinations(range(len(feature_indices)), 2):
         pd_bivariate = _calculate_pd_over_data(
             estimator,
             X=X,
-            feature_indices=feature_indices[[i, j]],
+            feature_indices=feature_indices[[j, k]],
             sample_weight=sample_weight,
         )
         num.append(
             np.average(
-                (pd_bivariate - pd_univariate[i] - pd_univariate[j]) ** 2,
+                (pd_bivariate - pd_univariate[j] - pd_univariate[k]) ** 2,
                 axis=0,
                 weights=sample_weight,
             )
@@ -218,10 +250,11 @@ def h_statistic(
     num = np.array(num)
     num[np.abs(num) < eps] = 0  # Round small numerators to 0
     denom = np.array(denom)
+    h2_stat = np.divide(num, denom, out=np.zeros_like(num), where=denom > 0)
 
     return Bunch(
         feature_pair=list(itertools.combinations(features, 2)),
+        h_squared_pairwise=h2_stat,
         numerator_pairwise=num,
         denominator_pairwise=denom,
-        h_squared_pairwise=num / denom,
     )

From a2b659d3ebd0b5e1b86847b6b9241bb130b725c7 Mon Sep 17 00:00:00 2001
From: Michael Mayer <mayermichael79@gmail.com>
Date: Mon, 12 Feb 2024 19:54:46 +0100
Subject: [PATCH 19/66] Fix typos in docstring

---
 sklearn/inspection/_h_statistic.py | 16 ++++++++--------
 1 file changed, 8 insertions(+), 8 deletions(-)

diff --git a/sklearn/inspection/_h_statistic.py b/sklearn/inspection/_h_statistic.py
index dbec153a71e68..4cbd8abf2c0a6 100644
--- a/sklearn/inspection/_h_statistic.py
+++ b/sklearn/inspection/_h_statistic.py
@@ -110,7 +110,7 @@ def h_statistic(
     precisely 0. The numerator (or its square root) provides an absolute measure
     of interaction strength, enabling direct comparison across feature pairs.
 
-    The computational complexity of :term:`h_statistic()` is :math:`O(p^2 n^2)`,
+    The computational complexity of the function is :math:`O(p^2 n^2)`,
     where :math:`p` denotes the number of features considered. The size of `n` is
     automatically controlled via `n_max=500`, while it is the user's responsibility
     to select only a subset of *important* features. It is crucial to focus on important
@@ -137,7 +137,7 @@ def h_statistic(
         See :term:`Glossary <random_state>`.
 
     sample_weight : array-like of shape (n_samples,), default=None
-        Sample weights used in calculating partial dependences.
+        Sample weights used in calculating partial dependencies.
 
     eps : float, default=1e-10
         Threshold below which numerator values are set to 0.
@@ -151,18 +151,18 @@ def h_statistic(
             The list contains tuples of feature pairs (indices) in the same order
             as all pairwise statistics.
 
+        h_squared_pairwise : ndarray of shape (n_pairs, ) or (n_pairs, output_dim)
+            Pairwise H-squared statistic. Useful to see which feature pair has
+            strongest relative interation (relative with respect to joint effect).
+            Calculated as numerator_pairwise / denominator_pairwise.
+
         numerator_pairwise : ndarray of shape (n_pairs, ) or (n_pairs, output_dim)
             Numerator of pairwise H-squared statistic.
             Useful to see which feature pair has strongest absolute interaction.
             Take square-root to get values on the scale of the predictions.
 
         denominator_pairwise : ndarray of shape (n_pairs, ) or (n_pairs, output_dim)
-            Denominator of pairwise H-squared statistic.
-
-        hsquared_pairwise : ndarray of shape (n_pairs, ) or (n_pairs, output_dim)
-            Pairwise H-squared statistic. Useful to see which feature pair has
-            strongest relative interation (relative with respect to joint effect).
-            Calculated as numerator_pairwise / denominator_pairwise.
+            Denominator of pairwise H-squared statistic (not of particular interest).
 
     References
     ----------

From 69111e3f145bd68ea30e738ca3805e206f1283b4 Mon Sep 17 00:00:00 2001
From: Michael Mayer <mayermichael79@gmail.com>
Date: Mon, 12 Feb 2024 20:54:38 +0100
Subject: [PATCH 20/66] Fix example output in docstring

---
 sklearn/inspection/_h_statistic.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/sklearn/inspection/_h_statistic.py b/sklearn/inspection/_h_statistic.py
index 4cbd8abf2c0a6..9a4930d051849 100644
--- a/sklearn/inspection/_h_statistic.py
+++ b/sklearn/inspection/_h_statistic.py
@@ -191,9 +191,9 @@ def h_statistic(
     >>> # their interaction. These two features also have strongest absolute
     >>> # interaction, see "numerator_pairwise":
     >>> # {'feature_pair': [(3, 8), (3, 2), (8, 2)],
+    >>> # 'h_squared_pairwise': array([0.00985985, 0.00927104, 0.03439926]),
     >>> # 'numerator_pairwise': array([ 1.2955532 ,  1.2419687 , 11.13358385]),
-    >>> # 'denominator_pairwise': array([131.39690331, 133.96210997, 323.6576595 ]),
-    >>> # 'h_squared_pairwise': array([0.00985985, 0.00927104, 0.03439926])}
+    >>> # 'denominator_pairwise': array([131.39690331, 133.96210997, 323.6576595 ])}
 
     """
     check_is_fitted(estimator)

From 0416de3ef4fdc48b748f96c2cac4102db20e734c Mon Sep 17 00:00:00 2001
From: Michael Mayer <mayermichael79@gmail.com>
Date: Sat, 13 Apr 2024 13:23:47 +0200
Subject: [PATCH 21/66] Apply suggestions from code review

Co-authored-by: Christian Lorentzen <lorentzen.ch@gmail.com>
---
 sklearn/inspection/_h_statistic.py           | 19 ++++++++++++++-----
 sklearn/inspection/tests/test_h_statistic.py |  1 -
 2 files changed, 14 insertions(+), 6 deletions(-)

diff --git a/sklearn/inspection/_h_statistic.py b/sklearn/inspection/_h_statistic.py
index 9a4930d051849..e63112146bff3 100644
--- a/sklearn/inspection/_h_statistic.py
+++ b/sklearn/inspection/_h_statistic.py
@@ -92,9 +92,9 @@ def h_statistic(
     sample_weight=None,
     eps=1e-10,
 ):
-    """Friedman and Popescu's H-statistic of pairwise interaction strength.
+    """Friedman's and Popescu's H-statistic of pairwise interaction strength.
 
-    Calculates Friedman and Popescu's H-statistic of interaction strength
+    Calculates Friedman's and Popescu's H-statistic of interaction strength
     for each feature pair j, k, see [FRI]_. The statistic is defined as::
 
         H_jk^2 = Numerator_jk / Denominator_jk, where
@@ -107,7 +107,7 @@ def h_statistic(
 
     It equals the proportion of effect variability between two features that cannot
     be explained by their main effects. When there is no interaction, the value is
-    precisely 0. The numerator (or its square root) provides an absolute measure
+    exactly 0. The numerator (or its square root) provides an absolute measure
     of interaction strength, enabling direct comparison across feature pairs.
 
     The computational complexity of the function is :math:`O(p^2 n^2)`,
@@ -122,7 +122,7 @@ def h_statistic(
     estimator : object
         An estimator that has already been :term:`fitted`.
 
-    X : ndarray or DataFrame, shape (n_observations, n_features)
+    X : {array-like or dataframe} of shape (n_samples, n_features)
         Data for which :term:`estimator` is able to calculate predictions.
 
     features : array-like of {int, str}, default=None
@@ -162,7 +162,7 @@ def h_statistic(
             Take square-root to get values on the scale of the predictions.
 
         denominator_pairwise : ndarray of shape (n_pairs, ) or (n_pairs, output_dim)
-            Denominator of pairwise H-squared statistic (not of particular interest).
+            Denominator of pairwise H-squared statistic. Used for appropriate normalization of H.
 
     References
     ----------
@@ -198,6 +198,15 @@ def h_statistic(
     """
     check_is_fitted(estimator)
 
+    if not (is_classifier(estimator) or is_regressor(estimator)):
+        raise ValueError("'estimator' must be a fitted regressor or classifier.")
+
+    if is_classifier(estimator) and isinstance(estimator.classes_[0], np.ndarray):
+        raise ValueError("Multiclass-multioutput estimators are not supported")
+    # Use check_array only on lists and other non-array-likes / sparse. Do not
+    # convert DataFrame into a NumPy array.
+    if not (hasattr(X, "__array__") or sparse.issparse(X)):
+        X = check_array(X, force_all_finite="allow-nan", dtype=object)
     if sample_weight is not None:
         sample_weight = _check_sample_weight(sample_weight, X)
 
diff --git a/sklearn/inspection/tests/test_h_statistic.py b/sklearn/inspection/tests/test_h_statistic.py
index 5d8a5b74631ca..ea6b9d0fae656 100644
--- a/sklearn/inspection/tests/test_h_statistic.py
+++ b/sklearn/inspection/tests/test_h_statistic.py
@@ -63,7 +63,6 @@ def test_h_statistic_additive_classification():
     expected_length = M_FEAT * (M_FEAT - 1) / 2
 
     assert result.h_squared_pairwise.shape == (expected_length, N_CLASSES)
-    print(result)
     assert_allclose(result.h_squared_pairwise, 0)
 
 

From 3fe5d6439132b2a09a869a240e7ccaeddbd783e7 Mon Sep 17 00:00:00 2001
From: Michael Mayer <mayermichael79@gmail.com>
Date: Sat, 13 Apr 2024 14:29:40 +0200
Subject: [PATCH 22/66] More changed from review

---
 sklearn/inspection/_h_statistic.py           | 56 ++++++++++++--------
 sklearn/inspection/tests/test_h_statistic.py | 26 ++++-----
 2 files changed, 48 insertions(+), 34 deletions(-)

diff --git a/sklearn/inspection/_h_statistic.py b/sklearn/inspection/_h_statistic.py
index e63112146bff3..2628ba5f36d86 100644
--- a/sklearn/inspection/_h_statistic.py
+++ b/sklearn/inspection/_h_statistic.py
@@ -1,11 +1,18 @@
-"""Friedman and Popescu's H-Statistic"""
+"""Friedman's and Popescu's H-Statistic"""
 
 import itertools
 
 import numpy as np
+from scipy import sparse
 
 from ..base import is_classifier, is_regressor
-from ..utils import Bunch, _get_column_indices, _safe_assign, _safe_indexing
+from ..utils import (
+    Bunch,
+    _get_column_indices,
+    _safe_assign,
+    _safe_indexing,
+    check_array,
+)
 from ..utils.random import sample_without_replacement
 from ..utils.validation import _check_sample_weight, check_is_fitted
 
@@ -13,15 +20,19 @@
 def _calculate_pd_brute_fast(estimator, X, feature_indices, grid, sample_weight=None):
     """Fast version of _calculate_partial_dependence_brute()
 
-    Returns np.array of size (n_grid, ) or (n_ngrid, output_dimension).
+    Returns np.array of size (n_grid, ) or (n_ngrid, output_dim).
     """
 
     if is_regressor(estimator):
-        pred_fun = estimator.predict
-    elif is_classifier(estimator) and hasattr(estimator, "predict_proba"):
-        pred_fun = estimator.predict_proba
-    else:
-        raise ValueError("The estimator has no predict or predict_proba method.")
+        if hasattr(estimator, "predict"):
+            pred_fun = estimator.predict
+        else:
+            raise ValueError("The regressor has no predict() method.")
+    elif is_classifier(estimator):
+        if hasattr(estimator, "predict_proba"):
+            pred_fun = estimator.predict_proba
+        else:
+            raise ValueError("The classifier has no predict_proba() method.")
 
     # X is stacked n_grid times, and grid columns are replaced by replicated grid
     n = X.shape[0]
@@ -48,7 +59,7 @@ def _calculate_pd_brute_fast(estimator, X, feature_indices, grid, sample_weight=
 def _calculate_pd_over_data(estimator, X, feature_indices, sample_weight=None):
     """Calculates centered partial dependence over the data distribution.
 
-    It returns a numpy array of size (n, ) or (n, output_dimension).
+    It returns a numpy array of size (n, ) or (n, output_dim).
     """
 
     # Select grid columns and remove duplicates (will compensate below)
@@ -85,11 +96,11 @@ def _calculate_pd_over_data(estimator, X, feature_indices, sample_weight=None):
 def h_statistic(
     estimator,
     X,
-    *,
     features=None,
+    *,
+    sample_weight=None,
     n_max=500,
     random_state=None,
-    sample_weight=None,
     eps=1e-10,
 ):
     """Friedman's and Popescu's H-statistic of pairwise interaction strength.
@@ -110,8 +121,8 @@ def h_statistic(
     exactly 0. The numerator (or its square root) provides an absolute measure
     of interaction strength, enabling direct comparison across feature pairs.
 
-    The computational complexity of the function is :math:`O(p^2 n^2)`,
-    where :math:`p` denotes the number of features considered. The size of `n` is
+    The computational complexity of the function is `O(p^2 n^2)`,
+    where `p` denotes the number of features considered. The size of `n` is
     automatically controlled via `n_max=500`, while it is the user's responsibility
     to select only a subset of *important* features. It is crucial to focus on important
     features because for weak predictors, the denominator might be small, and
@@ -129,6 +140,9 @@ def h_statistic(
         List of feature names or column indices used to calculate pairwise statistics.
         The default, None, will use all column indices of X.
 
+    sample_weight : array-like of shape (n_samples,), default=None
+        Sample weights used in calculating partial dependencies.
+
     n_max : int, default=500
         The number of rows to draw without replacement from X (and `sample_weight`).
 
@@ -136,9 +150,6 @@ def h_statistic(
         Pseudo-random number generator used for subsampling via `n_max`.
         See :term:`Glossary <random_state>`.
 
-    sample_weight : array-like of shape (n_samples,), default=None
-        Sample weights used in calculating partial dependencies.
-
     eps : float, default=1e-10
         Threshold below which numerator values are set to 0.
 
@@ -147,7 +158,7 @@ def h_statistic(
     result : :class:`~sklearn.utils.Bunch`
         Dictionary-like object, with the following attributes.
 
-        feature_pair : list of length n_feature_pairs
+        feature_pairs : list of length n_feature_pairs
             The list contains tuples of feature pairs (indices) in the same order
             as all pairwise statistics.
 
@@ -162,7 +173,8 @@ def h_statistic(
             Take square-root to get values on the scale of the predictions.
 
         denominator_pairwise : ndarray of shape (n_pairs, ) or (n_pairs, output_dim)
-            Denominator of pairwise H-squared statistic. Used for appropriate normalization of H.
+            Denominator of pairwise H-squared statistic. Used for appropriate
+            normalization of H.
 
     References
     ----------
@@ -203,10 +215,12 @@ def h_statistic(
 
     if is_classifier(estimator) and isinstance(estimator.classes_[0], np.ndarray):
         raise ValueError("Multiclass-multioutput estimators are not supported")
+
     # Use check_array only on lists and other non-array-likes / sparse. Do not
     # convert DataFrame into a NumPy array.
     if not (hasattr(X, "__array__") or sparse.issparse(X)):
         X = check_array(X, force_all_finite="allow-nan", dtype=object)
+
     if sample_weight is not None:
         sample_weight = _check_sample_weight(sample_weight, X)
 
@@ -230,10 +244,10 @@ def h_statistic(
 
     # CALCULATIONS
     pd_univariate = []
-    for ind in feature_indices:
+    for idx in feature_indices:
         pd_univariate.append(
             _calculate_pd_over_data(
-                estimator, X=X, feature_indices=[ind], sample_weight=sample_weight
+                estimator, X=X, feature_indices=[idx], sample_weight=sample_weight
             )
         )
 
@@ -262,7 +276,7 @@ def h_statistic(
     h2_stat = np.divide(num, denom, out=np.zeros_like(num), where=denom > 0)
 
     return Bunch(
-        feature_pair=list(itertools.combinations(features, 2)),
+        feature_pairs=list(itertools.combinations(features, 2)),
         h_squared_pairwise=h2_stat,
         numerator_pairwise=num,
         denominator_pairwise=denom,
diff --git a/sklearn/inspection/tests/test_h_statistic.py b/sklearn/inspection/tests/test_h_statistic.py
index ea6b9d0fae656..c6627cf1597b7 100644
--- a/sklearn/inspection/tests/test_h_statistic.py
+++ b/sklearn/inspection/tests/test_h_statistic.py
@@ -25,17 +25,17 @@
 @pytest.mark.parametrize("sample_weight", [None, "ones"])
 def test_h_statistic_additive_regression(est, features, n_max, sample_weight):
     # Checks that additive regressions get statistics of 0.
-    M_FEAT = 4
+    N_FEAT = 4
     N = 200
-    X, y = make_regression(n_samples=N, n_features=M_FEAT, random_state=0)
+    X, y = make_regression(n_samples=N, n_features=N_FEAT, random_state=0)
     w = np.ones(N) if sample_weight == "ones" else None
 
     model = est.fit(X, y, sample_weight=w)
     result = h_statistic(
-        model, X, features=features, random_state=1, n_max=n_max, sample_weight=w
+        model, X, features=features, sample_weight=w, n_max=n_max, random_state=1
     )
 
-    m = M_FEAT if features is None else len(features)
+    m = N_FEAT if features is None else len(features)
     expected_length = m * (m - 1) / 2
 
     assert result.h_squared_pairwise.shape == (expected_length,)
@@ -46,12 +46,12 @@ def test_h_statistic_additive_classification():
     # Checks that additive classification gets statistics of 0. The presence
     # of link functions (especially for GradientBoosting with more than 2 classes)
     # would make further tests tricky.
-    M_FEAT = 4
+    N_FEAT = 4
     N_CLASSES = 4
     X, y = make_classification(
         n_samples=200,
-        n_features=M_FEAT,
-        n_informative=M_FEAT,
+        n_features=N_FEAT,
+        n_informative=N_FEAT,
         n_redundant=0,
         n_classes=N_CLASSES,
         random_state=0,
@@ -60,7 +60,7 @@ def test_h_statistic_additive_classification():
     model.fit(X, y)
     result = h_statistic(model, X, random_state=1)
 
-    expected_length = M_FEAT * (M_FEAT - 1) / 2
+    expected_length = N_FEAT * (N_FEAT - 1) / 2
 
     assert result.h_squared_pairwise.shape == (expected_length, N_CLASSES)
     assert_allclose(result.h_squared_pairwise, 0)
@@ -78,14 +78,14 @@ def test_h_statistic_additive_classification():
 @pytest.mark.parametrize("sample_weight", [None, "ones"])
 def test_h_statistic_regression(est, features, n_max, sample_weight):
     # Tests that models with interactions will produce (some) positive values.
-    M_FEAT = 4
+    N_FEAT = 4
     N = 200
-    X, y = make_regression(n_samples=N, n_features=M_FEAT, random_state=0)
+    X, y = make_regression(n_samples=N, n_features=N_FEAT, random_state=0)
     w = np.ones(N) if sample_weight == "ones" else None
 
     model = est.fit(X, y, sample_weight=w)
     result = h_statistic(
-        model, X, features=features, random_state=1, n_max=n_max, sample_weight=w
+        model, X, features=features, sample_weight=w, n_max=n_max, random_state=1
     )
 
     assert any(result.h_squared_pairwise > 1e-5)
@@ -105,13 +105,13 @@ def test_h_statistic_equivalence_array_dataframe(n_max, sample_weight):
     # Numpy
     model_np = RandomForestRegressor(random_state=0, max_depth=4, n_estimators=20)
     model_np.fit(X, y, sample_weight=w)
-    result_np = h_statistic(model_np, X, random_state=1, n_max=n_max, sample_weight=w)
+    result_np = h_statistic(model_np, X, sample_weight=w, n_max=n_max, random_state=1)
 
     # Pandas
     model_pd = clone(model_np)
     model_pd.fit(X_df, y, sample_weight=w)
     result_pd = h_statistic(
-        model_pd, X_df, random_state=1, n_max=n_max, sample_weight=w
+        model_pd, X_df, sample_weight=w, n_max=n_max, random_state=1
     )
 
     assert_allclose(result_np.h_squared_pairwise, result_pd.h_squared_pairwise)

From c583aeb2d09bb710f39eb08340515a1a3d8fa014 Mon Sep 17 00:00:00 2001
From: Michael Mayer <mayermichael79@gmail.com>
Date: Sat, 13 Apr 2024 20:50:50 +0200
Subject: [PATCH 23/66] add validate_params()

---
 sklearn/inspection/_h_statistic.py | 24 +++++++++++++++++++++++-
 1 file changed, 23 insertions(+), 1 deletion(-)

diff --git a/sklearn/inspection/_h_statistic.py b/sklearn/inspection/_h_statistic.py
index 2628ba5f36d86..9f6c490916cc9 100644
--- a/sklearn/inspection/_h_statistic.py
+++ b/sklearn/inspection/_h_statistic.py
@@ -13,6 +13,13 @@
     _safe_indexing,
     check_array,
 )
+from ..utils._param_validation import (
+    HasMethods,
+    Integral,
+    Interval,
+    Real,
+    validate_params,
+)
 from ..utils.random import sample_without_replacement
 from ..utils.validation import _check_sample_weight, check_is_fitted
 
@@ -93,6 +100,21 @@ def _calculate_pd_over_data(estimator, X, feature_indices, sample_weight=None):
     return pd_values - column_means
 
 
+@validate_params(
+    {
+        "estimator": [
+            HasMethods(["fit", "predict"]),
+            HasMethods(["fit", "predict_proba"]),
+        ],
+        "X": ["array-like", "sparse matrix"],
+        "features": ["array-like", list, None],
+        "sample_weight": ["array-like", None],
+        "n_max": [Interval(Integral, 1, None, closed="left")],
+        "random_state": ["random_state"],
+        "eps": [Interval(Real, 0, None, closed="left")],
+    },
+    prefer_skip_nested_validation=True,
+)
 def h_statistic(
     estimator,
     X,
@@ -202,7 +224,7 @@ def h_statistic(
     >>> # For features (8, 2), 3.4% of the joint effect variability comes from
     >>> # their interaction. These two features also have strongest absolute
     >>> # interaction, see "numerator_pairwise":
-    >>> # {'feature_pair': [(3, 8), (3, 2), (8, 2)],
+    >>> # {'feature_pairs': [(3, 8), (3, 2), (8, 2)],
     >>> # 'h_squared_pairwise': array([0.00985985, 0.00927104, 0.03439926]),
     >>> # 'numerator_pairwise': array([ 1.2955532 ,  1.2419687 , 11.13358385]),
     >>> # 'denominator_pairwise': array([131.39690331, 133.96210997, 323.6576595 ])}

From a443f02fa8229d13e801afe672f7dcbe480ba58c Mon Sep 17 00:00:00 2001
From: Michael Mayer <mayermichael79@gmail.com>
Date: Sun, 14 Apr 2024 17:41:29 +0200
Subject: [PATCH 24/66] Add h_statistic to test_public_functions.py

---
 sklearn/tests/test_public_functions.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/sklearn/tests/test_public_functions.py b/sklearn/tests/test_public_functions.py
index 22e70b39e1c6f..f2d83448b56ea 100644
--- a/sklearn/tests/test_public_functions.py
+++ b/sklearn/tests/test_public_functions.py
@@ -196,6 +196,7 @@ def _check_function_param_validation(
     "sklearn.feature_selection.mutual_info_classif",
     "sklearn.feature_selection.mutual_info_regression",
     "sklearn.feature_selection.r_regression",
+    "sklearn.inspection.h_statistic",
     "sklearn.inspection.partial_dependence",
     "sklearn.inspection.permutation_importance",
     "sklearn.isotonic.check_increasing",

From a3aaed47c0778a9507ebe30a6deff54515a71bb2 Mon Sep 17 00:00:00 2001
From: Michael Mayer <mayermichael79@gmail.com>
Date: Sun, 14 Apr 2024 17:51:30 +0200
Subject: [PATCH 25/66] Possession apostrophs

---
 sklearn/inspection/_h_statistic.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/sklearn/inspection/_h_statistic.py b/sklearn/inspection/_h_statistic.py
index 9f6c490916cc9..85ec2e77dcaf9 100644
--- a/sklearn/inspection/_h_statistic.py
+++ b/sklearn/inspection/_h_statistic.py
@@ -1,4 +1,4 @@
-"""Friedman's and Popescu's H-Statistic"""
+"""Friedman and Popescu's H-Statistic"""
 
 import itertools
 
@@ -125,9 +125,9 @@ def h_statistic(
     random_state=None,
     eps=1e-10,
 ):
-    """Friedman's and Popescu's H-statistic of pairwise interaction strength.
+    """Friedman and Popescu's H-statistic of pairwise interaction strength.
 
-    Calculates Friedman's and Popescu's H-statistic of interaction strength
+    Calculates Friedman and Popescu's H-statistic of interaction strength
     for each feature pair j, k, see [FRI]_. The statistic is defined as::
 
         H_jk^2 = Numerator_jk / Denominator_jk, where

From 95ed5defc82383e475fb0f866244011aac04db72 Mon Sep 17 00:00:00 2001
From: Michael Mayer <mayermichael79@gmail.com>
Date: Sun, 14 Apr 2024 20:41:03 +0200
Subject: [PATCH 26/66] Add docu

---
 doc/images/friedmans_h_statistic.png | Bin 0 -> 30389 bytes
 doc/inspection.rst                   |   1 +
 doc/modules/h_statistic.rst          | 179 +++++++++++++++++++++++++++
 3 files changed, 180 insertions(+)
 create mode 100644 doc/images/friedmans_h_statistic.png
 create mode 100644 doc/modules/h_statistic.rst

diff --git a/doc/images/friedmans_h_statistic.png b/doc/images/friedmans_h_statistic.png
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diff --git a/doc/inspection.rst b/doc/inspection.rst
index 57c1cfc3275e8..dcf169377e320 100644
--- a/doc/inspection.rst
+++ b/doc/inspection.rst
@@ -29,3 +29,4 @@ to diagnose issues with model performance.
 
     modules/partial_dependence
     modules/permutation_importance
+    modules/h_statistic
diff --git a/doc/modules/h_statistic.rst b/doc/modules/h_statistic.rst
new file mode 100644
index 0000000000000..ee2b10ae8d351
--- /dev/null
+++ b/doc/modules/h_statistic.rst
@@ -0,0 +1,179 @@
+
+.. _h_statistic:
+
+===============================================================
+Friedman and Popescu's H-Statistic
+===============================================================
+
+.. currentmodule:: sklearn.inspection
+
+What is the difference between a white box model and a black box model?
+It is the many and complicated interaction effects of the latter.
+
+Such interaction effects can be visualized by two-dimensional or stratified
+partial dependence plots (PDP). But how to figure out between *which feature pairs*
+the strongest interactions occur?
+
+One approach is to study pairwise H-statistics, introduced by Friedman and Popescu
+in [F2008]_.
+
+The statistic
+=============
+
+The H-statistic of two features is the proportion of effect variability of the 
+two features coming from their pairwise interaction. 
+Effect variability is measured via partial dependence.
+
+Partial dependence
+------------------
+
+Let :math:`F: \mathbb{R}^p \to \mathbb{R}` denote the prediction function that 
+maps the :math:`p`-dimensional feature vector :math:`\mathbf x = (x_1, \dots, x_p)`
+to its prediction.
+Furthermore, let :math:`F_s(\mathbf x_s) = E_{\mathbf x_{\setminus s}}(F(\mathbf x_s, \mathbf x_{\setminus s}))`
+be the partial dependence function of :math:`F` on the feature subset 
+:math:`\mathbf x_s`, where :math:`s \subseteq \{1, \dots, p\}`, 
+Here, the expectation runs over the joint marginal distribution of features 
+:math:`\mathbf x_{\setminus s}` not in :math:`\mathbf x_s`.
+
+Given data, :math:`F_s(\mathbf x_s)` can be estimated by the empirical partial 
+dependence function
+
+.. math::
+  \hat F_s(\mathbf x_s) = \frac{1}{n} \sum_{i = 1}^n F(\mathbf x_s, \mathbf x_{i \setminus s}),
+
+where :math:`\mathbf x_{i\setminus s}`, :math:`i = 1, \dots, n`, 
+are the observed values of :math:`\mathbf x_{\setminus s}` in some "background" dataset.
+
+Pairwise H-statistic
+--------------------
+
+Following [F2008]_, if there are no interaction effects between features
+:math:`x_j` and :math:`x_k`, their two-dimensional partial dependence function
+:math:`F_{jk}` can be written as the sum of the univariate partial dependencies, i.e.,
+
+.. math::
+  F_{jk}(x_j, x_k) = F_j(x_j) + F_k(x_k).
+
+Correspondingly, Friedman and Popescu's H-statistic of pairwise interaction strength
+is defined as
+
+.. math::
+
+  H_{jk}^2 = A_{jk} / B_{jk},
+
+where
+
+.. math::
+
+  A_{jk} = \frac{1}{n} \sum_{i = 1}^n\big[\hat F_{jk}(x_{ij}, x_{ik}) - \hat F_j(x_{ij}) - \hat F_k(x_{ik})\big]^2
+
+and
+
+.. math::
+
+  B_{jk} = \frac{1}{n} \sum_{i = 1}^n\big[\hat F_{jk}(x_{ij}, x_{ik})\big]^2.
+
+Remarks
+=======
+
+1. Partial dependence functions and :math:`F` are centered to mean 0.
+2. Partial dependence functions and :math:`F` are evaluated over the data distribution.
+   This is different to partial dependence plots, where one uses a fixed grid.
+3. Weighted versions follow by replacing all arithmetic means by corresponding weighted means.
+4. Multi-output prediction (e.g., probabilistic classification) is handled component-wise.
+5. Due to undesired extrapolation of partial dependence functions, values above 1 may occur.
+
+Interpretation
+==============
+
+* The statistic provides the proportion of joint effect variability explained by the interaction.
+* A value of 0 means no interaction.
+* If main effects are weak, a small interaction effect can get a high value of the statistic.
+  Therefore, it often makes sense to study unnormalized statistics :math:`A_{jk}` or to
+  stay on the scale of the prediction :math:`\sqrt{A_{jk}}`.
+
+Workflow
+========
+
+Calculating all pairwise H-statistics has computational complexity of :math:`O(n^2p^2)`.
+Therefore, our implementation randomly selects ``n_max = 500`` rows from the provided dataset ``X``.
+Furthermore, if the number of features :math:`p` is large, use some feature importance measure
+to select the most important features and pass them via the ``features=None`` argument.
+
+Example
+=======
+
+Let's calculate pairwise H-statistics from a boosted trees model of diabetes data.
+There are :math:`p = 10` features, leading to 45 pairwise interactions. To save
+time, we select the top 6 predictors via permutation importance, leading to 15 pairs.
+
+    >>> import numpy as np
+    >>> from sklearn.ensemble import HistGradientBoostingRegressor
+    >>> from sklearn.inspection import permutation_importance, h_statistic
+    >>> from sklearn.datasets import load_diabetes
+    >>>
+    >>> X, y = load_diabetes(return_X_y=True)
+    >>> est = HistGradientBoostingRegressor(max_iter=100, max_depth=4).fit(X, y)
+    >>>
+    >>> # Get Friedman's H-squared for top m=6 predictors
+    >>> m = 6
+    >>> imp = permutation_importance(est, X, y, random_state=0)
+    >>> top_m = np.argsort(imp.importances_mean)[-m:]
+    >>> h_statistic(est, X=X, features=top_m, random_state=4)
+
+Then, we plot the statistics:
+
+    >>> import pandas as pd
+    >>> import matplotlib.pyplot as plt
+    >>>
+    >>> fig, axes = plt.subplots(1, 2, figsize=(8, 4), layout="tight")
+    >>>
+    >>> H_df = pd.DataFrame(H).set_index("feature_pairs")
+    >>>
+    >>> # H-squared (for interpretation)
+    >>> (
+    >>>     H_df["h_squared_pairwise"]
+    >>>     .sort_values()
+    >>>     .plot.barh(xlabel="Normalized $H^2$", ax=axes[0])
+    >>> )
+    >>>
+    >>> # Square-root of numerator (for comparison)
+    >>> (
+    >>>     np.sqrt(H_df["numerator_pairwise"])
+    >>>     .sort_values()
+    >>>     .plot.barh(xlabel="Unnormalized $H$", ax=axes[1])
+    >>> )
+
+.. image:: ../images/friedmans_h_statistic.png
+   :align: center
+
+The left plot shows that the interaction between features 0 and 1 explains
+about 15% of their joint effect variability. For the other interactions, its
+5% or less.
+
+The right plot shows :math:`\sqrt{A_{jk}}`. These values are all on the same
+scale and thus can be directly compared: The *strongest* interaction occurs between
+feature 2 and 3, even if it explains less than 5% of the joint effect variability.
+This happens because features 8 > 2 > 3 are the most important features according to
+permutation importance. Thus, a relatively weak interaction between the two very important
+features 2 and 3 is still stronger als the strong relative interaction strength
+between two quite unimportant features.
+
+Limitations
+===========
+
+1. H-statistics are based on partial dependence estimates. Therefore, they are
+   just as good or poor as these. The major problem of partial dependence is
+   the application of the model to unseen and/or impossible feature combinations.
+   H-statistics, which should actually lie in the range between 0 and 1,
+   can become greater than 1 in extreme cases.
+2. Due to their computational complexity, H-statistics are usually evaluated on
+   relatively small subsets of the data. Consequently, the estimates are
+   typically not very robust.
+
+.. topic:: References
+
+    .. [F2008] J. H. Friedman and B. E. Popescu,
+               "Predictive Learning via Rule Ensembles",
+               The Annals of Applied Statistics, 2(3), 916-954, 2008.
\ No newline at end of file

From 7f4527f3a47bcf825141b82dd86d4b6f22345d93 Mon Sep 17 00:00:00 2001
From: Michael Mayer <mayermichael79@gmail.com>
Date: Tue, 16 Apr 2024 22:38:37 +0200
Subject: [PATCH 27/66] Add entry to classes.rst

---
 doc/modules/classes.rst | 1 +
 1 file changed, 1 insertion(+)

diff --git a/doc/modules/classes.rst b/doc/modules/classes.rst
index 55336389f93d5..ba51cb775a2da 100644
--- a/doc/modules/classes.rst
+++ b/doc/modules/classes.rst
@@ -674,6 +674,7 @@ Kernels
 
    inspection.partial_dependence
    inspection.permutation_importance
+   inspection.h_statistic
 
 Plotting
 --------

From 0ee0f7a310f10c98a69cf87dbb5f805164bb6e17 Mon Sep 17 00:00:00 2001
From: Michael Mayer <mayermichael79@gmail.com>
Date: Tue, 16 Apr 2024 22:39:37 +0200
Subject: [PATCH 28/66] reorder position in classes.rst

---
 doc/modules/classes.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/modules/classes.rst b/doc/modules/classes.rst
index ba51cb775a2da..370b4cd284b50 100644
--- a/doc/modules/classes.rst
+++ b/doc/modules/classes.rst
@@ -672,9 +672,9 @@ Kernels
    :toctree: generated/
    :template: function.rst
 
+   inspection.h_statistic
    inspection.partial_dependence
    inspection.permutation_importance
-   inspection.h_statistic
 
 Plotting
 --------

From a93a4c9ce8eaf669e8b2ad7e16b56cfc0fa1999f Mon Sep 17 00:00:00 2001
From: Michael Mayer <mayermichael79@gmail.com>
Date: Fri, 19 Apr 2024 10:10:21 +0200
Subject: [PATCH 29/66] safe assign and indexing have moved

---
 sklearn/inspection/_h_statistic.py | 9 ++-------
 1 file changed, 2 insertions(+), 7 deletions(-)

diff --git a/sklearn/inspection/_h_statistic.py b/sklearn/inspection/_h_statistic.py
index 85ec2e77dcaf9..9149808311e89 100644
--- a/sklearn/inspection/_h_statistic.py
+++ b/sklearn/inspection/_h_statistic.py
@@ -6,13 +6,8 @@
 from scipy import sparse
 
 from ..base import is_classifier, is_regressor
-from ..utils import (
-    Bunch,
-    _get_column_indices,
-    _safe_assign,
-    _safe_indexing,
-    check_array,
-)
+from ..utils import Bunch, check_array
+from ..utils._indexing import _get_column_indices, _safe_assign, _safe_indexing
 from ..utils._param_validation import (
     HasMethods,
     Integral,

From 6b63a551121062af56318c4daf29f04fe376bad7 Mon Sep 17 00:00:00 2001
From: Michael Mayer <mayermichael79@gmail.com>
Date: Fri, 19 Apr 2024 12:13:12 +0200
Subject: [PATCH 30/66] Fix doctest failure

---
 sklearn/inspection/_h_statistic.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/sklearn/inspection/_h_statistic.py b/sklearn/inspection/_h_statistic.py
index 9149808311e89..be9b9bc568865 100644
--- a/sklearn/inspection/_h_statistic.py
+++ b/sklearn/inspection/_h_statistic.py
@@ -214,7 +214,7 @@ def h_statistic(
     >>> m = 3
     >>> imp = permutation_importance(est, X, y, random_state=0)
     >>> top_m = np.argsort(imp.importances_mean)[-m:]
-    >>> h_statistic(est, X=X, features=top_m, random_state=4)
+    >>> H = h_statistic(est, X=X, features=top_m, random_state=4)
 
     >>> # For features (8, 2), 3.4% of the joint effect variability comes from
     >>> # their interaction. These two features also have strongest absolute

From 3923d739b6c9425aaeb085098ef08a2a6b2fd486 Mon Sep 17 00:00:00 2001
From: Michael Mayer <mayermichael79@gmail.com>
Date: Fri, 19 Apr 2024 12:33:40 +0200
Subject: [PATCH 31/66] fix docstring failure attempt 2

---
 doc/modules/h_statistic.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/modules/h_statistic.rst b/doc/modules/h_statistic.rst
index ee2b10ae8d351..882138db0ec2d 100644
--- a/doc/modules/h_statistic.rst
+++ b/doc/modules/h_statistic.rst
@@ -120,7 +120,7 @@ time, we select the top 6 predictors via permutation importance, leading to 15 p
     >>> m = 6
     >>> imp = permutation_importance(est, X, y, random_state=0)
     >>> top_m = np.argsort(imp.importances_mean)[-m:]
-    >>> h_statistic(est, X=X, features=top_m, random_state=4)
+    >>> H = h_statistic(est, X=X, features=top_m, random_state=4)
 
 Then, we plot the statistics:
 

From d406dc641a2fc4b7e0d13c827e3d3f0815d5d654 Mon Sep 17 00:00:00 2001
From: Michael Mayer <mayermichael79@gmail.com>
Date: Fri, 19 Apr 2024 14:52:17 +0200
Subject: [PATCH 32/66] doc tests do not seem to allow multiline command in
 parantheses

---
 doc/modules/h_statistic.rst | 16 +++++-----------
 1 file changed, 5 insertions(+), 11 deletions(-)

diff --git a/doc/modules/h_statistic.rst b/doc/modules/h_statistic.rst
index 882138db0ec2d..325176611b0ee 100644
--- a/doc/modules/h_statistic.rst
+++ b/doc/modules/h_statistic.rst
@@ -132,18 +132,12 @@ Then, we plot the statistics:
     >>> H_df = pd.DataFrame(H).set_index("feature_pairs")
     >>>
     >>> # H-squared (for interpretation)
-    >>> (
-    >>>     H_df["h_squared_pairwise"]
-    >>>     .sort_values()
-    >>>     .plot.barh(xlabel="Normalized $H^2$", ax=axes[0])
-    >>> )
-    >>>
+    >>> H_df["h_squared_pairwise"].sort_values().plot.barh(
+    >>>     xlabel="Normalized $H^2$", ax=axes[0])
+    >>> 
     >>> # Square-root of numerator (for comparison)
-    >>> (
-    >>>     np.sqrt(H_df["numerator_pairwise"])
-    >>>     .sort_values()
-    >>>     .plot.barh(xlabel="Unnormalized $H$", ax=axes[1])
-    >>> )
+    >>> np.sqrt(H_df["numerator_pairwise"]).sort_values().plot.barh(
+    >>>     xlabel="Unnormalized $H$", ax=axes[1])
 
 .. image:: ../images/friedmans_h_statistic.png
    :align: center

From 70b9f6866818b3524f04b42e1886b2e10bc219ca Mon Sep 17 00:00:00 2001
From: Michael Mayer <mayermichael79@gmail.com>
Date: Fri, 19 Apr 2024 15:41:52 +0200
Subject: [PATCH 33/66] docstring checks do not like black

---
 doc/modules/h_statistic.rst | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/doc/modules/h_statistic.rst b/doc/modules/h_statistic.rst
index 325176611b0ee..d42af2e8c80f1 100644
--- a/doc/modules/h_statistic.rst
+++ b/doc/modules/h_statistic.rst
@@ -132,12 +132,12 @@ Then, we plot the statistics:
     >>> H_df = pd.DataFrame(H).set_index("feature_pairs")
     >>>
     >>> # H-squared (for interpretation)
-    >>> H_df["h_squared_pairwise"].sort_values().plot.barh(
-    >>>     xlabel="Normalized $H^2$", ax=axes[0])
+    >>> H2 = H_df["h_squared_pairwise"].sort_values()
+    >>> H2.plot.barh(xlabel="Normalized $H^2$", ax=axes[0])
     >>> 
     >>> # Square-root of numerator (for comparison)
-    >>> np.sqrt(H_df["numerator_pairwise"]).sort_values().plot.barh(
-    >>>     xlabel="Unnormalized $H$", ax=axes[1])
+    >>> H_num = np.sqrt(H_df["numerator_pairwise"]).sort_values()
+    >>> H_num.plot.barh(xlabel="Unnormalized $H$", ax=axes[1])
 
 .. image:: ../images/friedmans_h_statistic.png
    :align: center

From 3ed07af9c618bff3cd74579e6b0ca6605f23f0e1 Mon Sep 17 00:00:00 2001
From: Michael Mayer <mayermichael79@gmail.com>
Date: Fri, 19 Apr 2024 18:00:19 +0200
Subject: [PATCH 34/66] assign result of plot in docu

---
 doc/modules/h_statistic.rst | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/doc/modules/h_statistic.rst b/doc/modules/h_statistic.rst
index d42af2e8c80f1..a7ba97b01c384 100644
--- a/doc/modules/h_statistic.rst
+++ b/doc/modules/h_statistic.rst
@@ -133,11 +133,11 @@ Then, we plot the statistics:
     >>>
     >>> # H-squared (for interpretation)
     >>> H2 = H_df["h_squared_pairwise"].sort_values()
-    >>> H2.plot.barh(xlabel="Normalized $H^2$", ax=axes[0])
+    >>> _ = H2.plot.barh(xlabel="Normalized $H^2$", ax=axes[0])
     >>> 
     >>> # Square-root of numerator (for comparison)
     >>> H_num = np.sqrt(H_df["numerator_pairwise"]).sort_values()
-    >>> H_num.plot.barh(xlabel="Unnormalized $H$", ax=axes[1])
+    >>> _ = H_num.plot.barh(xlabel="Unnormalized $H$", ax=axes[1])
 
 .. image:: ../images/friedmans_h_statistic.png
    :align: center

From c7b798eddd2250556ec73ad3ede5be5d77f864f6 Mon Sep 17 00:00:00 2001
From: Michael Mayer <mayermichael79@gmail.com>
Date: Fri, 19 Apr 2024 18:32:56 +0200
Subject: [PATCH 35/66] superfluous newline in docstring of function

---
 sklearn/inspection/_h_statistic.py | 7 +++----
 1 file changed, 3 insertions(+), 4 deletions(-)

diff --git a/sklearn/inspection/_h_statistic.py b/sklearn/inspection/_h_statistic.py
index be9b9bc568865..752a11e2eb9db 100644
--- a/sklearn/inspection/_h_statistic.py
+++ b/sklearn/inspection/_h_statistic.py
@@ -123,7 +123,7 @@ def h_statistic(
     """Friedman and Popescu's H-statistic of pairwise interaction strength.
 
     Calculates Friedman and Popescu's H-statistic of interaction strength
-    for each feature pair j, k, see [FRI]_. The statistic is defined as::
+    for each feature pair j, k, see [FRI]_. The statistic is defined as:
 
         H_jk^2 = Numerator_jk / Denominator_jk, where
 
@@ -221,9 +221,8 @@ def h_statistic(
     >>> # interaction, see "numerator_pairwise":
     >>> # {'feature_pairs': [(3, 8), (3, 2), (8, 2)],
     >>> # 'h_squared_pairwise': array([0.00985985, 0.00927104, 0.03439926]),
-    >>> # 'numerator_pairwise': array([ 1.2955532 ,  1.2419687 , 11.13358385]),
-    >>> # 'denominator_pairwise': array([131.39690331, 133.96210997, 323.6576595 ])}
-
+    >>> # 'numerator_pairwise': array([1.2955532, 1.2419687, 11.13358385]),
+    >>> # 'denominator_pairwise': array([131.39690331, 133.96210997, 323.6576595])}
     """
     check_is_fitted(estimator)
 

From 7e4ff8b85ce5a971c96792ac95f888321223dc73 Mon Sep 17 00:00:00 2001
From: Michael Mayer <mayermichael79@gmail.com>
Date: Fri, 19 Apr 2024 21:25:59 +0200
Subject: [PATCH 36/66] Replace plot by print()

---
 doc/images/friedmans_h_statistic.png | Bin 30389 -> 0 bytes
 doc/modules/h_statistic.rst          |  92 ++++++++++++++-------------
 2 files changed, 47 insertions(+), 45 deletions(-)
 delete mode 100644 doc/images/friedmans_h_statistic.png

diff --git a/doc/images/friedmans_h_statistic.png b/doc/images/friedmans_h_statistic.png
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GIT binary patch
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diff --git a/doc/modules/h_statistic.rst b/doc/modules/h_statistic.rst
index a7ba97b01c384..71cf2cb6f92fd 100644
--- a/doc/modules/h_statistic.rst
+++ b/doc/modules/h_statistic.rst
@@ -108,51 +108,53 @@ Let's calculate pairwise H-statistics from a boosted trees model of diabetes dat
 There are :math:`p = 10` features, leading to 45 pairwise interactions. To save
 time, we select the top 6 predictors via permutation importance, leading to 15 pairs.
 
-    >>> import numpy as np
-    >>> from sklearn.ensemble import HistGradientBoostingRegressor
-    >>> from sklearn.inspection import permutation_importance, h_statistic
-    >>> from sklearn.datasets import load_diabetes
-    >>>
-    >>> X, y = load_diabetes(return_X_y=True)
-    >>> est = HistGradientBoostingRegressor(max_iter=100, max_depth=4).fit(X, y)
-    >>>
-    >>> # Get Friedman's H-squared for top m=6 predictors
-    >>> m = 6
-    >>> imp = permutation_importance(est, X, y, random_state=0)
-    >>> top_m = np.argsort(imp.importances_mean)[-m:]
-    >>> H = h_statistic(est, X=X, features=top_m, random_state=4)
-
-Then, we plot the statistics:
-
-    >>> import pandas as pd
-    >>> import matplotlib.pyplot as plt
-    >>>
-    >>> fig, axes = plt.subplots(1, 2, figsize=(8, 4), layout="tight")
-    >>>
-    >>> H_df = pd.DataFrame(H).set_index("feature_pairs")
-    >>>
-    >>> # H-squared (for interpretation)
-    >>> H2 = H_df["h_squared_pairwise"].sort_values()
-    >>> _ = H2.plot.barh(xlabel="Normalized $H^2$", ax=axes[0])
-    >>> 
-    >>> # Square-root of numerator (for comparison)
-    >>> H_num = np.sqrt(H_df["numerator_pairwise"]).sort_values()
-    >>> _ = H_num.plot.barh(xlabel="Unnormalized $H$", ax=axes[1])
-
-.. image:: ../images/friedmans_h_statistic.png
-   :align: center
-
-The left plot shows that the interaction between features 0 and 1 explains
-about 15% of their joint effect variability. For the other interactions, its
-5% or less.
-
-The right plot shows :math:`\sqrt{A_{jk}}`. These values are all on the same
-scale and thus can be directly compared: The *strongest* interaction occurs between
-feature 2 and 3, even if it explains less than 5% of the joint effect variability.
-This happens because features 8 > 2 > 3 are the most important features according to
-permutation importance. Thus, a relatively weak interaction between the two very important
-features 2 and 3 is still stronger als the strong relative interaction strength
-between two quite unimportant features.
+  >>> import numpy as np
+  >>> from sklearn.ensemble import HistGradientBoostingRegressor
+  >>> from sklearn.inspection import permutation_importance, h_statistic
+  >>> from sklearn.datasets import load_diabetes
+
+  >>> X, y = load_diabetes(return_X_y=True)
+  >>> est = HistGradientBoostingRegressor(max_iter=100, max_depth=4).fit(X, y)
+
+  >>> # Get Friedman's H-squared for top m=6 predictors
+  >>> m = 6
+  >>> imp = permutation_importance(est, X, y, random_state=0)
+  >>> top_m = np.argsort(imp.importances_mean)[-m:]
+  >>> H = h_statistic(est, X=X, features=top_m, random_state=4)
+
+Then, we print the statistics, sorted by relative importance:
+
+  >>> print(" Pair       H^2  Unnormalized H")
+  >>> for i in np.argsort(H["h_squared_pairwise"])[::-1]:
+  ...     print(f"{str(H['feature_pairs'][i]):<11}"
+  ...     f"{H['h_squared_pairwise'][i]:.3f}"
+  ...     f"    {np.sqrt(H['numerator_pairwise'][i]):.3f}")
+
+ Pair       H^2  Unnormalized H
+(1, 0)     0.155    3.947
+(0, 9)     0.059    2.097
+(0, 3)     0.054    3.279
+(3, 2)     0.043    6.952
+(9, 3)     0.037    2.805
+(1, 3)     0.028    2.367
+(9, 8)     0.022    4.866
+(2, 8)     0.021    6.524
+(0, 2)     0.019    3.341
+(3, 8)     0.015    4.622
+(1, 2)     0.010    2.558
+(0, 8)     0.009    2.803
+(9, 2)     0.006    1.942
+(1, 8)     0.002    1.234
+(1, 9)     0.002    0.453
+
+The column ``H^2`` shows that the interaction between features 0 and 1
+explains 15.5% of their joint effect variability. For the other pairs, it is
+clearly less. The column ``Unnormalized H`` (:math:`\sqrt{A_{jk}}`) additionally 
+shows that the interaction between features 2 and 3 is the largest in absolute terms.
+The reason for this is that these features belong to the most important predictors.
+Their weak relative interaction is still stronger than the strong relative interaction
+between features 0 and 1. Thus, it often makes sense to study both normalized and
+unnormalized statistics.
 
 Limitations
 ===========

From c5e56abf0da157331c077b082469cdfe272679bc Mon Sep 17 00:00:00 2001
From: Michael Mayer <mayermichael79@gmail.com>
Date: Fri, 19 Apr 2024 21:55:30 +0200
Subject: [PATCH 37/66] rst docu: reformat code

---
 doc/modules/h_statistic.rst | 71 +++++++++++++++++++------------------
 1 file changed, 36 insertions(+), 35 deletions(-)

diff --git a/doc/modules/h_statistic.rst b/doc/modules/h_statistic.rst
index 71cf2cb6f92fd..14b6ca97769c1 100644
--- a/doc/modules/h_statistic.rst
+++ b/doc/modules/h_statistic.rst
@@ -108,44 +108,45 @@ Let's calculate pairwise H-statistics from a boosted trees model of diabetes dat
 There are :math:`p = 10` features, leading to 45 pairwise interactions. To save
 time, we select the top 6 predictors via permutation importance, leading to 15 pairs.
 
-  >>> import numpy as np
-  >>> from sklearn.ensemble import HistGradientBoostingRegressor
-  >>> from sklearn.inspection import permutation_importance, h_statistic
-  >>> from sklearn.datasets import load_diabetes
-
-  >>> X, y = load_diabetes(return_X_y=True)
-  >>> est = HistGradientBoostingRegressor(max_iter=100, max_depth=4).fit(X, y)
-
-  >>> # Get Friedman's H-squared for top m=6 predictors
-  >>> m = 6
-  >>> imp = permutation_importance(est, X, y, random_state=0)
-  >>> top_m = np.argsort(imp.importances_mean)[-m:]
-  >>> H = h_statistic(est, X=X, features=top_m, random_state=4)
+    >>> import numpy as np
+    >>> from sklearn.ensemble import HistGradientBoostingRegressor
+    >>> from sklearn.inspection import permutation_importance, h_statistic
+    >>> from sklearn.datasets import load_diabetes
+  
+    >>> # Fit model
+    >>> X, y = load_diabetes(return_X_y=True)
+    >>> est = HistGradientBoostingRegressor(max_iter=100, max_depth=4).fit(X, y)
+  
+    >>> # Get Friedman's H-squared for top m=6 predictors
+    >>> m = 6
+    >>> imp = permutation_importance(est, X, y, random_state=0)
+    >>> top_m = np.argsort(imp.importances_mean)[-m:]
+    >>> H = h_statistic(est, X=X, features=top_m, random_state=4)
 
 Then, we print the statistics, sorted by relative importance:
 
-  >>> print(" Pair       H^2  Unnormalized H")
-  >>> for i in np.argsort(H["h_squared_pairwise"])[::-1]:
-  ...     print(f"{str(H['feature_pairs'][i]):<11}"
-  ...     f"{H['h_squared_pairwise'][i]:.3f}"
-  ...     f"    {np.sqrt(H['numerator_pairwise'][i]):.3f}")
-
- Pair       H^2  Unnormalized H
-(1, 0)     0.155    3.947
-(0, 9)     0.059    2.097
-(0, 3)     0.054    3.279
-(3, 2)     0.043    6.952
-(9, 3)     0.037    2.805
-(1, 3)     0.028    2.367
-(9, 8)     0.022    4.866
-(2, 8)     0.021    6.524
-(0, 2)     0.019    3.341
-(3, 8)     0.015    4.622
-(1, 2)     0.010    2.558
-(0, 8)     0.009    2.803
-(9, 2)     0.006    1.942
-(1, 8)     0.002    1.234
-(1, 9)     0.002    0.453
+    >>> print(" Pair       H^2  Unnormalized H")
+    >>> for i in np.argsort(H["h_squared_pairwise"])[::-1]:
+    ...     print(f"{str(H['feature_pairs'][i]):<11}"
+    ...     f"{H['h_squared_pairwise'][i]:.3f}"
+    ...     f"    {np.sqrt(H['numerator_pairwise'][i]):.3f}")
+    ...
+   Pair       H^2  Unnormalized H
+  (1, 0)     0.155    3.947
+  (0, 9)     0.059    2.097
+  (0, 3)     0.054    3.279
+  (3, 2)     0.043    6.952
+  (9, 3)     0.037    2.805
+  (1, 3)     0.028    2.367
+  (9, 8)     0.022    4.866
+  (2, 8)     0.021    6.524
+  (0, 2)     0.019    3.341
+  (3, 8)     0.015    4.622
+  (1, 2)     0.010    2.558
+  (0, 8)     0.009    2.803
+  (9, 2)     0.006    1.942
+  (1, 8)     0.002    1.234
+  (1, 9)     0.002    0.453
 
 The column ``H^2`` shows that the interaction between features 0 and 1
 explains 15.5% of their joint effect variability. For the other pairs, it is

From 9ed9b2bf71f06ca8e0f0733401fa2781c3f4eb08 Mon Sep 17 00:00:00 2001
From: Michael Mayer <mayermichael79@gmail.com>
Date: Fri, 19 Apr 2024 22:18:20 +0200
Subject: [PATCH 38/66] Change intendation in example output of rst docu

---
 doc/modules/h_statistic.rst | 32 ++++++++++++++++----------------
 1 file changed, 16 insertions(+), 16 deletions(-)

diff --git a/doc/modules/h_statistic.rst b/doc/modules/h_statistic.rst
index 14b6ca97769c1..b28893922192a 100644
--- a/doc/modules/h_statistic.rst
+++ b/doc/modules/h_statistic.rst
@@ -131,22 +131,22 @@ Then, we print the statistics, sorted by relative importance:
     ...     f"{H['h_squared_pairwise'][i]:.3f}"
     ...     f"    {np.sqrt(H['numerator_pairwise'][i]):.3f}")
     ...
-   Pair       H^2  Unnormalized H
-  (1, 0)     0.155    3.947
-  (0, 9)     0.059    2.097
-  (0, 3)     0.054    3.279
-  (3, 2)     0.043    6.952
-  (9, 3)     0.037    2.805
-  (1, 3)     0.028    2.367
-  (9, 8)     0.022    4.866
-  (2, 8)     0.021    6.524
-  (0, 2)     0.019    3.341
-  (3, 8)     0.015    4.622
-  (1, 2)     0.010    2.558
-  (0, 8)     0.009    2.803
-  (9, 2)     0.006    1.942
-  (1, 8)     0.002    1.234
-  (1, 9)     0.002    0.453
+    Pair       H^2  Unnormalized H
+    (1, 0)     0.155    3.947
+    (0, 9)     0.059    2.097
+    (0, 3)     0.054    3.279
+    (3, 2)     0.043    6.952
+    (9, 3)     0.037    2.805
+    (1, 3)     0.028    2.367
+    (9, 8)     0.022    4.866
+    (2, 8)     0.021    6.524
+    (0, 2)     0.019    3.341
+    (3, 8)     0.015    4.622
+    (1, 2)     0.010    2.558
+    (0, 8)     0.009    2.803
+    (9, 2)     0.006    1.942
+    (1, 8)     0.002    1.234
+    (1, 9)     0.002    0.453
 
 The column ``H^2`` shows that the interaction between features 0 and 1
 explains 15.5% of their joint effect variability. For the other pairs, it is

From 0b29fc8ccb49169679ec1edc5ceeb9bc146ed03a Mon Sep 17 00:00:00 2001
From: Michael Mayer <mayermichael79@gmail.com>
Date: Fri, 19 Apr 2024 22:50:40 +0200
Subject: [PATCH 39/66] doctest failure

---
 doc/modules/h_statistic.rst | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/doc/modules/h_statistic.rst b/doc/modules/h_statistic.rst
index b28893922192a..890f9f76d0485 100644
--- a/doc/modules/h_statistic.rst
+++ b/doc/modules/h_statistic.rst
@@ -125,13 +125,12 @@ time, we select the top 6 predictors via permutation importance, leading to 15 p
 
 Then, we print the statistics, sorted by relative importance:
 
-    >>> print(" Pair       H^2  Unnormalized H")
     >>> for i in np.argsort(H["h_squared_pairwise"])[::-1]:
     ...     print(f"{str(H['feature_pairs'][i]):<11}"
     ...     f"{H['h_squared_pairwise'][i]:.3f}"
     ...     f"    {np.sqrt(H['numerator_pairwise'][i]):.3f}")
     ...
-    Pair       H^2  Unnormalized H
+    # Pair      H^2  Unnormalized H
     (1, 0)     0.155    3.947
     (0, 9)     0.059    2.097
     (0, 3)     0.054    3.279

From 5845331d2ae333838754d5418b41c9ede4c72456 Mon Sep 17 00:00:00 2001
From: Michael Mayer <mayermichael79@gmail.com>
Date: Sat, 20 Apr 2024 08:02:37 +0200
Subject: [PATCH 40/66] documentation: image is better than print

---
 doc/images/h_statistic.png         | Bin 0 -> 30211 bytes
 doc/modules/h_statistic.rst        |  63 +++++++++++++----------------
 sklearn/inspection/_h_statistic.py |  18 +++++----
 3 files changed, 37 insertions(+), 44 deletions(-)
 create mode 100644 doc/images/h_statistic.png

diff --git a/doc/images/h_statistic.png b/doc/images/h_statistic.png
new file mode 100644
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diff --git a/doc/modules/h_statistic.rst b/doc/modules/h_statistic.rst
index 890f9f76d0485..9b85e5e15e9fa 100644
--- a/doc/modules/h_statistic.rst
+++ b/doc/modules/h_statistic.rst
@@ -24,8 +24,7 @@ The H-statistic of two features is the proportion of effect variability of the
 two features coming from their pairwise interaction. 
 Effect variability is measured via partial dependence.
 
-Partial dependence
-------------------
+**Partial dependence**
 
 Let :math:`F: \mathbb{R}^p \to \mathbb{R}` denote the prediction function that 
 maps the :math:`p`-dimensional feature vector :math:`\mathbf x = (x_1, \dots, x_p)`
@@ -45,8 +44,7 @@ dependence function
 where :math:`\mathbf x_{i\setminus s}`, :math:`i = 1, \dots, n`, 
 are the observed values of :math:`\mathbf x_{\setminus s}` in some "background" dataset.
 
-Pairwise H-statistic
---------------------
+**Pairwise H-statistic**
 
 Following [F2008]_, if there are no interaction effects between features
 :math:`x_j` and :math:`x_k`, their two-dimensional partial dependence function
@@ -123,38 +121,31 @@ time, we select the top 6 predictors via permutation importance, leading to 15 p
     >>> top_m = np.argsort(imp.importances_mean)[-m:]
     >>> H = h_statistic(est, X=X, features=top_m, random_state=4)
 
-Then, we print the statistics, sorted by relative importance:
-
-    >>> for i in np.argsort(H["h_squared_pairwise"])[::-1]:
-    ...     print(f"{str(H['feature_pairs'][i]):<11}"
-    ...     f"{H['h_squared_pairwise'][i]:.3f}"
-    ...     f"    {np.sqrt(H['numerator_pairwise'][i]):.3f}")
-    ...
-    # Pair      H^2  Unnormalized H
-    (1, 0)     0.155    3.947
-    (0, 9)     0.059    2.097
-    (0, 3)     0.054    3.279
-    (3, 2)     0.043    6.952
-    (9, 3)     0.037    2.805
-    (1, 3)     0.028    2.367
-    (9, 8)     0.022    4.866
-    (2, 8)     0.021    6.524
-    (0, 2)     0.019    3.341
-    (3, 8)     0.015    4.622
-    (1, 2)     0.010    2.558
-    (0, 8)     0.009    2.803
-    (9, 2)     0.006    1.942
-    (1, 8)     0.002    1.234
-    (1, 9)     0.002    0.453
-
-The column ``H^2`` shows that the interaction between features 0 and 1
-explains 15.5% of their joint effect variability. For the other pairs, it is
-clearly less. The column ``Unnormalized H`` (:math:`\sqrt{A_{jk}}`) additionally 
-shows that the interaction between features 2 and 3 is the largest in absolute terms.
-The reason for this is that these features belong to the most important predictors.
-Their weak relative interaction is still stronger than the strong relative interaction
-between features 0 and 1. Thus, it often makes sense to study both normalized and
-unnormalized statistics.
+Then, we plot all :math:`H_{jk}^2` and :math:`\sqrt{A_{jk}}` statistics:
+
+    >>> import matplotlib.pyplot as plt
+    >>> 
+    >>> fig, axes = plt.subplots(1, 2, figsize=(8, 4), layout="tight")
+    >>> 
+    >>> bar_labels = np.array([str(pair) for pair in H["feature_pairs"]])
+    >>> stats = (H["h_squared_pairwise"], np.sqrt(H["numerator_pairwise"]))
+    >>> 
+    >>> for ax, stat, name in zip(axes, stats, ("$H^2$", "Unnormalized $H$")):
+    >>>    idx = np.argsort(stat)
+    >>>    ax.barh(bar_labels[idx], stat[idx], color="orange")
+    >>>    ax.set(xlabel=name, title=name)
+
+.. image:: ../images/h_statistic.png
+   :align: center
+
+**The left plot** shows that the interaction between features 0 and 1
+explains about 16% of their joint effect variability. For the other pairs, it is
+clearly less. **The right plot** additionally shows that the interaction between
+features 2 and 3 is the largest in absolute terms. The reason for this is that
+these features belong to the most important predictors.
+Their weak relative interaction is still stronger than the strong relative
+interaction between features 0 and 1.
+Thus, it often makes sense to study both normalized and unnormalized statistics.
 
 Limitations
 ===========
diff --git a/sklearn/inspection/_h_statistic.py b/sklearn/inspection/_h_statistic.py
index 752a11e2eb9db..a6201e07ba1e3 100644
--- a/sklearn/inspection/_h_statistic.py
+++ b/sklearn/inspection/_h_statistic.py
@@ -207,6 +207,7 @@ def h_statistic(
     >>> from sklearn.inspection import permutation_importance, h_statistic
     >>> from sklearn.datasets import load_diabetes
     >>>
+    >>> # Fit model
     >>> X, y = load_diabetes(return_X_y=True)
     >>> est = HistGradientBoostingRegressor(max_iter=5, max_depth=2).fit(X, y)
     >>>
@@ -215,14 +216,15 @@ def h_statistic(
     >>> imp = permutation_importance(est, X, y, random_state=0)
     >>> top_m = np.argsort(imp.importances_mean)[-m:]
     >>> H = h_statistic(est, X=X, features=top_m, random_state=4)
-
-    >>> # For features (8, 2), 3.4% of the joint effect variability comes from
-    >>> # their interaction. These two features also have strongest absolute
-    >>> # interaction, see "numerator_pairwise":
-    >>> # {'feature_pairs': [(3, 8), (3, 2), (8, 2)],
-    >>> # 'h_squared_pairwise': array([0.00985985, 0.00927104, 0.03439926]),
-    >>> # 'numerator_pairwise': array([1.2955532, 1.2419687, 11.13358385]),
-    >>> # 'denominator_pairwise': array([131.39690331, 133.96210997, 323.6576595])}
+    >>> H
+    {'feature_pairs': [(3, 8), (3, 2), (8, 2)],
+     'h_squared_pairwise': array([0.00985985, 0.00927104, 0.03439926]),
+     'numerator_pairwise': array([ 1.2955532 ,  1.2419687 , 11.13358385]),
+     'denominator_pairwise': array([131.39690331, 133.96210997, 323.6576595 ])}
+
+    >>> # Interpretation: For features (8, 2), 3.4% of the joint effect variability
+    >>> # comes from their interaction. These two features also have strongest absolute
+    >>> # interaction across pairs ("numerator_pairwise").
     """
     check_is_fitted(estimator)
 

From 91c2d65a8177efa54aaa8ff9cb86b8779f5662e4 Mon Sep 17 00:00:00 2001
From: Michael Mayer <mayermichael79@gmail.com>
Date: Sat, 20 Apr 2024 10:16:56 +0200
Subject: [PATCH 41/66] Intendation fix

---
 doc/modules/h_statistic.rst | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/doc/modules/h_statistic.rst b/doc/modules/h_statistic.rst
index 9b85e5e15e9fa..aa3d85036dfc2 100644
--- a/doc/modules/h_statistic.rst
+++ b/doc/modules/h_statistic.rst
@@ -131,9 +131,9 @@ Then, we plot all :math:`H_{jk}^2` and :math:`\sqrt{A_{jk}}` statistics:
     >>> stats = (H["h_squared_pairwise"], np.sqrt(H["numerator_pairwise"]))
     >>> 
     >>> for ax, stat, name in zip(axes, stats, ("$H^2$", "Unnormalized $H$")):
-    >>>    idx = np.argsort(stat)
-    >>>    ax.barh(bar_labels[idx], stat[idx], color="orange")
-    >>>    ax.set(xlabel=name, title=name)
+    >>>     idx = np.argsort(stat)
+    >>>     ax.barh(bar_labels[idx], stat[idx], color="orange")
+    >>>     ax.set(xlabel=name, title=name)
 
 .. image:: ../images/h_statistic.png
    :align: center
@@ -163,4 +163,4 @@ Limitations
 
     .. [F2008] J. H. Friedman and B. E. Popescu,
                "Predictive Learning via Rule Ensembles",
-               The Annals of Applied Statistics, 2(3), 916-954, 2008.
\ No newline at end of file
+               The Annals of Applied Statistics, 2(3), 916-954, 2008.

From 25a3f6d5c5837199144bd8ccb733cb015a432b34 Mon Sep 17 00:00:00 2001
From: Michael Mayer <mayermichael79@gmail.com>
Date: Sat, 20 Apr 2024 14:50:12 +0200
Subject: [PATCH 42/66] fix doctest issues

---
 doc/modules/h_statistic.rst        | 19 ++++++++++---------
 sklearn/inspection/_h_statistic.py |  4 ----
 2 files changed, 10 insertions(+), 13 deletions(-)

diff --git a/doc/modules/h_statistic.rst b/doc/modules/h_statistic.rst
index aa3d85036dfc2..2e711b05aa171 100644
--- a/doc/modules/h_statistic.rst
+++ b/doc/modules/h_statistic.rst
@@ -110,11 +110,11 @@ time, we select the top 6 predictors via permutation importance, leading to 15 p
     >>> from sklearn.ensemble import HistGradientBoostingRegressor
     >>> from sklearn.inspection import permutation_importance, h_statistic
     >>> from sklearn.datasets import load_diabetes
-  
+
     >>> # Fit model
     >>> X, y = load_diabetes(return_X_y=True)
     >>> est = HistGradientBoostingRegressor(max_iter=100, max_depth=4).fit(X, y)
-  
+
     >>> # Get Friedman's H-squared for top m=6 predictors
     >>> m = 6
     >>> imp = permutation_importance(est, X, y, random_state=0)
@@ -124,16 +124,17 @@ time, we select the top 6 predictors via permutation importance, leading to 15 p
 Then, we plot all :math:`H_{jk}^2` and :math:`\sqrt{A_{jk}}` statistics:
 
     >>> import matplotlib.pyplot as plt
-    >>> 
-    >>> fig, axes = plt.subplots(1, 2, figsize=(8, 4), layout="tight")
-    >>> 
+    >>>
+    >>> fig, axes = plt.subplots(1, 2, figsize=(8, 4))
+    >>>
     >>> bar_labels = np.array([str(pair) for pair in H["feature_pairs"]])
     >>> stats = (H["h_squared_pairwise"], np.sqrt(H["numerator_pairwise"]))
-    >>> 
+    >>>
     >>> for ax, stat, name in zip(axes, stats, ("$H^2$", "Unnormalized $H$")):
-    >>>     idx = np.argsort(stat)
-    >>>     ax.barh(bar_labels[idx], stat[idx], color="orange")
-    >>>     ax.set(xlabel=name, title=name)
+    ...     idx = np.argsort(stat)
+    ...     ax.barh(bar_labels[idx], stat[idx], color="orange")
+    ...     ax.set(xlabel=name, title=name)
+    >>> _ = fig.tight_layout()
 
 .. image:: ../images/h_statistic.png
    :align: center
diff --git a/sklearn/inspection/_h_statistic.py b/sklearn/inspection/_h_statistic.py
index a6201e07ba1e3..999e7eadc78b8 100644
--- a/sklearn/inspection/_h_statistic.py
+++ b/sklearn/inspection/_h_statistic.py
@@ -206,12 +206,8 @@ def h_statistic(
     >>> from sklearn.ensemble import HistGradientBoostingRegressor
     >>> from sklearn.inspection import permutation_importance, h_statistic
     >>> from sklearn.datasets import load_diabetes
-    >>>
-    >>> # Fit model
     >>> X, y = load_diabetes(return_X_y=True)
     >>> est = HistGradientBoostingRegressor(max_iter=5, max_depth=2).fit(X, y)
-    >>>
-    >>> # Get Friedman's H-squared for top m=3 predictors
     >>> m = 3
     >>> imp = permutation_importance(est, X, y, random_state=0)
     >>> top_m = np.argsort(imp.importances_mean)[-m:]

From 72ce9c7de0caebf0872c088e425953be5fcc9415 Mon Sep 17 00:00:00 2001
From: Michael Mayer <mayermichael79@gmail.com>
Date: Fri, 26 Apr 2024 08:32:47 +0200
Subject: [PATCH 43/66] Review Lorentzen

---
 sklearn/inspection/_h_statistic.py           |  13 +-
 sklearn/inspection/tests/test_h_statistic.py | 126 +++++++++++++------
 2 files changed, 90 insertions(+), 49 deletions(-)

diff --git a/sklearn/inspection/_h_statistic.py b/sklearn/inspection/_h_statistic.py
index 999e7eadc78b8..07e0740471d88 100644
--- a/sklearn/inspection/_h_statistic.py
+++ b/sklearn/inspection/_h_statistic.py
@@ -39,9 +39,8 @@ def _calculate_pd_brute_fast(estimator, X, feature_indices, grid, sample_weight=
     # X is stacked n_grid times, and grid columns are replaced by replicated grid
     n = X.shape[0]
     n_grid = grid.shape[0]
-    X_eval = X.copy()
 
-    X_stacked = _safe_indexing(X_eval, np.tile(np.arange(n), n_grid), axis=0)
+    X_stacked = _safe_indexing(X, np.tile(np.arange(n), n_grid), axis=0)
     grid_stacked = _safe_indexing(grid, np.repeat(np.arange(n_grid), n), axis=0)
     _safe_assign(X_stacked, values=grid_stacked, column_indexer=feature_indices)
 
@@ -51,11 +50,11 @@ def _calculate_pd_brute_fast(estimator, X, feature_indices, grid, sample_weight=
         preds = preds[:, 1]
 
     # Partial dependences are averages per grid block
-    pd_values = [
-        np.average(Z, axis=0, weights=sample_weight) for Z in np.split(preds, n_grid)
-    ]
+    pd_values = np.fromiter(
+        [np.average(Z, axis=0, weights=sample_weight) for Z in np.split(preds, n_grid)]
+    )
 
-    return np.array(pd_values)
+    return pd_values
 
 
 def _calculate_pd_over_data(estimator, X, feature_indices, sample_weight=None):
@@ -246,8 +245,6 @@ def h_statistic(
         X = _safe_indexing(X, row_indices, axis=0)
         if sample_weight is not None:
             sample_weight = _safe_indexing(sample_weight, row_indices, axis=0)
-    else:
-        X = X.copy()
 
     if features is None:
         features = feature_indices = np.arange(X.shape[1])
diff --git a/sklearn/inspection/tests/test_h_statistic.py b/sklearn/inspection/tests/test_h_statistic.py
index c6627cf1597b7..abff222e221e7 100644
--- a/sklearn/inspection/tests/test_h_statistic.py
+++ b/sklearn/inspection/tests/test_h_statistic.py
@@ -13,6 +13,20 @@
 from sklearn.linear_model import LinearRegression
 
 
+class Deterministic(RegressorMixin):
+    """Create estimator from deteministic prediction function"""
+
+    def __init__(self, pred_fun):
+        self._pred_fun = pred_fun
+        self.is_fitted_ = True
+
+    def fit(self, *args, **kwargs):
+        return self
+
+    def predict(self, X):
+        return self._pred_fun(X)
+
+
 @pytest.mark.parametrize(
     "est",
     (
@@ -24,7 +38,7 @@
 @pytest.mark.parametrize("n_max", [500, 50])
 @pytest.mark.parametrize("sample_weight", [None, "ones"])
 def test_h_statistic_additive_regression(est, features, n_max, sample_weight):
-    # Checks that additive regressions get statistics of 0.
+    """Checks that additive regressions get statistics of 0."""
     N_FEAT = 4
     N = 200
     X, y = make_regression(n_samples=N, n_features=N_FEAT, random_state=0)
@@ -42,30 +56,6 @@ def test_h_statistic_additive_regression(est, features, n_max, sample_weight):
     assert_allclose(result.h_squared_pairwise, 0)
 
 
-def test_h_statistic_additive_classification():
-    # Checks that additive classification gets statistics of 0. The presence
-    # of link functions (especially for GradientBoosting with more than 2 classes)
-    # would make further tests tricky.
-    N_FEAT = 4
-    N_CLASSES = 4
-    X, y = make_classification(
-        n_samples=200,
-        n_features=N_FEAT,
-        n_informative=N_FEAT,
-        n_redundant=0,
-        n_classes=N_CLASSES,
-        random_state=0,
-    )
-    model = RandomForestClassifier(n_estimators=20, random_state=0, max_depth=1)
-    model.fit(X, y)
-    result = h_statistic(model, X, random_state=1)
-
-    expected_length = N_FEAT * (N_FEAT - 1) / 2
-
-    assert result.h_squared_pairwise.shape == (expected_length, N_CLASSES)
-    assert_allclose(result.h_squared_pairwise, 0)
-
-
 @pytest.mark.parametrize(
     "est",
     (
@@ -77,7 +67,7 @@ def test_h_statistic_additive_classification():
 @pytest.mark.parametrize("n_max", [500, 50])
 @pytest.mark.parametrize("sample_weight", [None, "ones"])
 def test_h_statistic_regression(est, features, n_max, sample_weight):
-    # Tests that models with interactions will produce (some) positive values.
+    """Tests that models with interactions will produce (some) positive values."""
     N_FEAT = 4
     N = 200
     X, y = make_regression(n_samples=N, n_features=N_FEAT, random_state=0)
@@ -91,10 +81,36 @@ def test_h_statistic_regression(est, features, n_max, sample_weight):
     assert any(result.h_squared_pairwise > 1e-5)
 
 
+def test_h_statistic_additive_classification():
+    """Checks that additive classification gets statistics of 0.
+
+    The presence of link functions (especially for GradientBoosting with
+    more than 2 classes) would make further tests tricky.
+    """
+    N_FEAT = 4
+    N_CLASSES = 4
+    X, y = make_classification(
+        n_samples=200,
+        n_features=N_FEAT,
+        n_informative=N_FEAT,
+        n_redundant=0,
+        n_classes=N_CLASSES,
+        random_state=0,
+    )
+    model = RandomForestClassifier(n_estimators=20, random_state=0, max_depth=1)
+    model.fit(X, y)
+    result = h_statistic(model, X, random_state=1)
+
+    expected_length = N_FEAT * (N_FEAT - 1) / 2
+
+    assert result.h_squared_pairwise.shape == (expected_length, N_CLASSES)
+    assert_allclose(result.h_squared_pairwise, 0)
+
+
 @pytest.mark.parametrize("sample_weight", [None, "ones"])
 @pytest.mark.parametrize("n_max", [500, 50])
 def test_h_statistic_equivalence_array_dataframe(n_max, sample_weight):
-    # Checks that index operations give the same for numpy arrays or dataframes.
+    """Checks that index operations give the same for numpy arrays or dataframes."""
     pd = pytest.importorskip("pandas")
 
     N = 200
@@ -118,7 +134,7 @@ def test_h_statistic_equivalence_array_dataframe(n_max, sample_weight):
 
 
 def test_h_statistic_reflect_interaction_constraints():
-    # Checks that interaction constraints are reflected.
+    """Checks that interaction constraints are reflected."""
     X, y = make_regression(n_samples=200, n_features=4, random_state=0)
 
     model = HistGradientBoostingRegressor(
@@ -132,9 +148,11 @@ def test_h_statistic_reflect_interaction_constraints():
 
 
 def test_h_statistic_matches_other_implementations():
-    # Checks for one specific example that the result matches the one from two
-    # independent R packages ({iml} of Christoph Molnar, {hstats} of Michael Mayer).
-    # The latter also allows for sample weights.
+    """Check against other implementations
+
+    Check for one specific example that the result matches the one from two
+    independent R packages ({iml} of Christoph Molnar, {hstats} of Michael Mayer).
+    The latter also allows for sample weights.
     # X <- data.frame(x1 = c(1, 1, 1, 2), x2 = c(0, 0, 2, 1), x3 = c(2, 2, 1, 4))
     # pred_fun <- function(model, newdata)
     #   newdata[, 1] + newdata[, 2] * sqrt(newdata[, 3])
@@ -147,16 +165,7 @@ def test_h_statistic_matches_other_implementations():
     # library(hstats)  # 1.1.2
     # h2_pairwise(hstats(X = X, pred_fun = pred_fun))           # x2:x3 -> 0.078125
     # h2_pairwise(hstats(X = X, pred_fun = pred_fun, w = 0:3))  # weights -> 0.1010093
-    class Deterministic(RegressorMixin):
-        def __init__(self, pred_fun):
-            self._pred_fun = pred_fun
-            self.is_fitted_ = True
-
-        def fit(self, *args, **kwargs):
-            return self
-
-        def predict(self, X):
-            return self._pred_fun(X)
+    """
 
     X = np.array([[1, 1, 1, 2], [0, 0, 2, 1], [2, 2, 1, 4]]).T
 
@@ -168,3 +177,38 @@ def predict(self, X):
         h_statistic(model, X=X, sample_weight=range(4)).h_squared_pairwise[2],
         0.1010093,
     )
+
+
+def test_h_statistic_matches_analytic_result():
+    """Check against analytic result
+
+    By definition of the H-squared, a model consisting of an interaction
+    and without main effects would result in a value of 1.
+    """
+
+    X = np.array([[1, 1, -1, -1], [1, -1, 1, -1]]).T
+
+    # Model consistent of pure interaction (and zero main effects)
+    model = Deterministic(lambda X: X[:, 0] * X[:, 1])
+
+    assert_allclose(h_statistic(model, X=X).h_squared_pairwise[0], 1)
+
+
+@pytest.mark.parametrize("sample_weight", [None, "ones"])
+@pytest.mark.parametrize("n_max", [500, 50])
+def test_h_statistic_does_not_change_pandas_input(n_max, sample_weight):
+    """Checks that pandas data is unchanged by the function call."""
+    pd = pytest.importorskip("pandas")
+
+    N = 200
+    X, y = make_regression(n_samples=N, n_features=4, random_state=0)
+    X_df = pd.DataFrame(X)
+    X_df_orig = X_df.copy()
+    w = np.ones(N) if sample_weight == "ones" else None
+
+    # Pandas
+    model = RandomForestRegressor(random_state=0, max_depth=4, n_estimators=20)
+    model.fit(X_df, y, sample_weight=w)
+    _ = h_statistic(model, X_df, sample_weight=w, n_max=n_max, random_state=1)
+
+    pd.testing.assert_frame_equal(X_df, X_df_orig)

From cde7dae6559f207218817803070528ec377cdb7e Mon Sep 17 00:00:00 2001
From: Michael Mayer <mayermichael79@gmail.com>
Date: Fri, 26 Apr 2024 09:48:31 +0200
Subject: [PATCH 44/66] switch to pred_fun argument in helper

---
 sklearn/inspection/_h_statistic.py           | 61 ++++++++++--------
 sklearn/inspection/tests/test_h_statistic.py | 66 +++++++++++---------
 2 files changed, 70 insertions(+), 57 deletions(-)

diff --git a/sklearn/inspection/_h_statistic.py b/sklearn/inspection/_h_statistic.py
index 07e0740471d88..f0856fa50f38c 100644
--- a/sklearn/inspection/_h_statistic.py
+++ b/sklearn/inspection/_h_statistic.py
@@ -19,23 +19,14 @@
 from ..utils.validation import _check_sample_weight, check_is_fitted
 
 
-def _calculate_pd_brute_fast(estimator, X, feature_indices, grid, sample_weight=None):
+def _calculate_pd_brute_fast(
+    pred_fun, X, feature_indices, grid, sample_weight=None, reduce_binary=False
+):
     """Fast version of _calculate_partial_dependence_brute()
 
     Returns np.array of size (n_grid, ) or (n_ngrid, output_dim).
     """
 
-    if is_regressor(estimator):
-        if hasattr(estimator, "predict"):
-            pred_fun = estimator.predict
-        else:
-            raise ValueError("The regressor has no predict() method.")
-    elif is_classifier(estimator):
-        if hasattr(estimator, "predict_proba"):
-            pred_fun = estimator.predict_proba
-        else:
-            raise ValueError("The classifier has no predict_proba() method.")
-
     # X is stacked n_grid times, and grid columns are replaced by replicated grid
     n = X.shape[0]
     n_grid = grid.shape[0]
@@ -46,18 +37,20 @@ def _calculate_pd_brute_fast(estimator, X, feature_indices, grid, sample_weight=
 
     # Predict on stacked data. Pick positive class probs for binary classification
     preds = pred_fun(X_stacked)
-    if is_classifier(estimator) and preds.shape[1] == 2:
+    if reduce_binary:
         preds = preds[:, 1]
 
     # Partial dependences are averages per grid block
-    pd_values = np.fromiter(
-        [np.average(Z, axis=0, weights=sample_weight) for Z in np.split(preds, n_grid)]
-    )
+    pd_values = [
+        np.average(Z, axis=0, weights=sample_weight) for Z in np.split(preds, n_grid)
+    ]
 
-    return pd_values
+    return np.array(pd_values)
 
 
-def _calculate_pd_over_data(estimator, X, feature_indices, sample_weight=None):
+def _calculate_pd_over_data(
+    pred_fun, X, feature_indices, sample_weight=None, reduce_binary=False
+):
     """Calculates centered partial dependence over the data distribution.
 
     It returns a numpy array of size (n, ) or (n, output_dim).
@@ -78,11 +71,12 @@ def _calculate_pd_over_data(estimator, X, feature_indices, sample_weight=None):
         compressed_grid = False
 
     pd_values = _calculate_pd_brute_fast(
-        estimator,
+        pred_fun,
         X=X,
         feature_indices=feature_indices,
         grid=grid,
         sample_weight=sample_weight,
+        reduce_binary=reduce_binary,
     )
 
     if compressed_grid:
@@ -221,13 +215,23 @@ def h_statistic(
     >>> # comes from their interaction. These two features also have strongest absolute
     >>> # interaction across pairs ("numerator_pairwise").
     """
-    check_is_fitted(estimator)
+    reduce_binary = False  # In binary classification, we only retain second class probs
 
-    if not (is_classifier(estimator) or is_regressor(estimator)):
-        raise ValueError("'estimator' must be a fitted regressor or classifier.")
+    check_is_fitted(estimator)
 
-    if is_classifier(estimator) and isinstance(estimator.classes_[0], np.ndarray):
-        raise ValueError("Multiclass-multioutput estimators are not supported")
+    if is_regressor(estimator):
+        pred_fun = getattr(estimator, "predict", None)
+        if pred_fun is None:
+            raise ValueError("The regressor has no predict() method.")
+    elif is_classifier(estimator):
+        if isinstance(estimator.classes_[0], np.ndarray):
+            raise ValueError("Multiclass-multioutput estimators are not supported")
+        reduce_binary = len(estimator.classes_) == 2
+        pred_fun = getattr(estimator, "predict_proba", None)
+        if pred_fun is None:
+            raise ValueError("The classifier has no predict_proba() method.")
+    else:
+        raise ValueError("'estimator' must be a regressor or classifier.")
 
     # Use check_array only on lists and other non-array-likes / sparse. Do not
     # convert DataFrame into a NumPy array.
@@ -258,7 +262,11 @@ def h_statistic(
     for idx in feature_indices:
         pd_univariate.append(
             _calculate_pd_over_data(
-                estimator, X=X, feature_indices=[idx], sample_weight=sample_weight
+                pred_fun,
+                X=X,
+                feature_indices=[idx],
+                sample_weight=sample_weight,
+                reduce_binary=reduce_binary,
             )
         )
 
@@ -267,10 +275,11 @@ def h_statistic(
 
     for j, k in itertools.combinations(range(len(feature_indices)), 2):
         pd_bivariate = _calculate_pd_over_data(
-            estimator,
+            pred_fun,
             X=X,
             feature_indices=feature_indices[[j, k]],
             sample_weight=sample_weight,
+            reduce_binary=reduce_binary,
         )
         num.append(
             np.average(
diff --git a/sklearn/inspection/tests/test_h_statistic.py b/sklearn/inspection/tests/test_h_statistic.py
index abff222e221e7..b1a931605604a 100644
--- a/sklearn/inspection/tests/test_h_statistic.py
+++ b/sklearn/inspection/tests/test_h_statistic.py
@@ -28,17 +28,23 @@ def predict(self, X):
 
 
 @pytest.mark.parametrize(
-    "est",
+    ["est", "is_additive"],
     (
-        LinearRegression(),
-        HistGradientBoostingRegressor(random_state=0, max_depth=1, max_iter=20),
+        [LinearRegression(), True],
+        [HistGradientBoostingRegressor(random_state=0, max_depth=1, max_iter=20), True],
+        [RandomForestRegressor(random_state=0, max_depth=4, n_estimators=20), False],
+        [HistGradientBoostingRegressor(random_state=0, max_iter=20), False],
     ),
 )
 @pytest.mark.parametrize("features", [None, [0, 1, 2]])
 @pytest.mark.parametrize("n_max", [500, 50])
 @pytest.mark.parametrize("sample_weight", [None, "ones"])
-def test_h_statistic_additive_regression(est, features, n_max, sample_weight):
-    """Checks that additive regressions get statistics of 0."""
+def test_h_statistic_regression(est, is_additive, features, n_max, sample_weight):
+    """Checks on regression.
+
+    Additive regression models should get statistics of 0, while non-additive
+    models should get a value > 0.
+    """
     N_FEAT = 4
     N = 200
     X, y = make_regression(n_samples=N, n_features=N_FEAT, random_state=0)
@@ -53,32 +59,10 @@ def test_h_statistic_additive_regression(est, features, n_max, sample_weight):
     expected_length = m * (m - 1) / 2
 
     assert result.h_squared_pairwise.shape == (expected_length,)
-    assert_allclose(result.h_squared_pairwise, 0)
-
-
-@pytest.mark.parametrize(
-    "est",
-    (
-        RandomForestRegressor(random_state=0, max_depth=4, n_estimators=20),
-        HistGradientBoostingRegressor(random_state=0, max_iter=20),
-    ),
-)
-@pytest.mark.parametrize("features", [None, [0, 1, 2]])
-@pytest.mark.parametrize("n_max", [500, 50])
-@pytest.mark.parametrize("sample_weight", [None, "ones"])
-def test_h_statistic_regression(est, features, n_max, sample_weight):
-    """Tests that models with interactions will produce (some) positive values."""
-    N_FEAT = 4
-    N = 200
-    X, y = make_regression(n_samples=N, n_features=N_FEAT, random_state=0)
-    w = np.ones(N) if sample_weight == "ones" else None
-
-    model = est.fit(X, y, sample_weight=w)
-    result = h_statistic(
-        model, X, features=features, sample_weight=w, n_max=n_max, random_state=1
-    )
-
-    assert any(result.h_squared_pairwise > 1e-5)
+    if is_additive:
+        assert_allclose(result.h_squared_pairwise, 0)
+    else:
+        assert any(result.h_squared_pairwise > 1e-5)
 
 
 def test_h_statistic_additive_classification():
@@ -107,6 +91,26 @@ def test_h_statistic_additive_classification():
     assert_allclose(result.h_squared_pairwise, 0)
 
 
+def test_h_statistic_binary_classification_shape():
+    """Checks that 0 class is dropped from binary classification."""
+    N_FEAT = 4
+    X, y = make_classification(
+        n_samples=200,
+        n_features=N_FEAT,
+        n_informative=N_FEAT,
+        n_redundant=0,
+        n_classes=2,
+        random_state=0,
+    )
+    model = RandomForestClassifier(n_estimators=20, random_state=0, max_depth=3)
+    model.fit(X, y)
+    result = h_statistic(model, X, random_state=1)
+
+    expected_length = N_FEAT * (N_FEAT - 1) / 2
+
+    assert result.h_squared_pairwise.shape == (expected_length,)
+
+
 @pytest.mark.parametrize("sample_weight", [None, "ones"])
 @pytest.mark.parametrize("n_max", [500, 50])
 def test_h_statistic_equivalence_array_dataframe(n_max, sample_weight):

From a3f1beb15c8f1fc9f8d8ca71b14d84c4122bdf5b Mon Sep 17 00:00:00 2001
From: Michael Mayer <mayermichael79@gmail.com>
Date: Fri, 26 Apr 2024 18:07:50 +0200
Subject: [PATCH 45/66] Initialize all resulting numpy arrays

---
 doc/modules/h_statistic.rst                  |  1 +
 sklearn/inspection/_h_statistic.py           | 74 ++++++++++++--------
 sklearn/inspection/tests/test_h_statistic.py |  4 +-
 3 files changed, 46 insertions(+), 33 deletions(-)

diff --git a/doc/modules/h_statistic.rst b/doc/modules/h_statistic.rst
index 2e711b05aa171..bedceb4bdb999 100644
--- a/doc/modules/h_statistic.rst
+++ b/doc/modules/h_statistic.rst
@@ -131,6 +131,7 @@ Then, we plot all :math:`H_{jk}^2` and :math:`\sqrt{A_{jk}}` statistics:
     >>> stats = (H["h_squared_pairwise"], np.sqrt(H["numerator_pairwise"]))
     >>>
     >>> for ax, stat, name in zip(axes, stats, ("$H^2$", "Unnormalized $H$")):
+    ...     stat = stat.ravel()
     ...     idx = np.argsort(stat)
     ...     ax.barh(bar_labels[idx], stat[idx], color="orange")
     ...     ax.set(xlabel=name, title=name)
diff --git a/sklearn/inspection/_h_statistic.py b/sklearn/inspection/_h_statistic.py
index f0856fa50f38c..f3e8ce1efc0ab 100644
--- a/sklearn/inspection/_h_statistic.py
+++ b/sklearn/inspection/_h_statistic.py
@@ -24,7 +24,7 @@ def _calculate_pd_brute_fast(
 ):
     """Fast version of _calculate_partial_dependence_brute()
 
-    Returns np.array of size (n_grid, ) or (n_ngrid, output_dim).
+    Returns np.array of size (n_grid, output_dim).
     """
 
     # X is stacked n_grid times, and grid columns are replaced by replicated grid
@@ -35,17 +35,20 @@ def _calculate_pd_brute_fast(
     grid_stacked = _safe_indexing(grid, np.repeat(np.arange(n_grid), n), axis=0)
     _safe_assign(X_stacked, values=grid_stacked, column_indexer=feature_indices)
 
-    # Predict on stacked data. Pick positive class probs for binary classification
     preds = pred_fun(X_stacked)
+
+    # Drop reduntant 0 class in binary classification
     if reduce_binary:
         preds = preds[:, 1]
 
     # Partial dependences are averages per grid block
-    pd_values = [
-        np.average(Z, axis=0, weights=sample_weight) for Z in np.split(preds, n_grid)
-    ]
+    pd_values = np.average(
+        preds.reshape(n_grid, preds.shape[0] // n_grid, -1),
+        axis=1,
+        weights=sample_weight,
+    )
 
-    return np.array(pd_values)
+    return pd_values
 
 
 def _calculate_pd_over_data(
@@ -53,7 +56,7 @@ def _calculate_pd_over_data(
 ):
     """Calculates centered partial dependence over the data distribution.
 
-    It returns a numpy array of size (n, ) or (n, output_dim).
+    It returns a numpy array of size (n, output_dim).
     """
 
     # Select grid columns and remove duplicates (will compensate below)
@@ -126,8 +129,8 @@ def h_statistic(
           functions centered to mean 0,
         - and the sums run over 1 <= i <= n, where n is the sample size.
 
-    It equals the proportion of effect variability between two features that cannot
-    be explained by their main effects. When there is no interaction, the value is
+    It equals the proportion of effect variability between two features unexplained
+    by their main effects. When there is no interaction, the value is
     exactly 0. The numerator (or its square root) provides an absolute measure
     of interaction strength, enabling direct comparison across feature pairs.
 
@@ -136,12 +139,12 @@ def h_statistic(
     automatically controlled via `n_max=500`, while it is the user's responsibility
     to select only a subset of *important* features. It is crucial to focus on important
     features because for weak predictors, the denominator might be small, and
-    even a weak interaction could result in a high Friedman's H, sometimes exceeding 1.
+    even a weak interaction could result in a high Friedman's H^2, sometimes exceeding 1.
 
     Parameters
     ----------
     estimator : object
-        An estimator that has already been :term:`fitted`.
+        A fitted estimator.
 
     X : {array-like or dataframe} of shape (n_samples, n_features)
         Data for which :term:`estimator` is able to calculate predictions.
@@ -166,23 +169,25 @@ def h_statistic(
     Returns
     -------
     result : :class:`~sklearn.utils.Bunch`
-        Dictionary-like object, with the following attributes.
+        Dictionary-like object, with the attributes listed below. Note that
+        `output_dim` equals the number of values predicted per observation.
+        For single-output regression and binary classification, `output_dim` is 1.
 
         feature_pairs : list of length n_feature_pairs
             The list contains tuples of feature pairs (indices) in the same order
             as all pairwise statistics.
 
-        h_squared_pairwise : ndarray of shape (n_pairs, ) or (n_pairs, output_dim)
+        h_squared_pairwise : ndarray of shape (n_pairs, output_dim)
             Pairwise H-squared statistic. Useful to see which feature pair has
             strongest relative interation (relative with respect to joint effect).
             Calculated as numerator_pairwise / denominator_pairwise.
 
-        numerator_pairwise : ndarray of shape (n_pairs, ) or (n_pairs, output_dim)
+        numerator_pairwise : ndarray of shape (n_pairs, output_dim)
             Numerator of pairwise H-squared statistic.
             Useful to see which feature pair has strongest absolute interaction.
             Take square-root to get values on the scale of the predictions.
 
-        denominator_pairwise : ndarray of shape (n_pairs, ) or (n_pairs, output_dim)
+        denominator_pairwise : ndarray of shape (n_pairs, output_dim)
             Denominator of pairwise H-squared statistic. Used for appropriate
             normalization of H.
 
@@ -206,10 +211,16 @@ def h_statistic(
     >>> top_m = np.argsort(imp.importances_mean)[-m:]
     >>> H = h_statistic(est, X=X, features=top_m, random_state=4)
     >>> H
-    {'feature_pairs': [(3, 8), (3, 2), (8, 2)],
-     'h_squared_pairwise': array([0.00985985, 0.00927104, 0.03439926]),
-     'numerator_pairwise': array([ 1.2955532 ,  1.2419687 , 11.13358385]),
-     'denominator_pairwise': array([131.39690331, 133.96210997, 323.6576595 ])}
+    feature_pairs': [(3, 8), (3, 2), (8, 2)],
+     'h_squared_pairwise': array([[0.00985985],
+             [0.00927104],
+             [0.03439926]]),
+     'numerator_pairwise': array([[ 1.2955532 ],
+             [ 1.2419687 ],
+             [11.13358385]]),
+     'denominator_pairwise': array([[131.39690331],
+             [133.96210997],
+             [323.6576595 ]])}
 
     >>> # Interpretation: For features (8, 2), 3.4% of the joint effect variability
     >>> # comes from their interaction. These two features also have strongest absolute
@@ -270,10 +281,15 @@ def h_statistic(
             )
         )
 
-    num = []
-    denom = []
+    n_features = len(features)
+    n_pairs = int(n_features * (n_features - 1) / 2)
+    output_dim = pd_univariate[0].shape[1]
+    output_shape = (n_pairs, output_dim)
 
-    for j, k in itertools.combinations(range(len(feature_indices)), 2):
+    num = np.empty(output_shape)
+    denom = np.empty(output_shape)
+
+    for i, (j, k) in enumerate(itertools.combinations(range(n_features), 2)):
         pd_bivariate = _calculate_pd_over_data(
             pred_fun,
             X=X,
@@ -281,18 +297,14 @@ def h_statistic(
             sample_weight=sample_weight,
             reduce_binary=reduce_binary,
         )
-        num.append(
-            np.average(
-                (pd_bivariate - pd_univariate[j] - pd_univariate[k]) ** 2,
-                axis=0,
-                weights=sample_weight,
-            )
+        num[i] = np.average(
+            (pd_bivariate - pd_univariate[j] - pd_univariate[k]) ** 2,
+            axis=0,
+            weights=sample_weight,
         )
-        denom.append(np.average(pd_bivariate**2, axis=0, weights=sample_weight))
+        denom[i] = np.average(pd_bivariate**2, axis=0, weights=sample_weight)
 
-    num = np.array(num)
     num[np.abs(num) < eps] = 0  # Round small numerators to 0
-    denom = np.array(denom)
     h2_stat = np.divide(num, denom, out=np.zeros_like(num), where=denom > 0)
 
     return Bunch(
diff --git a/sklearn/inspection/tests/test_h_statistic.py b/sklearn/inspection/tests/test_h_statistic.py
index b1a931605604a..4d9a52eebf3e5 100644
--- a/sklearn/inspection/tests/test_h_statistic.py
+++ b/sklearn/inspection/tests/test_h_statistic.py
@@ -58,7 +58,7 @@ def test_h_statistic_regression(est, is_additive, features, n_max, sample_weight
     m = N_FEAT if features is None else len(features)
     expected_length = m * (m - 1) / 2
 
-    assert result.h_squared_pairwise.shape == (expected_length,)
+    assert result.h_squared_pairwise.shape == (expected_length, 1)
     if is_additive:
         assert_allclose(result.h_squared_pairwise, 0)
     else:
@@ -108,7 +108,7 @@ def test_h_statistic_binary_classification_shape():
 
     expected_length = N_FEAT * (N_FEAT - 1) / 2
 
-    assert result.h_squared_pairwise.shape == (expected_length,)
+    assert result.h_squared_pairwise.shape == (expected_length, 1)
 
 
 @pytest.mark.parametrize("sample_weight", [None, "ones"])

From ffc6b77a5259ab503f4b013bf08f2e86ded93ef3 Mon Sep 17 00:00:00 2001
From: Michael Mayer <mayermichael79@gmail.com>
Date: Fri, 26 Apr 2024 18:11:57 +0200
Subject: [PATCH 46/66] Too long line in docstring

---
 sklearn/inspection/_h_statistic.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/sklearn/inspection/_h_statistic.py b/sklearn/inspection/_h_statistic.py
index f3e8ce1efc0ab..23505d81963fb 100644
--- a/sklearn/inspection/_h_statistic.py
+++ b/sklearn/inspection/_h_statistic.py
@@ -139,7 +139,7 @@ def h_statistic(
     automatically controlled via `n_max=500`, while it is the user's responsibility
     to select only a subset of *important* features. It is crucial to focus on important
     features because for weak predictors, the denominator might be small, and
-    even a weak interaction could result in a high Friedman's H^2, sometimes exceeding 1.
+    even a weak interaction could result in a high Friedman's H, sometimes exceeding 1.
 
     Parameters
     ----------

From ef203806151419f23a3f243f6ce7f5b345c86957 Mon Sep 17 00:00:00 2001
From: Michael Mayer <mayermichael79@gmail.com>
Date: Sat, 27 Apr 2024 07:18:09 +0200
Subject: [PATCH 47/66] Doctest failure

---
 sklearn/inspection/_h_statistic.py | 17 +++++++----------
 1 file changed, 7 insertions(+), 10 deletions(-)

diff --git a/sklearn/inspection/_h_statistic.py b/sklearn/inspection/_h_statistic.py
index 23505d81963fb..7a461c92cb209 100644
--- a/sklearn/inspection/_h_statistic.py
+++ b/sklearn/inspection/_h_statistic.py
@@ -211,16 +211,13 @@ def h_statistic(
     >>> top_m = np.argsort(imp.importances_mean)[-m:]
     >>> H = h_statistic(est, X=X, features=top_m, random_state=4)
     >>> H
-    feature_pairs': [(3, 8), (3, 2), (8, 2)],
-     'h_squared_pairwise': array([[0.00985985],
-             [0.00927104],
-             [0.03439926]]),
-     'numerator_pairwise': array([[ 1.2955532 ],
-             [ 1.2419687 ],
-             [11.13358385]]),
-     'denominator_pairwise': array([[131.39690331],
-             [133.96210997],
-             [323.6576595 ]])}
+    {'feature_pairs': [(3, 8), (3, 2), (8, 2)], 'h_squared_pairwise': array([[0.00985985],
+           [0.00927104],
+           [0.03439926]]), 'numerator_pairwise': array([[ 1.2955532 ],
+           [ 1.2419687 ],
+           [11.13358385]]), 'denominator_pairwise': array([[131.39690331],
+           [133.96210997],
+           [323.6576595 ]])}
 
     >>> # Interpretation: For features (8, 2), 3.4% of the joint effect variability
     >>> # comes from their interaction. These two features also have strongest absolute

From 3a34272d6952c4e0982ba16bbfc9b5d6af9e6662 Mon Sep 17 00:00:00 2001
From: Michael Mayer <mayermichael79@gmail.com>
Date: Sat, 27 Apr 2024 08:40:37 +0200
Subject: [PATCH 48/66] move example from rst file to
 plot_partial_dependence.py

---
 doc/modules/h_statistic.rst                   | 74 +++++--------------
 .../inspection/plot_partial_dependence.py     | 64 +++++++++++++++-
 2 files changed, 79 insertions(+), 59 deletions(-)

diff --git a/doc/modules/h_statistic.rst b/doc/modules/h_statistic.rst
index bedceb4bdb999..090594ef79a3f 100644
--- a/doc/modules/h_statistic.rst
+++ b/doc/modules/h_statistic.rst
@@ -15,14 +15,22 @@ partial dependence plots (PDP). But how to figure out between *which feature pai
 the strongest interactions occur?
 
 One approach is to study pairwise H-statistics, introduced by Friedman and Popescu
-in [F2008]_.
+in [F2008]_. The H-statistic of two features provides the proportion of effect
+variability of the two features coming from their pairwise interaction.
 
-The statistic
-=============
+The figure below shows H-statistics and their unnormalized counterparts for
+the bike sharing dataset, with a
+:class:`~sklearn.ensemble.HistGradientBoostingRegressor`:
 
-The H-statistic of two features is the proportion of effect variability of the 
-two features coming from their pairwise interaction. 
-Effect variability is measured via partial dependence.
+.. figure:: ../auto_examples/inspection/images/sphx_glr_plot_partial_dependence_007.png
+   :target: ../auto_examples/inspection/plot_partial_dependence.html
+   :align: center
+   :scale: 70
+
+The statistics have been compured for the five most important features.
+
+Mathematical definition
+=======================
 
 **Partial dependence**
 
@@ -99,56 +107,6 @@ Therefore, our implementation randomly selects ``n_max = 500`` rows from the pro
 Furthermore, if the number of features :math:`p` is large, use some feature importance measure
 to select the most important features and pass them via the ``features=None`` argument.
 
-Example
-=======
-
-Let's calculate pairwise H-statistics from a boosted trees model of diabetes data.
-There are :math:`p = 10` features, leading to 45 pairwise interactions. To save
-time, we select the top 6 predictors via permutation importance, leading to 15 pairs.
-
-    >>> import numpy as np
-    >>> from sklearn.ensemble import HistGradientBoostingRegressor
-    >>> from sklearn.inspection import permutation_importance, h_statistic
-    >>> from sklearn.datasets import load_diabetes
-
-    >>> # Fit model
-    >>> X, y = load_diabetes(return_X_y=True)
-    >>> est = HistGradientBoostingRegressor(max_iter=100, max_depth=4).fit(X, y)
-
-    >>> # Get Friedman's H-squared for top m=6 predictors
-    >>> m = 6
-    >>> imp = permutation_importance(est, X, y, random_state=0)
-    >>> top_m = np.argsort(imp.importances_mean)[-m:]
-    >>> H = h_statistic(est, X=X, features=top_m, random_state=4)
-
-Then, we plot all :math:`H_{jk}^2` and :math:`\sqrt{A_{jk}}` statistics:
-
-    >>> import matplotlib.pyplot as plt
-    >>>
-    >>> fig, axes = plt.subplots(1, 2, figsize=(8, 4))
-    >>>
-    >>> bar_labels = np.array([str(pair) for pair in H["feature_pairs"]])
-    >>> stats = (H["h_squared_pairwise"], np.sqrt(H["numerator_pairwise"]))
-    >>>
-    >>> for ax, stat, name in zip(axes, stats, ("$H^2$", "Unnormalized $H$")):
-    ...     stat = stat.ravel()
-    ...     idx = np.argsort(stat)
-    ...     ax.barh(bar_labels[idx], stat[idx], color="orange")
-    ...     ax.set(xlabel=name, title=name)
-    >>> _ = fig.tight_layout()
-
-.. image:: ../images/h_statistic.png
-   :align: center
-
-**The left plot** shows that the interaction between features 0 and 1
-explains about 16% of their joint effect variability. For the other pairs, it is
-clearly less. **The right plot** additionally shows that the interaction between
-features 2 and 3 is the largest in absolute terms. The reason for this is that
-these features belong to the most important predictors.
-Their weak relative interaction is still stronger than the strong relative
-interaction between features 0 and 1.
-Thus, it often makes sense to study both normalized and unnormalized statistics.
-
 Limitations
 ===========
 
@@ -161,6 +119,10 @@ Limitations
    relatively small subsets of the data. Consequently, the estimates are
    typically not very robust.
 
+.. topic:: Examples:
+
+ * :ref:`sphx_glr_auto_examples_inspection_plot_partial_dependence.py`
+
 .. topic:: References
 
     .. [F2008] J. H. Friedman and B. E. Popescu,
diff --git a/examples/inspection/plot_partial_dependence.py b/examples/inspection/plot_partial_dependence.py
index 4c3e0f409eeff..52ac3838ec4cd 100644
--- a/examples/inspection/plot_partial_dependence.py
+++ b/examples/inspection/plot_partial_dependence.py
@@ -16,9 +16,12 @@
 feature for each :term:`sample` separately, with one line per sample.
 Only one feature of interest is supported for ICE plots.
 
-This example shows how to obtain partial dependence and ICE plots from a
-:class:`~sklearn.neural_network.MLPRegressor` and a
-:class:`~sklearn.ensemble.HistGradientBoostingRegressor` trained on the
+Furthermore, the concept of partial dependence is used to measure interaction
+strength of feature pairs. The corresponding measure is called *H-statistic*
+and has been introduced in [4]_.
+
+To illustrate these methods, we use a :class:`~sklearn.neural_network.MLPRegressor`
+and a :class:`~sklearn.ensemble.HistGradientBoostingRegressor` trained on the
 bike sharing dataset. The example is inspired by [1]_.
 
 .. [1] `Molnar, Christoph. "Interpretable machine learning.
@@ -32,6 +35,10 @@
        "Peeking Inside the Black Box: Visualizing Statistical Learning With Plots of
        Individual Conditional Expectation". Journal of Computational and
        Graphical Statistics, 24(1): 44-65 <1309.6392>`
+
+.. [4] Friedman, J. H. and Popescu, B. E. (2008).
+       "Predictive Learning via Rule Ensembles".
+       The Annals of Applied Statistics, 2(3), 916-954, 2008.
 """
 
 # %%
@@ -567,3 +574,54 @@
 plt.show()
 
 # %%
+# Interaction strength
+# --------------------
+#
+# Above considerations show that comparing 2D PDPs with their univariate versions
+# give hints about feature interactions. This idea is formalized by Friedman
+# and Popescu's (pairwise) H-statistic, see [4]_.
+# It measures how well the 2D partial dependence function can be approximated
+# by the two one-dimensional partial dependence functions.
+# The resulting value is then normalized and can be interpreted as proportion of effect
+# variability explained by the interaction. Besides this relative measure, we advocate
+# to also consider unnormalized statistics. They can directly be compared between
+# feature pairs to see which interactions are strongest.
+#
+# Since computational burden is high, H-statistics are usually calculated only for
+# important features, e.g., selected by permutation importance. What do we get for
+# our model?
+print("Select five important features and crunch H-statistics...")
+
+from sklearn.inspection import h_statistic, permutation_importance
+
+tic = time()
+
+imp = permutation_importance(hgbdt_model, X=X_train, y=y_train, random_state=0)
+features = X_train.columns[np.argsort(imp.importances_mean)][-5:]
+
+H = h_statistic(hgbdt_model, X=X_train, features=features, random_state=0)
+
+print(f"done in {time() - tic:.3f}s")
+
+import matplotlib.pyplot as plt
+
+fig, axes = plt.subplots(1, 2, figsize=(8, 4))
+bar_labels = np.array([str(pair) for pair in H["feature_pairs"]])
+stats = (H["h_squared_pairwise"], np.sqrt(H["numerator_pairwise"]))
+
+for ax, stat, name in zip(axes, stats, ("$H^2$", "Unnormalized $H$")):
+    stat = stat.ravel()
+    idx = np.argsort(stat)
+    ax.barh(bar_labels[idx], stat[idx], color="orange")
+    ax.set(xlabel=name, title=name)
+_ = fig.tight_layout()
+
+# %%
+
+# **The left plot** shows that the interaction between 'workingday' and 'hour'
+# explains about 8% of their joint effect variability. For the other pairs, it is
+# less than 5%. **The right plot** additionally shows that the interaction between
+# 'workingday' and 'hour' is also largest in absolute terms (on the scale of the
+# predictions).
+
+# %%

From 4658ee63124e5ae41b6b97b49c648b08f4e8095c Mon Sep 17 00:00:00 2001
From: Michael Mayer <mayermichael79@gmail.com>
Date: Sat, 27 Apr 2024 08:46:24 +0200
Subject: [PATCH 49/66] Reformat example output

---
 sklearn/inspection/_h_statistic.py | 19 ++++++++++---------
 1 file changed, 10 insertions(+), 9 deletions(-)

diff --git a/sklearn/inspection/_h_statistic.py b/sklearn/inspection/_h_statistic.py
index 7a461c92cb209..d5535139d00fc 100644
--- a/sklearn/inspection/_h_statistic.py
+++ b/sklearn/inspection/_h_statistic.py
@@ -210,15 +210,16 @@ def h_statistic(
     >>> imp = permutation_importance(est, X, y, random_state=0)
     >>> top_m = np.argsort(imp.importances_mean)[-m:]
     >>> H = h_statistic(est, X=X, features=top_m, random_state=4)
-    >>> H
-    {'feature_pairs': [(3, 8), (3, 2), (8, 2)], 'h_squared_pairwise': array([[0.00985985],
-           [0.00927104],
-           [0.03439926]]), 'numerator_pairwise': array([[ 1.2955532 ],
-           [ 1.2419687 ],
-           [11.13358385]]), 'denominator_pairwise': array([[131.39690331],
-           [133.96210997],
-           [323.6576595 ]])}
-
+    >>> # {'feature_pairs': [(3, 8), (3, 2), (8, 2)],
+    >>> #  'h_squared_pairwise': array([[0.00985985],
+    >>> #         [0.00927104],
+    >>> #         [0.03439926]]),
+    >>> #  'numerator_pairwise': array([[ 1.2955532 ],
+    >>> #         [ 1.2419687 ],
+    >>> #         [11.13358385]]),
+    >>> #  'denominator_pairwise': array([[131.39690331],
+    >>> #         [133.96210997],
+    >>> #         [323.6576595 ]])}
     >>> # Interpretation: For features (8, 2), 3.4% of the joint effect variability
     >>> # comes from their interaction. These two features also have strongest absolute
     >>> # interaction across pairs ("numerator_pairwise").

From 9bc1ff8ee9d0f5a88f83c3197fa29a573f4cb204 Mon Sep 17 00:00:00 2001
From: Michael Mayer <mayermichael79@gmail.com>
Date: Sat, 27 Apr 2024 09:24:43 +0200
Subject: [PATCH 50/66] Fixing plot

---
 doc/modules/h_statistic.rst                    |  2 +-
 examples/inspection/plot_partial_dependence.py | 10 ++--------
 2 files changed, 3 insertions(+), 9 deletions(-)

diff --git a/doc/modules/h_statistic.rst b/doc/modules/h_statistic.rst
index 090594ef79a3f..c1e5f7a138579 100644
--- a/doc/modules/h_statistic.rst
+++ b/doc/modules/h_statistic.rst
@@ -22,7 +22,7 @@ The figure below shows H-statistics and their unnormalized counterparts for
 the bike sharing dataset, with a
 :class:`~sklearn.ensemble.HistGradientBoostingRegressor`:
 
-.. figure:: ../auto_examples/inspection/images/sphx_glr_plot_partial_dependence_007.png
+.. figure:: ../auto_examples/inspection/images/sphx_glr_plot_partial_dependence_010.png
    :target: ../auto_examples/inspection/plot_partial_dependence.html
    :align: center
    :scale: 70
diff --git a/examples/inspection/plot_partial_dependence.py b/examples/inspection/plot_partial_dependence.py
index 52ac3838ec4cd..2822b61bab770 100644
--- a/examples/inspection/plot_partial_dependence.py
+++ b/examples/inspection/plot_partial_dependence.py
@@ -36,9 +36,9 @@
        Individual Conditional Expectation". Journal of Computational and
        Graphical Statistics, 24(1): 44-65 <1309.6392>`
 
-.. [4] Friedman, J. H. and Popescu, B. E. (2008).
+.. [4] `Friedman, J. H. and Popescu, B. E. (2008).
        "Predictive Learning via Rule Ensembles".
-       The Annals of Applied Statistics, 2(3), 916-954, 2008.
+       The Annals of Applied Statistics, 2(3), 916-954, 2008.`
 """
 
 # %%
@@ -595,7 +595,6 @@
 from sklearn.inspection import h_statistic, permutation_importance
 
 tic = time()
-
 imp = permutation_importance(hgbdt_model, X=X_train, y=y_train, random_state=0)
 features = X_train.columns[np.argsort(imp.importances_mean)][-5:]
 
@@ -603,8 +602,6 @@
 
 print(f"done in {time() - tic:.3f}s")
 
-import matplotlib.pyplot as plt
-
 fig, axes = plt.subplots(1, 2, figsize=(8, 4))
 bar_labels = np.array([str(pair) for pair in H["feature_pairs"]])
 stats = (H["h_squared_pairwise"], np.sqrt(H["numerator_pairwise"]))
@@ -617,11 +614,8 @@
 _ = fig.tight_layout()
 
 # %%
-
 # **The left plot** shows that the interaction between 'workingday' and 'hour'
 # explains about 8% of their joint effect variability. For the other pairs, it is
 # less than 5%. **The right plot** additionally shows that the interaction between
 # 'workingday' and 'hour' is also largest in absolute terms (on the scale of the
 # predictions).
-
-# %%

From 9b8a1d63a249b6b150778b87a0245a045c8cbc6c Mon Sep 17 00:00:00 2001
From: Michael Mayer <mayermichael79@gmail.com>
Date: Sat, 27 Apr 2024 10:38:02 +0200
Subject: [PATCH 51/66] Fix reference

---
 examples/inspection/plot_partial_dependence.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/inspection/plot_partial_dependence.py b/examples/inspection/plot_partial_dependence.py
index 2822b61bab770..0e0e691d87067 100644
--- a/examples/inspection/plot_partial_dependence.py
+++ b/examples/inspection/plot_partial_dependence.py
@@ -38,7 +38,7 @@
 
 .. [4] `Friedman, J. H. and Popescu, B. E. (2008).
        "Predictive Learning via Rule Ensembles".
-       The Annals of Applied Statistics, 2(3), 916-954, 2008.`
+       The Annals of Applied Statistics, 2(3), 916-954, 2008.`_
 """
 
 # %%

From a5cfa08dfab549dfb2a49c32ec83cb27c7acb5c8 Mon Sep 17 00:00:00 2001
From: Michael Mayer <mayermichael79@gmail.com>
Date: Sat, 27 Apr 2024 11:34:36 +0200
Subject: [PATCH 52/66] maybe we need copy()

---
 examples/inspection/plot_partial_dependence.py | 4 ++--
 sklearn/inspection/_h_statistic.py             | 3 ++-
 2 files changed, 4 insertions(+), 3 deletions(-)

diff --git a/examples/inspection/plot_partial_dependence.py b/examples/inspection/plot_partial_dependence.py
index 0e0e691d87067..f90c0ec8efbf1 100644
--- a/examples/inspection/plot_partial_dependence.py
+++ b/examples/inspection/plot_partial_dependence.py
@@ -36,9 +36,9 @@
        Individual Conditional Expectation". Journal of Computational and
        Graphical Statistics, 24(1): 44-65 <1309.6392>`
 
-.. [4] `Friedman, J. H. and Popescu, B. E. (2008).
+.. [4] Friedman, J. H. and Popescu, B. E. (2008).
        "Predictive Learning via Rule Ensembles".
-       The Annals of Applied Statistics, 2(3), 916-954, 2008.`_
+       The Annals of Applied Statistics, 2(3), 916-954, 2008.
 """
 
 # %%
diff --git a/sklearn/inspection/_h_statistic.py b/sklearn/inspection/_h_statistic.py
index d5535139d00fc..b3b0d34145b5a 100644
--- a/sklearn/inspection/_h_statistic.py
+++ b/sklearn/inspection/_h_statistic.py
@@ -30,8 +30,9 @@ def _calculate_pd_brute_fast(
     # X is stacked n_grid times, and grid columns are replaced by replicated grid
     n = X.shape[0]
     n_grid = grid.shape[0]
+    X_eval = X.copy()
 
-    X_stacked = _safe_indexing(X, np.tile(np.arange(n), n_grid), axis=0)
+    X_stacked = _safe_indexing(X_eval, np.tile(np.arange(n), n_grid), axis=0)
     grid_stacked = _safe_indexing(grid, np.repeat(np.arange(n_grid), n), axis=0)
     _safe_assign(X_stacked, values=grid_stacked, column_indexer=feature_indices)
 

From f36a9136835ed52d955c9053302b9c8161042518 Mon Sep 17 00:00:00 2001
From: Michael Mayer <mayermichael79@gmail.com>
Date: Sat, 27 Apr 2024 12:05:51 +0200
Subject: [PATCH 53/66] Try second copy()

---
 sklearn/inspection/_h_statistic.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/sklearn/inspection/_h_statistic.py b/sklearn/inspection/_h_statistic.py
index b3b0d34145b5a..25c4ea8f652ba 100644
--- a/sklearn/inspection/_h_statistic.py
+++ b/sklearn/inspection/_h_statistic.py
@@ -259,6 +259,8 @@ def h_statistic(
         X = _safe_indexing(X, row_indices, axis=0)
         if sample_weight is not None:
             sample_weight = _safe_indexing(sample_weight, row_indices, axis=0)
+    else:
+        X = X.copy()
 
     if features is None:
         features = feature_indices = np.arange(X.shape[1])

From 54e98c8ab8089658bfdfca569d8da121b7c6b433 Mon Sep 17 00:00:00 2001
From: Michael Mayer <mayermichael79@gmail.com>
Date: Sat, 27 Apr 2024 12:29:58 +0200
Subject: [PATCH 54/66] Remove copy again

---
 sklearn/inspection/_h_statistic.py | 5 +----
 1 file changed, 1 insertion(+), 4 deletions(-)

diff --git a/sklearn/inspection/_h_statistic.py b/sklearn/inspection/_h_statistic.py
index 25c4ea8f652ba..d5535139d00fc 100644
--- a/sklearn/inspection/_h_statistic.py
+++ b/sklearn/inspection/_h_statistic.py
@@ -30,9 +30,8 @@ def _calculate_pd_brute_fast(
     # X is stacked n_grid times, and grid columns are replaced by replicated grid
     n = X.shape[0]
     n_grid = grid.shape[0]
-    X_eval = X.copy()
 
-    X_stacked = _safe_indexing(X_eval, np.tile(np.arange(n), n_grid), axis=0)
+    X_stacked = _safe_indexing(X, np.tile(np.arange(n), n_grid), axis=0)
     grid_stacked = _safe_indexing(grid, np.repeat(np.arange(n_grid), n), axis=0)
     _safe_assign(X_stacked, values=grid_stacked, column_indexer=feature_indices)
 
@@ -259,8 +258,6 @@ def h_statistic(
         X = _safe_indexing(X, row_indices, axis=0)
         if sample_weight is not None:
             sample_weight = _safe_indexing(sample_weight, row_indices, axis=0)
-    else:
-        X = X.copy()
 
     if features is None:
         features = feature_indices = np.arange(X.shape[1])

From 131910bff7c49c3e57e689ffeeb4b0e0e8b6d637 Mon Sep 17 00:00:00 2001
From: Michael Mayer <mayermichael79@gmail.com>
Date: Thu, 2 May 2024 20:26:24 +0200
Subject: [PATCH 55/66] fix dupe index issue with old pandas

---
 sklearn/inspection/_h_statistic.py | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/sklearn/inspection/_h_statistic.py b/sklearn/inspection/_h_statistic.py
index d5535139d00fc..3a108e5b45973 100644
--- a/sklearn/inspection/_h_statistic.py
+++ b/sklearn/inspection/_h_statistic.py
@@ -33,6 +33,10 @@ def _calculate_pd_brute_fast(
 
     X_stacked = _safe_indexing(X, np.tile(np.arange(n), n_grid), axis=0)
     grid_stacked = _safe_indexing(grid, np.repeat(np.arange(n_grid), n), axis=0)
+
+    if hasattr(X, "iloc"):  # pandas<2 does not allow "values" to have repeated indices
+        grid_stacked = grid_stacked.reset_index(drop=True)
+
     _safe_assign(X_stacked, values=grid_stacked, column_indexer=feature_indices)
 
     preds = pred_fun(X_stacked)

From 00f0eeda1a61f270219dff9ac88b9918bee978c2 Mon Sep 17 00:00:00 2001
From: Michael Mayer <mayermichael79@gmail.com>
Date: Fri, 17 May 2024 17:59:18 +0200
Subject: [PATCH 56/66] Fix typo

---
 sklearn/inspection/_h_statistic.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/sklearn/inspection/_h_statistic.py b/sklearn/inspection/_h_statistic.py
index 3a108e5b45973..16c5fe56103be 100644
--- a/sklearn/inspection/_h_statistic.py
+++ b/sklearn/inspection/_h_statistic.py
@@ -34,6 +34,7 @@ def _calculate_pd_brute_fast(
     X_stacked = _safe_indexing(X, np.tile(np.arange(n), n_grid), axis=0)
     grid_stacked = _safe_indexing(grid, np.repeat(np.arange(n_grid), n), axis=0)
 
+    # TODO: remove via https://github.com/scikit-learn/scikit-learn/issues/28931
     if hasattr(X, "iloc"):  # pandas<2 does not allow "values" to have repeated indices
         grid_stacked = grid_stacked.reset_index(drop=True)
 
@@ -183,7 +184,7 @@ def h_statistic(
 
         h_squared_pairwise : ndarray of shape (n_pairs, output_dim)
             Pairwise H-squared statistic. Useful to see which feature pair has
-            strongest relative interation (relative with respect to joint effect).
+            strongest relative interaction (relative with respect to joint effect).
             Calculated as numerator_pairwise / denominator_pairwise.
 
         numerator_pairwise : ndarray of shape (n_pairs, output_dim)

From 90ffd7b7785105ad1f8a69c847fbba1dba53c1e1 Mon Sep 17 00:00:00 2001
From: Michael Mayer <mayermichael79@gmail.com>
Date: Fri, 17 May 2024 19:22:58 +0200
Subject: [PATCH 57/66] add column names to pandas unit test

---
 sklearn/inspection/tests/test_h_statistic.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/sklearn/inspection/tests/test_h_statistic.py b/sklearn/inspection/tests/test_h_statistic.py
index 4d9a52eebf3e5..b561bed8d2f8e 100644
--- a/sklearn/inspection/tests/test_h_statistic.py
+++ b/sklearn/inspection/tests/test_h_statistic.py
@@ -119,7 +119,7 @@ def test_h_statistic_equivalence_array_dataframe(n_max, sample_weight):
 
     N = 200
     X, y = make_regression(n_samples=N, n_features=4, random_state=0)
-    X_df = pd.DataFrame(X)
+    X_df = pd.DataFrame(X, columns=list("abcd"))
     w = np.ones(N) if sample_weight == "ones" else None
 
     # Numpy

From 387d9976dd8a2d7e59f9e8bd0e73d41940acdd30 Mon Sep 17 00:00:00 2001
From: Michael Mayer <mayermichael79@gmail.com>
Date: Fri, 31 May 2024 12:05:24 +0200
Subject: [PATCH 58/66] Fix problem related to #28931

---
 sklearn/inspection/_h_statistic.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/sklearn/inspection/_h_statistic.py b/sklearn/inspection/_h_statistic.py
index 16c5fe56103be..0435416002442 100644
--- a/sklearn/inspection/_h_statistic.py
+++ b/sklearn/inspection/_h_statistic.py
@@ -37,7 +37,7 @@ def _calculate_pd_brute_fast(
     # TODO: remove via https://github.com/scikit-learn/scikit-learn/issues/28931
     if hasattr(X, "iloc"):  # pandas<2 does not allow "values" to have repeated indices
         grid_stacked = grid_stacked.reset_index(drop=True)
-
+        X_stacked = X_stacked.reset_index(drop=True)
     _safe_assign(X_stacked, values=grid_stacked, column_indexer=feature_indices)
 
     preds = pred_fun(X_stacked)

From ea50a48d03adfe91fc45617dd617d35bf2577ece Mon Sep 17 00:00:00 2001
From: Michael Mayer <mayermichael79@gmail.com>
Date: Fri, 31 May 2024 12:46:06 +0200
Subject: [PATCH 59/66] merge

---
 .github/workflows/wheels.yml                  |   10 +-
 .gitignore                                    |    4 +
 .pre-commit-config.yaml                       |    7 +
 README.rst                                    |    2 +-
 SECURITY.md                                   |    4 +-
 asv_benchmarks/benchmarks/linear_model.py     |    1 -
 benchmarks/bench_isotonic.py                  |    6 +-
 benchmarks/bench_saga.py                      |   11 +-
 build_tools/azure/debian_atlas_32bit_lock.txt |   11 +-
 .../azure/debian_atlas_32bit_requirements.txt |    2 +-
 ...latest_conda_forge_mkl_linux-64_conda.lock |   94 +-
 ...t_conda_forge_mkl_linux-64_environment.yml |    2 +-
 ...pylatest_conda_forge_mkl_osx-64_conda.lock |  100 +-
 ...est_conda_forge_mkl_osx-64_environment.yml |    2 +-
 ...latest_conda_mkl_no_openmp_environment.yml |    6 +-
 ...test_conda_mkl_no_openmp_osx-64_conda.lock |   64 +-
 ...latest_pip_openblas_pandas_environment.yml |    2 +-
 ...st_pip_openblas_pandas_linux-64_conda.lock |   64 +-
 .../pylatest_pip_scipy_dev_environment.yml    |    2 +-
 ...pylatest_pip_scipy_dev_linux-64_conda.lock |   54 +-
 ...in_conda_defaults_openblas_environment.yml |    8 +-
 ...onda_defaults_openblas_linux-64_conda.lock |   48 +-
 .../pymin_conda_forge_mkl_environment.yml     |    2 +-
 .../pymin_conda_forge_mkl_win-64_conda.lock   |   68 +-
 ...forge_openblas_ubuntu_2204_environment.yml |    2 +-
 ...e_openblas_ubuntu_2204_linux-64_conda.lock |   90 +-
 build_tools/azure/pypy3_environment.yml       |    2 +-
 build_tools/azure/pypy3_linux-64_conda.lock   |   46 +-
 build_tools/azure/python_nogil_lock.txt       |   19 +-
 build_tools/azure/ubuntu_atlas_lock.txt       |   15 +-
 .../azure/ubuntu_atlas_requirements.txt       |    4 +-
 build_tools/circle/build_doc.sh               |    8 +-
 build_tools/circle/doc_environment.yml        |    6 +-
 build_tools/circle/doc_linux-64_conda.lock    |  190 +-
 .../doc_min_dependencies_environment.yml      |   18 +-
 .../doc_min_dependencies_linux-64_conda.lock  |  154 +-
 build_tools/circle/list_versions.py           |   64 +-
 .../cirrus/pymin_conda_forge_environment.yml  |    2 +-
 ...pymin_conda_forge_linux-aarch64_conda.lock |   48 +-
 build_tools/generate_authors_table.py         |    6 +-
 build_tools/github/repair_windows_wheels.sh   |    1 +
 .../update_environments_and_lock_files.py     |   56 +-
 doc/Makefile                                  |    8 +
 doc/about.rst                                 |  666 +-
 doc/api/deprecated.rst.template               |   24 +
 doc/api/index.rst.template                    |   77 +
 doc/api/module.rst.template                   |   46 +
 doc/api_reference.py                          | 1335 ++++
 doc/common_pitfalls.rst                       |   71 +-
 doc/computing.rst                             |    6 -
 doc/computing/computational_performance.rst   |    4 -
 doc/computing/parallelism.rst                 |    4 -
 doc/computing/scaling_strategies.rst          |    4 -
 doc/conf.py                                   |  403 +-
 doc/contents.rst                              |   24 -
 doc/css/.gitkeep                              |    0
 doc/data_transforms.rst                       |    6 -
 doc/datasets.rst                              |    6 -
 doc/datasets/loading_other_datasets.rst       |   23 +-
 doc/datasets/real_world.rst                   |    4 -
 doc/datasets/sample_generators.rst            |    4 -
 doc/datasets/toy_dataset.rst                  |    4 -
 doc/developers/advanced_installation.rst      |    8 +-
 doc/developers/contributing.rst               |  910 ++-
 doc/developers/cython.rst                     |   13 +
 doc/developers/develop.rst                    |   10 +-
 doc/developers/index.rst                      |    7 -
 doc/developers/maintainer.rst                 |   24 +-
 doc/dispatching.rst                           |    6 -
 doc/faq.rst                                   |   46 +-
 doc/governance.rst                            |   91 +-
 doc/images/Tidelift-logo-on-light.svg         |   33 +
 doc/images/ml_map.png                         |  Bin 761071 -> 0 bytes
 doc/images/ml_map.svg                         |    4 +
 doc/includes/big_toc_css.rst                  |   40 -
 doc/includes/bigger_toc_css.rst               |   60 -
 doc/index.rst.template                        |   25 +
 doc/inspection.rst                            |   10 +-
 doc/install.rst                               |  304 +-
 doc/js/scripts/api-search.js                  |   12 +
 doc/js/scripts/dropdown.js                    |   61 +
 doc/js/scripts/vendor/svg-pan-zoom.min.js     |   31 +
 doc/js/scripts/version-switcher.js            |   40 +
 doc/{authors.rst => maintainers.rst}          |    0
 ..._emeritus.rst => maintainers_emeritus.rst} |    0
 doc/make.bat                                  |   27 +-
 doc/metadata_routing.rst                      |   10 +-
 doc/min_dependency_substitutions.rst.template |    3 +
 doc/min_dependency_table.rst.template         |   13 +
 doc/model_persistence.rst                     |  536 +-
 doc/model_selection.rst                       |    7 +-
 doc/modules/array_api.rst                     |    8 +-
 doc/modules/biclustering.rst                  |   42 +-
 doc/modules/calibration.rst                   |   96 +-
 doc/modules/classes.rst                       | 1905 ------
 doc/modules/classification_threshold.rst      |  157 +
 doc/modules/clustering.rst                    | 1020 ++-
 doc/modules/compose.rst                       |  215 +-
 doc/modules/covariance.rst                    |  103 +-
 doc/modules/cross_decomposition.rst           |   70 +-
 doc/modules/cross_validation.rst              |  140 +-
 doc/modules/decomposition.rst                 |  424 +-
 doc/modules/density.rst                       |   47 +-
 doc/modules/ensemble.rst                      |  729 +--
 doc/modules/feature_extraction.rst            |  481 +-
 doc/modules/feature_selection.rst             |  145 +-
 doc/modules/gaussian_process.rst              |  225 +-
 doc/modules/grid_search.rst                   |   83 +-
 doc/modules/impute.rst                        |   12 +-
 doc/modules/isotonic.rst                      |    4 +-
 doc/modules/kernel_approximation.rst          |   70 +-
 doc/modules/kernel_ridge.rst                  |   10 +-
 doc/modules/lda_qda.rst                       |   34 +-
 doc/modules/learning_curve.rst                |    8 +-
 doc/modules/linear_model.rst                  |  903 ++-
 doc/modules/manifold.rst                      |  586 +-
 doc/modules/metrics.rst                       |   20 +-
 doc/modules/mixture.rst                       |  278 +-
 doc/modules/model_evaluation.rst              |  863 ++-
 doc/modules/multiclass.rst                    |   43 +-
 doc/modules/naive_bayes.rst                   |  114 +-
 doc/modules/neighbors.rst                     |  375 +-
 doc/modules/neural_networks_supervised.rst    |  182 +-
 doc/modules/neural_networks_unsupervised.rst  |   22 +-
 doc/modules/outlier_detection.rst             |  110 +-
 doc/modules/partial_dependence.rst            |   63 +-
 doc/modules/permutation_importance.rst        |   92 +-
 doc/modules/preprocessing.rst                 |  387 +-
 doc/modules/random_projection.rst             |   57 +-
 doc/modules/semi_supervised.rst               |   40 +-
 doc/modules/sgd.rst                           |  169 +-
 doc/modules/svm.rst                           |  337 +-
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 doc/preface.rst                               |   32 -
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 doc/scss/install.scss                         |   20 +
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 doc/sphinxext/dropdown_anchors.py             |   78 +
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 doc/sphinxext/override_pst_pagetoc.py         |   84 +
 doc/supervised_learning.rst                   |    6 -
 doc/templates/base.rst                        |   36 +
 doc/templates/class.rst                       |   17 -
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 .../machine_learning_map/parse_path.py        |  192 -
 .../machine_learning_map/pyparsing.py         | 5715 -----------------
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 doc/tutorial/statistical_inference/index.rst  |   10 +-
 .../statistical_inference/model_selection.rst |   67 +-
 .../supervised_learning.rst                   |   11 +-
 doc/unsupervised_learning.rst                 |    6 -
 doc/user_guide.rst                            |   12 -
 doc/visualizations.rst                        |   16 +-
 doc/whats_new.rst                             |    1 +
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 doc/whats_new/v1.6.rst                        |  116 +
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 .../plot_classification_probability.py        |   15 +-
 .../covariance/plot_mahalanobis_distances.py  |   18 +-
 examples/ensemble/plot_adaboost_multiclass.py |   22 +-
 examples/ensemble/plot_hgbt_regression.py     |   59 +-
 examples/feature_selection/plot_rfe_digits.py |   36 +-
 examples/gaussian_process/plot_gpr_co2.py     |   52 +-
 .../inspection/plot_permutation_importance.py |    6 +-
 examples/linear_model/plot_lasso_lars_ic.py   |   10 +-
 .../linear_model/plot_logistic_multinomial.py |   20 +-
 ...plot_poisson_regression_non_normal_loss.py |    7 +-
 examples/linear_model/plot_sgd_iris.py        |    1 -
 ...sparse_logistic_regression_20newsgroups.py |   16 +-
 ...lot_tweedie_regression_insurance_claims.py |    7 +-
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 .../plot_cost_sensitive_learning.py           |  702 ++
 examples/model_selection/plot_cv_indices.py   |    5 +-
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 ...ot_permutation_tests_for_classification.py |   10 +-
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 meson.build                                   |    2 +-
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 setup.cfg                                     |    2 +-
 sklearn/__init__.py                           |   22 +-
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 sklearn/base.py                               |   43 +-
 sklearn/calibration.py                        |    6 +-
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 .../tests/test_feature_agglomeration.py       |   16 +-
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 sklearn/cluster/tests/test_mean_shift.py      |    9 +
 sklearn/compose/__init__.py                   |    6 +-
 sklearn/compose/_column_transformer.py        |  198 +-
 .../compose/tests/test_column_transformer.py  |  190 +-
 sklearn/covariance/__init__.py                |   11 +-
 sklearn/covariance/_shrunk_covariance.py      |    4 +-
 sklearn/cross_decomposition/__init__.py       |    2 +
 sklearn/cross_decomposition/_pls.py           |    5 +-
 sklearn/cross_decomposition/tests/test_pls.py |   24 +-
 sklearn/datasets/__init__.py                  |    6 +-
 sklearn/datasets/_lfw.py                      |    3 +
 sklearn/datasets/descr/breast_cancer.rst      |   28 +-
 sklearn/datasets/descr/california_housing.rst |    6 +-
 sklearn/datasets/descr/digits.rst             |   28 +-
 sklearn/datasets/descr/iris.rst               |   36 +-
 sklearn/datasets/descr/kddcup99.rst           |   18 +-
 sklearn/datasets/descr/lfw.rst                |  136 +-
 sklearn/datasets/descr/linnerud.rst           |   10 +-
 sklearn/datasets/descr/rcv1.rst               |    8 +-
 .../datasets/descr/species_distributions.rst  |    8 +-
 sklearn/datasets/descr/twenty_newsgroups.rst  |  396 +-
 sklearn/datasets/descr/wine_data.rst          |   42 +-
 sklearn/decomposition/__init__.py             |    8 +-
 sklearn/decomposition/_kernel_pca.py          |   12 +-
 sklearn/decomposition/_nmf.py                 |   35 +-
 sklearn/decomposition/tests/test_nmf.py       |   21 +-
 sklearn/decomposition/tests/test_pca.py       |   46 +-
 sklearn/discriminant_analysis.py              |    4 +-
 sklearn/dummy.py                              |    2 +
 sklearn/ensemble/__init__.py                  |    5 +-
 sklearn/ensemble/_base.py                     |    2 +-
 sklearn/ensemble/_forest.py                   |   10 +-
 sklearn/ensemble/_gb.py                       |    5 +
 .../gradient_boosting.py                      |   13 +-
 .../tests/test_monotonic_contraints.py        |   39 +-
 sklearn/ensemble/_stacking.py                 |  228 +-
 sklearn/ensemble/_voting.py                   |    2 +-
 sklearn/ensemble/_weight_boosting.py          |    8 +
 sklearn/ensemble/tests/test_stacking.py       |  120 +
 sklearn/exceptions.py                         |    5 +-
 sklearn/experimental/__init__.py              |   10 +-
 sklearn/feature_extraction/__init__.py        |    6 +-
 sklearn/feature_extraction/image.py           |    5 +-
 sklearn/feature_extraction/tests/test_text.py |   42 -
 sklearn/feature_extraction/text.py            |   50 +-
 sklearn/feature_selection/__init__.py         |    8 +-
 sklearn/feature_selection/_from_model.py      |    4 +-
 sklearn/feature_selection/_rfe.py             |    2 +-
 sklearn/gaussian_process/__init__.py          |    7 +-
 sklearn/gaussian_process/_gpr.py              |    4 +-
 sklearn/gaussian_process/kernels.py           |    5 +-
 sklearn/impute/__init__.py                    |    2 +-
 sklearn/inspection/__init__.py                |    2 +-
 sklearn/isotonic.py                           |   26 +-
 sklearn/kernel_approximation.py               |   87 +-
 sklearn/kernel_ridge.py                       |    2 +-
 sklearn/linear_model/__init__.py              |    4 +-
 sklearn/linear_model/_base.py                 |   77 +-
 sklearn/linear_model/_bayes.py                |    3 +
 sklearn/linear_model/_coordinate_descent.py   |    5 +-
 sklearn/linear_model/_glm/__init__.py         |    4 +-
 sklearn/linear_model/_glm/_newton_solver.py   |    5 +-
 sklearn/linear_model/_glm/glm.py              |    6 +-
 sklearn/linear_model/_glm/tests/__init__.py   |    3 +-
 sklearn/linear_model/_glm/tests/test_glm.py   |    6 +-
 sklearn/linear_model/_least_angle.py          |   10 +-
 sklearn/linear_model/_logistic.py             |  201 +-
 sklearn/linear_model/_omp.py                  |    2 +-
 sklearn/linear_model/_passive_aggressive.py   |    4 +
 sklearn/linear_model/_ridge.py                |  300 +-
 sklearn/linear_model/_sag.py                  |    5 +-
 sklearn/linear_model/_theil_sen.py            |   18 +-
 sklearn/linear_model/tests/test_logistic.py   |  221 +-
 sklearn/linear_model/tests/test_ridge.py      |  236 +-
 sklearn/linear_model/tests/test_sag.py        |  113 +-
 sklearn/linear_model/tests/test_theil_sen.py  |    8 +
 sklearn/manifold/__init__.py                  |    4 +-
 sklearn/manifold/_barnes_hut_tsne.pyx         |   11 +-
 sklearn/metrics/__init__.py                   |    7 +-
 sklearn/metrics/_classification.py            |  162 +-
 .../_argkmin.pyx.tp                           |    4 +-
 .../_argkmin_classmode.pyx.tp                 |    4 +-
 .../_radius_neighbors.pyx.tp                  |    4 +-
 .../_radius_neighbors_classmode.pyx.tp        |    4 +-
 .../_pairwise_distances_reduction/meson.build |    7 +-
 sklearn/metrics/_ranking.py                   |    4 +-
 sklearn/metrics/_regression.py                |   58 +-
 sklearn/metrics/_scorer.py                    |  124 +-
 sklearn/metrics/cluster/__init__.py           |    9 +-
 sklearn/metrics/cluster/_supervised.py        |    4 +-
 sklearn/metrics/pairwise.py                   |   15 +-
 sklearn/metrics/tests/test_classification.py  |  305 +-
 sklearn/metrics/tests/test_common.py          |  133 +-
 .../test_pairwise_distances_reduction.py      |    4 +-
 sklearn/metrics/tests/test_ranking.py         |    2 +-
 sklearn/metrics/tests/test_score_objects.py   |   83 +-
 sklearn/mixture/__init__.py                   |    4 +-
 sklearn/model_selection/__init__.py           |    8 +
 .../_classification_threshold.py              | 1019 +++
 sklearn/model_selection/_search.py            |   17 +-
 sklearn/model_selection/_split.py             |    2 +-
 sklearn/model_selection/_validation.py        |  147 +-
 .../tests/test_classification_threshold.py    |  724 +++
 sklearn/model_selection/tests/test_search.py  |   68 +
 .../model_selection/tests/test_validation.py  |   93 +-
 sklearn/multiclass.py                         |   21 +-
 sklearn/multioutput.py                        |   13 +-
 sklearn/naive_bayes.py                        |    6 +-
 sklearn/neighbors/__init__.py                 |    5 +-
 sklearn/neighbors/_binary_tree.pxi.tp         |    5 +-
 sklearn/neighbors/_lof.py                     |    8 +
 sklearn/neighbors/_nearest_centroid.py        |   71 +-
 sklearn/neighbors/_quad_tree.pyx              |    4 +-
 sklearn/neighbors/meson.build                 |    2 +-
 sklearn/neighbors/tests/test_lof.py           |   34 +
 .../neighbors/tests/test_nearest_centroid.py  |   22 +-
 sklearn/neighbors/tests/test_neighbors.py     |   20 +-
 sklearn/neural_network/__init__.py            |    5 +-
 sklearn/pipeline.py                           |   54 +-
 sklearn/preprocessing/__init__.py             |    5 +-
 sklearn/preprocessing/_discretization.py      |   15 +-
 .../preprocessing/_function_transformer.py    |    2 +-
 sklearn/preprocessing/_label.py               |   23 +-
 sklearn/preprocessing/tests/test_data.py      |    4 +
 .../tests/test_discretization.py              |   20 +
 sklearn/preprocessing/tests/test_label.py     |   52 +-
 sklearn/random_projection.py                  |    7 +-
 sklearn/semi_supervised/__init__.py           |    9 +-
 sklearn/svm/__init__.py                       |    4 +-
 sklearn/tests/metadata_routing_common.py      |   50 +-
 sklearn/tests/test_base.py                    |   62 +-
 sklearn/tests/test_common.py                  |   19 +-
 sklearn/tests/test_isotonic.py                |   32 +-
 sklearn/tests/test_kernel_approximation.py    |   13 -
 sklearn/tests/test_metadata_routing.py        |   56 +-
 .../test_metaestimators_metadata_routing.py   |   25 +-
 sklearn/tests/test_pipeline.py                |   21 +
 sklearn/tests/test_public_functions.py        |    1 +
 sklearn/tree/__init__.py                      |    5 +-
 sklearn/utils/__init__.py                     |    4 +-
 sklearn/utils/_array_api.py                   |  213 +-
 sklearn/utils/_encode.py                      |   62 +-
 sklearn/utils/_indexing.py                    |    5 +-
 sklearn/utils/_metadata_requests.py           |  112 +-
 sklearn/utils/_mocking.py                     |   26 +-
 sklearn/utils/_pprint.py                      |    5 +-
 sklearn/utils/_response.py                    |   17 +-
 sklearn/utils/_set_output.py                  |    4 +-
 sklearn/utils/_show_versions.py               |    3 +-
 sklearn/utils/arrayfuncs.pyx                  |    6 +-
 sklearn/utils/class_weight.py                 |    5 +-
 sklearn/utils/deprecation.py                  |   19 +
 sklearn/utils/discovery.py                    |    5 +-
 sklearn/utils/estimator_checks.py             |   17 +-
 sklearn/utils/extmath.py                      |    5 +-
 sklearn/utils/fixes.py                        |   39 +-
 sklearn/utils/graph.py                        |    4 +-
 sklearn/utils/metadata_routing.py             |    5 +-
 sklearn/utils/metaestimators.py               |    4 +-
 sklearn/utils/multiclass.py                   |    6 +-
 sklearn/utils/parallel.py                     |    4 +-
 sklearn/utils/random.py                       |    4 +-
 sklearn/utils/sparsefuncs.py                  |    5 +-
 sklearn/utils/sparsefuncs_fast.pyx            |    5 +-
 sklearn/utils/tests/test_array_api.py         |   84 +
 sklearn/utils/tests/test_arrayfuncs.py        |    4 +-
 sklearn/utils/tests/test_mask.py              |   19 +
 sklearn/utils/tests/test_missing.py           |   27 +
 sklearn/utils/tests/test_mocking.py           |   13 +-
 sklearn/utils/tests/test_multiclass.py        |    2 +-
 sklearn/utils/tests/test_response.py          |   60 +-
 sklearn/utils/tests/test_show_versions.py     |    3 +-
 sklearn/utils/tests/test_utils.py             |  148 +-
 sklearn/utils/tests/test_validation.py        |   63 +
 sklearn/utils/validation.py                   |   20 +-
 409 files changed, 18247 insertions(+), 21170 deletions(-)
 create mode 100644 doc/api/deprecated.rst.template
 create mode 100644 doc/api/index.rst.template
 create mode 100644 doc/api/module.rst.template
 create mode 100644 doc/api_reference.py
 delete mode 100644 doc/contents.rst
 create mode 100644 doc/css/.gitkeep
 create mode 100644 doc/images/Tidelift-logo-on-light.svg
 delete mode 100644 doc/images/ml_map.png
 create mode 100644 doc/images/ml_map.svg
 delete mode 100644 doc/includes/big_toc_css.rst
 delete mode 100644 doc/includes/bigger_toc_css.rst
 create mode 100644 doc/index.rst.template
 create mode 100644 doc/js/scripts/api-search.js
 create mode 100644 doc/js/scripts/dropdown.js
 create mode 100644 doc/js/scripts/vendor/svg-pan-zoom.min.js
 create mode 100644 doc/js/scripts/version-switcher.js
 rename doc/{authors.rst => maintainers.rst} (100%)
 rename doc/{authors_emeritus.rst => maintainers_emeritus.rst} (100%)
 create mode 100644 doc/min_dependency_substitutions.rst.template
 create mode 100644 doc/min_dependency_table.rst.template
 delete mode 100644 doc/modules/classes.rst
 create mode 100644 doc/modules/classification_threshold.rst
 delete mode 100644 doc/preface.rst
 create mode 100644 doc/scss/api-search.scss
 create mode 100644 doc/scss/api.scss
 create mode 100644 doc/scss/colors.scss
 create mode 100644 doc/scss/custom.scss
 create mode 100644 doc/scss/index.scss
 create mode 100644 doc/scss/install.scss
 delete mode 100644 doc/sphinxext/add_toctree_functions.py
 create mode 100644 doc/sphinxext/autoshortsummary.py
 create mode 100644 doc/sphinxext/dropdown_anchors.py
 create mode 100644 doc/sphinxext/move_gallery_links.py
 create mode 100644 doc/sphinxext/override_pst_pagetoc.py
 create mode 100644 doc/templates/base.rst
 delete mode 100644 doc/templates/class.rst
 delete mode 100644 doc/templates/class_with_call.rst
 delete mode 100644 doc/templates/deprecated_class.rst
 delete mode 100644 doc/templates/deprecated_class_with_call.rst
 delete mode 100644 doc/templates/deprecated_class_without_init.rst
 delete mode 100644 doc/templates/deprecated_function.rst
 delete mode 100644 doc/templates/display_all_class_methods.rst
 delete mode 100644 doc/templates/display_only_from_estimator.rst
 delete mode 100644 doc/templates/function.rst
 delete mode 100755 doc/templates/generate_deprecated.sh
 delete mode 100644 doc/themes/scikit-learn-modern/javascript.html
 delete mode 100644 doc/themes/scikit-learn-modern/layout.html
 delete mode 100644 doc/themes/scikit-learn-modern/nav.html
 delete mode 100644 doc/themes/scikit-learn-modern/search.html
 delete mode 100644 doc/themes/scikit-learn-modern/static/css/theme.css
 delete mode 100644 doc/themes/scikit-learn-modern/static/css/vendor/bootstrap.min.css
 delete mode 100644 doc/themes/scikit-learn-modern/static/js/details-permalink.js
 delete mode 100644 doc/themes/scikit-learn-modern/static/js/vendor/bootstrap.min.js
 delete mode 100644 doc/themes/scikit-learn-modern/static/js/vendor/jquery-3.6.3.slim.min.js
 delete mode 100644 doc/themes/scikit-learn-modern/theme.conf
 delete mode 100644 doc/tune_toc.rst
 delete mode 100644 doc/tutorial/machine_learning_map/ML_MAPS_README.txt
 create mode 100644 doc/tutorial/machine_learning_map/README.md
 delete mode 100644 doc/tutorial/machine_learning_map/parse_path.py
 delete mode 100644 doc/tutorial/machine_learning_map/pyparsing.py
 delete mode 100644 doc/tutorial/machine_learning_map/svg2imagemap.py
 create mode 100644 doc/whats_new/v1.6.rst
 create mode 100644 examples/model_selection/plot_cost_sensitive_learning.py
 create mode 100644 examples/model_selection/plot_tuned_decision_threshold.py
 create mode 100644 examples/release_highlights/plot_release_highlights_1_5_0.py
 delete mode 100644 examples/svm/plot_svm_nonlinear.py
 create mode 100644 sklearn/model_selection/_classification_threshold.py
 create mode 100644 sklearn/model_selection/tests/test_classification_threshold.py
 create mode 100644 sklearn/utils/tests/test_mask.py
 create mode 100644 sklearn/utils/tests/test_missing.py

diff --git a/.github/workflows/wheels.yml b/.github/workflows/wheels.yml
index 1c4255706972f..8bd7ffc17beca 100644
--- a/.github/workflows/wheels.yml
+++ b/.github/workflows/wheels.yml
@@ -53,8 +53,6 @@ jobs:
       matrix:
         include:
           # Window 64 bit
-          # Note: windows-2019 is needed for older Python versions:
-          # https://github.com/scikit-learn/scikit-learn/issues/22530
           - os: windows-latest
             python: 39
             platform_id: win_amd64
@@ -90,16 +88,16 @@ jobs:
             manylinux_image: manylinux2014
 
           # MacOS x86_64
-          - os: macos-latest
+          - os: macos-12
             python: 39
             platform_id: macosx_x86_64
-          - os: macos-latest
+          - os: macos-12
             python: 310
             platform_id: macosx_x86_64
-          - os: macos-latest
+          - os: macos-12
             python: 311
             platform_id: macosx_x86_64
-          - os: macos-latest
+          - os: macos-12
             python: 312
             platform_id: macosx_x86_64
 
diff --git a/.gitignore b/.gitignore
index 9f3b453bbfd74..61c89bcb96491 100644
--- a/.gitignore
+++ b/.gitignore
@@ -15,9 +15,13 @@ dist/
 MANIFEST
 doc/sg_execution_times.rst
 doc/_build/
+doc/api/*.rst
 doc/auto_examples/
+doc/css/*
+!doc/css/.gitkeep
 doc/modules/generated/
 doc/datasets/generated/
+doc/index.rst
 doc/min_dependency_table.rst
 doc/min_dependency_substitutions.rst
 *.pdf
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 31af43b6bbab0..abe14acc7778c 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -27,3 +27,10 @@ repos:
     # TODO: add the double-quote-cython-strings hook when it's usability has improved:
     # possibility to pass a directory and use it as a check instead of auto-formatter.
     -   id: cython-lint
+-   repo: https://github.com/pre-commit/mirrors-prettier
+    rev: v2.7.1
+    hooks:
+    -   id: prettier
+        files: ^doc/scss/|^doc/js/scripts/
+        exclude: ^doc/js/scripts/vendor/
+        types_or: ["scss", "javascript"]
diff --git a/README.rst b/README.rst
index 3f9a4ad726806..4ac297063c26e 100644
--- a/README.rst
+++ b/README.rst
@@ -36,7 +36,7 @@
 .. |NumPyMinVersion| replace:: 1.19.5
 .. |SciPyMinVersion| replace:: 1.6.0
 .. |JoblibMinVersion| replace:: 1.2.0
-.. |ThreadpoolctlMinVersion| replace:: 2.0.0
+.. |ThreadpoolctlMinVersion| replace:: 3.1.0
 .. |MatplotlibMinVersion| replace:: 3.3.4
 .. |Scikit-ImageMinVersion| replace:: 0.17.2
 .. |PandasMinVersion| replace:: 1.1.5
diff --git a/SECURITY.md b/SECURITY.md
index 18bb99ea3c15c..c961966d3ea5e 100644
--- a/SECURITY.md
+++ b/SECURITY.md
@@ -4,8 +4,8 @@
 
 | Version       | Supported          |
 | ------------- | ------------------ |
-| 1.4.2         | :white_check_mark: |
-| < 1.4.2       | :x:                |
+| 1.5.0         | :white_check_mark: |
+| < 1.5.0       | :x:                |
 
 ## Reporting a Vulnerability
 
diff --git a/asv_benchmarks/benchmarks/linear_model.py b/asv_benchmarks/benchmarks/linear_model.py
index 7e7b9d33540c6..24153895611df 100644
--- a/asv_benchmarks/benchmarks/linear_model.py
+++ b/asv_benchmarks/benchmarks/linear_model.py
@@ -52,7 +52,6 @@ def make_estimator(self, params):
         estimator = LogisticRegression(
             solver=solver,
             penalty=penalty,
-            multi_class="multinomial",
             tol=0.01,
             n_jobs=n_jobs,
             random_state=0,
diff --git a/benchmarks/bench_isotonic.py b/benchmarks/bench_isotonic.py
index 556c452fa3323..be2ff6548cb92 100644
--- a/benchmarks/bench_isotonic.py
+++ b/benchmarks/bench_isotonic.py
@@ -13,7 +13,7 @@
 
 import argparse
 import gc
-from datetime import datetime
+from timeit import default_timer
 
 import matplotlib.pyplot as plt
 import numpy as np
@@ -52,9 +52,9 @@ def bench_isotonic_regression(Y):
     """
     gc.collect()
 
-    tstart = datetime.now()
+    tstart = default_timer()
     isotonic_regression(Y)
-    return (datetime.now() - tstart).total_seconds()
+    return default_timer() - tstart
 
 
 if __name__ == "__main__":
diff --git a/benchmarks/bench_saga.py b/benchmarks/bench_saga.py
index c5b3e7728e2ec..97d4ba7b4b75b 100644
--- a/benchmarks/bench_saga.py
+++ b/benchmarks/bench_saga.py
@@ -20,6 +20,7 @@
 from sklearn.linear_model import LogisticRegression
 from sklearn.metrics import log_loss
 from sklearn.model_selection import train_test_split
+from sklearn.multiclass import OneVsRestClassifier
 from sklearn.preprocessing import LabelBinarizer, LabelEncoder
 from sklearn.utils.extmath import safe_sparse_dot, softmax
 from sklearn.utils.parallel import Parallel, delayed
@@ -95,7 +96,6 @@ def fit_single(
         else:
             lr = LogisticRegression(
                 solver=solver,
-                multi_class=multi_class,
                 C=C,
                 penalty=penalty,
                 fit_intercept=False,
@@ -103,6 +103,8 @@ def fit_single(
                 max_iter=this_max_iter,
                 random_state=42,
             )
+            if multi_class == "ovr":
+                lr = OneVsRestClassifier(lr)
 
         # Makes cpu cache even for all fit calls
         X_train.max()
@@ -118,8 +120,12 @@ def fit_single(
             except NotImplementedError:
                 # Lightning predict_proba is not implemented for n_classes > 2
                 y_pred = _predict_proba(lr, X)
+            if isinstance(lr, OneVsRestClassifier):
+                coef = np.concatenate([est.coef_ for est in lr.estimators_])
+            else:
+                coef = lr.coef_
             score = log_loss(y, y_pred, normalize=False) / n_samples
-            score += 0.5 * alpha * np.sum(lr.coef_**2) + beta * np.sum(np.abs(lr.coef_))
+            score += 0.5 * alpha * np.sum(coef**2) + beta * np.sum(np.abs(coef))
             scores.append(score)
         train_score, test_score = tuple(scores)
 
@@ -133,6 +139,7 @@ def fit_single(
 
 
 def _predict_proba(lr, X):
+    """Predict proba for lightning for n_classes >=3."""
     pred = safe_sparse_dot(X, lr.coef_.T)
     if hasattr(lr, "intercept_"):
         pred += lr.intercept_
diff --git a/build_tools/azure/debian_atlas_32bit_lock.txt b/build_tools/azure/debian_atlas_32bit_lock.txt
index 40e0ff4e25cb8..92b4b864fb9f5 100644
--- a/build_tools/azure/debian_atlas_32bit_lock.txt
+++ b/build_tools/azure/debian_atlas_32bit_lock.txt
@@ -6,7 +6,7 @@
 #
 attrs==23.2.0
     # via pytest
-coverage==7.4.4
+coverage==7.5.2
     # via pytest-cov
 cython==3.0.10
     # via -r build_tools/azure/debian_atlas_32bit_requirements.txt
@@ -16,19 +16,20 @@ joblib==1.2.0
     # via -r build_tools/azure/debian_atlas_32bit_requirements.txt
 meson==1.4.0
     # via meson-python
-meson-python==0.15.0
+meson-python==0.16.0
     # via -r build_tools/azure/debian_atlas_32bit_requirements.txt
 ninja==1.11.1.1
     # via -r build_tools/azure/debian_atlas_32bit_requirements.txt
 packaging==24.0
     # via
+    #   meson-python
     #   pyproject-metadata
     #   pytest
-pluggy==1.4.0
+pluggy==1.5.0
     # via pytest
 py==1.11.0
     # via pytest
-pyproject-metadata==0.7.1
+pyproject-metadata==0.8.0
     # via meson-python
 pytest==7.1.2
     # via
@@ -36,7 +37,7 @@ pytest==7.1.2
     #   pytest-cov
 pytest-cov==2.9.0
     # via -r build_tools/azure/debian_atlas_32bit_requirements.txt
-threadpoolctl==2.2.0
+threadpoolctl==3.1.0
     # via -r build_tools/azure/debian_atlas_32bit_requirements.txt
 tomli==2.0.1
     # via
diff --git a/build_tools/azure/debian_atlas_32bit_requirements.txt b/build_tools/azure/debian_atlas_32bit_requirements.txt
index d1bc22529d4f4..615193a71fc6b 100644
--- a/build_tools/azure/debian_atlas_32bit_requirements.txt
+++ b/build_tools/azure/debian_atlas_32bit_requirements.txt
@@ -3,7 +3,7 @@
 # build_tools/update_environments_and_lock_files.py
 cython==3.0.10  # min
 joblib==1.2.0  # min
-threadpoolctl==2.2.0
+threadpoolctl==3.1.0
 pytest==7.1.2  # min
 pytest-cov==2.9.0  # min
 ninja
diff --git a/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock b/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock
index 5f8dda6fa9748..bf4b0087c662b 100644
--- a/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock
+++ b/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock
@@ -1,21 +1,21 @@
 # Generated by conda-lock.
 # platform: linux-64
-# input_hash: 2622dc7361d0af53cfb31534b939a13e48192a3260137ba4ec20083659c2e5fa
+# input_hash: 50fed47bc507d9ee3dbf5ff7a2247cb88944928bd5797e534ebdf8ece2d858ec
 @EXPLICIT
 https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2#d7c89558ba9fa0495403155b64376d81
 https://conda.anaconda.org/conda-forge/linux-64/ca-certificates-2024.2.2-hbcca054_0.conda#2f4327a1cbe7f022401b236e915a5fef
 https://conda.anaconda.org/conda-forge/noarch/font-ttf-dejavu-sans-mono-2.37-hab24e00_0.tar.bz2#0c96522c6bdaed4b1566d11387caaf45
 https://conda.anaconda.org/conda-forge/noarch/font-ttf-inconsolata-3.000-h77eed37_0.tar.bz2#34893075a5c9e55cdafac56607368fc6
 https://conda.anaconda.org/conda-forge/noarch/font-ttf-source-code-pro-2.038-h77eed37_0.tar.bz2#4d59c254e01d9cde7957100457e2d5fb
-https://conda.anaconda.org/conda-forge/noarch/font-ttf-ubuntu-0.83-h77eed37_1.conda#6185f640c43843e5ad6fd1c5372c3f80
-https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.40-h41732ed_0.conda#7aca3059a1729aa76c597603f10b0dd3
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+https://conda.anaconda.org/conda-forge/noarch/setuptools-70.0.0-pyhd8ed1ab_0.conda#c8ddb4f34a208df4dd42509a0f6a1c89
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-https://conda.anaconda.org/conda-forge/noarch/threadpoolctl-3.4.0-pyhc1e730c_0.conda#b296278eef667c673bf51de6535bad88
+https://conda.anaconda.org/conda-forge/linux-64/tbb-2021.12.0-h297d8ca_1.conda#3ff978d8994f591818a506640c6a7071
+https://conda.anaconda.org/conda-forge/noarch/threadpoolctl-3.5.0-pyhc1e730c_0.conda#df68d78237980a159bd7149f33c0e8fd
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@@ -172,10 +172,10 @@ https://conda.anaconda.org/conda-forge/linux-64/xorg-libxrender-0.9.11-hd590300_
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-https://conda.anaconda.org/conda-forge/linux-64/coverage-7.4.4-py311h459d7ec_0.conda#1aa22cb84e68841ec206ee066457bdf0
-https://conda.anaconda.org/conda-forge/linux-64/fonttools-4.51.0-py311h459d7ec_0.conda#17e1997cc17c571d5ad27bd0159f616c
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-https://conda.anaconda.org/conda-forge/noarch/joblib-1.4.0-pyhd8ed1ab_0.conda#e0ed1bf13ce3a440e022157bf4764465
+https://conda.anaconda.org/conda-forge/linux-64/coverage-7.5.2-py311h331c9d8_0.conda#6afe87fd0c278ed708393cc1cf085146
+https://conda.anaconda.org/conda-forge/linux-64/fonttools-4.52.1-py311h331c9d8_0.conda#bb70cda5777de08084a402a2cdc13935
+https://conda.anaconda.org/conda-forge/linux-64/glib-2.80.2-hf974151_0.conda#d427988dc3dbd0a4c136f52db356cc6a
+https://conda.anaconda.org/conda-forge/noarch/joblib-1.4.2-pyhd8ed1ab_0.conda#25df261d4523d9f9783bcdb7208d872f
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@@ -184,22 +184,22 @@ https://conda.anaconda.org/conda-forge/noarch/meson-1.4.0-pyhd8ed1ab_0.conda#52a
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+https://conda.anaconda.org/conda-forge/noarch/pyproject-metadata-0.8.0-pyhd8ed1ab_0.conda#573fe09d7bd0cd4bcc210d8369b5ca47
+https://conda.anaconda.org/conda-forge/noarch/pytest-8.2.1-pyhd8ed1ab_0.conda#e4418e8bdbaa8eea28e047531e6763c8
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+https://conda.anaconda.org/conda-forge/linux-64/gst-plugins-base-1.24.3-h9ad1361_0.conda#8fb0e954c616bb0f9389efac4b4ed44b
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@@ -209,13 +209,13 @@ https://conda.anaconda.org/conda-forge/linux-64/qt-main-5.15.8-hc9dc06e_21.conda
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+https://conda.anaconda.org/conda-forge/linux-64/pandas-2.2.2-py311h14de704_1.conda#84e2dd379d4edec4dd6382861486104d
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+https://conda.anaconda.org/conda-forge/linux-64/matplotlib-3.8.4-py311h38be061_2.conda#7667100b9559c1b7a40c728cd72dabdf
diff --git a/build_tools/azure/pylatest_conda_forge_mkl_linux-64_environment.yml b/build_tools/azure/pylatest_conda_forge_mkl_linux-64_environment.yml
index 30686a983ab35..30a2fe1d1812a 100644
--- a/build_tools/azure/pylatest_conda_forge_mkl_linux-64_environment.yml
+++ b/build_tools/azure/pylatest_conda_forge_mkl_linux-64_environment.yml
@@ -14,7 +14,7 @@ dependencies:
   - matplotlib
   - pandas
   - pyamg
-  - pytest<8
+  - pytest
   - pytest-xdist
   - pillow
   - pip
diff --git a/build_tools/azure/pylatest_conda_forge_mkl_osx-64_conda.lock b/build_tools/azure/pylatest_conda_forge_mkl_osx-64_conda.lock
index 72289e4ce587a..833ce559e02b9 100644
--- a/build_tools/azure/pylatest_conda_forge_mkl_osx-64_conda.lock
+++ b/build_tools/azure/pylatest_conda_forge_mkl_osx-64_conda.lock
@@ -1,55 +1,52 @@
 # Generated by conda-lock.
 # platform: osx-64
-# input_hash: 05036df523e23d48cff7b6355ca081c5e5b41d8c5078cb9e1352f79e661d0549
+# input_hash: e7c2bc2b07721ef735f30d3b1cf0b2a780b5bf5c138d9d18ad174611bfbd32bf
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+https://conda.anaconda.org/conda-forge/osx-64/fortran-compiler-1.7.0-h6c2ab21_1.conda#48319058089f492d5059e04494b81ed9
+https://conda.anaconda.org/conda-forge/osx-64/compilers-1.7.0-h694c41f_1.conda#875e9b06186a41d55b96b9c1a52f15be
diff --git a/build_tools/azure/pylatest_conda_forge_mkl_osx-64_environment.yml b/build_tools/azure/pylatest_conda_forge_mkl_osx-64_environment.yml
index cfa1b7689a4ad..ad177e4ed391b 100644
--- a/build_tools/azure/pylatest_conda_forge_mkl_osx-64_environment.yml
+++ b/build_tools/azure/pylatest_conda_forge_mkl_osx-64_environment.yml
@@ -14,7 +14,7 @@ dependencies:
   - matplotlib
   - pandas
   - pyamg
-  - pytest<8
+  - pytest
   - pytest-xdist
   - pillow
   - pip
diff --git a/build_tools/azure/pylatest_conda_mkl_no_openmp_environment.yml b/build_tools/azure/pylatest_conda_mkl_no_openmp_environment.yml
index 9c46400c2d3c6..7e85b28b3f6c4 100644
--- a/build_tools/azure/pylatest_conda_mkl_no_openmp_environment.yml
+++ b/build_tools/azure/pylatest_conda_mkl_no_openmp_environment.yml
@@ -9,19 +9,19 @@ dependencies:
   - blas[build=mkl]
   - scipy<1.12
   - joblib
-  - threadpoolctl
   - matplotlib
   - pandas
   - pyamg
-  - pytest<8
+  - pytest
   - pytest-xdist
   - pillow
   - pip
   - ninja
-  - meson-python
   - pytest-cov
   - coverage
   - ccache
   - pip
   - pip:
     - cython
+    - threadpoolctl
+    - meson-python
diff --git a/build_tools/azure/pylatest_conda_mkl_no_openmp_osx-64_conda.lock b/build_tools/azure/pylatest_conda_mkl_no_openmp_osx-64_conda.lock
index aa946a23c4650..956622fe0008e 100644
--- a/build_tools/azure/pylatest_conda_mkl_no_openmp_osx-64_conda.lock
+++ b/build_tools/azure/pylatest_conda_mkl_no_openmp_osx-64_conda.lock
@@ -1,81 +1,77 @@
 # Generated by conda-lock.
 # platform: osx-64
-# input_hash: d3fadab6d5d5d715660beb53286e2687b018b5195ef7ce47928bb79a96ee851b
+# input_hash: 33a102d2ccde4e14e315a98b50340349af349f802403dd49589375b2b889f2d3
 @EXPLICIT
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+https://repo.anaconda.com/pkgs/main/osx-64/bzip2-1.0.8-h6c40b1e_6.conda#96224786021d0765ce05818fa3c59bdb
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+https://repo.anaconda.com/pkgs/main/osx-64/libbrotlicommon-1.0.9-h6c40b1e_8.conda#8e86dfa34b08bc664b19e1499e5465b8
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-https://repo.anaconda.com/pkgs/main/osx-64/libffi-3.4.4-hecd8cb5_0.conda#c20b2687118c471b1d70067ef2b2703f
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-https://repo.anaconda.com/pkgs/main/osx-64/openssl-3.0.13-hca72f7f_0.conda#08b109f010b97ce6cef211e235177175
+https://repo.anaconda.com/pkgs/main/osx-64/openssl-3.0.13-hca72f7f_2.conda#4f840ec6217dff98040ff6be19cf3afb
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+https://repo.anaconda.com/pkgs/main/osx-64/tk-8.6.14-h4d00af3_0.conda#a2c03940c2ae54614301ec82e6a98d75
+https://repo.anaconda.com/pkgs/main/osx-64/brotli-bin-1.0.9-h6c40b1e_8.conda#11053f9c6b8d8a8348d0c33450c23ce9
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-https://repo.anaconda.com/pkgs/main/osx-64/sqlite-3.41.2-h6c40b1e_0.conda#6947a501943529c7536b7e4ba53802c1
-https://repo.anaconda.com/pkgs/main/osx-64/zstd-1.5.5-hc035e20_0.conda#5e0b7ddb1b7dc6b630e1f9a03499c19c
-https://repo.anaconda.com/pkgs/main/osx-64/brotli-1.0.9-hca72f7f_7.conda#68e54d12ec67591deb2ffd70348fb00f
+https://repo.anaconda.com/pkgs/main/osx-64/sqlite-3.45.3-h6c40b1e_0.conda#2edf909b937b3aad48322c9cb2e8f1a0
+https://repo.anaconda.com/pkgs/main/osx-64/zstd-1.5.5-hc035e20_2.conda#c033bf68c12f8c71fd916f000f3dc118
+https://repo.anaconda.com/pkgs/main/osx-64/brotli-1.0.9-h6c40b1e_8.conda#10f89677a3898d0113dc354adf643df3
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-https://repo.anaconda.com/pkgs/main/osx-64/python-3.12.2-hd58486a_0.conda#21efba1355d32906d082aaff16698961
+https://repo.anaconda.com/pkgs/main/osx-64/python-3.12.3-hd58486a_1.conda#cdc61e8f6c2d77b3b263e720048c4b54
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-https://repo.anaconda.com/pkgs/main/osx-64/joblib-1.2.0-py312hecd8cb5_0.conda#aeeb33f85c1e6776700b67a4762d2e6d
+https://repo.anaconda.com/pkgs/main/osx-64/joblib-1.4.0-py312hecd8cb5_0.conda#0af12a3a87d9c8051ae6ba2ed2c3882a
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-https://repo.anaconda.com/pkgs/main/osx-64/pytz-2023.3.post1-py312hecd8cb5_0.conda#2636382c9a424f69cbc36b1c5dc1f2fc
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+https://repo.anaconda.com/pkgs/main/osx-64/pytz-2024.1-py312hecd8cb5_0.conda#2b28ec0e0d07f5c0c701f75200b1e8b6
+https://repo.anaconda.com/pkgs/main/osx-64/setuptools-69.5.1-py312hecd8cb5_0.conda#5c7c7ef1e0762e3ca1f543d28310946f
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-https://repo.anaconda.com/pkgs/main/noarch/threadpoolctl-2.2.0-pyh0d69192_0.conda#bbfdbae4934150b902f97daaf287efe2
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-https://repo.anaconda.com/pkgs/main/osx-64/wheel-0.41.2-py312hecd8cb5_0.conda#e7aea266d81142e2bb0bbc2280e64526
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-https://repo.anaconda.com/pkgs/main/osx-64/meson-1.3.1-py312hecd8cb5_0.conda#43963a2b38becce4caa95434b8c96837
+https://repo.anaconda.com/pkgs/main/osx-64/unicodedata2-15.1.0-py312h6c40b1e_0.conda#65bd2cb787fc99662d9bb6e6520c5826
+https://repo.anaconda.com/pkgs/main/osx-64/wheel-0.43.0-py312hecd8cb5_0.conda#c0bdd5748b170523232e8ad1d667136c
+https://repo.anaconda.com/pkgs/main/osx-64/fonttools-4.51.0-py312h6c40b1e_0.conda#8f55fa86b73e8a7f4403503f9b7a9959
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-https://repo.anaconda.com/pkgs/main/osx-64/pip-23.3.1-py312hecd8cb5_0.conda#efc3db40cac09f74bb480d28d3a0b260
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+https://repo.anaconda.com/pkgs/main/osx-64/pillow-10.3.0-py312h6c40b1e_0.conda#fe883fa4247d35fe6de49f713529ca02
+https://repo.anaconda.com/pkgs/main/osx-64/pip-24.0-py312hecd8cb5_0.conda#7a8e0b1d3742ddf1c8aa97fbaa158039
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-https://repo.anaconda.com/pkgs/main/noarch/python-dateutil-2.8.2-pyhd3eb1b0_0.conda#211ee00320b08a1ac9fea6677649f6c9
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+https://repo.anaconda.com/pkgs/main/osx-64/python-dateutil-2.9.0post0-py312hecd8cb5_2.conda#1047dde28f78127dd9f6121e882926dd
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-https://repo.anaconda.com/pkgs/main/osx-64/matplotlib-3.8.0-py312hecd8cb5_0.conda#64ffa3462aace0fc2d5fa5bff15f63f6
-https://repo.anaconda.com/pkgs/main/osx-64/matplotlib-base-3.8.0-py312h7f12edd_0.conda#bda389e5a1ff69f763911cf90102893b
+https://repo.anaconda.com/pkgs/main/osx-64/matplotlib-3.8.4-py312hecd8cb5_0.conda#6886c230c2ec2f47621b5cca4c7d493a
+https://repo.anaconda.com/pkgs/main/osx-64/matplotlib-base-3.8.4-py312h7f12edd_0.conda#a4eee14a4dcaa89b306ca33d2d479fa4
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@@ -84,3 +80,7 @@ https://repo.anaconda.com/pkgs/main/osx-64/scipy-1.11.4-py312h81688c2_0.conda#7d
 https://repo.anaconda.com/pkgs/main/osx-64/pandas-2.2.1-py312he282a81_0.conda#021b70a1e40efb75b89eb8ebdb347132
 https://repo.anaconda.com/pkgs/main/osx-64/pyamg-4.2.3-py312h44cbcf4_0.conda#3bdc7be74087b3a5a83c520a74e1e8eb
 # pip cython @ https://files.pythonhosted.org/packages/d5/6d/06c08d75adb98cdf72af18801e193d22580cc86ca553610f430f18ea26b3/Cython-3.0.10-cp312-cp312-macosx_10_9_x86_64.whl#sha256=8f2864ab5fcd27a346f0b50f901ebeb8f60b25a60a575ccfd982e7f3e9674914
+# pip meson @ https://files.pythonhosted.org/packages/33/75/b1a37fa7b2dbca8c0dbb04d5cdd7e2720c8ef6febe41b4a74866350e041c/meson-1.4.0-py3-none-any.whl#sha256=476a458d51fcfa322a6bdc64da5138997c542d08e6b2e49b9fa68c46fd7c4475
+# pip threadpoolctl @ https://files.pythonhosted.org/packages/4b/2c/ffbf7a134b9ab11a67b0cf0726453cedd9c5043a4fe7a35d1cefa9a1bcfb/threadpoolctl-3.5.0-py3-none-any.whl#sha256=56c1e26c150397e58c4926da8eeee87533b1e32bef131bd4bf6a2f45f3185467
+# pip pyproject-metadata @ https://files.pythonhosted.org/packages/aa/5f/bb5970d3d04173b46c9037109f7f05fc8904ff5be073ee49bb6ff00301bc/pyproject_metadata-0.8.0-py3-none-any.whl#sha256=ad858d448e1d3a1fb408ac5bac9ea7743e7a8bbb472f2693aaa334d2db42f526
+# pip meson-python @ https://files.pythonhosted.org/packages/91/c0/104cb6244c83fe6bc3886f144cc433db0c0c78efac5dc00e409a5a08c87d/meson_python-0.16.0-py3-none-any.whl#sha256=842dc9f5dc29e55fc769ff1b6fe328412fe6c870220fc321060a1d2d395e69e8
diff --git a/build_tools/azure/pylatest_pip_openblas_pandas_environment.yml b/build_tools/azure/pylatest_pip_openblas_pandas_environment.yml
index 0f82886f4acb2..adb7add7622e1 100644
--- a/build_tools/azure/pylatest_pip_openblas_pandas_environment.yml
+++ b/build_tools/azure/pylatest_pip_openblas_pandas_environment.yml
@@ -16,7 +16,7 @@ dependencies:
     - matplotlib
     - pandas
     - pyamg
-    - pytest<8
+    - pytest
     - pytest-xdist
     - pillow
     - ninja
diff --git a/build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock b/build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock
index b2c4d1c5163e4..fb409d975c5a4 100644
--- a/build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock
+++ b/build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock
@@ -1,6 +1,6 @@
 # Generated by conda-lock.
 # platform: linux-64
-# input_hash: d4063b0b99f7a39e30c5f6e2d9c5dd293d9b206ce326841bf811534ea1be79f0
+# input_hash: 37f8029b6bb116e0d5856093424791a8c1ddc3f493e20fcb5d02cd32d516523d
 @EXPLICIT
 https://repo.anaconda.com/pkgs/main/linux-64/_libgcc_mutex-0.1-main.conda#c3473ff8bdb3d124ed5ff11ec380d6f9
 https://repo.anaconda.com/pkgs/main/linux-64/ca-certificates-2024.3.11-h06a4308_0.conda#08529eb3504712baabcbda266a19feb7
@@ -10,33 +10,33 @@ https://repo.anaconda.com/pkgs/main/linux-64/libgomp-11.2.0-h1234567_1.conda#b37
 https://repo.anaconda.com/pkgs/main/linux-64/libstdcxx-ng-11.2.0-h1234567_1.conda#57623d10a70e09e1d048c2b2b6f4e2dd
 https://repo.anaconda.com/pkgs/main/linux-64/_openmp_mutex-5.1-1_gnu.conda#71d281e9c2192cb3fa425655a8defb85
 https://repo.anaconda.com/pkgs/main/linux-64/libgcc-ng-11.2.0-h1234567_1.conda#a87728dabf3151fb9cfa990bd2eb0464
-https://repo.anaconda.com/pkgs/main/linux-64/libffi-3.4.4-h6a678d5_0.conda#06e288f9250abef59b9a367d151fc339
+https://repo.anaconda.com/pkgs/main/linux-64/libffi-3.4.4-h6a678d5_1.conda#70646cc713f0c43926cfdcfe9b695fe0
 https://repo.anaconda.com/pkgs/main/linux-64/ncurses-6.4-h6a678d5_0.conda#5558eec6e2191741a92f832ea826251c
-https://repo.anaconda.com/pkgs/main/linux-64/openssl-3.0.13-h7f8727e_0.conda#c73d46a4d666da0ae3dcd3fd8f805122
-https://repo.anaconda.com/pkgs/main/linux-64/xz-5.4.6-h5eee18b_0.conda#81a9916f581d4da15a3839216a487c66
-https://repo.anaconda.com/pkgs/main/linux-64/zlib-1.2.13-h5eee18b_0.conda#333e31fbfbb5057c92fa845ad6adef93
+https://repo.anaconda.com/pkgs/main/linux-64/openssl-3.0.13-h7f8727e_2.conda#0019453c25f5e72129f99236e60febaa
+https://repo.anaconda.com/pkgs/main/linux-64/xz-5.4.6-h5eee18b_1.conda#1562802f843297ee776a50b9329597ed
+https://repo.anaconda.com/pkgs/main/linux-64/zlib-1.2.13-h5eee18b_1.conda#92e42d8310108b0a440fb2e60b2b2a25
 https://repo.anaconda.com/pkgs/main/linux-64/ccache-3.7.9-hfe4627d_0.conda#bef6fc681c273bb7bd0c67d1a591365e
 https://repo.anaconda.com/pkgs/main/linux-64/readline-8.2-h5eee18b_0.conda#be42180685cce6e6b0329201d9f48efb
-https://repo.anaconda.com/pkgs/main/linux-64/tk-8.6.12-h1ccaba5_0.conda#fa10ff4aa631fa4aa090a6234d7770b9
-https://repo.anaconda.com/pkgs/main/linux-64/sqlite-3.41.2-h5eee18b_0.conda#c7086c9ceb6cfe1c4c729a774a2d88a5
-https://repo.anaconda.com/pkgs/main/linux-64/python-3.9.19-h955ad1f_0.conda#33cb019c40e3409df392c99e3c34f352
-https://repo.anaconda.com/pkgs/main/linux-64/setuptools-68.2.2-py39h06a4308_0.conda#5b42cae5548732ae5c167bb1066085de
-https://repo.anaconda.com/pkgs/main/linux-64/wheel-0.41.2-py39h06a4308_0.conda#ec1b8213c3585defaa6042ed2f95861d
-https://repo.anaconda.com/pkgs/main/linux-64/pip-23.3.1-py39h06a4308_0.conda#685007e3dae59d211620f19926577bd6
+https://repo.anaconda.com/pkgs/main/linux-64/tk-8.6.14-h39e8969_0.conda#78dbc5e3c69143ebc037fc5d5b22e597
+https://repo.anaconda.com/pkgs/main/linux-64/sqlite-3.45.3-h5eee18b_0.conda#acf93d6aceb74d6110e20b44cc45939e
+https://repo.anaconda.com/pkgs/main/linux-64/python-3.9.19-h955ad1f_1.conda#4b453281859c293c9d577271f3b18a0d
+https://repo.anaconda.com/pkgs/main/linux-64/setuptools-69.5.1-py39h06a4308_0.conda#3eb144d481b39c0fbbced789dd9b76b3
+https://repo.anaconda.com/pkgs/main/linux-64/wheel-0.43.0-py39h06a4308_0.conda#40bb60408c7433d767fd8c65b35bc4a0
+https://repo.anaconda.com/pkgs/main/linux-64/pip-24.0-py39h06a4308_0.conda#7f8ce3af15cfecd12e4dda8c5cef5fb7
 # pip alabaster @ https://files.pythonhosted.org/packages/32/34/d4e1c02d3bee589efb5dfa17f88ea08bdb3e3eac12bc475462aec52ed223/alabaster-0.7.16-py3-none-any.whl#sha256=b46733c07dce03ae4e150330b975c75737fa60f0a7c591b6c8bf4928a28e2c92
-# pip babel @ https://files.pythonhosted.org/packages/0d/35/4196b21041e29a42dc4f05866d0c94fa26c9da88ce12c38c2265e42c82fb/Babel-2.14.0-py3-none-any.whl#sha256=efb1a25b7118e67ce3a259bed20545c29cb68be8ad2c784c83689981b7a57287
+# pip babel @ https://files.pythonhosted.org/packages/27/45/377f7e32a5c93d94cd56542349b34efab5ca3f9e2fd5a68c5e93169aa32d/Babel-2.15.0-py3-none-any.whl#sha256=08706bdad8d0a3413266ab61bd6c34d0c28d6e1e7badf40a2cebe67644e2e1fb
 # pip certifi @ https://files.pythonhosted.org/packages/ba/06/a07f096c664aeb9f01624f858c3add0a4e913d6c96257acb4fce61e7de14/certifi-2024.2.2-py3-none-any.whl#sha256=dc383c07b76109f368f6106eee2b593b04a011ea4d55f652c6ca24a754d1cdd1
 # pip charset-normalizer @ https://files.pythonhosted.org/packages/98/69/5d8751b4b670d623aa7a47bef061d69c279e9f922f6705147983aa76c3ce/charset_normalizer-3.3.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=b261ccdec7821281dade748d088bb6e9b69e6d15b30652b74cbbac25e280b796
 # pip cycler @ https://files.pythonhosted.org/packages/e7/05/c19819d5e3d95294a6f5947fb9b9629efb316b96de511b418c53d245aae6/cycler-0.12.1-py3-none-any.whl#sha256=85cef7cff222d8644161529808465972e51340599459b8ac3ccbac5a854e0d30
 # pip cython @ https://files.pythonhosted.org/packages/a7/f5/3dde4d96076888ceaa981827b098274c2b45ddd4b20d75a8cfaa92b91eec/Cython-3.0.10-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=651a15a8534ebfb9b58cb0b87c269c70984b6f9c88bfe65e4f635f0e3f07dfcd
-# pip docutils @ https://files.pythonhosted.org/packages/26/87/f238c0670b94533ac0353a4e2a1a771a0cc73277b88bff23d3ae35a256c1/docutils-0.20.1-py3-none-any.whl#sha256=96f387a2c5562db4476f09f13bbab2192e764cac08ebbf3a34a95d9b1e4a59d6
-# pip exceptiongroup @ https://files.pythonhosted.org/packages/b8/9a/5028fd52db10e600f1c4674441b968cf2ea4959085bfb5b99fb1250e5f68/exceptiongroup-1.2.0-py3-none-any.whl#sha256=4bfd3996ac73b41e9b9628b04e079f193850720ea5945fc96a08633c66912f14
+# pip docutils @ https://files.pythonhosted.org/packages/8f/d7/9322c609343d929e75e7e5e6255e614fcc67572cfd083959cdef3b7aad79/docutils-0.21.2-py3-none-any.whl#sha256=dafca5b9e384f0e419294eb4d2ff9fa826435bf15f15b7bd45723e8ad76811b2
+# pip exceptiongroup @ https://files.pythonhosted.org/packages/01/90/79fe92dd413a9cab314ef5c591b5aa9b9ba787ae4cadab75055b0ae00b33/exceptiongroup-1.2.1-py3-none-any.whl#sha256=5258b9ed329c5bbdd31a309f53cbfb0b155341807f6ff7606a1e801a891b29ad
 # pip execnet @ https://files.pythonhosted.org/packages/43/09/2aea36ff60d16dd8879bdb2f5b3ee0ba8d08cbbdcdfe870e695ce3784385/execnet-2.1.1-py3-none-any.whl#sha256=26dee51f1b80cebd6d0ca8e74dd8745419761d3bef34163928cbebbdc4749fdc
-# pip fonttools @ https://files.pythonhosted.org/packages/8b/c6/636f008104908a93b80419f756be755bb91df4b8a0c88d5158bb52c82c3a/fonttools-4.51.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=0d145976194a5242fdd22df18a1b451481a88071feadf251221af110ca8f00ce
+# pip fonttools @ https://files.pythonhosted.org/packages/22/56/8b2ffe792b0a11d481ed797e0c246ee914b86370e317476c84d4fc537d95/fonttools-4.52.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=ee2a8c1101d06cc8fca7851dceb67afd53dd6fc0288bacaa632e647bc5afff58
 # pip idna @ https://files.pythonhosted.org/packages/e5/3e/741d8c82801c347547f8a2a06aa57dbb1992be9e948df2ea0eda2c8b79e8/idna-3.7-py3-none-any.whl#sha256=82fee1fc78add43492d3a1898bfa6d8a904cc97d8427f683ed8e798d07761aa0
 # pip imagesize @ https://files.pythonhosted.org/packages/ff/62/85c4c919272577931d407be5ba5d71c20f0b616d31a0befe0ae45bb79abd/imagesize-1.4.1-py2.py3-none-any.whl#sha256=0d8d18d08f840c19d0ee7ca1fd82490fdc3729b7ac93f49870406ddde8ef8d8b
 # pip iniconfig @ https://files.pythonhosted.org/packages/ef/a6/62565a6e1cf69e10f5727360368e451d4b7f58beeac6173dc9db836a5b46/iniconfig-2.0.0-py3-none-any.whl#sha256=b6a85871a79d2e3b22d2d1b94ac2824226a63c6b741c88f7ae975f18b6778374
-# pip joblib @ https://files.pythonhosted.org/packages/ae/e2/4dea6313ef2b38442fccbbaf4017e50a6c3c8a50e8ee9b512783e5c90409/joblib-1.4.0-py3-none-any.whl#sha256=42942470d4062537be4d54c83511186da1fc14ba354961a2114da91efa9a4ed7
+# pip joblib @ https://files.pythonhosted.org/packages/91/29/df4b9b42f2be0b623cbd5e2140cafcaa2bef0759a00b7b70104dcfe2fb51/joblib-1.4.2-py3-none-any.whl#sha256=06d478d5674cbc267e7496a410ee875abd68e4340feff4490bcb7afb88060ae6
 # pip kiwisolver @ https://files.pythonhosted.org/packages/c0/a8/841594f11d0b88d8aeb26991bc4dac38baa909dc58d0c4262a4f7893bcbf/kiwisolver-1.4.5-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl#sha256=6c3bd3cde54cafb87d74d8db50b909705c62b17c2099b8f2e25b461882e544ff
 # pip markupsafe @ https://files.pythonhosted.org/packages/5f/5a/360da85076688755ea0cceb92472923086993e86b5613bbae9fbc14136b0/MarkupSafe-2.1.5-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=17b950fccb810b3293638215058e432159d2b71005c74371d784862b7e4683f3
 # pip meson @ https://files.pythonhosted.org/packages/33/75/b1a37fa7b2dbca8c0dbb04d5cdd7e2720c8ef6febe41b4a74866350e041c/meson-1.4.0-py3-none-any.whl#sha256=476a458d51fcfa322a6bdc64da5138997c542d08e6b2e49b9fa68c46fd7c4475
@@ -45,8 +45,8 @@ https://repo.anaconda.com/pkgs/main/linux-64/pip-23.3.1-py39h06a4308_0.conda#685
 # pip numpy @ https://files.pythonhosted.org/packages/54/30/c2a907b9443cf42b90c17ad10c1e8fa801975f01cb9764f3f8eb8aea638b/numpy-1.26.4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=f870204a840a60da0b12273ef34f7051e98c3b5961b61b0c2c1be6dfd64fbcd3
 # pip packaging @ https://files.pythonhosted.org/packages/49/df/1fceb2f8900f8639e278b056416d49134fb8d84c5942ffaa01ad34782422/packaging-24.0-py3-none-any.whl#sha256=2ddfb553fdf02fb784c234c7ba6ccc288296ceabec964ad2eae3777778130bc5
 # pip pillow @ https://files.pythonhosted.org/packages/f5/6d/52e82352670e850f468de9e6bccced4202a09f58e7ea5ecdbf08283d85cb/pillow-10.3.0-cp39-cp39-manylinux_2_28_x86_64.whl#sha256=1dfc94946bc60ea375cc39cff0b8da6c7e5f8fcdc1d946beb8da5c216156ddd8
-# pip pluggy @ https://files.pythonhosted.org/packages/a5/5b/0cc789b59e8cc1bf288b38111d002d8c5917123194d45b29dcdac64723cc/pluggy-1.4.0-py3-none-any.whl#sha256=7db9f7b503d67d1c5b95f59773ebb58a8c1c288129a88665838012cfb07b8981
-# pip pygments @ https://files.pythonhosted.org/packages/97/9c/372fef8377a6e340b1704768d20daaded98bf13282b5327beb2e2fe2c7ef/pygments-2.17.2-py3-none-any.whl#sha256=b27c2826c47d0f3219f29554824c30c5e8945175d888647acd804ddd04af846c
+# pip pluggy @ https://files.pythonhosted.org/packages/88/5f/e351af9a41f866ac3f1fac4ca0613908d9a41741cfcf2228f4ad853b697d/pluggy-1.5.0-py3-none-any.whl#sha256=44e1ad92c8ca002de6377e165f3e0f1be63266ab4d554740532335b9d75ea669
+# pip pygments @ https://files.pythonhosted.org/packages/f7/3f/01c8b82017c199075f8f788d0d906b9ffbbc5a47dc9918a945e13d5a2bda/pygments-2.18.0-py3-none-any.whl#sha256=b8e6aca0523f3ab76fee51799c488e38782ac06eafcf95e7ba832985c8e7b13a
 # pip pyparsing @ https://files.pythonhosted.org/packages/9d/ea/6d76df31432a0e6fdf81681a895f009a4bb47b3c39036db3e1b528191d52/pyparsing-3.1.2-py3-none-any.whl#sha256=f9db75911801ed778fe61bb643079ff86601aca99fcae6345aa67292038fb742
 # pip pytz @ https://files.pythonhosted.org/packages/9c/3d/a121f284241f08268b21359bd425f7d4825cffc5ac5cd0e1b3d82ffd2b10/pytz-2024.1-py2.py3-none-any.whl#sha256=328171f4e3623139da4983451950b28e95ac706e13f3f2630a879749e7a8b319
 # pip six @ https://files.pythonhosted.org/packages/d9/5a/e7c31adbe875f2abbb91bd84cf2dc52d792b5a01506781dbcf25c91daf11/six-1.16.0-py2.py3-none-any.whl#sha256=8abb2f1d86890a2dfb989f9a77cfcfd3e47c2a354b01111771326f8aa26e0254
@@ -58,31 +58,31 @@ https://repo.anaconda.com/pkgs/main/linux-64/pip-23.3.1-py39h06a4308_0.conda#685
 # pip sphinxcontrib-qthelp @ https://files.pythonhosted.org/packages/80/b3/1beac14a88654d2e5120d0143b49be5ad450b86eb1963523d8dbdcc51eb2/sphinxcontrib_qthelp-1.0.7-py3-none-any.whl#sha256=e2ae3b5c492d58fcbd73281fbd27e34b8393ec34a073c792642cd8e529288182
 # pip sphinxcontrib-serializinghtml @ https://files.pythonhosted.org/packages/38/24/228bb903ea87b9e08ab33470e6102402a644127108c7117ac9c00d849f82/sphinxcontrib_serializinghtml-1.1.10-py3-none-any.whl#sha256=326369b8df80a7d2d8d7f99aa5ac577f51ea51556ed974e7716cfd4fca3f6cb7
 # pip tabulate @ https://files.pythonhosted.org/packages/40/44/4a5f08c96eb108af5cb50b41f76142f0afa346dfa99d5296fe7202a11854/tabulate-0.9.0-py3-none-any.whl#sha256=024ca478df22e9340661486f85298cff5f6dcdba14f3813e8830015b9ed1948f
-# pip threadpoolctl @ https://files.pythonhosted.org/packages/1e/84/ccd9b08653022b7785b6e3ee070ffb2825841e0dc119be22f0840b2b35cb/threadpoolctl-3.4.0-py3-none-any.whl#sha256=8f4c689a65b23e5ed825c8436a92b818aac005e0f3715f6a1664d7c7ee29d262
+# pip threadpoolctl @ https://files.pythonhosted.org/packages/4b/2c/ffbf7a134b9ab11a67b0cf0726453cedd9c5043a4fe7a35d1cefa9a1bcfb/threadpoolctl-3.5.0-py3-none-any.whl#sha256=56c1e26c150397e58c4926da8eeee87533b1e32bef131bd4bf6a2f45f3185467
 # pip tomli @ https://files.pythonhosted.org/packages/97/75/10a9ebee3fd790d20926a90a2547f0bf78f371b2f13aa822c759680ca7b9/tomli-2.0.1-py3-none-any.whl#sha256=939de3e7a6161af0c887ef91b7d41a53e7c5a1ca976325f429cb46ea9bc30ecc
 # pip tzdata @ https://files.pythonhosted.org/packages/65/58/f9c9e6be752e9fcb8b6a0ee9fb87e6e7a1f6bcab2cdc73f02bb7ba91ada0/tzdata-2024.1-py2.py3-none-any.whl#sha256=9068bc196136463f5245e51efda838afa15aaeca9903f49050dfa2679db4d252
 # pip urllib3 @ https://files.pythonhosted.org/packages/a2/73/a68704750a7679d0b6d3ad7aa8d4da8e14e151ae82e6fee774e6e0d05ec8/urllib3-2.2.1-py3-none-any.whl#sha256=450b20ec296a467077128bff42b73080516e71b56ff59a60a02bef2232c4fa9d
-# pip zipp @ https://files.pythonhosted.org/packages/c2/0a/ba9d0ee9536d3ef73a3448e931776e658b36f128d344e175bc32b092a8bf/zipp-3.18.1-py3-none-any.whl#sha256=206f5a15f2af3dbaee80769fb7dc6f249695e940acca08dfb2a4769fe61e538b
+# pip zipp @ https://files.pythonhosted.org/packages/7f/2d/670176f39d6613af2908b5bc31c9974588208de1fcc4117dfae08623d188/zipp-3.19.0-py3-none-any.whl#sha256=96dc6ad62f1441bcaccef23b274ec471518daf4fbbc580341204936a5a3dddec
 # pip contourpy @ https://files.pythonhosted.org/packages/31/a2/2f12e3a6e45935ff694654b710961b03310b0e1ec997ee9f416d3c873f87/contourpy-1.2.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=e1d59258c3c67c865435d8fbeb35f8c59b8bef3d6f46c1f29f6123556af28445
-# pip coverage @ https://files.pythonhosted.org/packages/5b/ec/9bd500128995e9eec2ab50361ce8b853bab2b4839316ddcfd6a34f5bbfed/coverage-7.4.4-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=ff7687ca3d7028d8a5f0ebae95a6e4827c5616b31a4ee1192bdfde697db110d4
-# pip imageio @ https://files.pythonhosted.org/packages/02/25/66533a8390e3763cf8254dee143dbf8a830391ea60d2762512ba7f9ddfbe/imageio-2.34.0-py3-none-any.whl#sha256=08082bf47ccb54843d9c73fe9fc8f3a88c72452ab676b58aca74f36167e8ccba
+# pip coverage @ https://files.pythonhosted.org/packages/b2/31/aa090d5e5a4e1a0e2d517f73a737a6d4b4975ca1f2b9cea9cb985b3ef307/coverage-7.5.2-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=24bb4c7859a3f757a116521d4d3a8a82befad56ea1bdacd17d6aafd113b0071e
+# pip imageio @ https://files.pythonhosted.org/packages/a3/b6/39c7dad203d9984225f47e0aa39ac3ba3a47c77a02d0ef2a7be691855a06/imageio-2.34.1-py3-none-any.whl#sha256=408c1d4d62f72c9e8347e7d1ca9bc11d8673328af3913868db3b828e28b40a4c
 # pip importlib-metadata @ https://files.pythonhosted.org/packages/2d/0a/679461c511447ffaf176567d5c496d1de27cbe34a87df6677d7171b2fbd4/importlib_metadata-7.1.0-py3-none-any.whl#sha256=30962b96c0c223483ed6cc7280e7f0199feb01a0e40cfae4d4450fc6fab1f570
 # pip importlib-resources @ https://files.pythonhosted.org/packages/75/06/4df55e1b7b112d183f65db9503bff189e97179b256e1ea450a3c365241e0/importlib_resources-6.4.0-py3-none-any.whl#sha256=50d10f043df931902d4194ea07ec57960f66a80449ff867bfe782b4c486ba78c
-# pip jinja2 @ https://files.pythonhosted.org/packages/30/6d/6de6be2d02603ab56e72997708809e8a5b0fbfee080735109b40a3564843/Jinja2-3.1.3-py3-none-any.whl#sha256=7d6d50dd97d52cbc355597bd845fabfbac3f551e1f99619e39a35ce8c370b5fa
+# pip jinja2 @ https://files.pythonhosted.org/packages/31/80/3a54838c3fb461f6fec263ebf3a3a41771bd05190238de3486aae8540c36/jinja2-3.1.4-py3-none-any.whl#sha256=bc5dd2abb727a5319567b7a813e6a2e7318c39f4f487cfe6c89c6f9c7d25197d
 # pip lazy-loader @ https://files.pythonhosted.org/packages/83/60/d497a310bde3f01cb805196ac61b7ad6dc5dcf8dce66634dc34364b20b4f/lazy_loader-0.4-py3-none-any.whl#sha256=342aa8e14d543a154047afb4ba8ef17f5563baad3fc610d7b15b213b0f119efc
-# pip pyproject-metadata @ https://files.pythonhosted.org/packages/c4/cb/4678dfd70cd2f2d8969e571cdc1bb1e9293c698f8d1cf428fadcf48d6e9f/pyproject_metadata-0.7.1-py3-none-any.whl#sha256=28691fbb36266a819ec56c9fa1ecaf36f879d6944dfde5411e87fc4ff793aa60
-# pip pytest @ https://files.pythonhosted.org/packages/51/ff/f6e8b8f39e08547faece4bd80f89d5a8de68a38b2d179cc1c4490ffa3286/pytest-7.4.4-py3-none-any.whl#sha256=b090cdf5ed60bf4c45261be03239c2c1c22df034fbffe691abe93cd80cea01d8
+# pip pyproject-metadata @ https://files.pythonhosted.org/packages/aa/5f/bb5970d3d04173b46c9037109f7f05fc8904ff5be073ee49bb6ff00301bc/pyproject_metadata-0.8.0-py3-none-any.whl#sha256=ad858d448e1d3a1fb408ac5bac9ea7743e7a8bbb472f2693aaa334d2db42f526
+# pip pytest @ https://files.pythonhosted.org/packages/b4/c1/27a1274b73712232328cb5115030057b7dec377f36a518c83f2e01d4f305/pytest-8.2.1-py3-none-any.whl#sha256=faccc5d332b8c3719f40283d0d44aa5cf101cec36f88cde9ed8f2bc0538612b1
 # pip python-dateutil @ https://files.pythonhosted.org/packages/ec/57/56b9bcc3c9c6a792fcbaf139543cee77261f3651ca9da0c93f5c1221264b/python_dateutil-2.9.0.post0-py2.py3-none-any.whl#sha256=a8b2bc7bffae282281c8140a97d3aa9c14da0b136dfe83f850eea9a5f7470427
-# pip requests @ https://files.pythonhosted.org/packages/70/8e/0e2d847013cb52cd35b38c009bb167a1a26b2ce6cd6965bf26b47bc0bf44/requests-2.31.0-py3-none-any.whl#sha256=58cd2187c01e70e6e26505bca751777aa9f2ee0b7f4300988b709f44e013003f
-# pip scipy @ https://files.pythonhosted.org/packages/c6/ba/a778e6c0020d728c119b0379805a357135fe8c9bc87fdb7e0750ca11319f/scipy-1.13.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=28e286bf9ac422d6beb559bc61312c348ca9b0f0dae0d7c5afde7f722d6ea13d
-# pip tifffile @ https://files.pythonhosted.org/packages/cd/0b/33610b4d0d1bb83a6bfd20ed838f52e02a44e9b439116cd4f3d424e81a80/tifffile-2024.2.12-py3-none-any.whl#sha256=870998f82fbc94ff7c3528884c1b0ae54863504ff51dbebea431ac3fa8fb7c21
+# pip requests @ https://files.pythonhosted.org/packages/c3/20/748e38b466e0819491f0ce6e90ebe4184966ee304fe483e2c414b0f4ef07/requests-2.32.2-py3-none-any.whl#sha256=fc06670dd0ed212426dfeb94fc1b983d917c4f9847c863f313c9dfaaffb7c23c
+# pip scipy @ https://files.pythonhosted.org/packages/35/f5/d0ad1a96f80962ba65e2ce1de6a1e59edecd1f0a7b55990ed208848012e0/scipy-1.13.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=637e98dcf185ba7f8e663e122ebf908c4702420477ae52a04f9908707456ba4d
+# pip tifffile @ https://files.pythonhosted.org/packages/d9/6c/740c07588434e86028c24b0653c1eb6b46904d9ce585a20f07590620ec41/tifffile-2024.5.22-py3-none-any.whl#sha256=e281781c15d7d197d7e12749849c965651413aa905f97a48b0f84bd90a3b4c6f
 # pip lightgbm @ https://files.pythonhosted.org/packages/ba/11/cb8b67f3cbdca05b59a032bb57963d4fe8c8d18c3870f30bed005b7f174d/lightgbm-4.3.0-py3-none-manylinux_2_28_x86_64.whl#sha256=104496a3404cb2452d3412cbddcfbfadbef9c372ea91e3a9b8794bcc5183bf07
-# pip matplotlib @ https://files.pythonhosted.org/packages/5e/2c/513395a63a9e1124a5648addbf73be23cc603f955af026b04416da98dc96/matplotlib-3.8.4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=606e3b90897554c989b1e38a258c626d46c873523de432b1462f295db13de6f9
-# pip meson-python @ https://files.pythonhosted.org/packages/1f/60/b10b11ab470a690d5777310d6cfd1c9bdbbb0a1313a78c34a1e82e0b9d27/meson_python-0.15.0-py3-none-any.whl#sha256=3ae38253ff02b2e947a05e362a2eaf5a9a09d133c5666b4123399ee5fbf2e591
+# pip matplotlib @ https://files.pythonhosted.org/packages/d3/6d/45837c5b3d0005a5a9b04729b218a16bf3aa195701c6b33b2cc39ae943b6/matplotlib-3.9.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=81c40af649d19c85f8073e25e5806926986806fa6d54be506fbf02aef47d5a89
+# pip meson-python @ https://files.pythonhosted.org/packages/91/c0/104cb6244c83fe6bc3886f144cc433db0c0c78efac5dc00e409a5a08c87d/meson_python-0.16.0-py3-none-any.whl#sha256=842dc9f5dc29e55fc769ff1b6fe328412fe6c870220fc321060a1d2d395e69e8
 # pip pandas @ https://files.pythonhosted.org/packages/bb/30/f6f1f1ac36250f50c421b1b6af08c35e5a8b5a84385ef928625336b93e6f/pandas-2.2.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=66b479b0bd07204e37583c191535505410daa8df638fd8e75ae1b383851fe921
 # pip pyamg @ https://files.pythonhosted.org/packages/68/a9/aed9f557e7eb779d2cb4fa090663f8540979e0c04dadd16e9a0bdc9632c5/pyamg-5.1.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=5817d4567fb240dab4779bb1630bbb3035b3827731fcdaeb9ecc9c8814319995
 # pip pytest-cov @ https://files.pythonhosted.org/packages/78/3a/af5b4fa5961d9a1e6237b530eb87dd04aea6eb83da09d2a4073d81b54ccf/pytest_cov-5.0.0-py3-none-any.whl#sha256=4f0764a1219df53214206bf1feea4633c3b558a2925c8b59f144f682861ce652
-# pip pytest-xdist @ https://files.pythonhosted.org/packages/50/37/125fe5ec459321e2d48a0c38672cfc2419ad87d580196fd894e5f25230b0/pytest_xdist-3.5.0-py3-none-any.whl#sha256=d075629c7e00b611df89f490a5063944bee7a4362a5ff11c7cc7824a03dfce24
+# pip pytest-xdist @ https://files.pythonhosted.org/packages/6d/82/1d96bf03ee4c0fdc3c0cbe61470070e659ca78dc0086fb88b66c185e2449/pytest_xdist-3.6.1-py3-none-any.whl#sha256=9ed4adfb68a016610848639bb7e02c9352d5d9f03d04809919e2dafc3be4cca7
 # pip scikit-image @ https://files.pythonhosted.org/packages/a3/7e/4cd853a855ac34b4ef3ef6a5c3d1c2e96eaca1154fc6be75db55ffa87393/scikit_image-0.22.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=3b7a6c89e8d6252332121b58f50e1625c35f7d6a85489c0b6b7ee4f5155d547a
-# pip sphinx @ https://files.pythonhosted.org/packages/b2/b6/8ed35256aa530a9d3da15d20bdc0ba888d5364441bb50a5a83ee7827affe/sphinx-7.2.6-py3-none-any.whl#sha256=1e09160a40b956dc623c910118fa636da93bd3ca0b9876a7b3df90f07d691560
+# pip sphinx @ https://files.pythonhosted.org/packages/b4/fa/130c32ed94cf270e3d0b9ded16fb7b2c8fea86fa7263c29a696a30c1dde7/sphinx-7.3.7-py3-none-any.whl#sha256=413f75440be4cacf328f580b4274ada4565fb2187d696a84970c23f77b64d8c3
 # pip numpydoc @ https://files.pythonhosted.org/packages/f0/fa/dcfe0f65660661db757ee9ebd84e170ff98edd5d80235f62457d9088f85f/numpydoc-1.7.0-py3-none-any.whl#sha256=5a56419d931310d79a06cfc2a126d1558700feeb9b4f3d8dcae1a8134be829c9
diff --git a/build_tools/azure/pylatest_pip_scipy_dev_environment.yml b/build_tools/azure/pylatest_pip_scipy_dev_environment.yml
index 7d8e7a66d987e..01709b79e3720 100644
--- a/build_tools/azure/pylatest_pip_scipy_dev_environment.yml
+++ b/build_tools/azure/pylatest_pip_scipy_dev_environment.yml
@@ -9,7 +9,7 @@ dependencies:
   - pip
   - pip:
     - threadpoolctl
-    - pytest<8
+    - pytest
     - pytest-xdist
     - pip
     - ninja
diff --git a/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock b/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock
index b5f1d7613fa72..370dd97916518 100644
--- a/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock
+++ b/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock
@@ -1,6 +1,6 @@
 # Generated by conda-lock.
 # platform: linux-64
-# input_hash: 777413179f12c3f7972520657eb2c826ffd6ff4c15e5da73631696b7ef07c3f2
+# input_hash: 8a4a203136d97ff3b2c8657fce2dd2228215bfbf9c1cfbe271e401f934bdf1a7
 @EXPLICIT
 https://repo.anaconda.com/pkgs/main/linux-64/_libgcc_mutex-0.1-main.conda#c3473ff8bdb3d124ed5ff11ec380d6f9
 https://repo.anaconda.com/pkgs/main/linux-64/ca-certificates-2024.3.11-h06a4308_0.conda#08529eb3504712baabcbda266a19feb7
@@ -10,28 +10,28 @@ https://repo.anaconda.com/pkgs/main/linux-64/libgomp-11.2.0-h1234567_1.conda#b37
 https://repo.anaconda.com/pkgs/main/linux-64/libstdcxx-ng-11.2.0-h1234567_1.conda#57623d10a70e09e1d048c2b2b6f4e2dd
 https://repo.anaconda.com/pkgs/main/linux-64/_openmp_mutex-5.1-1_gnu.conda#71d281e9c2192cb3fa425655a8defb85
 https://repo.anaconda.com/pkgs/main/linux-64/libgcc-ng-11.2.0-h1234567_1.conda#a87728dabf3151fb9cfa990bd2eb0464
-https://repo.anaconda.com/pkgs/main/linux-64/bzip2-1.0.8-h5eee18b_5.conda#9c8dec113089c4aca7392c6a3864f505
-https://repo.anaconda.com/pkgs/main/linux-64/expat-2.5.0-h6a678d5_0.conda#9a21d99d49a0a556cf9590430dec8ec0
-https://repo.anaconda.com/pkgs/main/linux-64/libffi-3.4.4-h6a678d5_0.conda#06e288f9250abef59b9a367d151fc339
+https://repo.anaconda.com/pkgs/main/linux-64/bzip2-1.0.8-h5eee18b_6.conda#f21a3ff51c1b271977f53ce956a69297
+https://repo.anaconda.com/pkgs/main/linux-64/expat-2.6.2-h6a678d5_0.conda#55049db2772dae035f6b8a95f72b5970
+https://repo.anaconda.com/pkgs/main/linux-64/libffi-3.4.4-h6a678d5_1.conda#70646cc713f0c43926cfdcfe9b695fe0
 https://repo.anaconda.com/pkgs/main/linux-64/libuuid-1.41.5-h5eee18b_0.conda#4a6a2354414c9080327274aa514e5299
 https://repo.anaconda.com/pkgs/main/linux-64/ncurses-6.4-h6a678d5_0.conda#5558eec6e2191741a92f832ea826251c
-https://repo.anaconda.com/pkgs/main/linux-64/openssl-3.0.13-h7f8727e_0.conda#c73d46a4d666da0ae3dcd3fd8f805122
-https://repo.anaconda.com/pkgs/main/linux-64/xz-5.4.6-h5eee18b_0.conda#81a9916f581d4da15a3839216a487c66
-https://repo.anaconda.com/pkgs/main/linux-64/zlib-1.2.13-h5eee18b_0.conda#333e31fbfbb5057c92fa845ad6adef93
+https://repo.anaconda.com/pkgs/main/linux-64/openssl-3.0.13-h7f8727e_2.conda#0019453c25f5e72129f99236e60febaa
+https://repo.anaconda.com/pkgs/main/linux-64/xz-5.4.6-h5eee18b_1.conda#1562802f843297ee776a50b9329597ed
+https://repo.anaconda.com/pkgs/main/linux-64/zlib-1.2.13-h5eee18b_1.conda#92e42d8310108b0a440fb2e60b2b2a25
 https://repo.anaconda.com/pkgs/main/linux-64/ccache-3.7.9-hfe4627d_0.conda#bef6fc681c273bb7bd0c67d1a591365e
 https://repo.anaconda.com/pkgs/main/linux-64/readline-8.2-h5eee18b_0.conda#be42180685cce6e6b0329201d9f48efb
-https://repo.anaconda.com/pkgs/main/linux-64/tk-8.6.12-h1ccaba5_0.conda#fa10ff4aa631fa4aa090a6234d7770b9
-https://repo.anaconda.com/pkgs/main/linux-64/sqlite-3.41.2-h5eee18b_0.conda#c7086c9ceb6cfe1c4c729a774a2d88a5
-https://repo.anaconda.com/pkgs/main/linux-64/python-3.12.2-h996f2a0_0.conda#bc4748d0d26253c8499a3abeca289469
-https://repo.anaconda.com/pkgs/main/linux-64/setuptools-68.2.2-py312h06a4308_0.conda#83ba634cde4f30d9e0b88e4ac9716ca4
-https://repo.anaconda.com/pkgs/main/linux-64/wheel-0.41.2-py312h06a4308_0.conda#b2c4f82880d58d679f3982370d80c0e2
-https://repo.anaconda.com/pkgs/main/linux-64/pip-23.3.1-py312h06a4308_0.conda#e1d44bca4a257e84af33503233491107
+https://repo.anaconda.com/pkgs/main/linux-64/tk-8.6.14-h39e8969_0.conda#78dbc5e3c69143ebc037fc5d5b22e597
+https://repo.anaconda.com/pkgs/main/linux-64/sqlite-3.45.3-h5eee18b_0.conda#acf93d6aceb74d6110e20b44cc45939e
+https://repo.anaconda.com/pkgs/main/linux-64/python-3.12.3-h996f2a0_1.conda#0e22ed7e6df024e4f7467e75c8575301
+https://repo.anaconda.com/pkgs/main/linux-64/setuptools-69.5.1-py312h06a4308_0.conda#ce85d9a864a73e0b12d31a97733c9fca
+https://repo.anaconda.com/pkgs/main/linux-64/wheel-0.43.0-py312h06a4308_0.conda#18d5f3b68a175c72576876db4afc9e9e
+https://repo.anaconda.com/pkgs/main/linux-64/pip-24.0-py312h06a4308_0.conda#6d9697bb8b9f3212be10b3b8e01a12b9
 # pip alabaster @ https://files.pythonhosted.org/packages/32/34/d4e1c02d3bee589efb5dfa17f88ea08bdb3e3eac12bc475462aec52ed223/alabaster-0.7.16-py3-none-any.whl#sha256=b46733c07dce03ae4e150330b975c75737fa60f0a7c591b6c8bf4928a28e2c92
-# pip babel @ https://files.pythonhosted.org/packages/0d/35/4196b21041e29a42dc4f05866d0c94fa26c9da88ce12c38c2265e42c82fb/Babel-2.14.0-py3-none-any.whl#sha256=efb1a25b7118e67ce3a259bed20545c29cb68be8ad2c784c83689981b7a57287
+# pip babel @ https://files.pythonhosted.org/packages/27/45/377f7e32a5c93d94cd56542349b34efab5ca3f9e2fd5a68c5e93169aa32d/Babel-2.15.0-py3-none-any.whl#sha256=08706bdad8d0a3413266ab61bd6c34d0c28d6e1e7badf40a2cebe67644e2e1fb
 # pip certifi @ https://files.pythonhosted.org/packages/ba/06/a07f096c664aeb9f01624f858c3add0a4e913d6c96257acb4fce61e7de14/certifi-2024.2.2-py3-none-any.whl#sha256=dc383c07b76109f368f6106eee2b593b04a011ea4d55f652c6ca24a754d1cdd1
 # pip charset-normalizer @ https://files.pythonhosted.org/packages/ee/fb/14d30eb4956408ee3ae09ad34299131fb383c47df355ddb428a7331cfa1e/charset_normalizer-3.3.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=90d558489962fd4918143277a773316e56c72da56ec7aa3dc3dbbe20fdfed15b
-# pip coverage @ https://files.pythonhosted.org/packages/98/79/185cb42910b6a2b2851980407c8445ac0da0750dff65e420e86f973c8396/coverage-7.4.4-cp312-cp312-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=ce4b94265ca988c3f8e479e741693d143026632672e3ff924f25fab50518dd51
-# pip docutils @ https://files.pythonhosted.org/packages/26/87/f238c0670b94533ac0353a4e2a1a771a0cc73277b88bff23d3ae35a256c1/docutils-0.20.1-py3-none-any.whl#sha256=96f387a2c5562db4476f09f13bbab2192e764cac08ebbf3a34a95d9b1e4a59d6
+# pip coverage @ https://files.pythonhosted.org/packages/36/2e/f52bd5573fdf4b527ed0255ec8efc5fae77f5d0b086c41abae6eb175b426/coverage-7.5.2-cp312-cp312-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=3bb5b92a0ab3d22dfdbfe845e2fef92717b067bdf41a5b68c7e3e857c0cff1a4
+# pip docutils @ https://files.pythonhosted.org/packages/8f/d7/9322c609343d929e75e7e5e6255e614fcc67572cfd083959cdef3b7aad79/docutils-0.21.2-py3-none-any.whl#sha256=dafca5b9e384f0e419294eb4d2ff9fa826435bf15f15b7bd45723e8ad76811b2
 # pip execnet @ https://files.pythonhosted.org/packages/43/09/2aea36ff60d16dd8879bdb2f5b3ee0ba8d08cbbdcdfe870e695ce3784385/execnet-2.1.1-py3-none-any.whl#sha256=26dee51f1b80cebd6d0ca8e74dd8745419761d3bef34163928cbebbdc4749fdc
 # pip idna @ https://files.pythonhosted.org/packages/e5/3e/741d8c82801c347547f8a2a06aa57dbb1992be9e948df2ea0eda2c8b79e8/idna-3.7-py3-none-any.whl#sha256=82fee1fc78add43492d3a1898bfa6d8a904cc97d8427f683ed8e798d07761aa0
 # pip imagesize @ https://files.pythonhosted.org/packages/ff/62/85c4c919272577931d407be5ba5d71c20f0b616d31a0befe0ae45bb79abd/imagesize-1.4.1-py2.py3-none-any.whl#sha256=0d8d18d08f840c19d0ee7ca1fd82490fdc3729b7ac93f49870406ddde8ef8d8b
@@ -40,9 +40,9 @@ https://repo.anaconda.com/pkgs/main/linux-64/pip-23.3.1-py312h06a4308_0.conda#e1
 # pip meson @ https://files.pythonhosted.org/packages/33/75/b1a37fa7b2dbca8c0dbb04d5cdd7e2720c8ef6febe41b4a74866350e041c/meson-1.4.0-py3-none-any.whl#sha256=476a458d51fcfa322a6bdc64da5138997c542d08e6b2e49b9fa68c46fd7c4475
 # pip ninja @ https://files.pythonhosted.org/packages/6d/92/8d7aebd4430ab5ff65df2bfee6d5745f95c004284db2d8ca76dcbfd9de47/ninja-1.11.1.1-py2.py3-none-manylinux1_x86_64.manylinux_2_5_x86_64.whl#sha256=84502ec98f02a037a169c4b0d5d86075eaf6afc55e1879003d6cab51ced2ea4b
 # pip packaging @ https://files.pythonhosted.org/packages/49/df/1fceb2f8900f8639e278b056416d49134fb8d84c5942ffaa01ad34782422/packaging-24.0-py3-none-any.whl#sha256=2ddfb553fdf02fb784c234c7ba6ccc288296ceabec964ad2eae3777778130bc5
-# pip platformdirs @ https://files.pythonhosted.org/packages/55/72/4898c44ee9ea6f43396fbc23d9bfaf3d06e01b83698bdf2e4c919deceb7c/platformdirs-4.2.0-py3-none-any.whl#sha256=0614df2a2f37e1a662acbd8e2b25b92ccf8632929bc6d43467e17fe89c75e068
-# pip pluggy @ https://files.pythonhosted.org/packages/a5/5b/0cc789b59e8cc1bf288b38111d002d8c5917123194d45b29dcdac64723cc/pluggy-1.4.0-py3-none-any.whl#sha256=7db9f7b503d67d1c5b95f59773ebb58a8c1c288129a88665838012cfb07b8981
-# pip pygments @ https://files.pythonhosted.org/packages/97/9c/372fef8377a6e340b1704768d20daaded98bf13282b5327beb2e2fe2c7ef/pygments-2.17.2-py3-none-any.whl#sha256=b27c2826c47d0f3219f29554824c30c5e8945175d888647acd804ddd04af846c
+# pip platformdirs @ https://files.pythonhosted.org/packages/68/13/2aa1f0e1364feb2c9ef45302f387ac0bd81484e9c9a4c5688a322fbdfd08/platformdirs-4.2.2-py3-none-any.whl#sha256=2d7a1657e36a80ea911db832a8a6ece5ee53d8de21edd5cc5879af6530b1bfee
+# pip pluggy @ https://files.pythonhosted.org/packages/88/5f/e351af9a41f866ac3f1fac4ca0613908d9a41741cfcf2228f4ad853b697d/pluggy-1.5.0-py3-none-any.whl#sha256=44e1ad92c8ca002de6377e165f3e0f1be63266ab4d554740532335b9d75ea669
+# pip pygments @ https://files.pythonhosted.org/packages/f7/3f/01c8b82017c199075f8f788d0d906b9ffbbc5a47dc9918a945e13d5a2bda/pygments-2.18.0-py3-none-any.whl#sha256=b8e6aca0523f3ab76fee51799c488e38782ac06eafcf95e7ba832985c8e7b13a
 # pip six @ https://files.pythonhosted.org/packages/d9/5a/e7c31adbe875f2abbb91bd84cf2dc52d792b5a01506781dbcf25c91daf11/six-1.16.0-py2.py3-none-any.whl#sha256=8abb2f1d86890a2dfb989f9a77cfcfd3e47c2a354b01111771326f8aa26e0254
 # pip snowballstemmer @ https://files.pythonhosted.org/packages/ed/dc/c02e01294f7265e63a7315fe086dd1df7dacb9f840a804da846b96d01b96/snowballstemmer-2.2.0-py2.py3-none-any.whl#sha256=c8e1716e83cc398ae16824e5572ae04e0d9fc2c6b985fb0f900f5f0c96ecba1a
 # pip sphinxcontrib-applehelp @ https://files.pythonhosted.org/packages/56/89/fea3fbf6785b388e6cb8a1beaf62f96e80b37311bdeed6e133388a732426/sphinxcontrib_applehelp-1.0.8-py3-none-any.whl#sha256=cb61eb0ec1b61f349e5cc36b2028e9e7ca765be05e49641c97241274753067b4
@@ -52,16 +52,16 @@ https://repo.anaconda.com/pkgs/main/linux-64/pip-23.3.1-py312h06a4308_0.conda#e1
 # pip sphinxcontrib-qthelp @ https://files.pythonhosted.org/packages/80/b3/1beac14a88654d2e5120d0143b49be5ad450b86eb1963523d8dbdcc51eb2/sphinxcontrib_qthelp-1.0.7-py3-none-any.whl#sha256=e2ae3b5c492d58fcbd73281fbd27e34b8393ec34a073c792642cd8e529288182
 # pip sphinxcontrib-serializinghtml @ https://files.pythonhosted.org/packages/38/24/228bb903ea87b9e08ab33470e6102402a644127108c7117ac9c00d849f82/sphinxcontrib_serializinghtml-1.1.10-py3-none-any.whl#sha256=326369b8df80a7d2d8d7f99aa5ac577f51ea51556ed974e7716cfd4fca3f6cb7
 # pip tabulate @ https://files.pythonhosted.org/packages/40/44/4a5f08c96eb108af5cb50b41f76142f0afa346dfa99d5296fe7202a11854/tabulate-0.9.0-py3-none-any.whl#sha256=024ca478df22e9340661486f85298cff5f6dcdba14f3813e8830015b9ed1948f
-# pip threadpoolctl @ https://files.pythonhosted.org/packages/1e/84/ccd9b08653022b7785b6e3ee070ffb2825841e0dc119be22f0840b2b35cb/threadpoolctl-3.4.0-py3-none-any.whl#sha256=8f4c689a65b23e5ed825c8436a92b818aac005e0f3715f6a1664d7c7ee29d262
+# pip threadpoolctl @ https://files.pythonhosted.org/packages/4b/2c/ffbf7a134b9ab11a67b0cf0726453cedd9c5043a4fe7a35d1cefa9a1bcfb/threadpoolctl-3.5.0-py3-none-any.whl#sha256=56c1e26c150397e58c4926da8eeee87533b1e32bef131bd4bf6a2f45f3185467
 # pip urllib3 @ https://files.pythonhosted.org/packages/a2/73/a68704750a7679d0b6d3ad7aa8d4da8e14e151ae82e6fee774e6e0d05ec8/urllib3-2.2.1-py3-none-any.whl#sha256=450b20ec296a467077128bff42b73080516e71b56ff59a60a02bef2232c4fa9d
-# pip jinja2 @ https://files.pythonhosted.org/packages/30/6d/6de6be2d02603ab56e72997708809e8a5b0fbfee080735109b40a3564843/Jinja2-3.1.3-py3-none-any.whl#sha256=7d6d50dd97d52cbc355597bd845fabfbac3f551e1f99619e39a35ce8c370b5fa
-# pip pyproject-metadata @ https://files.pythonhosted.org/packages/c4/cb/4678dfd70cd2f2d8969e571cdc1bb1e9293c698f8d1cf428fadcf48d6e9f/pyproject_metadata-0.7.1-py3-none-any.whl#sha256=28691fbb36266a819ec56c9fa1ecaf36f879d6944dfde5411e87fc4ff793aa60
-# pip pytest @ https://files.pythonhosted.org/packages/51/ff/f6e8b8f39e08547faece4bd80f89d5a8de68a38b2d179cc1c4490ffa3286/pytest-7.4.4-py3-none-any.whl#sha256=b090cdf5ed60bf4c45261be03239c2c1c22df034fbffe691abe93cd80cea01d8
+# pip jinja2 @ https://files.pythonhosted.org/packages/31/80/3a54838c3fb461f6fec263ebf3a3a41771bd05190238de3486aae8540c36/jinja2-3.1.4-py3-none-any.whl#sha256=bc5dd2abb727a5319567b7a813e6a2e7318c39f4f487cfe6c89c6f9c7d25197d
+# pip pyproject-metadata @ https://files.pythonhosted.org/packages/aa/5f/bb5970d3d04173b46c9037109f7f05fc8904ff5be073ee49bb6ff00301bc/pyproject_metadata-0.8.0-py3-none-any.whl#sha256=ad858d448e1d3a1fb408ac5bac9ea7743e7a8bbb472f2693aaa334d2db42f526
+# pip pytest @ https://files.pythonhosted.org/packages/b4/c1/27a1274b73712232328cb5115030057b7dec377f36a518c83f2e01d4f305/pytest-8.2.1-py3-none-any.whl#sha256=faccc5d332b8c3719f40283d0d44aa5cf101cec36f88cde9ed8f2bc0538612b1
 # pip python-dateutil @ https://files.pythonhosted.org/packages/ec/57/56b9bcc3c9c6a792fcbaf139543cee77261f3651ca9da0c93f5c1221264b/python_dateutil-2.9.0.post0-py2.py3-none-any.whl#sha256=a8b2bc7bffae282281c8140a97d3aa9c14da0b136dfe83f850eea9a5f7470427
-# pip requests @ https://files.pythonhosted.org/packages/70/8e/0e2d847013cb52cd35b38c009bb167a1a26b2ce6cd6965bf26b47bc0bf44/requests-2.31.0-py3-none-any.whl#sha256=58cd2187c01e70e6e26505bca751777aa9f2ee0b7f4300988b709f44e013003f
-# pip meson-python @ https://files.pythonhosted.org/packages/1f/60/b10b11ab470a690d5777310d6cfd1c9bdbbb0a1313a78c34a1e82e0b9d27/meson_python-0.15.0-py3-none-any.whl#sha256=3ae38253ff02b2e947a05e362a2eaf5a9a09d133c5666b4123399ee5fbf2e591
+# pip requests @ https://files.pythonhosted.org/packages/c3/20/748e38b466e0819491f0ce6e90ebe4184966ee304fe483e2c414b0f4ef07/requests-2.32.2-py3-none-any.whl#sha256=fc06670dd0ed212426dfeb94fc1b983d917c4f9847c863f313c9dfaaffb7c23c
+# pip meson-python @ https://files.pythonhosted.org/packages/91/c0/104cb6244c83fe6bc3886f144cc433db0c0c78efac5dc00e409a5a08c87d/meson_python-0.16.0-py3-none-any.whl#sha256=842dc9f5dc29e55fc769ff1b6fe328412fe6c870220fc321060a1d2d395e69e8
 # pip pooch @ https://files.pythonhosted.org/packages/f4/72/8ae0f1ba4ce6a4f6d4d01a60a9fdf690fde188c45c1872b0b4ddb0607ace/pooch-1.8.1-py3-none-any.whl#sha256=6b56611ac320c239faece1ac51a60b25796792599ce5c0b1bb87bf01df55e0a9
 # pip pytest-cov @ https://files.pythonhosted.org/packages/78/3a/af5b4fa5961d9a1e6237b530eb87dd04aea6eb83da09d2a4073d81b54ccf/pytest_cov-5.0.0-py3-none-any.whl#sha256=4f0764a1219df53214206bf1feea4633c3b558a2925c8b59f144f682861ce652
-# pip pytest-xdist @ https://files.pythonhosted.org/packages/50/37/125fe5ec459321e2d48a0c38672cfc2419ad87d580196fd894e5f25230b0/pytest_xdist-3.5.0-py3-none-any.whl#sha256=d075629c7e00b611df89f490a5063944bee7a4362a5ff11c7cc7824a03dfce24
-# pip sphinx @ https://files.pythonhosted.org/packages/b2/b6/8ed35256aa530a9d3da15d20bdc0ba888d5364441bb50a5a83ee7827affe/sphinx-7.2.6-py3-none-any.whl#sha256=1e09160a40b956dc623c910118fa636da93bd3ca0b9876a7b3df90f07d691560
+# pip pytest-xdist @ https://files.pythonhosted.org/packages/6d/82/1d96bf03ee4c0fdc3c0cbe61470070e659ca78dc0086fb88b66c185e2449/pytest_xdist-3.6.1-py3-none-any.whl#sha256=9ed4adfb68a016610848639bb7e02c9352d5d9f03d04809919e2dafc3be4cca7
+# pip sphinx @ https://files.pythonhosted.org/packages/b4/fa/130c32ed94cf270e3d0b9ded16fb7b2c8fea86fa7263c29a696a30c1dde7/sphinx-7.3.7-py3-none-any.whl#sha256=413f75440be4cacf328f580b4274ada4565fb2187d696a84970c23f77b64d8c3
 # pip numpydoc @ https://files.pythonhosted.org/packages/f0/fa/dcfe0f65660661db757ee9ebd84e170ff98edd5d80235f62457d9088f85f/numpydoc-1.7.0-py3-none-any.whl#sha256=5a56419d931310d79a06cfc2a126d1558700feeb9b4f3d8dcae1a8134be829c9
diff --git a/build_tools/azure/pymin_conda_defaults_openblas_environment.yml b/build_tools/azure/pymin_conda_defaults_openblas_environment.yml
index a422a0a539c53..a82ba18e27980 100644
--- a/build_tools/azure/pymin_conda_defaults_openblas_environment.yml
+++ b/build_tools/azure/pymin_conda_defaults_openblas_environment.yml
@@ -8,11 +8,11 @@ dependencies:
   - numpy=1.21
   - blas[build=openblas]
   - scipy=1.7
-  - joblib
-  - threadpoolctl=2.2.0
+  - cython=3.0.10  # min
+  - joblib=1.2.0  # min
   - matplotlib=3.3.4  # min
   - pyamg
-  - pytest<8
+  - pytest
   - pytest-xdist
   - pillow
   - pytest-cov
@@ -20,4 +20,4 @@ dependencies:
   - ccache
   - pip
   - pip:
-    - cython==3.0.10  # min
+    - threadpoolctl==3.1.0  # min
diff --git a/build_tools/azure/pymin_conda_defaults_openblas_linux-64_conda.lock b/build_tools/azure/pymin_conda_defaults_openblas_linux-64_conda.lock
index 4e64af1960718..deb9f11010bd9 100644
--- a/build_tools/azure/pymin_conda_defaults_openblas_linux-64_conda.lock
+++ b/build_tools/azure/pymin_conda_defaults_openblas_linux-64_conda.lock
@@ -1,6 +1,6 @@
 # Generated by conda-lock.
 # platform: linux-64
-# input_hash: 3d6bcb087065974114c1567c8dccd947a7376a7503b3514d82904299b651692d
+# input_hash: be0f080ab1974d224952262cd9179ff999d10108421d7e7ded2435e6f08edf0e
 @EXPLICIT
 https://repo.anaconda.com/pkgs/main/linux-64/_libgcc_mutex-0.1-main.conda#c3473ff8bdb3d124ed5ff11ec380d6f9
 https://repo.anaconda.com/pkgs/main/linux-64/blas-1.0-openblas.conda#9ddfcaef10d79366c90128f5dc444be8
@@ -13,41 +13,41 @@ https://repo.anaconda.com/pkgs/main/linux-64/libgomp-11.2.0-h1234567_1.conda#b37
 https://repo.anaconda.com/pkgs/main/linux-64/libstdcxx-ng-11.2.0-h1234567_1.conda#57623d10a70e09e1d048c2b2b6f4e2dd
 https://repo.anaconda.com/pkgs/main/linux-64/_openmp_mutex-5.1-1_gnu.conda#71d281e9c2192cb3fa425655a8defb85
 https://repo.anaconda.com/pkgs/main/linux-64/libgcc-ng-11.2.0-h1234567_1.conda#a87728dabf3151fb9cfa990bd2eb0464
-https://repo.anaconda.com/pkgs/main/linux-64/bzip2-1.0.8-h5eee18b_5.conda#9c8dec113089c4aca7392c6a3864f505
-https://repo.anaconda.com/pkgs/main/linux-64/expat-2.5.0-h6a678d5_0.conda#9a21d99d49a0a556cf9590430dec8ec0
+https://repo.anaconda.com/pkgs/main/linux-64/bzip2-1.0.8-h5eee18b_6.conda#f21a3ff51c1b271977f53ce956a69297
+https://repo.anaconda.com/pkgs/main/linux-64/expat-2.6.2-h6a678d5_0.conda#55049db2772dae035f6b8a95f72b5970
 https://repo.anaconda.com/pkgs/main/linux-64/fftw-3.3.9-h5eee18b_2.conda#db1df41113accc18ec59a99f1631bfcd
 https://repo.anaconda.com/pkgs/main/linux-64/icu-73.1-h6a678d5_0.conda#6d09df641fc23f7d277a04dc7ea32dd4
 https://repo.anaconda.com/pkgs/main/linux-64/jpeg-9e-h5eee18b_1.conda#ac373800fda872108412d1ccfe3fa572
 https://repo.anaconda.com/pkgs/main/linux-64/lerc-3.0-h295c915_0.conda#b97309770412f10bed8d9448f6f98f87
 https://repo.anaconda.com/pkgs/main/linux-64/libdeflate-1.17-h5eee18b_1.conda#82831ef0b6c9595382d74e0c281f6742
-https://repo.anaconda.com/pkgs/main/linux-64/libffi-3.4.4-h6a678d5_0.conda#06e288f9250abef59b9a367d151fc339
-https://repo.anaconda.com/pkgs/main/linux-64/libiconv-1.16-h7f8727e_2.conda#80d4bc7d7e58b5f0be41d763f60994f5
+https://repo.anaconda.com/pkgs/main/linux-64/libffi-3.4.4-h6a678d5_1.conda#70646cc713f0c43926cfdcfe9b695fe0
+https://repo.anaconda.com/pkgs/main/linux-64/libiconv-1.16-h5eee18b_3.conda#197b1a0886a31fccab2167340528eebc
 https://repo.anaconda.com/pkgs/main/linux-64/libopenblas-0.3.21-h043d6bf_0.conda#7f7324dcc3c4761a14f3e4ac443235a7
 https://repo.anaconda.com/pkgs/main/linux-64/libuuid-1.41.5-h5eee18b_0.conda#4a6a2354414c9080327274aa514e5299
 https://repo.anaconda.com/pkgs/main/linux-64/libwebp-base-1.3.2-h5eee18b_0.conda#9179fc7baefa1e027f572edbc519d805
 https://repo.anaconda.com/pkgs/main/linux-64/libxcb-1.15-h7f8727e_0.conda#ada518dcadd6aaee9aae47ba9a671553
-https://repo.anaconda.com/pkgs/main/linux-64/lz4-c-1.9.4-h6a678d5_0.conda#53915e9402180a7f22ea619c41089520
+https://repo.anaconda.com/pkgs/main/linux-64/lz4-c-1.9.4-h6a678d5_1.conda#2ee58861f2b92b868ce761abb831819d
 https://repo.anaconda.com/pkgs/main/linux-64/ncurses-6.4-h6a678d5_0.conda#5558eec6e2191741a92f832ea826251c
-https://repo.anaconda.com/pkgs/main/linux-64/openssl-3.0.13-h7f8727e_0.conda#c73d46a4d666da0ae3dcd3fd8f805122
-https://repo.anaconda.com/pkgs/main/linux-64/xz-5.4.6-h5eee18b_0.conda#81a9916f581d4da15a3839216a487c66
-https://repo.anaconda.com/pkgs/main/linux-64/zlib-1.2.13-h5eee18b_0.conda#333e31fbfbb5057c92fa845ad6adef93
+https://repo.anaconda.com/pkgs/main/linux-64/openssl-3.0.13-h7f8727e_2.conda#0019453c25f5e72129f99236e60febaa
+https://repo.anaconda.com/pkgs/main/linux-64/xz-5.4.6-h5eee18b_1.conda#1562802f843297ee776a50b9329597ed
+https://repo.anaconda.com/pkgs/main/linux-64/zlib-1.2.13-h5eee18b_1.conda#92e42d8310108b0a440fb2e60b2b2a25
 https://repo.anaconda.com/pkgs/main/linux-64/ccache-3.7.9-hfe4627d_0.conda#bef6fc681c273bb7bd0c67d1a591365e
 https://repo.anaconda.com/pkgs/main/linux-64/libcups-2.4.2-h2d74bed_1.conda#3f265c2172a9e8c90a74037b6fa13685
 https://repo.anaconda.com/pkgs/main/linux-64/libedit-3.1.20230828-h5eee18b_0.conda#850eb5a9d2d7d3c66cce12e84406ca08
 https://repo.anaconda.com/pkgs/main/linux-64/libllvm14-14.0.6-hdb19cb5_3.conda#aefea2b45cf32f12b4f1ffaa70aa3201
 https://repo.anaconda.com/pkgs/main/linux-64/libpng-1.6.39-h5eee18b_0.conda#f6aee38184512eb05b06c2e94d39ab22
-https://repo.anaconda.com/pkgs/main/linux-64/libxml2-2.10.4-hf1b16e4_1.conda#e87849ce513f9968794f20bba620e6a4
-https://repo.anaconda.com/pkgs/main/linux-64/pcre2-10.42-hebb0a14_0.conda#fca6dea6ce1eddd0876a024f62c5097a
+https://repo.anaconda.com/pkgs/main/linux-64/libxml2-2.10.4-hfdd30dd_2.conda#ff7a0e3b92afb3c99b82c9f0ba8b5670
+https://repo.anaconda.com/pkgs/main/linux-64/pcre2-10.42-hebb0a14_1.conda#727e15c3cfa02b032da4eb0c1123e977
 https://repo.anaconda.com/pkgs/main/linux-64/readline-8.2-h5eee18b_0.conda#be42180685cce6e6b0329201d9f48efb
-https://repo.anaconda.com/pkgs/main/linux-64/tk-8.6.12-h1ccaba5_0.conda#fa10ff4aa631fa4aa090a6234d7770b9
-https://repo.anaconda.com/pkgs/main/linux-64/zstd-1.5.5-hc292b87_0.conda#0f59d57dc21f585f4c282d60dfb46505
+https://repo.anaconda.com/pkgs/main/linux-64/tk-8.6.14-h39e8969_0.conda#78dbc5e3c69143ebc037fc5d5b22e597
+https://repo.anaconda.com/pkgs/main/linux-64/zstd-1.5.5-hc292b87_2.conda#3b7fe809e5b429b4f90fe064842a2370
 https://repo.anaconda.com/pkgs/main/linux-64/freetype-2.12.1-h4a9f257_0.conda#bdc7b5952e9c5dca01bc2f4ccef2f974
 https://repo.anaconda.com/pkgs/main/linux-64/krb5-1.20.1-h143b758_1.conda#cf1accc86321fa25d6b978cc748039ae
 https://repo.anaconda.com/pkgs/main/linux-64/libclang13-14.0.6-default_he11475f_1.conda#44890feda1cf51639d9c94afbacce011
 https://repo.anaconda.com/pkgs/main/linux-64/libglib-2.78.4-hdc74915_0.conda#2f6d27741e931d5b6ba56e1a1312aaf0
 https://repo.anaconda.com/pkgs/main/linux-64/libtiff-4.5.1-h6a678d5_0.conda#235a671f74f0c4ecad9f9b3b107e3566
 https://repo.anaconda.com/pkgs/main/linux-64/libxkbcommon-1.0.1-h5eee18b_1.conda#888b2e8f1bbf21017c503826e2d24b50
-https://repo.anaconda.com/pkgs/main/linux-64/sqlite-3.41.2-h5eee18b_0.conda#c7086c9ceb6cfe1c4c729a774a2d88a5
+https://repo.anaconda.com/pkgs/main/linux-64/sqlite-3.45.3-h5eee18b_0.conda#acf93d6aceb74d6110e20b44cc45939e
 https://repo.anaconda.com/pkgs/main/linux-64/cyrus-sasl-2.1.28-h52b45da_1.conda#d634af1577e4008f9228ae96ce671c44
 https://repo.anaconda.com/pkgs/main/linux-64/fontconfig-2.14.1-h4c34cd2_2.conda#f0b472f5b544f8d57beb09ed4a2932e1
 https://repo.anaconda.com/pkgs/main/linux-64/glib-tools-2.78.4-h6a678d5_0.conda#3dbe6227cd59818dca9afb75ccb70708
@@ -55,9 +55,10 @@ https://repo.anaconda.com/pkgs/main/linux-64/lcms2-2.12-h3be6417_0.conda#719db47
 https://repo.anaconda.com/pkgs/main/linux-64/libclang-14.0.6-default_hc6dbbc7_1.conda#8f12583c4027b2861cff470f6b8837c4
 https://repo.anaconda.com/pkgs/main/linux-64/libpq-12.17-hdbd6064_0.conda#6bed363e25859faff66bf546a11c10e8
 https://repo.anaconda.com/pkgs/main/linux-64/openjpeg-2.4.0-h3ad879b_0.conda#86baecb47ecaa7f7ff2657a1f03b90c9
-https://repo.anaconda.com/pkgs/main/linux-64/python-3.9.19-h955ad1f_0.conda#33cb019c40e3409df392c99e3c34f352
+https://repo.anaconda.com/pkgs/main/linux-64/python-3.9.19-h955ad1f_1.conda#4b453281859c293c9d577271f3b18a0d
 https://repo.anaconda.com/pkgs/main/linux-64/certifi-2024.2.2-py39h06a4308_0.conda#2bc1db9166ecbb968f61252e6f08c2ce
 https://repo.anaconda.com/pkgs/main/noarch/cycler-0.11.0-pyhd3eb1b0_0.conda#f5e365d2cdb66d547eb8c3ab93843aab
+https://repo.anaconda.com/pkgs/main/linux-64/cython-3.0.10-py39h5eee18b_0.conda#1419a658ed2b4d5c3ac1964f33143b64
 https://repo.anaconda.com/pkgs/main/linux-64/exceptiongroup-1.2.0-py39h06a4308_0.conda#960e2cb83ac5134df8e593a130aa11af
 https://repo.anaconda.com/pkgs/main/noarch/execnet-1.9.0-pyhd3eb1b0_0.conda#f895937671af67cebb8af617494b3513
 https://repo.anaconda.com/pkgs/main/linux-64/glib-2.78.4-h6a678d5_0.conda#045ff487547f7b2b7ff01648681b8ebe
@@ -65,27 +66,26 @@ https://repo.anaconda.com/pkgs/main/noarch/iniconfig-1.1.1-pyhd3eb1b0_0.tar.bz2#
 https://repo.anaconda.com/pkgs/main/linux-64/joblib-1.2.0-py39h06a4308_0.conda#ac1f5687d70aa1128cbecb26bc9e559d
 https://repo.anaconda.com/pkgs/main/linux-64/kiwisolver-1.4.4-py39h6a678d5_0.conda#3d57aedbfbd054ce57fb3c1e4448828c
 https://repo.anaconda.com/pkgs/main/linux-64/mysql-5.7.24-h721c034_2.conda#dfc19ca2466d275c4c1f73b62c57f37b
-https://repo.anaconda.com/pkgs/main/linux-64/numpy-base-1.21.6-py39h375b286_0.conda#4ceaa5d6e6307fe06961d555f78b266f
+https://repo.anaconda.com/pkgs/main/linux-64/numpy-base-1.21.6-py39h375b286_1.conda#0061d9193658774ab79fc85d143a94fc
 https://repo.anaconda.com/pkgs/main/linux-64/packaging-23.2-py39h06a4308_0.conda#b3f88f45f31bde016e49be3e941e5272
-https://repo.anaconda.com/pkgs/main/linux-64/pillow-10.2.0-py39h5eee18b_0.conda#fca2a1c44d16ec4b8ba71759b4ba9ba4
+https://repo.anaconda.com/pkgs/main/linux-64/pillow-10.3.0-py39h5eee18b_0.conda#b346d6c71267c1553b6c18d3db5fdf6d
 https://repo.anaconda.com/pkgs/main/linux-64/pluggy-1.0.0-py39h06a4308_1.conda#fb4fed11ed43cf727dbd51883cc1d9fa
 https://repo.anaconda.com/pkgs/main/linux-64/ply-3.11-py39h06a4308_0.conda#6c89bf6d2fdf6d24126e34cb83fd10f1
 https://repo.anaconda.com/pkgs/main/linux-64/pyparsing-3.0.9-py39h06a4308_0.conda#3a0537468e59760404f63b4f04369828
 https://repo.anaconda.com/pkgs/main/linux-64/pyqt5-sip-12.13.0-py39h5eee18b_0.conda#256840c3841b52346ea5743be8490ede
-https://repo.anaconda.com/pkgs/main/linux-64/setuptools-68.2.2-py39h06a4308_0.conda#5b42cae5548732ae5c167bb1066085de
+https://repo.anaconda.com/pkgs/main/linux-64/setuptools-69.5.1-py39h06a4308_0.conda#3eb144d481b39c0fbbced789dd9b76b3
 https://repo.anaconda.com/pkgs/main/noarch/six-1.16.0-pyhd3eb1b0_1.conda#34586824d411d36af2fa40e799c172d0
-https://repo.anaconda.com/pkgs/main/noarch/threadpoolctl-2.2.0-pyh0d69192_0.conda#bbfdbae4934150b902f97daaf287efe2
 https://repo.anaconda.com/pkgs/main/noarch/toml-0.10.2-pyhd3eb1b0_0.conda#cda05f5f6d8509529d1a2743288d197a
 https://repo.anaconda.com/pkgs/main/linux-64/tomli-2.0.1-py39h06a4308_0.conda#b06dffe7ddca2645ed72f5116f0a087d
 https://repo.anaconda.com/pkgs/main/linux-64/tornado-6.3.3-py39h5eee18b_0.conda#9c4bd985bb8adcd12f47e790e95a9333
-https://repo.anaconda.com/pkgs/main/linux-64/wheel-0.41.2-py39h06a4308_0.conda#ec1b8213c3585defaa6042ed2f95861d
+https://repo.anaconda.com/pkgs/main/linux-64/wheel-0.43.0-py39h06a4308_0.conda#40bb60408c7433d767fd8c65b35bc4a0
 https://repo.anaconda.com/pkgs/main/linux-64/coverage-7.2.2-py39h5eee18b_0.conda#e9da151b7e1f56be2cb569c65949a1d2
 https://repo.anaconda.com/pkgs/main/linux-64/dbus-1.13.18-hb2f20db_0.conda#6a6a6f1391f807847404344489ef6cf4
 https://repo.anaconda.com/pkgs/main/linux-64/gstreamer-1.14.1-h5eee18b_1.conda#f2f26e6f869b5d87f41bd059fae47c3e
-https://repo.anaconda.com/pkgs/main/linux-64/numpy-1.21.6-py39hac523dd_0.conda#a03c1fe16cf2558bca3838062c334d7d
-https://repo.anaconda.com/pkgs/main/linux-64/pip-23.3.1-py39h06a4308_0.conda#685007e3dae59d211620f19926577bd6
+https://repo.anaconda.com/pkgs/main/linux-64/numpy-1.21.6-py39hac523dd_1.conda#f379f92039f666828a193fadd18c9819
+https://repo.anaconda.com/pkgs/main/linux-64/pip-24.0-py39h06a4308_0.conda#7f8ce3af15cfecd12e4dda8c5cef5fb7
 https://repo.anaconda.com/pkgs/main/linux-64/pytest-7.4.0-py39h06a4308_0.conda#99d92a7a39f7e615de84f8cc5606c49a
-https://repo.anaconda.com/pkgs/main/noarch/python-dateutil-2.8.2-pyhd3eb1b0_0.conda#211ee00320b08a1ac9fea6677649f6c9
+https://repo.anaconda.com/pkgs/main/linux-64/python-dateutil-2.9.0post0-py39h06a4308_2.conda#d6566c6d6a0140e45f22787ae58ed6e8
 https://repo.anaconda.com/pkgs/main/linux-64/sip-6.7.12-py39h6a678d5_0.conda#6988a3e12fcacfedcac523c1e4c3167c
 https://repo.anaconda.com/pkgs/main/linux-64/gst-plugins-base-1.14.1-h6a678d5_1.conda#afd9cbe949d670d24cc0a007aaec1fe1
 https://repo.anaconda.com/pkgs/main/linux-64/matplotlib-base-3.3.4-py39h62a2d02_0.conda#dbab28222c740af8e21a3e5e2882c178
@@ -96,4 +96,4 @@ https://repo.anaconda.com/pkgs/main/linux-64/pyamg-4.2.3-py39h79cecc1_0.conda#af
 https://repo.anaconda.com/pkgs/main/linux-64/qt-main-5.15.2-h53bd1ea_10.conda#bd0c79e82df6323f638bdcb871891b61
 https://repo.anaconda.com/pkgs/main/linux-64/pyqt-5.15.10-py39h6a678d5_0.conda#52da5ff9b1144b078d2f41bab0b213f2
 https://repo.anaconda.com/pkgs/main/linux-64/matplotlib-3.3.4-py39h06a4308_0.conda#384fc5e01ebfcf30e7161119d3029b5a
-# pip cython @ https://files.pythonhosted.org/packages/a7/f5/3dde4d96076888ceaa981827b098274c2b45ddd4b20d75a8cfaa92b91eec/Cython-3.0.10-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=651a15a8534ebfb9b58cb0b87c269c70984b6f9c88bfe65e4f635f0e3f07dfcd
+# pip threadpoolctl @ https://files.pythonhosted.org/packages/61/cf/6e354304bcb9c6413c4e02a747b600061c21d38ba51e7e544ac7bc66aecc/threadpoolctl-3.1.0-py3-none-any.whl#sha256=8b99adda265feb6773280df41eece7b2e6561b772d21ffd52e372f999024907b
diff --git a/build_tools/azure/pymin_conda_forge_mkl_environment.yml b/build_tools/azure/pymin_conda_forge_mkl_environment.yml
index fbad1d5bd42a8..a219e4b3daa8f 100644
--- a/build_tools/azure/pymin_conda_forge_mkl_environment.yml
+++ b/build_tools/azure/pymin_conda_forge_mkl_environment.yml
@@ -12,7 +12,7 @@ dependencies:
   - joblib
   - threadpoolctl
   - matplotlib
-  - pytest<8
+  - pytest
   - pytest-xdist
   - pillow
   - pip
diff --git a/build_tools/azure/pymin_conda_forge_mkl_win-64_conda.lock b/build_tools/azure/pymin_conda_forge_mkl_win-64_conda.lock
index c6983ba7a9f51..f4c2f51b1ea88 100644
--- a/build_tools/azure/pymin_conda_forge_mkl_win-64_conda.lock
+++ b/build_tools/azure/pymin_conda_forge_mkl_win-64_conda.lock
@@ -1,22 +1,20 @@
 # Generated by conda-lock.
 # platform: win-64
-# input_hash: 4a2ac6360285edd6c1e8182dd51ef698c0041fa9843e4ad9d9bc9dec6a7c8d1d
+# input_hash: ea607aaeb7b1d1f8a1f821a9f505b3601083a218ec4763e2d72d3d3d800e718c
 @EXPLICIT
 https://conda.anaconda.org/conda-forge/win-64/ca-certificates-2024.2.2-h56e8100_0.conda#63da060240ab8087b60d1357051ea7d6
-https://conda.anaconda.org/conda-forge/win-64/intel-openmp-2024.1.0-h57928b3_965.conda#c66eb2fd33b999ccc258aef85689758e
-https://conda.anaconda.org/conda-forge/win-64/libasprintf-0.22.5-h5728263_2.conda#75a6982b9ff0a8db0f53303527b07af8
+https://conda.anaconda.org/conda-forge/win-64/intel-openmp-2024.1.0-h57928b3_966.conda#35d7ea07ad6c878bd7240d2d6c1b8657
 https://conda.anaconda.org/conda-forge/win-64/mkl-include-2024.1.0-h66d3029_692.conda#60233966dc7c0261c9a443120b43c477
 https://conda.anaconda.org/conda-forge/win-64/msys2-conda-epoch-20160418-1.tar.bz2#b0309b72560df66f71a9d5e34a5efdfa
 https://conda.anaconda.org/conda-forge/win-64/python_abi-3.9-4_cp39.conda#948b0d93d4ab1372d8fd45e1560afd47
 https://conda.anaconda.org/conda-forge/noarch/tzdata-2024a-h0c530f3_0.conda#161081fc7cec0bfda0d86d7cb595f8d8
 https://conda.anaconda.org/conda-forge/win-64/ucrt-10.0.22621.0-h57928b3_0.tar.bz2#72608f6cd3e5898229c3ea16deb1ac43
-https://conda.anaconda.org/conda-forge/win-64/libasprintf-devel-0.22.5-h5728263_2.conda#8377da2cc31200d7181d2e48d60e4c7b
 https://conda.anaconda.org/conda-forge/win-64/m2w64-gmp-6.1.0-2.tar.bz2#53a1c73e1e3d185516d7e3af177596d9
 https://conda.anaconda.org/conda-forge/win-64/m2w64-libwinpthread-git-5.0.0.4634.697f757-2.tar.bz2#774130a326dee16f1ceb05cc687ee4f0
-https://conda.anaconda.org/conda-forge/win-64/vc14_runtime-14.38.33130-h82b7239_18.conda#8be79fdd2725ddf7bbf8a27a4c1f79ba
+https://conda.anaconda.org/conda-forge/win-64/vc14_runtime-14.38.33135-h835141b_20.conda#e971b35a5765862fabc4ba6e5ddf9470
 https://conda.anaconda.org/conda-forge/win-64/m2w64-gcc-libs-core-5.3.0-7.tar.bz2#4289d80fb4d272f1f3b56cfe87ac90bd
-https://conda.anaconda.org/conda-forge/win-64/vc-14.3-hcf57466_18.conda#20e1e652a4c740fa719002a8449994a2
-https://conda.anaconda.org/conda-forge/win-64/vs2015_runtime-14.38.33130-hcb4865c_18.conda#10d42885e3ed84e575b454db30f1aa93
+https://conda.anaconda.org/conda-forge/win-64/vc-14.3-ha32ba9b_20.conda#2abfb5cb1b9d41a50f765d60f0be563d
+https://conda.anaconda.org/conda-forge/win-64/vs2015_runtime-14.38.33135-h22015db_20.conda#bb4f5ab332e46e1b022d8842e72905b1
 https://conda.anaconda.org/conda-forge/win-64/bzip2-1.0.8-hcfcfb64_5.conda#26eb8ca6ea332b675e11704cce84a3be
 https://conda.anaconda.org/conda-forge/win-64/icu-73.2-h63175ca_0.conda#0f47d9e3192d9e09ae300da0d28e0f56
 https://conda.anaconda.org/conda-forge/win-64/lerc-4.0.0-h63175ca_0.tar.bz2#1900cb3cab5055833cfddb0ba233b074
@@ -26,12 +24,12 @@ https://conda.anaconda.org/conda-forge/win-64/libffi-3.4.2-h8ffe710_5.tar.bz2#2c
 https://conda.anaconda.org/conda-forge/win-64/libiconv-1.17-hcfcfb64_2.conda#e1eb10b1cca179f2baa3601e4efc8712
 https://conda.anaconda.org/conda-forge/win-64/libjpeg-turbo-3.0.0-hcfcfb64_1.conda#3f1b948619c45b1ca714d60c7389092c
 https://conda.anaconda.org/conda-forge/win-64/libogg-1.3.4-h8ffe710_1.tar.bz2#04286d905a0dcb7f7d4a12bdfe02516d
-https://conda.anaconda.org/conda-forge/win-64/libsqlite-3.45.2-hcfcfb64_0.conda#f95359f8dc5abf7da7776ece9ef10bc5
-https://conda.anaconda.org/conda-forge/win-64/libwebp-base-1.3.2-hcfcfb64_1.conda#fdf80cb33c32d4d002bb89c37cfff5b7
+https://conda.anaconda.org/conda-forge/win-64/libsqlite-3.45.3-hcfcfb64_0.conda#73f5dc8e2d55d9a1e14b11f49c3b4a28
+https://conda.anaconda.org/conda-forge/win-64/libwebp-base-1.4.0-hcfcfb64_0.conda#abd61d0ab127ec5cd68f62c2969e6f34
 https://conda.anaconda.org/conda-forge/win-64/libzlib-1.2.13-hcfcfb64_5.conda#5fdb9c6a113b6b6cb5e517fd972d5f41
 https://conda.anaconda.org/conda-forge/win-64/m2w64-gcc-libgfortran-5.3.0-6.tar.bz2#066552ac6b907ec6d72c0ddab29050dc
-https://conda.anaconda.org/conda-forge/win-64/ninja-1.11.1-h91493d7_0.conda#44a99ef26178ea98626ff8e027702795
-https://conda.anaconda.org/conda-forge/win-64/openssl-3.2.1-hcfcfb64_1.conda#958e0418e93e50c575bff70fbcaa12d8
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@@ -54,24 +52,22 @@ https://conda.anaconda.org/conda-forge/win-64/cython-3.0.10-py39h99910a6_0.conda
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+https://conda.anaconda.org/conda-forge/noarch/setuptools-70.0.0-pyhd8ed1ab_0.conda#c8ddb4f34a208df4dd42509a0f6a1c89
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-https://conda.anaconda.org/conda-forge/noarch/threadpoolctl-3.4.0-pyhc1e730c_0.conda#b296278eef667c673bf51de6535bad88
+https://conda.anaconda.org/conda-forge/noarch/threadpoolctl-3.5.0-pyhc1e730c_0.conda#df68d78237980a159bd7149f33c0e8fd
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@@ -81,35 +77,33 @@ https://conda.anaconda.org/conda-forge/win-64/xorg-libxau-1.0.11-hcd874cb_0.cond
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-https://conda.anaconda.org/conda-forge/win-64/glib-tools-2.80.0-h0a98069_4.conda#44cd44c004db728bf5788c1d46ce7334
+https://conda.anaconda.org/conda-forge/win-64/coverage-7.5.2-py39ha55e580_0.conda#efb1e63bf5157f005afcc40778efaff5
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@@ -118,7 +112,7 @@ https://conda.anaconda.org/conda-forge/win-64/qt-main-5.15.8-hcef0176_21.conda#7
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+https://conda.anaconda.org/conda-forge/win-64/matplotlib-base-3.8.4-py39he1095e7_2.conda#5c813b5da86f186d8026b6de6429c212
+https://conda.anaconda.org/conda-forge/win-64/matplotlib-3.8.4-py39hcbf5309_2.conda#1ecee90b529cb69ec4e95add23323110
diff --git a/build_tools/azure/pymin_conda_forge_openblas_ubuntu_2204_environment.yml b/build_tools/azure/pymin_conda_forge_openblas_ubuntu_2204_environment.yml
index 855909a2c262a..38737e7c9c0b0 100644
--- a/build_tools/azure/pymin_conda_forge_openblas_ubuntu_2204_environment.yml
+++ b/build_tools/azure/pymin_conda_forge_openblas_ubuntu_2204_environment.yml
@@ -14,7 +14,7 @@ dependencies:
   - matplotlib
   - pandas
   - pyamg
-  - pytest<8
+  - pytest
   - pytest-xdist
   - pillow
   - pip
diff --git a/build_tools/azure/pymin_conda_forge_openblas_ubuntu_2204_linux-64_conda.lock b/build_tools/azure/pymin_conda_forge_openblas_ubuntu_2204_linux-64_conda.lock
index 7f06496a7e247..66cd5eced566b 100644
--- a/build_tools/azure/pymin_conda_forge_openblas_ubuntu_2204_linux-64_conda.lock
+++ b/build_tools/azure/pymin_conda_forge_openblas_ubuntu_2204_linux-64_conda.lock
@@ -1,21 +1,21 @@
 # Generated by conda-lock.
 # platform: linux-64
-# input_hash: a64ed7d3cc839a12cb1faa238a89d4aec55abc43d335791f0e8422f5722ff662
+# input_hash: 3974f9847d888a2fd37ba5fcfb76cb09bba4c9b84b6200932500fc94e3b0c4ae
 @EXPLICIT
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 https://conda.anaconda.org/conda-forge/linux-64/tornado-6.4-py39hd1e30aa_0.conda#1e865e9188204cdfb1fd2531780add88
@@ -155,12 +155,12 @@ https://conda.anaconda.org/conda-forge/linux-64/xorg-libxrender-0.9.11-hd590300_
 https://conda.anaconda.org/conda-forge/noarch/zipp-3.17.0-pyhd8ed1ab_0.conda#2e4d6bc0b14e10f895fc6791a7d9b26a
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 https://conda.anaconda.org/conda-forge/linux-64/cairo-1.18.0-h3faef2a_0.conda#f907bb958910dc404647326ca80c263e
-https://conda.anaconda.org/conda-forge/linux-64/fonttools-4.51.0-py39hd1e30aa_0.conda#79f5dd8778873faa54e8f7b2729fe8a6
-https://conda.anaconda.org/conda-forge/linux-64/glib-2.80.0-hf2295e7_4.conda#5521382ee30b96b35eb0037fc3c4f2b4
+https://conda.anaconda.org/conda-forge/linux-64/fonttools-4.52.1-py39hd3abc70_0.conda#66b6088c2446c25e714829a289070499
+https://conda.anaconda.org/conda-forge/linux-64/glib-2.80.2-hf974151_0.conda#d427988dc3dbd0a4c136f52db356cc6a
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-https://conda.anaconda.org/conda-forge/noarch/jinja2-3.1.3-pyhd8ed1ab_0.conda#e7d8df6509ba635247ff9aea31134262
-https://conda.anaconda.org/conda-forge/noarch/joblib-1.4.0-pyhd8ed1ab_0.conda#e0ed1bf13ce3a440e022157bf4764465
+https://conda.anaconda.org/conda-forge/noarch/jinja2-3.1.4-pyhd8ed1ab_0.conda#7b86ecb7d3557821c649b3c31e3eb9f2
+https://conda.anaconda.org/conda-forge/noarch/joblib-1.4.2-pyhd8ed1ab_0.conda#25df261d4523d9f9783bcdb7208d872f
 https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.9.0-22_linux64_openblas.conda#4b31699e0ec5de64d5896e580389c9a1
 https://conda.anaconda.org/conda-forge/linux-64/libgcrypt-1.10.3-hd590300_0.conda#32d16ad533c59bb0a3c5ffaf16110829
 https://conda.anaconda.org/conda-forge/linux-64/liblapack-3.9.0-22_linux64_openblas.conda#b083767b6c877e24ee597d93b87ab838
@@ -169,37 +169,37 @@ https://conda.anaconda.org/conda-forge/linux-64/libxkbcommon-1.7.0-h662e7e4_0.co
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 https://conda.anaconda.org/conda-forge/noarch/pip-24.0-pyhd8ed1ab_0.conda#f586ac1e56c8638b64f9c8122a7b8a67
-https://conda.anaconda.org/conda-forge/noarch/pyproject-metadata-0.7.1-pyhd8ed1ab_0.conda#dcb27826ffc94d5f04e241322239983b
-https://conda.anaconda.org/conda-forge/noarch/pytest-7.4.4-pyhd8ed1ab_0.conda#a9d145de8c5f064b5fa68fb34725d9f4
+https://conda.anaconda.org/conda-forge/noarch/pyproject-metadata-0.8.0-pyhd8ed1ab_0.conda#573fe09d7bd0cd4bcc210d8369b5ca47
+https://conda.anaconda.org/conda-forge/noarch/pytest-8.2.1-pyhd8ed1ab_0.conda#e4418e8bdbaa8eea28e047531e6763c8
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-https://conda.anaconda.org/conda-forge/noarch/sphinx-7.2.6-pyhd8ed1ab_0.conda#bbfd1120d1824d2d073bc65935f0e4c0
+https://conda.anaconda.org/conda-forge/noarch/sphinx-7.3.7-pyhd8ed1ab_0.conda#7b1465205e28d75d2c0e1a868ee00a67
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diff --git a/build_tools/azure/pypy3_environment.yml b/build_tools/azure/pypy3_environment.yml
index 285f1b0d51d17..84784d5a90b20 100644
--- a/build_tools/azure/pypy3_environment.yml
+++ b/build_tools/azure/pypy3_environment.yml
@@ -14,7 +14,7 @@ dependencies:
   - threadpoolctl
   - matplotlib
   - pyamg
-  - pytest<8
+  - pytest
   - pytest-xdist
   - pip
   - ninja
diff --git a/build_tools/azure/pypy3_linux-64_conda.lock b/build_tools/azure/pypy3_linux-64_conda.lock
index 23710cfe35cb8..a47c89e5a7aab 100644
--- a/build_tools/azure/pypy3_linux-64_conda.lock
+++ b/build_tools/azure/pypy3_linux-64_conda.lock
@@ -1,27 +1,27 @@
 # Generated by conda-lock.
 # platform: linux-64
-# input_hash: c4b15c5bfeffe4d558e4ece0c996e6cc04c00369326c72d19780ffc0209bd591
+# input_hash: cb8a71fc5a5762d803c62e60f01aaf1788c4357c1233fd623cecb1225076b9b5
 @EXPLICIT
 https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2#d7c89558ba9fa0495403155b64376d81
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-https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-ng-13.2.0-h7e041cc_5.conda#f6f6600d18a4047b54f803cf708b868a
+https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-ng-13.2.0-hc0a3c3a_7.conda#53ebd4c833fa01cb2c6353e99f905406
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+https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-13.2.0-hca663fb_7.conda#c0bd771f09a326fdcd95a60b617795bf
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-https://conda.anaconda.org/conda-forge/linux-64/libwebp-base-1.3.2-hd590300_1.conda#049b7df8bae5e184d1de42cdf64855f8
+https://conda.anaconda.org/conda-forge/linux-64/libwebp-base-1.4.0-hd590300_0.conda#b26e8aa824079e1be0294e7152ca4559
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-https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.4.20240210-h59595ed_0.conda#97da8860a0da5413c7c98a3b3838a645
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-https://conda.anaconda.org/conda-forge/linux-64/openssl-3.2.1-hd590300_1.conda#9d731343cff6ee2e5a25c4a091bf8e2a
+https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.5-h59595ed_0.conda#fcea371545eda051b6deafb24889fc69
+https://conda.anaconda.org/conda-forge/linux-64/ninja-1.12.1-h297d8ca_0.conda#3aa1c7e292afeff25a0091ddd7c69b72
+https://conda.anaconda.org/conda-forge/linux-64/openssl-3.3.0-h4ab18f5_2.conda#b8934d399b56d73e323403e183d009c5
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@@ -32,22 +32,22 @@ https://conda.anaconda.org/conda-forge/linux-64/xz-5.2.6-h166bdaf_0.tar.bz2#2161
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+https://conda.anaconda.org/conda-forge/linux-64/libgfortran-ng-13.2.0-h69a702a_7.conda#1b84f26d9f4f6026e179e7805d5a15cd
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-https://conda.anaconda.org/conda-forge/linux-64/libsqlite-3.45.2-h2797004_0.conda#866983a220e27a80cb75e85cb30466a1
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 https://conda.anaconda.org/conda-forge/linux-64/libtiff-4.6.0-h1dd3fc0_3.conda#66f03896ffbe1a110ffda05c7a856504
-https://conda.anaconda.org/conda-forge/linux-64/llvm-openmp-18.1.3-h4dfa4b3_0.conda#d39965123dffcad4d750989be65bcb7c
-https://conda.anaconda.org/conda-forge/linux-64/sqlite-3.45.2-h2c6b66d_0.conda#1423efca06ed343c1da0fc429bae0779
+https://conda.anaconda.org/conda-forge/linux-64/llvm-openmp-18.1.5-ha31de31_0.conda#b923cdb6e567ada84f991ffcc5848afb
+https://conda.anaconda.org/conda-forge/linux-64/sqlite-3.45.3-h2c6b66d_0.conda#be7d70f2db41b674733667bdd69bd000
 https://conda.anaconda.org/conda-forge/linux-64/xorg-libx11-1.8.9-h8ee46fc_0.conda#077b6e8ad6a3ddb741fce2496dd01bec
 https://conda.anaconda.org/conda-forge/linux-64/brotli-1.1.0-hd590300_1.conda#f27a24d46e3ea7b70a1f98e50c62508f
 https://conda.anaconda.org/conda-forge/linux-64/ccache-4.9.1-h1fcd64f_0.conda#3620f564bcf28c3524951b6f64f5c5ac
@@ -72,12 +72,12 @@ https://conda.anaconda.org/conda-forge/noarch/munkres-1.1.4-pyh9f0ad1d_0.tar.bz2
 https://conda.anaconda.org/conda-forge/linux-64/numpy-1.26.4-py39h6dedee3_0.conda#557d64563e84ff21b14f586c7f662b7f
 https://conda.anaconda.org/conda-forge/noarch/packaging-24.0-pyhd8ed1ab_0.conda#248f521b64ce055e7feae3105e7abeb8
 https://conda.anaconda.org/conda-forge/linux-64/pillow-10.3.0-py39h90a76f3_0.conda#799e6519cfffe2784db27b1db2ef33f3
-https://conda.anaconda.org/conda-forge/noarch/pluggy-1.4.0-pyhd8ed1ab_0.conda#139e9feb65187e916162917bb2484976
+https://conda.anaconda.org/conda-forge/noarch/pluggy-1.5.0-pyhd8ed1ab_0.conda#d3483c8fc2dc2cc3f5cf43e26d60cabf
 https://conda.anaconda.org/conda-forge/noarch/pyparsing-3.1.2-pyhd8ed1ab_0.conda#b9a4dacf97241704529131a0dfc0494f
 https://conda.anaconda.org/conda-forge/noarch/pypy-7.3.15-1_pypy39.conda#a418a6c16bd6f7ed56b92194214791a0
-https://conda.anaconda.org/conda-forge/noarch/setuptools-69.5.1-pyhd8ed1ab_0.conda#7462280d81f639363e6e63c81276bd9e
+https://conda.anaconda.org/conda-forge/noarch/setuptools-70.0.0-pyhd8ed1ab_0.conda#c8ddb4f34a208df4dd42509a0f6a1c89
 https://conda.anaconda.org/conda-forge/noarch/six-1.16.0-pyh6c4a22f_0.tar.bz2#e5f25f8dbc060e9a8d912e432202afc2
-https://conda.anaconda.org/conda-forge/noarch/threadpoolctl-3.4.0-pyhc1e730c_0.conda#b296278eef667c673bf51de6535bad88
+https://conda.anaconda.org/conda-forge/noarch/threadpoolctl-3.5.0-pyhc1e730c_0.conda#df68d78237980a159bd7149f33c0e8fd
 https://conda.anaconda.org/conda-forge/noarch/tomli-2.0.1-pyhd8ed1ab_0.tar.bz2#5844808ffab9ebdb694585b50ba02a96
 https://conda.anaconda.org/conda-forge/linux-64/tornado-6.4-py39hf860d4a_0.conda#e7fded713fb466e1e0670afce1761b47
 https://conda.anaconda.org/conda-forge/linux-64/unicodedata2-15.1.0-py39hf860d4a_0.conda#f699157518d28d00c87542b4ec1273be
@@ -87,17 +87,17 @@ https://conda.anaconda.org/conda-forge/linux-64/blas-devel-3.9.0-22_linux64_open
 https://conda.anaconda.org/conda-forge/linux-64/contourpy-1.2.1-py39ha90811c_0.conda#07ed14c8326da42356514bcbc0b04802
 https://conda.anaconda.org/conda-forge/linux-64/fonttools-4.51.0-py39hf860d4a_0.conda#63421b4dd7222fad555e34ec9af015a1
 https://conda.anaconda.org/conda-forge/noarch/importlib_resources-6.4.0-pyhd8ed1ab_0.conda#c5d3907ad8bd7bf557521a1833cf7e6d
-https://conda.anaconda.org/conda-forge/noarch/joblib-1.4.0-pyhd8ed1ab_0.conda#e0ed1bf13ce3a440e022157bf4764465
+https://conda.anaconda.org/conda-forge/noarch/joblib-1.4.2-pyhd8ed1ab_0.conda#25df261d4523d9f9783bcdb7208d872f
 https://conda.anaconda.org/conda-forge/noarch/meson-1.4.0-pyhd8ed1ab_0.conda#52a0660cfa40b45bf254ecc3374cb2e0
 https://conda.anaconda.org/conda-forge/noarch/pip-24.0-pyhd8ed1ab_0.conda#f586ac1e56c8638b64f9c8122a7b8a67
-https://conda.anaconda.org/conda-forge/noarch/pyproject-metadata-0.7.1-pyhd8ed1ab_0.conda#dcb27826ffc94d5f04e241322239983b
-https://conda.anaconda.org/conda-forge/noarch/pytest-7.4.4-pyhd8ed1ab_0.conda#a9d145de8c5f064b5fa68fb34725d9f4
+https://conda.anaconda.org/conda-forge/noarch/pyproject-metadata-0.8.0-pyhd8ed1ab_0.conda#573fe09d7bd0cd4bcc210d8369b5ca47
+https://conda.anaconda.org/conda-forge/noarch/pytest-8.2.1-pyhd8ed1ab_0.conda#e4418e8bdbaa8eea28e047531e6763c8
 https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0-pyhd8ed1ab_0.conda#2cf4264fffb9e6eff6031c5b6884d61c
 https://conda.anaconda.org/conda-forge/linux-64/scipy-1.12.0-py39h6dedee3_2.conda#6c5d74bac41838f4377dfd45085e1fec
 https://conda.anaconda.org/conda-forge/linux-64/blas-2.122-openblas.conda#5065468105542a8b23ea47bd8b6fa55f
 https://conda.anaconda.org/conda-forge/noarch/importlib-resources-6.4.0-pyhd8ed1ab_0.conda#dcbadab7a68738a028e195ab68ab2d2e
-https://conda.anaconda.org/conda-forge/noarch/meson-python-0.15.0-pyh0c530f3_0.conda#3bc64565ca78ce3bb80248d09926d8f9
-https://conda.anaconda.org/conda-forge/linux-64/pyamg-5.1.0-py39h5fd064f_0.conda#04676d2a49da3cb608af77e04b796ce1
+https://conda.anaconda.org/conda-forge/noarch/meson-python-0.16.0-pyh0c530f3_0.conda#e16f0dbf502da873be9f9adb0dc52547
+https://conda.anaconda.org/conda-forge/linux-64/pyamg-5.1.0-py39h3c335be_1.conda#7278eb55a7e97a0ba2376a6c608e7c46
 https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-3.5.0-pyhd8ed1ab_0.conda#d5f595da2daead898ca958ac62f0307b
-https://conda.anaconda.org/conda-forge/linux-64/matplotlib-base-3.8.4-py39h4e7d633_0.conda#58272019e595dde98d0844ae3ebf0cfe
-https://conda.anaconda.org/conda-forge/linux-64/matplotlib-3.8.4-py39h4162558_0.conda#b0f7702a174422ff1db58190495fd766
+https://conda.anaconda.org/conda-forge/linux-64/matplotlib-base-3.8.4-py39h6fb8a73_2.conda#3212f51613e10b3ee319f3f2bf8ee5a8
+https://conda.anaconda.org/conda-forge/linux-64/matplotlib-3.8.4-py39h4162558_2.conda#05babd7bae196648bfc6b7e3d9ea7630
diff --git a/build_tools/azure/python_nogil_lock.txt b/build_tools/azure/python_nogil_lock.txt
index 03cd4f2e0c346..7f67a48842dea 100644
--- a/build_tools/azure/python_nogil_lock.txt
+++ b/build_tools/azure/python_nogil_lock.txt
@@ -13,15 +13,15 @@ cycler==0.12.1
     # via matplotlib
 cython==3.0.10
     # via -r /scikit-learn/build_tools/azure/python_nogil_requirements.txt
-exceptiongroup==1.2.0
+exceptiongroup==1.2.1
     # via pytest
-execnet==2.0.2
+execnet==2.1.1
     # via pytest-xdist
-fonttools==4.50.0
+fonttools==4.51.0
     # via matplotlib
 iniconfig==2.0.0
     # via pytest
-joblib==1.3.2
+joblib==1.4.2
     # via -r /scikit-learn/build_tools/azure/python_nogil_requirements.txt
 kiwisolver==1.4.4
     # via matplotlib
@@ -29,7 +29,7 @@ matplotlib==3.6.2
     # via -r /scikit-learn/build_tools/azure/python_nogil_requirements.txt
 meson==1.4.0
     # via meson-python
-meson-python==0.15.0
+meson-python==0.16.0
     # via -r /scikit-learn/build_tools/azure/python_nogil_requirements.txt
 ninja==1.11.1.1
     # via -r /scikit-learn/build_tools/azure/python_nogil_requirements.txt
@@ -42,21 +42,22 @@ numpy==1.24.0
 packaging==24.0
     # via
     #   matplotlib
+    #   meson-python
     #   pyproject-metadata
     #   pytest
 pillow==9.5.0
     # via matplotlib
-pluggy==1.4.0
+pluggy==1.5.0
     # via pytest
 pyparsing==3.1.2
     # via matplotlib
-pyproject-metadata==0.7.1
+pyproject-metadata==0.8.0
     # via meson-python
 pytest==7.4.4
     # via
     #   -r /scikit-learn/build_tools/azure/python_nogil_requirements.txt
     #   pytest-xdist
-pytest-xdist==3.5.0
+pytest-xdist==3.6.1
     # via -r /scikit-learn/build_tools/azure/python_nogil_requirements.txt
 python-dateutil==2.9.0.post0
     # via matplotlib
@@ -64,7 +65,7 @@ scipy==1.9.3
     # via -r /scikit-learn/build_tools/azure/python_nogil_requirements.txt
 six==1.16.0
     # via python-dateutil
-threadpoolctl==3.4.0
+threadpoolctl==3.5.0
     # via -r /scikit-learn/build_tools/azure/python_nogil_requirements.txt
 tomli==2.0.1
     # via
diff --git a/build_tools/azure/ubuntu_atlas_lock.txt b/build_tools/azure/ubuntu_atlas_lock.txt
index aa17f49e75936..e66f5ca8943fd 100644
--- a/build_tools/azure/ubuntu_atlas_lock.txt
+++ b/build_tools/azure/ubuntu_atlas_lock.txt
@@ -6,7 +6,7 @@
 #
 cython==3.0.10
     # via -r build_tools/azure/ubuntu_atlas_requirements.txt
-exceptiongroup==1.2.0
+exceptiongroup==1.2.1
     # via pytest
 execnet==2.1.1
     # via pytest-xdist
@@ -16,25 +16,26 @@ joblib==1.2.0
     # via -r build_tools/azure/ubuntu_atlas_requirements.txt
 meson==1.4.0
     # via meson-python
-meson-python==0.15.0
+meson-python==0.16.0
     # via -r build_tools/azure/ubuntu_atlas_requirements.txt
 ninja==1.11.1.1
     # via -r build_tools/azure/ubuntu_atlas_requirements.txt
 packaging==24.0
     # via
+    #   meson-python
     #   pyproject-metadata
     #   pytest
-pluggy==1.4.0
+pluggy==1.5.0
     # via pytest
-pyproject-metadata==0.7.1
+pyproject-metadata==0.8.0
     # via meson-python
-pytest==7.4.4
+pytest==8.2.1
     # via
     #   -r build_tools/azure/ubuntu_atlas_requirements.txt
     #   pytest-xdist
-pytest-xdist==3.5.0
+pytest-xdist==3.6.1
     # via -r build_tools/azure/ubuntu_atlas_requirements.txt
-threadpoolctl==2.0.0
+threadpoolctl==3.1.0
     # via -r build_tools/azure/ubuntu_atlas_requirements.txt
 tomli==2.0.1
     # via
diff --git a/build_tools/azure/ubuntu_atlas_requirements.txt b/build_tools/azure/ubuntu_atlas_requirements.txt
index aab362dda0bf2..dfb0cfebc54d1 100644
--- a/build_tools/azure/ubuntu_atlas_requirements.txt
+++ b/build_tools/azure/ubuntu_atlas_requirements.txt
@@ -3,8 +3,8 @@
 # build_tools/update_environments_and_lock_files.py
 cython==3.0.10  # min
 joblib==1.2.0  # min
-threadpoolctl==2.0.0  # min
-pytest<8
+threadpoolctl==3.1.0  # min
+pytest
 pytest-xdist
 ninja
 meson-python
diff --git a/build_tools/circle/build_doc.sh b/build_tools/circle/build_doc.sh
index 35fee3ae50b65..c569f4913d4d8 100755
--- a/build_tools/circle/build_doc.sh
+++ b/build_tools/circle/build_doc.sh
@@ -190,17 +190,13 @@ export OMP_NUM_THREADS=1
 if [[ "$CIRCLE_BRANCH" =~ ^main$ && -z "$CI_PULL_REQUEST" ]]
 then
     # List available documentation versions if on main
-    python build_tools/circle/list_versions.py > doc/versions.rst
+    python build_tools/circle/list_versions.py --json doc/js/versions.json --rst doc/versions.rst
 fi
 
 
 # The pipefail is requested to propagate exit code
 set -o pipefail && cd doc && make $make_args 2>&1 | tee ~/log.txt
 
-# Insert the version warning for deployment
-find _build/html/stable -name "*.html" | xargs sed -i '/<\/body>/ i \
-\    <script src="https://scikit-learn.org/versionwarning.js"></script>'
-
 cd -
 set +o pipefail
 
@@ -244,7 +240,7 @@ then
     (
     echo '<html><body><ul>'
     echo "$affected" | sed 's|.*|<li><a href="&">&</a> [<a href="https://scikit-learn.org/dev/&">dev</a>, <a href="https://scikit-learn.org/stable/&">stable</a>]</li>|'
-    echo '</ul><p>General: <a href="index.html">Home</a> | <a href="modules/classes.html">API Reference</a> | <a href="auto_examples/index.html">Examples</a></p>'
+    echo '</ul><p>General: <a href="index.html">Home</a> | <a href="api/index.html">API Reference</a> | <a href="auto_examples/index.html">Examples</a></p>'
     echo '<strong>Sphinx Warnings in affected files</strong><ul>'
     echo "$warnings" | sed 's/\/home\/circleci\/project\//<li>/g'
     echo '</ul></body></html>'
diff --git a/build_tools/circle/doc_environment.yml b/build_tools/circle/doc_environment.yml
index 4df22341635a3..ea930fadcb528 100644
--- a/build_tools/circle/doc_environment.yml
+++ b/build_tools/circle/doc_environment.yml
@@ -14,7 +14,7 @@ dependencies:
   - matplotlib
   - pandas
   - pyamg
-  - pytest<8
+  - pytest
   - pytest-xdist
   - pillow
   - pip
@@ -33,7 +33,11 @@ dependencies:
   - polars
   - pooch
   - sphinxext-opengraph
+  - sphinx-remove-toctrees
+  - sphinx-design
+  - pydata-sphinx-theme
   - pip
   - pip:
     - jupyterlite-sphinx
     - jupyterlite-pyodide-kernel
+    - sphinxcontrib-sass
diff --git a/build_tools/circle/doc_linux-64_conda.lock b/build_tools/circle/doc_linux-64_conda.lock
index 984cca332fc7f..0d925375b643b 100644
--- a/build_tools/circle/doc_linux-64_conda.lock
+++ b/build_tools/circle/doc_linux-64_conda.lock
@@ -1,31 +1,31 @@
 # Generated by conda-lock.
 # platform: linux-64
-# input_hash: b57888763997b08b2f240b5ff1ed6afcf88685f3d8c791ea8eba4d80483c43d0
+# input_hash: f6f3862aafcafa139a322e498517c3db58e1b8db95f1b1ca8c18f5b70d446dc9
 @EXPLICIT
 https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2#d7c89558ba9fa0495403155b64376d81
 https://conda.anaconda.org/conda-forge/linux-64/ca-certificates-2024.2.2-hbcca054_0.conda#2f4327a1cbe7f022401b236e915a5fef
 https://conda.anaconda.org/conda-forge/noarch/font-ttf-dejavu-sans-mono-2.37-hab24e00_0.tar.bz2#0c96522c6bdaed4b1566d11387caaf45
 https://conda.anaconda.org/conda-forge/noarch/font-ttf-inconsolata-3.000-h77eed37_0.tar.bz2#34893075a5c9e55cdafac56607368fc6
 https://conda.anaconda.org/conda-forge/noarch/font-ttf-source-code-pro-2.038-h77eed37_0.tar.bz2#4d59c254e01d9cde7957100457e2d5fb
-https://conda.anaconda.org/conda-forge/noarch/font-ttf-ubuntu-0.83-h77eed37_1.conda#6185f640c43843e5ad6fd1c5372c3f80
+https://conda.anaconda.org/conda-forge/noarch/font-ttf-ubuntu-0.83-h77eed37_2.conda#cbbe59391138ea5ad3658c76912e147f
 https://conda.anaconda.org/conda-forge/noarch/kernel-headers_linux-64-2.6.32-he073ed8_17.conda#d731b543793afc0433c4fd593e693fce
-https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.40-h41732ed_0.conda#7aca3059a1729aa76c597603f10b0dd3
-https://conda.anaconda.org/conda-forge/noarch/libgcc-devel_linux-64-12.3.0-h8bca6fd_105.conda#e12ce6b051085b8f27e239f5e5f5bce5
-https://conda.anaconda.org/conda-forge/noarch/libstdcxx-devel_linux-64-12.3.0-h8bca6fd_105.conda#b3c6062c84a8e172555ee104ea6a01ab
-https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-ng-13.2.0-h7e041cc_5.conda#f6f6600d18a4047b54f803cf708b868a
+https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.40-hf3520f5_1.conda#33b7851c39c25da14f6a233a8ccbeeca
+https://conda.anaconda.org/conda-forge/noarch/libgcc-devel_linux-64-12.3.0-h0223996_107.conda#851e9651c9e4cd5dc19f80398eba9a1c
+https://conda.anaconda.org/conda-forge/noarch/libstdcxx-devel_linux-64-12.3.0-h0223996_107.conda#167a1f5d77d8f3c2a638f7eb418429f1
+https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-ng-13.2.0-hc0a3c3a_7.conda#53ebd4c833fa01cb2c6353e99f905406
 https://conda.anaconda.org/conda-forge/linux-64/python_abi-3.9-4_cp39.conda#bfe4b3259a8ac6cdf0037752904da6a7
 https://conda.anaconda.org/conda-forge/noarch/tzdata-2024a-h0c530f3_0.conda#161081fc7cec0bfda0d86d7cb595f8d8
 https://conda.anaconda.org/conda-forge/noarch/fonts-conda-forge-1-0.tar.bz2#f766549260d6815b0c52253f1fb1bb29
-https://conda.anaconda.org/conda-forge/linux-64/libgomp-13.2.0-h807b86a_5.conda#d211c42b9ce49aee3734fdc828731689
+https://conda.anaconda.org/conda-forge/linux-64/libgomp-13.2.0-h77fa898_7.conda#abf3fec87c2563697defa759dec3d639
 https://conda.anaconda.org/conda-forge/noarch/sysroot_linux-64-2.12-he073ed8_17.conda#595db67e32b276298ff3d94d07d47fbf
-https://conda.anaconda.org/conda-forge/linux-64/binutils_impl_linux-64-2.40-hf600244_0.conda#33084421a8c0af6aef1b439707f7662a
+https://conda.anaconda.org/conda-forge/linux-64/binutils_impl_linux-64-2.40-ha1999f0_1.conda#e901545940ebdc5c40017fab53642b3c
 https://conda.anaconda.org/conda-forge/noarch/fonts-conda-ecosystem-1-0.tar.bz2#fee5683a3f04bd15cbd8318b096a27ab
-https://conda.anaconda.org/conda-forge/linux-64/binutils-2.40-hdd6e379_0.conda#ccc940fddbc3fcd3d79cd4c654c4b5c4
+https://conda.anaconda.org/conda-forge/linux-64/binutils-2.40-h4852527_1.conda#dfaea5684bbbbf0b64a4c31f984d0661
 https://conda.anaconda.org/conda-forge/linux-64/binutils_linux-64-2.40-hdade7a5_3.conda#2d9a60578bc28469d9aeef9aea5520c3
 https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-2_kmp_llvm.tar.bz2#562b26ba2e19059551a811e72ab7f793
-https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-13.2.0-h807b86a_5.conda#d4ff227c46917d3b4565302a2bbb276b
+https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-13.2.0-h77fa898_7.conda#72ec1b1b04c4d15d4204ece1ecea5978
 https://conda.anaconda.org/conda-forge/linux-64/alsa-lib-1.2.11-hd590300_1.conda#0bb492cca54017ea314b809b1ee3a176
-https://conda.anaconda.org/conda-forge/linux-64/aom-3.8.2-h59595ed_0.conda#625e1fed28a5139aed71b3a76117ef84
+https://conda.anaconda.org/conda-forge/linux-64/aom-3.9.0-hac33072_0.conda#93a3bf248e5bc729807db198a9c89f07
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+https://conda.anaconda.org/conda-forge/noarch/sphinx-design-0.5.0-pyhd8ed1ab_0.conda#264b3c697fa9cdade87eb0abe4440d54
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+https://conda.anaconda.org/conda-forge/noarch/sphinx-remove-toctrees-1.0.0.post1-pyhd8ed1ab_0.conda#6dee8412218288a17f99f2cfffab334d
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+https://conda.anaconda.org/conda-forge/noarch/sphinx-7.3.7-pyhd8ed1ab_0.conda#7b1465205e28d75d2c0e1a868ee00a67
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 # pip attrs @ https://files.pythonhosted.org/packages/e0/44/827b2a91a5816512fcaf3cc4ebc465ccd5d598c45cefa6703fcf4a79018f/attrs-23.2.0-py3-none-any.whl#sha256=99b87a485a5820b23b879f04c2305b44b951b502fd64be915879d77a7e8fc6f1
@@ -272,6 +278,7 @@ https://conda.anaconda.org/conda-forge/noarch/sphinxext-opengraph-0.9.1-pyhd8ed1
 # pip json5 @ https://files.pythonhosted.org/packages/8a/3c/4f8791ee53ab9eeb0b022205aa79387119a74cc9429582ce04098e6fc540/json5-0.9.25-py3-none-any.whl#sha256=34ed7d834b1341a86987ed52f3f76cd8ee184394906b6e22a1e0deb9ab294e8f
 # pip jsonpointer @ https://files.pythonhosted.org/packages/12/f6/0232cc0c617e195f06f810534d00b74d2f348fe71b2118009ad8ad31f878/jsonpointer-2.4-py2.py3-none-any.whl#sha256=15d51bba20eea3165644553647711d150376234112651b4f1811022aecad7d7a
 # pip jupyterlab-pygments @ https://files.pythonhosted.org/packages/b1/dd/ead9d8ea85bf202d90cc513b533f9c363121c7792674f78e0d8a854b63b4/jupyterlab_pygments-0.3.0-py3-none-any.whl#sha256=841a89020971da1d8693f1a99997aefc5dc424bb1b251fd6322462a1b8842780
+# pip libsass @ https://files.pythonhosted.org/packages/fd/5a/eb5b62641df0459a3291fc206cf5bd669c0feed7814dded8edef4ade8512/libsass-0.23.0-cp38-abi3-manylinux_2_5_x86_64.manylinux1_x86_64.whl#sha256=4a218406d605f325d234e4678bd57126a66a88841cb95bee2caeafdc6f138306
 # pip mistune @ https://files.pythonhosted.org/packages/f0/74/c95adcdf032956d9ef6c89a9b8a5152bf73915f8c633f3e3d88d06bd699c/mistune-3.0.2-py3-none-any.whl#sha256=71481854c30fdbc938963d3605b72501f5c10a9320ecd412c121c163a1c7d205
 # pip overrides @ https://files.pythonhosted.org/packages/2c/ab/fc8290c6a4c722e5514d80f62b2dc4c4df1a68a41d1364e625c35990fcf3/overrides-7.7.0-py3-none-any.whl#sha256=c7ed9d062f78b8e4c1a7b70bd8796b35ead4d9f510227ef9c5dc7626c60d7e49
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@@ -282,41 +289,40 @@ https://conda.anaconda.org/conda-forge/noarch/sphinxext-opengraph-0.9.1-pyhd8ed1
 # pip python-json-logger @ https://files.pythonhosted.org/packages/35/a6/145655273568ee78a581e734cf35beb9e33a370b29c5d3c8fee3744de29f/python_json_logger-2.0.7-py3-none-any.whl#sha256=f380b826a991ebbe3de4d897aeec42760035ac760345e57b812938dc8b35e2bd
 # pip pyyaml @ https://files.pythonhosted.org/packages/7d/39/472f2554a0f1e825bd7c5afc11c817cd7a2f3657460f7159f691fbb37c51/PyYAML-6.0.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=bc1bf2925a1ecd43da378f4db9e4f799775d6367bdb94671027b73b393a7c42c
 # pip rfc3986-validator @ https://files.pythonhosted.org/packages/9e/51/17023c0f8f1869d8806b979a2bffa3f861f26a3f1a66b094288323fba52f/rfc3986_validator-0.1.1-py2.py3-none-any.whl#sha256=2f235c432ef459970b4306369336b9d5dbdda31b510ca1e327636e01f528bfa9
-# pip rpds-py @ https://files.pythonhosted.org/packages/fd/ea/92231b62681961812e9fbd8ef9be7137856784406bf6a384976bb7b46472/rpds_py-0.18.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=ddc2f4dfd396c7bfa18e6ce371cba60e4cf9d2e5cdb71376aa2da264605b60b9
+# pip rpds-py @ https://files.pythonhosted.org/packages/97/b1/12238bd8cdf3cef71e85188af133399bfde1bddf319007361cc869d6f6a7/rpds_py-0.18.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=e4c39ad2f512b4041343ea3c7894339e4ca7839ac38ca83d68a832fc8b3748ab
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-# pip soupsieve @ https://files.pythonhosted.org/packages/4c/f3/038b302fdfbe3be7da016777069f26ceefe11a681055ea1f7817546508e3/soupsieve-2.5-py3-none-any.whl#sha256=eaa337ff55a1579b6549dc679565eac1e3d000563bcb1c8ab0d0fefbc0c2cdc7
-# pip traitlets @ https://files.pythonhosted.org/packages/7c/c4/366a09036c07f46eb8c9b2af39c97f502ef24f11f2a6e4d763655d9f2708/traitlets-5.14.2-py3-none-any.whl#sha256=fcdf85684a772ddeba87db2f398ce00b40ff550d1528c03c14dbf6a02003cd80
+# pip traitlets @ https://files.pythonhosted.org/packages/00/c0/8f5d070730d7836adc9c9b6408dec68c6ced86b304a9b26a14df072a6e8c/traitlets-5.14.3-py3-none-any.whl#sha256=b74e89e397b1ed28cc831db7aea759ba6640cb3de13090ca145426688ff1ac4f
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-# pip websocket-client @ https://files.pythonhosted.org/packages/1e/70/1e88138a9afbed1d37093b85f0bebc3011623c4f47c166431599fe9d6c93/websocket_client-1.7.0-py3-none-any.whl#sha256=f4c3d22fec12a2461427a29957ff07d35098ee2d976d3ba244e688b8b4057588
-# pip anyio @ https://files.pythonhosted.org/packages/14/fd/2f20c40b45e4fb4324834aea24bd4afdf1143390242c0b33774da0e2e34f/anyio-4.3.0-py3-none-any.whl#sha256=048e05d0f6caeed70d731f3db756d35dcc1f35747c8c403364a8332c630441b8
+# pip websocket-client @ https://files.pythonhosted.org/packages/5a/84/44687a29792a70e111c5c477230a72c4b957d88d16141199bf9acb7537a3/websocket_client-1.8.0-py3-none-any.whl#sha256=17b44cc997f5c498e809b22cdf2d9c7a9e71c02c8cc2b6c56e7c2d1239bfa526
+# pip anyio @ https://files.pythonhosted.org/packages/7b/a2/10639a79341f6c019dedc95bd48a4928eed9f1d1197f4c04f546fc7ae0ff/anyio-4.4.0-py3-none-any.whl#sha256=c1b2d8f46a8a812513012e1107cb0e68c17159a7a594208005a57dc776e1bdc7
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-# pip beautifulsoup4 @ https://files.pythonhosted.org/packages/b1/fe/e8c672695b37eecc5cbf43e1d0638d88d66ba3a44c4d321c796f4e59167f/beautifulsoup4-4.12.3-py3-none-any.whl#sha256=b80878c9f40111313e55da8ba20bdba06d8fa3969fc68304167741bbf9e082ed
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-# pip referencing @ https://files.pythonhosted.org/packages/42/8e/ae1de7b12223986e949bdb886c004de7c304b6fa94de5b87c926c1099656/referencing-0.34.0-py3-none-any.whl#sha256=d53ae300ceddd3169f1ffa9caf2cb7b769e92657e4fafb23d34b93679116dfd4
+# pip referencing @ https://files.pythonhosted.org/packages/b7/59/2056f61236782a2c86b33906c025d4f4a0b17be0161b63b70fd9e8775d36/referencing-0.35.1-py3-none-any.whl#sha256=eda6d3234d62814d1c64e305c1331c9a3a6132da475ab6382eaa997b21ee75de
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+# pip sphinxcontrib-sass @ https://files.pythonhosted.org/packages/2e/87/7c2eb08e3ca1d6baae32c0a5e005330fe1cec93a36aa085e714c3b3a3c7d/sphinxcontrib_sass-0.3.4-py2.py3-none-any.whl#sha256=a0c79a44ae8b8935c02dc340ebe40c9e002c839331201c899dc93708970c355a
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-# pip tinycss2 @ https://files.pythonhosted.org/packages/da/99/fd23634d6962c2791fb8cb6ccae1f05dcbfc39bce36bba8b1c9a8d92eae8/tinycss2-1.2.1-py3-none-any.whl#sha256=2b80a96d41e7c3914b8cda8bc7f705a4d9c49275616e886103dd839dfc847847
+# pip tinycss2 @ https://files.pythonhosted.org/packages/2c/4d/0db5b8a613d2a59bbc29bc5bb44a2f8070eb9ceab11c50d477502a8a0092/tinycss2-1.3.0-py3-none-any.whl#sha256=54a8dbdffb334d536851be0226030e9505965bb2f30f21a4a82c55fb2a80fae7
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 # pip jupyterlite-core @ https://files.pythonhosted.org/packages/05/d2/1d59d9a70d684b1eb3eb3a0b80a36b4e1d691e94af5d53aee56b1ad5240b/jupyterlite_core-0.3.0-py3-none-any.whl#sha256=247cc34ae6fedda41b15ce4778997164508b2039bc92480665cadfe955193467
-# pip pyzmq @ https://files.pythonhosted.org/packages/76/8b/6fca99e22c6316917de32b17be299dea431544209d619da16b6d9ec85c83/pyzmq-25.1.2-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl#sha256=c0b5ca88a8928147b7b1e2dfa09f3b6c256bc1135a1338536cbc9ea13d3b7add
+# pip pyzmq @ https://files.pythonhosted.org/packages/64/b8/1c181c13e118cabccfd25bd3e169e44958c649180b0d78b798a66899e08b/pyzmq-26.0.3-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl#sha256=b3cd31f859b662ac5d7f4226ec7d8bd60384fa037fc02aee6ff0b53ba29a3ba8
 # pip argon2-cffi @ https://files.pythonhosted.org/packages/a4/6a/e8a041599e78b6b3752da48000b14c8d1e8a04ded09c88c714ba047f34f5/argon2_cffi-23.1.0-py3-none-any.whl#sha256=c670642b78ba29641818ab2e68bd4e6a78ba53b7eff7b4c3815ae16abf91c7ea
-# pip jsonschema @ https://files.pythonhosted.org/packages/39/9d/b035d024c62c85f2e2d4806a59ca7b8520307f34e0932fbc8cc75fe7b2d9/jsonschema-4.21.1-py3-none-any.whl#sha256=7996507afae316306f9e2290407761157c6f78002dcf7419acb99822143d1c6f
-# pip jupyter-client @ https://files.pythonhosted.org/packages/75/6d/d7b55b9c1ac802ab066b3e5015e90faab1fffbbd67a2af498ffc6cc81c97/jupyter_client-8.6.1-py3-none-any.whl#sha256=3b7bd22f058434e3b9a7ea4b1500ed47de2713872288c0d511d19926f99b459f
+# pip jsonschema @ https://files.pythonhosted.org/packages/c8/2f/324fab4be6fe37fb7b521546e8a557e6cf08c1c1b3d0b4839a00f589d9ef/jsonschema-4.22.0-py3-none-any.whl#sha256=ff4cfd6b1367a40e7bc6411caec72effadd3db0bbe5017de188f2d6108335802
+# pip jupyter-client @ https://files.pythonhosted.org/packages/cf/d3/c4bb02580bc0db807edb9a29b2d0c56031be1ef0d804336deb2699a470f6/jupyter_client-8.6.2-py3-none-any.whl#sha256=50cbc5c66fd1b8f65ecb66bc490ab73217993632809b6e505687de18e9dea39f
 # pip jupyterlite-pyodide-kernel @ https://files.pythonhosted.org/packages/83/bf/749279904094015d5cb7e030dd7a111f8b013b9f1809d954d04ebe0c1197/jupyterlite_pyodide_kernel-0.3.1-py3-none-any.whl#sha256=ac9d9dd95adcced57d465a7b298f220d8785845c017ad3abf2a3677ff02631c6
 # pip jupyter-events @ https://files.pythonhosted.org/packages/a5/94/059180ea70a9a326e1815176b2370da56376da347a796f8c4f0b830208ef/jupyter_events-0.10.0-py3-none-any.whl#sha256=4b72130875e59d57716d327ea70d3ebc3af1944d3717e5a498b8a06c6c159960
 # pip nbformat @ https://files.pythonhosted.org/packages/a9/82/0340caa499416c78e5d8f5f05947ae4bc3cba53c9f038ab6e9ed964e22f1/nbformat-5.10.4-py3-none-any.whl#sha256=3b48d6c8fbca4b299bf3982ea7db1af21580e4fec269ad087b9e81588891200b
 # pip nbclient @ https://files.pythonhosted.org/packages/66/e8/00517a23d3eeaed0513e718fbc94aab26eaa1758f5690fc8578839791c79/nbclient-0.10.0-py3-none-any.whl#sha256=f13e3529332a1f1f81d82a53210322476a168bb7090a0289c795fe9cc11c9d3f
-# pip nbconvert @ https://files.pythonhosted.org/packages/23/8a/8d67cbd984739247e4b205c1143e2f71b25b4f71e180fe70f7cb2cf02633/nbconvert-7.16.3-py3-none-any.whl#sha256=ddeff14beeeedf3dd0bc506623e41e4507e551736de59df69a91f86700292b3b
+# pip nbconvert @ https://files.pythonhosted.org/packages/b8/bb/bb5b6a515d1584aa2fd89965b11db6632e4bdc69495a52374bcc36e56cfa/nbconvert-7.16.4-py3-none-any.whl#sha256=05873c620fe520b6322bf8a5ad562692343fe3452abda5765c7a34b7d1aa3eb3
 # pip jupyter-server @ https://files.pythonhosted.org/packages/07/46/6bb926b3bf878bf687b952fb6a4c09d014b4575a25960f2cd1a61793763f/jupyter_server-2.14.0-py3-none-any.whl#sha256=fb6be52c713e80e004fac34b35a0990d6d36ba06fd0a2b2ed82b899143a64210
-# pip jupyterlab-server @ https://files.pythonhosted.org/packages/6a/c9/b270a875916b18f137bb30ecd05031a2f05c95d47a8e8fbd3f805a72f593/jupyterlab_server-2.26.0-py3-none-any.whl#sha256=54622cbd330526a385ee0c1fdccdff3a1e7219bf3e864a335284a1270a1973df
-# pip jupyterlite-sphinx @ https://files.pythonhosted.org/packages/38/c9/5f1142c005cf8d75830b10029e53f074324bc85cfca1f1d0f22a207b771c/jupyterlite_sphinx-0.9.3-py3-none-any.whl#sha256=be6332d16490ea2fa90b78187a2c5e1c357195966a25741d60b1790346571041
+# pip jupyterlab-server @ https://files.pythonhosted.org/packages/cb/46/d5ffd7c0f63db4e9f0982c3d58efeea10fc5f47e79fb328431df78843772/jupyterlab_server-2.27.2-py3-none-any.whl#sha256=54aa2d64fd86383b5438d9f0c032f043c4d8c0264b8af9f60bd061157466ea43
+# pip jupyterlite-sphinx @ https://files.pythonhosted.org/packages/71/2c/bd797dc46a7281d43444c79ff312d4f8d27d41a0de05f48cad81c7939966/jupyterlite_sphinx-0.15.0-py3-none-any.whl#sha256=344d1f9ee5a20b141a4a4139874eae30a68216f0c995d03ea2e3b3e9d29c4cd5
diff --git a/build_tools/circle/doc_min_dependencies_environment.yml b/build_tools/circle/doc_min_dependencies_environment.yml
index 298a60e8ec4ff..e27c3a700fdad 100644
--- a/build_tools/circle/doc_min_dependencies_environment.yml
+++ b/build_tools/circle/doc_min_dependencies_environment.yml
@@ -14,7 +14,7 @@ dependencies:
   - matplotlib=3.3.4  # min
   - pandas=1.1.5  # min
   - pyamg
-  - pytest<8
+  - pytest
   - pytest-xdist
   - pillow
   - pip
@@ -24,14 +24,18 @@ dependencies:
   - seaborn
   - memory_profiler
   - compilers
-  - sphinx=6.0.0  # min
-  - sphinx-gallery=0.15.0  # min
+  - sphinx=7.3.7  # min
+  - sphinx-gallery=0.16.0  # min
   - sphinx-copybutton=0.5.2  # min
   - numpydoc=1.2.0  # min
-  - sphinx-prompt=1.3.0  # min
+  - sphinx-prompt=1.4.0  # min
   - plotly=5.14.0  # min
-  - polars=0.19.12  # min
-  - pooch
+  - polars=0.20.23  # min
+  - pooch=1.6.0  # min
+  - sphinx-remove-toctrees=1.0.0.post1  # min
+  - sphinx-design=0.5.0  # min
+  - pydata-sphinx-theme=0.15.3  # min
   - pip
   - pip:
-    - sphinxext-opengraph==0.4.2  # min
+    - sphinxext-opengraph==0.9.1  # min
+    - sphinxcontrib-sass==0.3.4  # min
diff --git a/build_tools/circle/doc_min_dependencies_linux-64_conda.lock b/build_tools/circle/doc_min_dependencies_linux-64_conda.lock
index 3bc6b95df2fe4..3708a86ff6a7a 100644
--- a/build_tools/circle/doc_min_dependencies_linux-64_conda.lock
+++ b/build_tools/circle/doc_min_dependencies_linux-64_conda.lock
@@ -1,30 +1,30 @@
 # Generated by conda-lock.
 # platform: linux-64
-# input_hash: 32601810330a8200864f7908d07d870a3a58931be4f833691b2b5c7937f2d330
+# input_hash: aa64e81a701c97b7c4cf149f108c3ca59fc65572bfda79dbaeb2d093afc8a665
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-https://conda.anaconda.org/conda-forge/noarch/font-ttf-ubuntu-0.83-h77eed37_1.conda#6185f640c43843e5ad6fd1c5372c3f80
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-https://conda.anaconda.org/conda-forge/linux-64/binutils-2.40-hdd6e379_0.conda#ccc940fddbc3fcd3d79cd4c654c4b5c4
+https://conda.anaconda.org/conda-forge/linux-64/binutils-2.40-h4852527_1.conda#dfaea5684bbbbf0b64a4c31f984d0661
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-https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-13.2.0-h807b86a_5.conda#d4ff227c46917d3b4565302a2bbb276b
+https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-13.2.0-h77fa898_7.conda#72ec1b1b04c4d15d4204ece1ecea5978
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@@ -39,23 +39,23 @@ https://conda.anaconda.org/conda-forge/linux-64/libdeflate-1.20-hd590300_0.conda
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-https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-13.2.0-ha4646dd_5.conda#7a6bd7a12a4bd359e2afe6c0fa1acace
+https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-13.2.0-hca663fb_7.conda#c0bd771f09a326fdcd95a60b617795bf
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-https://conda.anaconda.org/conda-forge/linux-64/libsanitizer-12.3.0-h0f45ef3_5.conda#11d1ceacff40054d5a74b12975d76f20
+https://conda.anaconda.org/conda-forge/linux-64/libsanitizer-12.3.0-hb8811af_7.conda#ee573415c47ce17f65101d0b3fba396d
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-https://conda.anaconda.org/conda-forge/linux-64/libzlib-1.2.13-hd590300_5.conda#f36c115f1ee199da648e0597ec2047ad
+https://conda.anaconda.org/conda-forge/linux-64/libzlib-1.2.13-h4ab18f5_6.conda#27329162c0dc732bcf67a4e0cd488125
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-https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.4.20240210-h59595ed_0.conda#97da8860a0da5413c7c98a3b3838a645
-https://conda.anaconda.org/conda-forge/linux-64/ninja-1.11.1-h924138e_0.conda#73a4953a2d9c115bdc10ff30a52f675f
+https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.5-h59595ed_0.conda#fcea371545eda051b6deafb24889fc69
+https://conda.anaconda.org/conda-forge/linux-64/ninja-1.12.1-h297d8ca_0.conda#3aa1c7e292afeff25a0091ddd7c69b72
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-https://conda.anaconda.org/conda-forge/linux-64/openssl-3.2.1-hd590300_1.conda#9d731343cff6ee2e5a25c4a091bf8e2a
+https://conda.anaconda.org/conda-forge/linux-64/openssl-3.3.0-h4ab18f5_3.conda#12ea6d0d4ed54530eaed18e4835c1f7c
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@@ -69,40 +69,40 @@ https://conda.anaconda.org/conda-forge/linux-64/xorg-xproto-7.0.31-h7f98852_1007
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+https://conda.anaconda.org/conda-forge/linux-64/gcc_impl_linux-64-12.3.0-h58ffeeb_7.conda#95f78565a09852783d3e90e0389cfa5f
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-https://conda.anaconda.org/conda-forge/linux-64/cxx-compiler-1.7.0-h00ab1b0_0.conda#b4537c98cb59f8725b0e1e65816b4a28
+https://conda.anaconda.org/conda-forge/linux-64/cxx-compiler-1.7.0-h00ab1b0_1.conda#28de2e073db9ca9b72858bee9fb6f571
 https://conda.anaconda.org/conda-forge/linux-64/cytoolz-0.12.3-py39hd1e30aa_0.conda#dc0fb8e157c7caba4c98f1e1f9d2e5f4
-https://conda.anaconda.org/conda-forge/linux-64/fortran-compiler-1.7.0-heb67821_0.conda#7ef7c0f111dad1c8006504a0f1ccd820
-https://conda.anaconda.org/conda-forge/linux-64/glib-2.80.0-hf2295e7_4.conda#5521382ee30b96b35eb0037fc3c4f2b4
+https://conda.anaconda.org/conda-forge/linux-64/fortran-compiler-1.7.0-heb67821_1.conda#cf4b0e7c4c78bb0662aed9b27c414a3c
+https://conda.anaconda.org/conda-forge/linux-64/glib-2.80.2-hf974151_0.conda#d427988dc3dbd0a4c136f52db356cc6a
 https://conda.anaconda.org/conda-forge/noarch/importlib-metadata-7.1.0-pyha770c72_0.conda#0896606848b2dc5cebdf111b6543aa04
-https://conda.anaconda.org/conda-forge/noarch/jinja2-3.1.3-pyhd8ed1ab_0.conda#e7d8df6509ba635247ff9aea31134262
-https://conda.anaconda.org/conda-forge/noarch/joblib-1.4.0-pyhd8ed1ab_0.conda#e0ed1bf13ce3a440e022157bf4764465
+https://conda.anaconda.org/conda-forge/noarch/jinja2-3.1.4-pyhd8ed1ab_0.conda#7b86ecb7d3557821c649b3c31e3eb9f2
+https://conda.anaconda.org/conda-forge/noarch/joblib-1.4.2-pyhd8ed1ab_0.conda#25df261d4523d9f9783bcdb7208d872f
 https://conda.anaconda.org/conda-forge/linux-64/libgcrypt-1.10.3-hd590300_0.conda#32d16ad533c59bb0a3c5ffaf16110829
 https://conda.anaconda.org/conda-forge/linux-64/libsndfile-1.2.2-hc60ed4a_1.conda#ef1910918dd895516a769ed36b5b3a4e
 https://conda.anaconda.org/conda-forge/linux-64/libxkbcommon-1.7.0-h662e7e4_0.conda#b32c0da42b1f24a98577bb3d7fc0b995
 https://conda.anaconda.org/conda-forge/noarch/memory_profiler-0.61.0-pyhd8ed1ab_0.tar.bz2#8b45f9f2b2f7a98b0ec179c8991a4a9b
 https://conda.anaconda.org/conda-forge/noarch/meson-1.4.0-pyhd8ed1ab_0.conda#52a0660cfa40b45bf254ecc3374cb2e0
-https://conda.anaconda.org/conda-forge/linux-64/mkl-2024.1.0-ha957f24_692.conda#e7f5c5cda17c6f5047db27d44367c19d
-https://conda.anaconda.org/conda-forge/noarch/partd-1.4.1-pyhd8ed1ab_0.conda#acf4b7c0bcd5fa3b0e05801c4d2accd6
+https://conda.anaconda.org/conda-forge/linux-64/mkl-2024.1.0-ha957f24_693.conda#ff0f4abf6f94e36a918f1ef4dbeb9769
+https://conda.anaconda.org/conda-forge/noarch/partd-1.4.2-pyhd8ed1ab_0.conda#0badf9c54e24cecfb0ad2f99d680c163
 https://conda.anaconda.org/conda-forge/linux-64/pillow-10.3.0-py39h90c7501_0.conda#1e3b6af9592be71ce19f0a6aae05d97b
 https://conda.anaconda.org/conda-forge/noarch/pip-24.0-pyhd8ed1ab_0.conda#f586ac1e56c8638b64f9c8122a7b8a67
 https://conda.anaconda.org/conda-forge/noarch/plotly-5.14.0-pyhd8ed1ab_0.conda#6a7bcc42ef58dd6cf3da9333ea102433
-https://conda.anaconda.org/conda-forge/noarch/pyproject-metadata-0.7.1-pyhd8ed1ab_0.conda#dcb27826ffc94d5f04e241322239983b
-https://conda.anaconda.org/conda-forge/noarch/pytest-7.4.4-pyhd8ed1ab_0.conda#a9d145de8c5f064b5fa68fb34725d9f4
+https://conda.anaconda.org/conda-forge/noarch/pyproject-metadata-0.8.0-pyhd8ed1ab_0.conda#573fe09d7bd0cd4bcc210d8369b5ca47
+https://conda.anaconda.org/conda-forge/noarch/pytest-8.2.1-pyhd8ed1ab_0.conda#e4418e8bdbaa8eea28e047531e6763c8
 https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0-pyhd8ed1ab_0.conda#2cf4264fffb9e6eff6031c5b6884d61c
 https://conda.anaconda.org/conda-forge/linux-64/sip-6.7.12-py39h3d6467e_0.conda#e667a3ab0df62c54e60e1843d2e6defb
 https://conda.anaconda.org/conda-forge/noarch/urllib3-2.2.1-pyhd8ed1ab_0.conda#08807a87fa7af10754d46f63b368e016
-https://conda.anaconda.org/conda-forge/linux-64/compilers-1.7.0-ha770c72_0.conda#81458b3aed8ab8711951ec3c0c04e097
-https://conda.anaconda.org/conda-forge/linux-64/gstreamer-1.24.1-h98fc4e7_1.conda#b04b5cdf3ba01430db27979250bc5a1d
-https://conda.anaconda.org/conda-forge/linux-64/harfbuzz-8.3.0-h3d44ed6_0.conda#5a6f6c00ef982a9bc83558d9ac8f64a0
+https://conda.anaconda.org/conda-forge/linux-64/compilers-1.7.0-ha770c72_1.conda#d8d07866ac3b5b6937213c89a1874f08
+https://conda.anaconda.org/conda-forge/linux-64/gstreamer-1.24.4-haf2f30d_0.conda#926c2c7ee7a0b48d6d70783a33f7bc80
+https://conda.anaconda.org/conda-forge/linux-64/harfbuzz-8.5.0-hfac3d4d_0.conda#f5126317dd0ce0ba26945e411ecc6960
 https://conda.anaconda.org/conda-forge/noarch/importlib_metadata-7.1.0-hd8ed1ab_0.conda#6ef2b72d291b39e479d7694efa2b2b98
 https://conda.anaconda.org/conda-forge/linux-64/libblas-3.9.0-22_linux64_mkl.conda#eb6deb4ba6f92ea3f31c09cb8b764738
 https://conda.anaconda.org/conda-forge/linux-64/libsystemd0-255-h3516f8a_1.conda#3366af27f0b593544a6cd453c7932ac5
-https://conda.anaconda.org/conda-forge/noarch/meson-python-0.15.0-pyh0c530f3_0.conda#3bc64565ca78ce3bb80248d09926d8f9
-https://conda.anaconda.org/conda-forge/linux-64/mkl-devel-2024.1.0-ha770c72_692.conda#56142862a71bcfdd6ef2ce95c8e90755
+https://conda.anaconda.org/conda-forge/noarch/meson-python-0.16.0-pyh0c530f3_0.conda#e16f0dbf502da873be9f9adb0dc52547
+https://conda.anaconda.org/conda-forge/linux-64/mkl-devel-2024.1.0-ha770c72_693.conda#7f422e2cf549a3fb920c95288393870d
 https://conda.anaconda.org/conda-forge/linux-64/pyqt5-sip-12.12.2-py39h3d6467e_5.conda#93aff412f3e49fdb43361c0215cbd72d
 https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-3.5.0-pyhd8ed1ab_0.conda#d5f595da2daead898ca958ac62f0307b
-https://conda.anaconda.org/conda-forge/noarch/requests-2.31.0-pyhd8ed1ab_0.conda#a30144e4156cdbb236f99ebb49828f8b
-https://conda.anaconda.org/conda-forge/noarch/dask-core-2024.4.1-pyhd8ed1ab_0.conda#52387f00fee8dcd5cf75f8886025293f
-https://conda.anaconda.org/conda-forge/linux-64/gst-plugins-base-1.24.1-hfa15dee_1.conda#a6dd2bbc684913e2bef0a54ce56fcbfb
+https://conda.anaconda.org/conda-forge/noarch/requests-2.32.2-pyhd8ed1ab_0.conda#e1643b34b19df8c028a4f00bf5df58a6
+https://conda.anaconda.org/conda-forge/noarch/dask-core-2024.5.1-pyhd8ed1ab_0.conda#d4f60ccc5421472d2583efd9ce39d8b1
+https://conda.anaconda.org/conda-forge/linux-64/gst-plugins-base-1.24.4-h9ad1361_0.conda#147cce520ec59367549fd0d96d404213
 https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.9.0-22_linux64_mkl.conda#d6f942423116553f068b2f2d93ffea2e
 https://conda.anaconda.org/conda-forge/linux-64/liblapack-3.9.0-22_linux64_mkl.conda#4edf2e7ce63920e4f539d12e32fb478e
-https://conda.anaconda.org/conda-forge/noarch/pooch-1.8.1-pyhd8ed1ab_0.conda#d15917f33140f8d2ac9ca44db7ec8a25
+https://conda.anaconda.org/conda-forge/noarch/pooch-1.6.0-pyhd8ed1ab_0.tar.bz2#6429e1d1091c51f626b5dcfdd38bf429
 https://conda.anaconda.org/conda-forge/linux-64/pulseaudio-client-17.0-hb77b528_0.conda#07f45f1be1c25345faddb8db0de8039b
 https://conda.anaconda.org/conda-forge/linux-64/liblapacke-3.9.0-22_linux64_mkl.conda#aa0a5a70e1c957d5911e76ac98e471e1
 https://conda.anaconda.org/conda-forge/linux-64/numpy-1.19.5-py39hd249d9e_3.tar.bz2#0cf333996ebdeeba8d1c8c1c0ee9eff9
 https://conda.anaconda.org/conda-forge/linux-64/qt-main-5.15.8-hc9dc06e_21.conda#b325046180590c868ce0dbf267b82eb8
 https://conda.anaconda.org/conda-forge/linux-64/blas-devel-3.9.0-22_linux64_mkl.conda#3cb0e51433c88d2f4cdfb50c5c08a683
-https://conda.anaconda.org/conda-forge/linux-64/imagecodecs-lite-2019.12.3-py39hd257fcd_5.tar.bz2#32dba66d6abc2b4b5b019c9e54307312
-https://conda.anaconda.org/conda-forge/noarch/imageio-2.34.0-pyh4b66e23_0.conda#b8853659d596f967c661f544dd89ede7
+https://conda.anaconda.org/conda-forge/linux-64/imagecodecs-lite-2019.12.3-py39hd92a3bb_8.conda#5eb64443d4d973c31e179a498e1bb4a2
+https://conda.anaconda.org/conda-forge/noarch/imageio-2.34.1-pyh4b66e23_0.conda#bcf6a6f4c6889ca083e8d33afbafb8d5
 https://conda.anaconda.org/conda-forge/linux-64/matplotlib-base-3.3.4-py39h2fa2bec_0.tar.bz2#9ec0b2186fab9121c54f4844f93ee5b7
 https://conda.anaconda.org/conda-forge/linux-64/pandas-1.1.5-py39hde0f152_0.tar.bz2#79fc4b5b3a865b90dd3701cecf1ad33c
 https://conda.anaconda.org/conda-forge/noarch/patsy-0.5.6-pyhd8ed1ab_0.conda#a5b55d1cb110cdcedc748b5c3e16e687
-https://conda.anaconda.org/conda-forge/linux-64/polars-0.19.12-py39h90d8ae4_0.conda#191828961c95f8d59fa2b86a590f9905
+https://conda.anaconda.org/conda-forge/linux-64/polars-0.20.23-py39ha963410_0.conda#4871f09d653e979d598d2d4cd5fa868d
 https://conda.anaconda.org/conda-forge/linux-64/pyqt-5.15.9-py39h52134e7_5.conda#e1f148e57d071b09187719df86f513c1
 https://conda.anaconda.org/conda-forge/linux-64/pywavelets-1.3.0-py39hd257fcd_1.tar.bz2#c4b698994b2d8d2e659ae02202e6abe4
 https://conda.anaconda.org/conda-forge/linux-64/scipy-1.6.0-py39hee8e79c_0.tar.bz2#3afcb78281836e61351a2924f3230060
@@ -236,13 +239,18 @@ https://conda.anaconda.org/conda-forge/noarch/tifffile-2020.6.3-py_0.tar.bz2#1fb
 https://conda.anaconda.org/conda-forge/linux-64/scikit-image-0.17.2-py39hde0f152_4.tar.bz2#2a58a7e382317b03f023b2fddf40f8a1
 https://conda.anaconda.org/conda-forge/noarch/seaborn-0.12.2-hd8ed1ab_0.conda#50847a47c07812f88581081c620f5160
 https://conda.anaconda.org/conda-forge/noarch/numpydoc-1.2-pyhd8ed1ab_0.tar.bz2#025ad7ca2c7f65007ab6b6f5d93a56eb
+https://conda.anaconda.org/conda-forge/noarch/pydata-sphinx-theme-0.15.3-pyhd8ed1ab_0.conda#55e445f4fcb07f2471fb0e1102d36488
 https://conda.anaconda.org/conda-forge/noarch/sphinx-copybutton-0.5.2-pyhd8ed1ab_0.conda#ac832cc43adc79118cf6e23f1f9b8995
-https://conda.anaconda.org/conda-forge/noarch/sphinx-gallery-0.15.0-pyhd8ed1ab_0.conda#1a49ca9515ef9a96edff2eea06143dc6
-https://conda.anaconda.org/conda-forge/noarch/sphinx-prompt-1.3.0-py_0.tar.bz2#9363002e2a134a287af4e32ff0f26cdc
+https://conda.anaconda.org/conda-forge/noarch/sphinx-design-0.5.0-pyhd8ed1ab_0.conda#264b3c697fa9cdade87eb0abe4440d54
+https://conda.anaconda.org/conda-forge/noarch/sphinx-gallery-0.16.0-pyhd8ed1ab_0.conda#add28691ee89e875b190eda07929d5d4
+https://conda.anaconda.org/conda-forge/noarch/sphinx-prompt-1.4.0-pyhd8ed1ab_0.tar.bz2#88ee91e8679603f2a5bd036d52919cc2
+https://conda.anaconda.org/conda-forge/noarch/sphinx-remove-toctrees-1.0.0.post1-pyhd8ed1ab_0.conda#6dee8412218288a17f99f2cfffab334d
 https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-applehelp-1.0.8-pyhd8ed1ab_0.conda#611a35a27914fac3aa37611a6fe40bb5
 https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-devhelp-1.0.6-pyhd8ed1ab_0.conda#d7e4954df0d3aea2eacc7835ad12671d
 https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-htmlhelp-2.0.5-pyhd8ed1ab_0.conda#7e1e7437273682ada2ed5e9e9714b140
 https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-qthelp-1.0.7-pyhd8ed1ab_0.conda#26acae54b06f178681bfb551760f5dd1
-https://conda.anaconda.org/conda-forge/noarch/sphinx-6.0.0-pyhd8ed1ab_2.conda#ac1d3b55da1669ee3a56973054fd7efb
+https://conda.anaconda.org/conda-forge/noarch/sphinx-7.3.7-pyhd8ed1ab_0.conda#7b1465205e28d75d2c0e1a868ee00a67
 https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-serializinghtml-1.1.10-pyhd8ed1ab_0.conda#e507335cb4ca9cff4c3d0fa9cdab255e
-# pip sphinxext-opengraph @ https://files.pythonhosted.org/packages/50/ac/c105ed3e0a00b14b28c0aa630935af858fd8a32affeff19574b16e2c6ae8/sphinxext_opengraph-0.4.2-py3-none-any.whl#sha256=a51f2604f9a5b6c0d25d3a88e694d5c02e20812dc0e482adf96c8628f9109357
+# pip libsass @ https://files.pythonhosted.org/packages/fd/5a/eb5b62641df0459a3291fc206cf5bd669c0feed7814dded8edef4ade8512/libsass-0.23.0-cp38-abi3-manylinux_2_5_x86_64.manylinux1_x86_64.whl#sha256=4a218406d605f325d234e4678bd57126a66a88841cb95bee2caeafdc6f138306
+# pip sphinxcontrib-sass @ https://files.pythonhosted.org/packages/2e/87/7c2eb08e3ca1d6baae32c0a5e005330fe1cec93a36aa085e714c3b3a3c7d/sphinxcontrib_sass-0.3.4-py2.py3-none-any.whl#sha256=a0c79a44ae8b8935c02dc340ebe40c9e002c839331201c899dc93708970c355a
+# pip sphinxext-opengraph @ https://files.pythonhosted.org/packages/92/0a/970b80b4fa1feeb6deb6f2e22d4cb14e388b27b315a1afdb9db930ff91a4/sphinxext_opengraph-0.9.1-py3-none-any.whl#sha256=b3b230cc6a5b5189139df937f0d9c7b23c7c204493b22646273687969dcb760e
diff --git a/build_tools/circle/list_versions.py b/build_tools/circle/list_versions.py
index 345e08b4bece4..e1f8d54b84ec5 100755
--- a/build_tools/circle/list_versions.py
+++ b/build_tools/circle/list_versions.py
@@ -1,6 +1,11 @@
 #!/usr/bin/env python3
 
-# List all available versions of the documentation
+# Write the available versions page (--rst) and the version switcher JSON (--json).
+# Version switcher see:
+# https://pydata-sphinx-theme.readthedocs.io/en/stable/user_guide/version-dropdown.html
+# https://pydata-sphinx-theme.readthedocs.io/en/stable/user_guide/announcements.html#announcement-banners
+
+import argparse
 import json
 import re
 import sys
@@ -52,14 +57,19 @@ def get_file_size(version):
             return human_readable_data_quantity(path_details["size"], 1000)
 
 
-print(":orphan:")
-print()
-heading = "Available documentation for Scikit-learn"
-print(heading)
-print("=" * len(heading))
-print()
-print("Web-based documentation is available for versions listed below:")
-print()
+parser = argparse.ArgumentParser()
+parser.add_argument("--rst", type=str, required=True)
+parser.add_argument("--json", type=str, required=True)
+args = parser.parse_args()
+
+heading = "Available documentation for scikit-learn"
+json_content = []
+rst_content = [
+    ":orphan:\n",
+    heading,
+    "=" * len(heading) + "\n",
+    "Web-based documentation is available for versions listed below:\n",
+]
 
 ROOT_URL = (
     "https://api.github.com/repos/scikit-learn/scikit-learn.github.io/contents/"  # noqa
@@ -93,8 +103,9 @@ def get_file_size(version):
 
 # Output in order: dev, stable, decreasing other version
 seen = set()
-for name in NAMED_DIRS + sorted(
-    (k for k in dirs if k[:1].isdigit()), key=parse_version, reverse=True
+for i, name in enumerate(
+    NAMED_DIRS
+    + sorted((k for k in dirs if k[:1].isdigit()), key=parse_version, reverse=True)
 ):
     version_num, file_size = dirs[name]
     if version_num in seen:
@@ -102,17 +113,32 @@ def get_file_size(version):
         continue
     else:
         seen.add(version_num)
-    name_display = "" if name[:1].isdigit() else " (%s)" % name
-    path = "https://scikit-learn.org/%s/" % name
-    out = "* `Scikit-learn %s%s documentation <%s>`_" % (
-        version_num,
-        name_display,
-        path,
-    )
+
+    full_name = f"{version_num}" if name[:1].isdigit() else f"{version_num} ({name})"
+    path = f"https://scikit-learn.org/{name}/"
+
+    # Update JSON for the version switcher; only keep the 8 latest versions to avoid
+    # overloading the version switcher dropdown
+    if i < 8:
+        info = {"name": full_name, "version": version_num, "url": path}
+        if name == "stable":
+            info["preferred"] = True
+        json_content.append(info)
+
+    # Printout for the historical version page
+    out = f"* `scikit-learn {full_name} documentation <{path}>`_"
     if file_size is not None:
         file_extension = get_file_extension(version_num)
         out += (
             f" (`{file_extension.upper()} {file_size} <{path}/"
             f"_downloads/scikit-learn-docs.{file_extension}>`_)"
         )
-    print(out)
+    rst_content.append(out)
+
+with open(args.rst, "w", encoding="utf-8") as f:
+    f.write("\n".join(rst_content) + "\n")
+print(f"Written {args.rst}")
+
+with open(args.json, "w", encoding="utf-8") as f:
+    json.dump(json_content, f, indent=2)
+print(f"Written {args.json}")
diff --git a/build_tools/cirrus/pymin_conda_forge_environment.yml b/build_tools/cirrus/pymin_conda_forge_environment.yml
index 684c4636daad4..e41cc7f610ac0 100644
--- a/build_tools/cirrus/pymin_conda_forge_environment.yml
+++ b/build_tools/cirrus/pymin_conda_forge_environment.yml
@@ -12,7 +12,7 @@ dependencies:
   - joblib
   - threadpoolctl
   - matplotlib
-  - pytest<8
+  - pytest
   - pytest-xdist
   - pillow
   - pip
diff --git a/build_tools/cirrus/pymin_conda_forge_linux-aarch64_conda.lock b/build_tools/cirrus/pymin_conda_forge_linux-aarch64_conda.lock
index 62e2d83af5c45..5d0c005fbdb17 100644
--- a/build_tools/cirrus/pymin_conda_forge_linux-aarch64_conda.lock
+++ b/build_tools/cirrus/pymin_conda_forge_linux-aarch64_conda.lock
@@ -1,48 +1,48 @@
 # Generated by conda-lock.
 # platform: linux-aarch64
-# input_hash: 80459c6003cbcd22780a22a62ed5cc116e951d5c2c14602af1281434263b9138
+# input_hash: 2d8c526ab7c0c2f0ca509bfec3f035e5bd33b8096f194f0747f167c8aff66383
 @EXPLICIT
 https://conda.anaconda.org/conda-forge/linux-aarch64/ca-certificates-2024.2.2-hcefe29a_0.conda#57c226edb90c4e973b9b7503537dd339
-https://conda.anaconda.org/conda-forge/linux-aarch64/ld_impl_linux-aarch64-2.40-h2d8c526_0.conda#16246d69e945d0b1969a6099e7c5d457
-https://conda.anaconda.org/conda-forge/linux-aarch64/libstdcxx-ng-13.2.0-h9a76618_5.conda#1b79d37dce0fad96bdf3de03925f43b4
+https://conda.anaconda.org/conda-forge/linux-aarch64/ld_impl_linux-aarch64-2.40-h9fc2d93_1.conda#8a90b17a5d12ba55b7d90534f38579d0
+https://conda.anaconda.org/conda-forge/linux-aarch64/libstdcxx-ng-13.2.0-h3f4de04_7.conda#2a54872c7fab2db99b0074212d8efe64
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-https://conda.anaconda.org/conda-forge/linux-aarch64/libgcc-ng-13.2.0-hf8544c7_5.conda#dee934e640275d9e74e7bbd455f25162
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+https://conda.anaconda.org/conda-forge/linux-aarch64/libgfortran5-13.2.0-h87d9d71_7.conda#423eb7de085dd6b46928723edf5f8767
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-https://conda.anaconda.org/conda-forge/linux-aarch64/libwebp-base-1.3.2-h31becfc_1.conda#675c1f4aa320704b899f4eb350a69418
+https://conda.anaconda.org/conda-forge/linux-aarch64/libwebp-base-1.4.0-h31becfc_0.conda#5fd7ab3e5f382c70607fbac6335e6e19
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-https://conda.anaconda.org/conda-forge/linux-aarch64/ncurses-6.4.20240210-h0425590_0.conda#c1a1612ddaee95c83abfa0b2ec858626
-https://conda.anaconda.org/conda-forge/linux-aarch64/ninja-1.11.1-hdd96247_0.conda#58f4c67113cda9171e3c03d3e62731e1
-https://conda.anaconda.org/conda-forge/linux-aarch64/openssl-3.2.1-h31becfc_1.conda#e95eb18d256edc72058e0dc9be5338a0
+https://conda.anaconda.org/conda-forge/linux-aarch64/ncurses-6.5-h0425590_0.conda#38362af7bfac0efef69675acee564458
+https://conda.anaconda.org/conda-forge/linux-aarch64/ninja-1.12.1-h70be974_0.conda#216635cea46498d8045c7cf0f03eaf72
+https://conda.anaconda.org/conda-forge/linux-aarch64/openssl-3.3.0-h68df207_3.conda#f2dec4814ac9649cd3d3e474cd0b9a58
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-https://conda.anaconda.org/conda-forge/linux-aarch64/libgfortran-ng-13.2.0-he9431aa_5.conda#fab7c6a8c84492e18cbe578820e97a56
+https://conda.anaconda.org/conda-forge/linux-aarch64/libgfortran-ng-13.2.0-he9431aa_7.conda#d714db6ba9d67d55d21cf96316714ec8
 https://conda.anaconda.org/conda-forge/linux-aarch64/libpng-1.6.43-h194ca79_0.conda#1123e504d9254dd9494267ab9aba95f0
-https://conda.anaconda.org/conda-forge/linux-aarch64/libsqlite-3.45.2-h194ca79_0.conda#bf4c96a21fbfc6a6ef6a7781a534a4e0
+https://conda.anaconda.org/conda-forge/linux-aarch64/libsqlite-3.45.3-h194ca79_0.conda#fb35b8afbe9e92467ac7b5608d60b775
 https://conda.anaconda.org/conda-forge/linux-aarch64/libxcb-1.15-h2a766a3_0.conda#eb3d8c8170e3d03f2564ed2024aa00c8
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-https://conda.anaconda.org/conda-forge/linux-aarch64/zstd-1.5.5-h4c53e97_0.conda#b74eb9dbb5c3c15cb3cee7cbdf198c75
+https://conda.anaconda.org/conda-forge/linux-aarch64/zstd-1.5.6-h02f22dd_0.conda#be8d5f8cf21aed237b8b182ea86b3dd6
 https://conda.anaconda.org/conda-forge/linux-aarch64/brotli-bin-1.1.0-h31becfc_1.conda#9e4a13596ab651ea8d77aae023d0ce3f
 https://conda.anaconda.org/conda-forge/linux-aarch64/freetype-2.12.1-hf0a5ef3_2.conda#a5ab74c5bd158c3d5532b66d8d83d907
 https://conda.anaconda.org/conda-forge/linux-aarch64/libhiredis-1.0.2-h05efe27_0.tar.bz2#a87f068744fd20334cd41489eb163bee
 https://conda.anaconda.org/conda-forge/linux-aarch64/libopenblas-0.3.27-pthreads_h5a5ec62_0.conda#ffecca8f4f31cd50b92c0e6e6bfe4416
 https://conda.anaconda.org/conda-forge/linux-aarch64/libtiff-4.6.0-hf980d43_3.conda#b6f3abf5726ae33094bee238b4eb492f
-https://conda.anaconda.org/conda-forge/linux-aarch64/llvm-openmp-18.1.3-h8b0cb96_0.conda#cd4d2b7580dd020814ea34ebbbca8c5e
+https://conda.anaconda.org/conda-forge/linux-aarch64/llvm-openmp-18.1.6-h767c9be_0.conda#67c8fa683c39feeae5d0e915d79fb061
 https://conda.anaconda.org/conda-forge/linux-aarch64/python-3.9.19-h4ac3b42_0_cpython.conda#1501507cd9451472ec8900d587ce872f
 https://conda.anaconda.org/conda-forge/linux-aarch64/brotli-1.1.0-h31becfc_1.conda#e41f5862ac746428407f3fd44d2ed01f
 https://conda.anaconda.org/conda-forge/linux-aarch64/ccache-4.9.1-h6552966_0.conda#758b202f61f6bbfd2c6adf0fde043276
@@ -60,35 +60,35 @@ https://conda.anaconda.org/conda-forge/noarch/munkres-1.1.4-pyh9f0ad1d_0.tar.bz2
 https://conda.anaconda.org/conda-forge/linux-aarch64/openblas-0.3.27-pthreads_h339cbfa_0.conda#cb06c34a3056f59e9e244c20836add8a
 https://conda.anaconda.org/conda-forge/linux-aarch64/openjpeg-2.5.2-h0d9d63b_0.conda#fd2898519e839d5ceb778343f39a3176
 https://conda.anaconda.org/conda-forge/noarch/packaging-24.0-pyhd8ed1ab_0.conda#248f521b64ce055e7feae3105e7abeb8
-https://conda.anaconda.org/conda-forge/noarch/pluggy-1.4.0-pyhd8ed1ab_0.conda#139e9feb65187e916162917bb2484976
+https://conda.anaconda.org/conda-forge/noarch/pluggy-1.5.0-pyhd8ed1ab_0.conda#d3483c8fc2dc2cc3f5cf43e26d60cabf
 https://conda.anaconda.org/conda-forge/noarch/pyparsing-3.1.2-pyhd8ed1ab_0.conda#b9a4dacf97241704529131a0dfc0494f
-https://conda.anaconda.org/conda-forge/noarch/setuptools-69.5.1-pyhd8ed1ab_0.conda#7462280d81f639363e6e63c81276bd9e
+https://conda.anaconda.org/conda-forge/noarch/setuptools-70.0.0-pyhd8ed1ab_0.conda#c8ddb4f34a208df4dd42509a0f6a1c89
 https://conda.anaconda.org/conda-forge/noarch/six-1.16.0-pyh6c4a22f_0.tar.bz2#e5f25f8dbc060e9a8d912e432202afc2
-https://conda.anaconda.org/conda-forge/noarch/threadpoolctl-3.4.0-pyhc1e730c_0.conda#b296278eef667c673bf51de6535bad88
+https://conda.anaconda.org/conda-forge/noarch/threadpoolctl-3.5.0-pyhc1e730c_0.conda#df68d78237980a159bd7149f33c0e8fd
 https://conda.anaconda.org/conda-forge/noarch/tomli-2.0.1-pyhd8ed1ab_0.tar.bz2#5844808ffab9ebdb694585b50ba02a96
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 https://conda.anaconda.org/conda-forge/linux-aarch64/unicodedata2-15.1.0-py39h898b7ef_0.conda#8c072c9329aeea97a46005625267a851
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-https://conda.anaconda.org/conda-forge/linux-aarch64/fonttools-4.51.0-py39h898b7ef_0.conda#7b6a069c66a729454fb4c534ed145dcd
+https://conda.anaconda.org/conda-forge/linux-aarch64/fonttools-4.52.1-py39he257ee7_0.conda#06188260ef5320c7d6eb4f1467224bb0
 https://conda.anaconda.org/conda-forge/noarch/importlib_resources-6.4.0-pyhd8ed1ab_0.conda#c5d3907ad8bd7bf557521a1833cf7e6d
-https://conda.anaconda.org/conda-forge/noarch/joblib-1.4.0-pyhd8ed1ab_0.conda#e0ed1bf13ce3a440e022157bf4764465
+https://conda.anaconda.org/conda-forge/noarch/joblib-1.4.2-pyhd8ed1ab_0.conda#25df261d4523d9f9783bcdb7208d872f
 https://conda.anaconda.org/conda-forge/linux-aarch64/libcblas-3.9.0-22_linuxaarch64_openblas.conda#fbe7fe553f2cc78a0311e009b26f180d
 https://conda.anaconda.org/conda-forge/linux-aarch64/liblapack-3.9.0-22_linuxaarch64_openblas.conda#8c709d281609792c39b1d5c0241f90f1
 https://conda.anaconda.org/conda-forge/noarch/meson-1.4.0-pyhd8ed1ab_0.conda#52a0660cfa40b45bf254ecc3374cb2e0
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 https://conda.anaconda.org/conda-forge/noarch/pip-24.0-pyhd8ed1ab_0.conda#f586ac1e56c8638b64f9c8122a7b8a67
-https://conda.anaconda.org/conda-forge/noarch/pyproject-metadata-0.7.1-pyhd8ed1ab_0.conda#dcb27826ffc94d5f04e241322239983b
-https://conda.anaconda.org/conda-forge/noarch/pytest-7.4.4-pyhd8ed1ab_0.conda#a9d145de8c5f064b5fa68fb34725d9f4
+https://conda.anaconda.org/conda-forge/noarch/pyproject-metadata-0.8.0-pyhd8ed1ab_0.conda#573fe09d7bd0cd4bcc210d8369b5ca47
+https://conda.anaconda.org/conda-forge/noarch/pytest-8.2.1-pyhd8ed1ab_0.conda#e4418e8bdbaa8eea28e047531e6763c8
 https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0-pyhd8ed1ab_0.conda#2cf4264fffb9e6eff6031c5b6884d61c
 https://conda.anaconda.org/conda-forge/noarch/importlib-resources-6.4.0-pyhd8ed1ab_0.conda#dcbadab7a68738a028e195ab68ab2d2e
 https://conda.anaconda.org/conda-forge/linux-aarch64/liblapacke-3.9.0-22_linuxaarch64_openblas.conda#5acf669e0be669f30f4b813d2ecda7b8
-https://conda.anaconda.org/conda-forge/noarch/meson-python-0.15.0-pyh0c530f3_0.conda#3bc64565ca78ce3bb80248d09926d8f9
+https://conda.anaconda.org/conda-forge/noarch/meson-python-0.16.0-pyh0c530f3_0.conda#e16f0dbf502da873be9f9adb0dc52547
 https://conda.anaconda.org/conda-forge/linux-aarch64/numpy-1.26.4-py39h91c28bb_0.conda#d88e195f11a9f27e649aea408b54cb48
 https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-3.5.0-pyhd8ed1ab_0.conda#d5f595da2daead898ca958ac62f0307b
 https://conda.anaconda.org/conda-forge/linux-aarch64/blas-devel-3.9.0-22_linuxaarch64_openblas.conda#a5b77b6c6807661afd716f33e85814b3
 https://conda.anaconda.org/conda-forge/linux-aarch64/contourpy-1.2.1-py39hd16970a_0.conda#66b9718539ecdd38876b0176c315bcad
-https://conda.anaconda.org/conda-forge/linux-aarch64/scipy-1.13.0-py39h91c28bb_0.conda#2b6f1ed053a61c2447304e4b810fc397
+https://conda.anaconda.org/conda-forge/linux-aarch64/scipy-1.13.1-py39hb921187_0.conda#1aac9080de661e03d286f18fb71e5240
 https://conda.anaconda.org/conda-forge/linux-aarch64/blas-2.122-openblas.conda#65bc48b3bc85f8eeeab54311443a83aa
-https://conda.anaconda.org/conda-forge/linux-aarch64/matplotlib-base-3.8.4-py39h8e43113_0.conda#f397ddfe5c551732de61a92106a14cf3
-https://conda.anaconda.org/conda-forge/linux-aarch64/matplotlib-3.8.4-py39ha65689a_0.conda#d501bb96ff505fdd431fd8fdac8efbf9
+https://conda.anaconda.org/conda-forge/linux-aarch64/matplotlib-base-3.8.4-py39hf44f4b6_2.conda#fadf734d38ed608c9f0b5c91fe79cfb4
+https://conda.anaconda.org/conda-forge/linux-aarch64/matplotlib-3.8.4-py39ha65689a_2.conda#c0472e3c4b3f007de6d643317c30963b
diff --git a/build_tools/generate_authors_table.py b/build_tools/generate_authors_table.py
index 28bb267b6f721..483dc3739506e 100644
--- a/build_tools/generate_authors_table.py
+++ b/build_tools/generate_authors_table.py
@@ -214,11 +214,13 @@ def generate_list(contributors):
         documentation_team,
     ) = get_contributors()
 
-    with open(REPO_FOLDER / "doc" / "authors.rst", "w+", encoding="utf-8") as rst_file:
+    with open(
+        REPO_FOLDER / "doc" / "maintainers.rst", "w+", encoding="utf-8"
+    ) as rst_file:
         rst_file.write(generate_table(core_devs))
 
     with open(
-        REPO_FOLDER / "doc" / "authors_emeritus.rst", "w+", encoding="utf-8"
+        REPO_FOLDER / "doc" / "maintainers_emeritus.rst", "w+", encoding="utf-8"
     ) as rst_file:
         rst_file.write(generate_list(emeritus))
 
diff --git a/build_tools/github/repair_windows_wheels.sh b/build_tools/github/repair_windows_wheels.sh
index cdd0c0c79d8c4..8f51a34d4039b 100755
--- a/build_tools/github/repair_windows_wheels.sh
+++ b/build_tools/github/repair_windows_wheels.sh
@@ -8,6 +8,7 @@ DEST_DIR=$2
 
 # By default, the Windows wheels are not repaired.
 # In this case, we need to vendor VCRUNTIME140.dll
+pip install wheel
 wheel unpack "$WHEEL"
 WHEEL_DIRNAME=$(ls -d scikit_learn-*)
 python build_tools/github/vendor.py "$WHEEL_DIRNAME"
diff --git a/build_tools/update_environments_and_lock_files.py b/build_tools/update_environments_and_lock_files.py
index fd77cfd3c0721..ce457cabb1e53 100644
--- a/build_tools/update_environments_and_lock_files.py
+++ b/build_tools/update_environments_and_lock_files.py
@@ -82,12 +82,7 @@
 
 docstring_test_dependencies = ["sphinx", "numpydoc"]
 
-default_package_constraints = {
-    # TODO: somehow pytest 8 does not seem to work with meson editable
-    # install. Exit code is 5, i.e. no test collected
-    # This would be fixed by https://github.com/mesonbuild/meson-python/pull/569
-    "pytest": "<8",
-}
+default_package_constraints = {}
 
 
 def remove_from(alist, to_remove):
@@ -141,7 +136,10 @@ def remove_from(alist, to_remove):
         "folder": "build_tools/azure",
         "platform": "osx-64",
         "channel": "defaults",
-        "conda_dependencies": remove_from(common_dependencies, ["cython"]) + ["ccache"],
+        "conda_dependencies": remove_from(
+            common_dependencies, ["cython", "threadpoolctl", "meson-python"]
+        )
+        + ["ccache"],
         "package_constraints": {
             "blas": "[build=mkl]",
             # scipy 1.12.x crashes on this platform (https://github.com/scipy/scipy/pull/20086)
@@ -149,9 +147,9 @@ def remove_from(alist, to_remove):
             # channel.
             "scipy": "<1.12",
         },
-        # TODO: put cython back to conda dependencies when required version is
-        # available on the main channel
-        "pip_dependencies": ["cython"],
+        # TODO: put cython, threadpoolctl and meson-python back to conda
+        # dependencies when required version is available on the main channel
+        "pip_dependencies": ["cython", "threadpoolctl", "meson-python"],
     },
     {
         "name": "pymin_conda_defaults_openblas",
@@ -161,7 +159,8 @@ def remove_from(alist, to_remove):
         "platform": "linux-64",
         "channel": "defaults",
         "conda_dependencies": remove_from(
-            common_dependencies, ["pandas", "cython", "pip", "ninja", "meson-python"]
+            common_dependencies,
+            ["pandas", "threadpoolctl", "pip", "ninja", "meson-python"],
         )
         + ["ccache"],
         "package_constraints": {
@@ -170,12 +169,13 @@ def remove_from(alist, to_remove):
             "numpy": "1.21",  # the min version is not available on the defaults channel
             "scipy": "1.7",  # the min version has some low level crashes
             "matplotlib": "min",
-            "threadpoolctl": "2.2.0",
             "cython": "min",
+            "joblib": "min",
+            "threadpoolctl": "min",
         },
-        # TODO: put cython back to conda dependencies when required version is
-        # available on the main channel
-        "pip_dependencies": ["cython"],
+        # TODO: put pip dependencies back to conda dependencies when required
+        # version is available on the defaults channel.
+        "pip_dependencies": ["threadpoolctl"],
     },
     {
         "name": "pymin_conda_forge_openblas_ubuntu_2204",
@@ -302,8 +302,14 @@ def remove_from(alist, to_remove):
             "plotly",
             "polars",
             "pooch",
+            "sphinx-remove-toctrees",
+            "sphinx-design",
+            "pydata-sphinx-theme",
+        ],
+        "pip_dependencies": [
+            "sphinxext-opengraph",
+            "sphinxcontrib-sass",
         ],
-        "pip_dependencies": ["sphinxext-opengraph"],
         "package_constraints": {
             "python": "3.9",
             "numpy": "min",
@@ -320,6 +326,11 @@ def remove_from(alist, to_remove):
             "sphinxext-opengraph": "min",
             "plotly": "min",
             "polars": "min",
+            "pooch": "min",
+            "sphinx-design": "min",
+            "sphinxcontrib-sass": "min",
+            "sphinx-remove-toctrees": "min",
+            "pydata-sphinx-theme": "min",
         },
     },
     {
@@ -344,8 +355,15 @@ def remove_from(alist, to_remove):
             "polars",
             "pooch",
             "sphinxext-opengraph",
+            "sphinx-remove-toctrees",
+            "sphinx-design",
+            "pydata-sphinx-theme",
+        ],
+        "pip_dependencies": [
+            "jupyterlite-sphinx",
+            "jupyterlite-pyodide-kernel",
+            "sphinxcontrib-sass",
         ],
-        "pip_dependencies": ["jupyterlite-sphinx", "jupyterlite-pyodide-kernel"],
         "package_constraints": {
             "python": "3.9",
         },
@@ -381,7 +399,7 @@ def remove_from(alist, to_remove):
         ],
         "package_constraints": {
             "joblib": "min",
-            "threadpoolctl": "2.2.0",
+            "threadpoolctl": "3.1.0",
             "pytest": "min",
             "pytest-cov": "min",
             # no pytest-xdist because it causes issue on 32bit
@@ -421,7 +439,7 @@ def execute_command(command_list):
     )
 
     out, err = proc.communicate()
-    out, err = out.decode(), err.decode()
+    out, err = out.decode(errors="replace"), err.decode(errors="replace")
 
     if proc.returncode != 0:
         command_str = " ".join(command_list)
diff --git a/doc/Makefile b/doc/Makefile
index 44f02585f6205..f84d3c78b8051 100644
--- a/doc/Makefile
+++ b/doc/Makefile
@@ -47,9 +47,17 @@ help:
 
 clean:
 	-rm -rf $(BUILDDIR)/*
+	@echo "Removed $(BUILDDIR)/*"
 	-rm -rf auto_examples/
+	@echo "Removed auto_examples/"
 	-rm -rf generated/*
+	@echo "Removed generated/"
 	-rm -rf modules/generated/
+	@echo "Removed modules/generated/"
+	-rm -rf css/styles/
+	@echo "Removed css/styles/"
+	-rm -rf api/*.rst
+	@echo "Removed api/*.rst"
 
 # Default to SPHINX_NUMJOBS=1 for full documentation build. Using
 # SPHINX_NUMJOBS!=1 may actually slow down the build, or cause weird issues in
diff --git a/doc/about.rst b/doc/about.rst
index 2a689646c1abb..47d57e4737318 100644
--- a/doc/about.rst
+++ b/doc/about.rst
@@ -13,8 +13,8 @@ this project as part of his thesis.
 In 2010 Fabian Pedregosa, Gael Varoquaux, Alexandre Gramfort and Vincent
 Michel of INRIA took leadership of the project and made the first public
 release, February the 1st 2010. Since then, several releases have appeared
-following a ~ 3-month cycle, and a thriving international community has
-been leading the development.
+following an approximately 3-month cycle, and a thriving international
+community has been leading the development.
 
 Governance
 ----------
@@ -22,32 +22,48 @@ Governance
 The decision making process and governance structure of scikit-learn is laid
 out in the :ref:`governance document <governance>`.
 
-Authors
--------
+.. The "author" anchors below is there to ensure that old html links (in
+   the form of "about.html#author" still work)
+
+.. _authors:
+
+The people behind scikit-learn
+------------------------------
+
+Scikit-learn is a community project, developed by a large group of
+people, all across the world. A few teams, listed below, have central
+roles, however a more complete list of contributors can be found `on
+github
+<https://github.com/scikit-learn/scikit-learn/graphs/contributors>`__.
 
-The following people are currently core contributors to scikit-learn's development
-and maintenance:
+Maintainers Team
+................
 
-.. include:: authors.rst
+The following people are currently maintainers, in charge of
+consolidating scikit-learn's development and maintenance:
 
-Please do not email the authors directly to ask for assistance or report issues.
-Instead, please see `What's the best way to ask questions about scikit-learn
-<https://scikit-learn.org/stable/faq.html#what-s-the-best-way-to-get-help-on-scikit-learn-usage>`_
-in the FAQ.
+.. include:: maintainers.rst
+
+.. note::
+
+  Please do not email the authors directly to ask for assistance or report issues.
+  Instead, please see `What's the best way to ask questions about scikit-learn
+  <https://scikit-learn.org/stable/faq.html#what-s-the-best-way-to-get-help-on-scikit-learn-usage>`_
+  in the FAQ.
 
 .. seealso::
 
-   :ref:`How you can contribute to the project <contributing>`
+  How you can :ref:`contribute to the project <contributing>`.
 
 Documentation Team
-------------------
+..................
 
 The following people help with documenting the project:
 
 .. include:: documentation_team.rst
 
 Contributor Experience Team
----------------------------
+...........................
 
 The following people are active contributors who also help with
 :ref:`triaging issues <bug_triaging>`, PRs, and general
@@ -56,24 +72,23 @@ maintenance:
 .. include:: contributor_experience_team.rst
 
 Communication Team
-------------------
+..................
 
 The following people help with :ref:`communication around scikit-learn
 <communication_team>`.
 
 .. include:: communication_team.rst
 
-
 Emeritus Core Developers
-------------------------
+........................
 
 The following people have been active contributors in the past, but are no
 longer active in the project:
 
-.. include:: authors_emeritus.rst
+.. include:: maintainers_emeritus.rst
 
 Emeritus Communication Team
----------------------------
+...........................
 
 The following people have been active in the communication team in the
 past, but no longer have communication responsibilities:
@@ -81,7 +96,7 @@ past, but no longer have communication responsibilities:
 .. include:: communication_team_emeritus.rst
 
 Emeritus Contributor Experience Team
-------------------------------------
+....................................
 
 The following people have been active in the contributor experience team in the
 past:
@@ -143,462 +158,305 @@ High quality PNG and SVG logos are available in the `doc/logos/
 source directory.
 
 .. image:: images/scikit-learn-logo-notext.png
-   :align: center
+  :align: center
 
 Funding
 -------
-Scikit-Learn is a community driven project, however institutional and private
+
+Scikit-learn is a community driven project, however institutional and private
 grants help to assure its sustainability.
 
 The project would like to thank the following funders.
 
 ...................................
 
+.. div:: sk-text-image-grid-small
 
-.. raw:: html
-
-   <div class="sk-sponsor-div">
-   <div class="sk-sponsor-div-box">
-
-`:probabl. <https://probabl.ai>`_ funds Adrin Jalali, Arturo Amor,
-François Goupil, Guillaume Lemaitre, Jérémie du Boisberranger, Olivier Grisel, and
-Stefanie Senger.
+  .. div:: text-box
 
-.. raw:: html
-
-   </div>
+    `:probabl. <https://probabl.ai>`_ funds Adrin Jalali, Arturo Amor, François Goupil,
+    Guillaume Lemaitre, Jérémie du Boisberranger, Olivier Grisel, and Stefanie Senger.
 
-   <div class="sk-sponsor-div-box">
+  .. div:: image-box
 
-.. image:: images/probabl.png
-   :width: 75pt
-   :align: center
-   :target: https://probabl.ai
-
-.. raw:: html
-
-   </div>
-   </div>
+    .. image:: images/probabl.png
+      :target: https://probabl.ai
 
 ..........
 
-.. raw:: html
-
-   <div class="sk-sponsor-div">
-   <div class="sk-sponsor-div-box">
-
-The `Members <https://scikit-learn.fondation-inria.fr/en/home/#sponsors>`_ of
-the `Scikit-Learn Consortium at Inria Foundation
-<https://scikit-learn.fondation-inria.fr/en/home/>`_ help at maintaining and
-improving the project through their financial support.
-
-.. raw:: html
-
-   </div>
-
 .. |chanel| image:: images/chanel.png
-   :width: 55pt
-   :target: https://www.chanel.com
+  :target: https://www.chanel.com
 
 .. |axa| image:: images/axa.png
-   :width: 40pt
-   :target: https://www.axa.fr/
+  :target: https://www.axa.fr/
 
 .. |bnp| image:: images/bnp.png
-   :width: 120pt
-   :target: https://www.bnpparibascardif.com/
+  :target: https://www.bnpparibascardif.com/
 
 .. |dataiku| image:: images/dataiku.png
-   :width: 55pt
-   :target: https://www.dataiku.com/
+  :target: https://www.dataiku.com/
 
 .. |hf| image:: images/huggingface_logo-noborder.png
-   :width: 55pt
-   :target: https://huggingface.co
+  :target: https://huggingface.co
 
 .. |nvidia| image:: images/nvidia.png
-   :width: 55pt
-   :target: https://www.nvidia.com
+  :target: https://www.nvidia.com
 
 .. |inria| image:: images/inria-logo.jpg
-   :width: 75pt
-   :target: https://www.inria.fr
-
+  :target: https://www.inria.fr
 
 .. raw:: html
 
-   <div class="sk-sponsor-div-box">
-
-.. table::
-   :class: sk-sponsor-table
-
-   +----------+-----------+
-   |       |chanel|       |
-   +----------+-----------+
-   |                      |
-   +----------+-----------+
-   |  |axa|   |    |bnp|  |
-   +----------+-----------+
-   |                      |
-   +----------+-----------+
-   | |nvidia| |    |hf|   |
-   +----------+-----------+
-   |                      |
-   +----------+-----------+
-   |       |dataiku|      |
-   +----------+-----------+
-   |                      |
-   +----------+-----------+
-   |        |inria|       |
-   +----------+-----------+
+  <style>
+    table.image-subtable tr {
+      border-color: transparent;
+    }
 
-.. raw:: html
+    table.image-subtable td {
+      width: 50%;
+      vertical-align: middle;
+      text-align: center;
+    }
 
-   </div>
-   </div>
+    table.image-subtable td img {
+      max-height: 40px !important;
+      max-width: 90% !important;
+    }
+  </style>
 
-..........
+.. div:: sk-text-image-grid-small
 
-.. raw:: html
+  .. div:: text-box
 
-   <div class="sk-sponsor-div">
-   <div class="sk-sponsor-div-box">
-
-`NVidia <https://nvidia.com>`_ funds Tim Head since 2022
-and is part of the scikit-learn consortium at Inria.
-
-.. raw:: html
+    The `Members <https://scikit-learn.fondation-inria.fr/en/home/#sponsors>`_ of
+    the `Scikit-learn Consortium at Inria Foundation
+    <https://scikit-learn.fondation-inria.fr/en/home/>`_ help at maintaining and
+    improving the project through their financial support.
 
-   </div>
+  .. div:: image-box
 
-   <div class="sk-sponsor-div-box">
+    .. table::
+      :class: image-subtable
 
-.. image:: images/nvidia.png
-   :width: 55pt
-   :align: center
-   :target: https://nvidia.com
+      +----------+-----------+
+      |       |chanel|       |
+      +----------+-----------+
+      |  |axa|   |    |bnp|  |
+      +----------+-----------+
+      | |nvidia| |    |hf|   |
+      +----------+-----------+
+      |       |dataiku|      |
+      +----------+-----------+
+      |        |inria|       |
+      +----------+-----------+
 
-.. raw:: html
+..........
 
-   </div>
-   </div>
+.. div:: sk-text-image-grid-small
 
-..........
+  .. div:: text-box
 
-.. raw:: html
+    `NVidia <https://nvidia.com>`_ funds Tim Head since 2022
+    and is part of the scikit-learn consortium at Inria.
 
-   <div class="sk-sponsor-div">
-   <div class="sk-sponsor-div-box">
+  .. div:: image-box
 
-`Microsoft <https://microsoft.com/>`_ funds Andreas Müller since 2020.
+    .. image:: images/nvidia.png
+      :target: https://nvidia.com
 
-.. raw:: html
+..........
 
-   </div>
+.. div:: sk-text-image-grid-small
 
-   <div class="sk-sponsor-div-box">
+  .. div:: text-box
 
-.. image:: images/microsoft.png
-   :width: 100pt
-   :align: center
-   :target: https://www.microsoft.com/
+    `Microsoft <https://microsoft.com/>`_ funds Andreas Müller since 2020.
 
-.. raw:: html
+  .. div:: image-box
 
-   </div>
-   </div>
+    .. image:: images/microsoft.png
+      :target: https://microsoft.com
 
 ...........
 
-.. raw:: html
+.. div:: sk-text-image-grid-small
 
-   <div class="sk-sponsor-div">
-   <div class="sk-sponsor-div-box">
+  .. div:: text-box
 
-`Quansight Labs <https://labs.quansight.org>`_ funds Lucy Liu since 2022.
+    `Quansight Labs <https://labs.quansight.org>`_ funds Lucy Liu since 2022.
 
-.. raw:: html
+  .. div:: image-box
 
-   </div>
+    .. image:: images/quansight-labs.png
+      :target: https://labs.quansight.org
 
-   <div class="sk-sponsor-div-box">
+...........
 
-.. image:: images/quansight-labs.png
-   :width: 100pt
-   :align: center
-   :target: https://labs.quansight.org
+.. div:: sk-text-image-grid-small
 
-.. raw:: html
+  .. div:: text-box
 
-   </div>
-   </div>
+    `Tidelift <https://tidelift.com/>`_ supports the project via their service
+    agreement.
 
-Past Sponsors
-.............
+  .. div:: image-box
 
-.. raw:: html
+    .. image:: images/Tidelift-logo-on-light.svg
+      :target: https://tidelift.com/
 
-   <div class="sk-sponsor-div">
-   <div class="sk-sponsor-div-box">
+...........
 
-`Quansight Labs <https://labs.quansight.org>`_ funded Meekail Zain in 2022 and 2023 and,
-funded Thomas J. Fan from 2021 to 2023.
 
-.. raw:: html
+Past Sponsors
+.............
 
-   </div>
+.. div:: sk-text-image-grid-small
 
-   <div class="sk-sponsor-div-box">
+  .. div:: text-box
 
-.. image:: images/quansight-labs.png
-   :width: 100pt
-   :align: center
-   :target: https://labs.quansight.org
+    `Quansight Labs <https://labs.quansight.org>`_ funded Meekail Zain in 2022 and 2023,
+    and funded Thomas J. Fan from 2021 to 2023.
 
-.. raw:: html
+  .. div:: image-box
 
-   </div>
-   </div>
+    .. image:: images/quansight-labs.png
+      :target: https://labs.quansight.org
 
 ...........
 
-.. raw:: html
-
-   <div class="sk-sponsor-div">
-   <div class="sk-sponsor-div-box">
-
-`Columbia University <https://columbia.edu/>`_ funded Andreas Müller
-(2016-2020).
+.. div:: sk-text-image-grid-small
 
-.. raw:: html
+  .. div:: text-box
 
-   </div>
+    `Columbia University <https://columbia.edu/>`_ funded Andreas Müller
+    (2016-2020).
 
-   <div class="sk-sponsor-div-box">
+  .. div:: image-box
 
-.. image:: images/columbia.png
-   :width: 50pt
-   :align: center
-   :target: https://www.columbia.edu/
-
-.. raw:: html
-
-   </div>
-   </div>
+    .. image:: images/columbia.png
+      :target: https://columbia.edu
 
 ........
 
-.. raw:: html
+.. div:: sk-text-image-grid-small
 
-   <div class="sk-sponsor-div">
-   <div class="sk-sponsor-div-box">
+  .. div:: text-box
 
-`The University of Sydney <https://sydney.edu.au/>`_ funded Joel Nothman
-(2017-2021).
+    `The University of Sydney <https://sydney.edu.au/>`_ funded Joel Nothman
+    (2017-2021).
 
-.. raw:: html
+  .. div:: image-box
 
-   </div>
-
-   <div class="sk-sponsor-div-box">
-
-.. image:: images/sydney-primary.jpeg
-   :width: 100pt
-   :align: center
-   :target: https://sydney.edu.au/
-
-.. raw:: html
-
-   </div>
-   </div>
+    .. image:: images/sydney-primary.jpeg
+      :target: https://sydney.edu.au/
 
 ...........
 
-.. raw:: html
-
-   <div class="sk-sponsor-div">
-   <div class="sk-sponsor-div-box">
-
-Andreas Müller received a grant to improve scikit-learn from the
-`Alfred P. Sloan Foundation <https://sloan.org>`_ .
-This grant supported the position of Nicolas Hug and Thomas J. Fan.
-
-.. raw:: html
-
-   </div>
+.. div:: sk-text-image-grid-small
 
-   <div class="sk-sponsor-div-box">
+  .. div:: text-box
 
-.. image:: images/sloan_banner.png
-   :width: 100pt
-   :align: center
-   :target: https://sloan.org/
+    Andreas Müller received a grant to improve scikit-learn from the
+    `Alfred P. Sloan Foundation <https://sloan.org>`_ .
+    This grant supported the position of Nicolas Hug and Thomas J. Fan.
 
-.. raw:: html
+  .. div:: image-box
 
-   </div>
-   </div>
+    .. image:: images/sloan_banner.png
+      :target: https://sloan.org/
 
 .............
 
-.. raw:: html
-
-   <div class="sk-sponsor-div">
-   <div class="sk-sponsor-div-box">
-
-`INRIA <https://www.inria.fr>`_ actively supports this project. It has
-provided funding for Fabian Pedregosa (2010-2012), Jaques Grobler
-(2012-2013) and Olivier Grisel (2013-2017) to work on this project
-full-time. It also hosts coding sprints and other events.
+.. div:: sk-text-image-grid-small
 
-.. raw:: html
-
-   </div>
+  .. div:: text-box
 
-   <div class="sk-sponsor-div-box">
+    `INRIA <https://www.inria.fr>`_ actively supports this project. It has
+    provided funding for Fabian Pedregosa (2010-2012), Jaques Grobler
+    (2012-2013) and Olivier Grisel (2013-2017) to work on this project
+    full-time. It also hosts coding sprints and other events.
 
-.. image:: images/inria-logo.jpg
-   :width: 100pt
-   :align: center
-   :target: https://www.inria.fr
-
-.. raw:: html
+  .. div:: image-box
 
-   </div>
-   </div>
+    .. image:: images/inria-logo.jpg
+      :target: https://www.inria.fr
 
 .....................
 
-.. raw:: html
-
-   <div class="sk-sponsor-div">
-   <div class="sk-sponsor-div-box">
-
-`Paris-Saclay Center for Data Science
-<http://www.datascience-paris-saclay.fr/>`_
-funded one year for a developer to work on the project full-time
-(2014-2015), 50% of the time of Guillaume Lemaitre (2016-2017) and 50% of the
-time of Joris van den Bossche (2017-2018).
-
-.. raw:: html
+.. div:: sk-text-image-grid-small
 
-   </div>
-   <div class="sk-sponsor-div-box">
+  .. div:: text-box
 
-.. image:: images/cds-logo.png
-   :width: 100pt
-   :align: center
-   :target: http://www.datascience-paris-saclay.fr/
+    `Paris-Saclay Center for Data Science <http://www.datascience-paris-saclay.fr/>`_
+    funded one year for a developer to work on the project full-time (2014-2015), 50%
+    of the time of Guillaume Lemaitre (2016-2017) and 50% of the time of Joris van den
+    Bossche (2017-2018).
 
-.. raw:: html
+  .. div:: image-box
 
-   </div>
-   </div>
+    .. image:: images/cds-logo.png
+      :target: http://www.datascience-paris-saclay.fr/
 
 ..........................
 
-.. raw:: html
+.. div:: sk-text-image-grid-small
 
-   <div class="sk-sponsor-div">
-   <div class="sk-sponsor-div-box">
+  .. div:: text-box
 
-`NYU Moore-Sloan Data Science Environment <https://cds.nyu.edu/mooresloan/>`_
-funded Andreas Mueller (2014-2016) to work on this project. The Moore-Sloan
-Data Science Environment also funds several students to work on the project
-part-time.
+    `NYU Moore-Sloan Data Science Environment <https://cds.nyu.edu/mooresloan/>`_
+    funded Andreas Mueller (2014-2016) to work on this project. The Moore-Sloan
+    Data Science Environment also funds several students to work on the project
+    part-time.
 
-.. raw:: html
+  .. div:: image-box
 
-   </div>
-   <div class="sk-sponsor-div-box">
-
-.. image:: images/nyu_short_color.png
-   :width: 100pt
-   :align: center
-   :target: https://cds.nyu.edu/mooresloan/
-
-.. raw:: html
-
-   </div>
-   </div>
+    .. image:: images/nyu_short_color.png
+      :target: https://cds.nyu.edu/mooresloan/
 
 ........................
 
-.. raw:: html
-
-   <div class="sk-sponsor-div">
-   <div class="sk-sponsor-div-box">
+.. div:: sk-text-image-grid-small
 
-`Télécom Paristech <https://www.telecom-paristech.fr/>`_ funded Manoj Kumar
-(2014), Tom Dupré la Tour (2015), Raghav RV (2015-2017), Thierry Guillemot
-(2016-2017) and Albert Thomas (2017) to work on scikit-learn.
+  .. div:: text-box
 
-.. raw:: html
+    `Télécom Paristech <https://www.telecom-paristech.fr/>`_ funded Manoj Kumar
+    (2014), Tom Dupré la Tour (2015), Raghav RV (2015-2017), Thierry Guillemot
+    (2016-2017) and Albert Thomas (2017) to work on scikit-learn.
 
-   </div>
-   <div class="sk-sponsor-div-box">
+  .. div:: image-box
 
-.. image:: images/telecom.png
-   :width: 50pt
-   :align: center
-   :target: https://www.telecom-paristech.fr/
-
-.. raw:: html
-
-   </div>
-   </div>
+    .. image:: images/telecom.png
+      :target: https://www.telecom-paristech.fr/
 
 .....................
 
-.. raw:: html
-
-   <div class="sk-sponsor-div">
-   <div class="sk-sponsor-div-box">
-
-`The Labex DigiCosme <https://digicosme.lri.fr>`_ funded Nicolas Goix
-(2015-2016), Tom Dupré la Tour (2015-2016 and 2017-2018), Mathurin Massias
-(2018-2019) to work part time on scikit-learn during their PhDs. It also
-funded a scikit-learn coding sprint in 2015.
+.. div:: sk-text-image-grid-small
 
-.. raw:: html
+  .. div:: text-box
 
-   </div>
-   <div class="sk-sponsor-div-box">
+    `The Labex DigiCosme <https://digicosme.lri.fr>`_ funded Nicolas Goix
+    (2015-2016), Tom Dupré la Tour (2015-2016 and 2017-2018), Mathurin Massias
+    (2018-2019) to work part time on scikit-learn during their PhDs. It also
+    funded a scikit-learn coding sprint in 2015.
 
-.. image:: images/digicosme.png
-   :width: 100pt
-   :align: center
-   :target: https://digicosme.lri.fr
+  .. div:: image-box
 
-.. raw:: html
-
-   </div>
-   </div>
+    .. image:: images/digicosme.png
+      :target: https://digicosme.lri.fr
 
 .....................
 
-.. raw:: html
-
-   <div class="sk-sponsor-div">
-   <div class="sk-sponsor-div-box">
+.. div:: sk-text-image-grid-small
 
-`The Chan-Zuckerberg Initiative <https://chanzuckerberg.com/>`_ funded Nicolas
-Hug to work full-time on scikit-learn in 2020.
+  .. div:: text-box
 
-.. raw:: html
+    `The Chan-Zuckerberg Initiative <https://chanzuckerberg.com/>`_ funded Nicolas
+    Hug to work full-time on scikit-learn in 2020.
 
-   </div>
-   <div class="sk-sponsor-div-box">
-
-.. image:: images/czi_logo.svg
-   :width: 100pt
-   :align: center
-   :target: https://chanzuckerberg.com
-
-.. raw:: html
+  .. div:: image-box
 
-   </div>
-   </div>
+    .. image:: images/czi_logo.svg
+      :target: https://chanzuckerberg.com
 
 ......................
 
@@ -609,9 +467,9 @@ program.
 
 - 2007 - David Cournapeau
 - 2011 - `Vlad Niculae`_
-- 2012 - `Vlad Niculae`_, Immanuel Bayer.
+- 2012 - `Vlad Niculae`_, Immanuel Bayer
 - 2013 - Kemal Eren, Nicolas Trésegnie
-- 2014 - Hamzeh Alsalhi, Issam Laradji, Maheshakya Wijewardena, Manoj Kumar.
+- 2014 - Hamzeh Alsalhi, Issam Laradji, Maheshakya Wijewardena, Manoj Kumar
 - 2015 - `Raghav RV <https://github.com/raghavrv>`_, Wei Xue
 - 2016 - `Nelson Liu <http://nelsonliu.me>`_, `YenChen Lin <https://yenchenlin.me/>`_
 
@@ -630,86 +488,112 @@ The following organizations funded the scikit-learn consortium at Inria in
 the past:
 
 .. |msn| image:: images/microsoft.png
-   :width: 100pt
-   :target: https://www.microsoft.com/
+  :target: https://www.microsoft.com/
 
 .. |bcg| image:: images/bcg.png
-   :width: 100pt
-   :target: https://www.bcg.com/beyond-consulting/bcg-gamma/default.aspx
+  :target: https://www.bcg.com/beyond-consulting/bcg-gamma/default.aspx
 
 .. |fujitsu| image:: images/fujitsu.png
-   :width: 100pt
-   :target: https://www.fujitsu.com/global/
+  :target: https://www.fujitsu.com/global/
 
 .. |aphp| image:: images/logo_APHP_text.png
-   :width: 150pt
-   :target: https://aphp.fr/
+  :target: https://aphp.fr/
 
+.. raw:: html
+
+  <style>
+    div.image-subgrid img {
+      max-height: 50px;
+      max-width: 90%;
+    }
+  </style>
+
+.. grid:: 2 2 4 4
+  :class-row: image-subgrid
+  :gutter: 1
+
+  .. grid-item::
+    :class: sd-text-center
+    :child-align: center
+
+    |msn|
+
+  .. grid-item::
+    :class: sd-text-center
+    :child-align: center
+
+    |bcg|
+
+  .. grid-item::
+    :class: sd-text-center
+    :child-align: center
 
-|bcg| |msn| |fujitsu| |aphp|
+    |fujitsu|
+
+  .. grid-item::
+    :class: sd-text-center
+    :child-align: center
+
+    |aphp|
 
 
 Sprints
 -------
 
-The International 2019 Paris sprint was kindly hosted by `AXA <https://www.axa.fr/>`_.
-Also some participants could attend thanks to the support of the `Alfred P.
-Sloan Foundation <https://sloan.org>`_, the `Python Software
-Foundation <https://www.python.org/psf/>`_ (PSF) and the `DATAIA Institute
-<https://dataia.eu/en>`_.
-
-.....................
+- The International 2019 Paris sprint was kindly hosted by `AXA <https://www.axa.fr/>`_.
+  Also some participants could attend thanks to the support of the `Alfred P.
+  Sloan Foundation <https://sloan.org>`_, the `Python Software
+  Foundation <https://www.python.org/psf/>`_ (PSF) and the `DATAIA Institute
+  <https://dataia.eu/en>`_.
 
-The 2013 International Paris Sprint was made possible thanks to the support of
-`Télécom Paristech <https://www.telecom-paristech.fr/>`_, `tinyclues
-<https://www.tinyclues.com/>`_, the `French Python Association
-<https://www.afpy.org/>`_ and the `Fonds de la Recherche Scientifique
-<https://www.frs-fnrs.be>`_.
+- The 2013 International Paris Sprint was made possible thanks to the support of
+  `Télécom Paristech <https://www.telecom-paristech.fr/>`_, `tinyclues
+  <https://www.tinyclues.com/>`_, the `French Python Association
+  <https://www.afpy.org/>`_ and the `Fonds de la Recherche Scientifique
+  <https://www.frs-fnrs.be>`_.
 
-..............
+- The 2011 International Granada sprint was made possible thanks to the support
+  of the `PSF <https://www.python.org/psf/>`_ and `tinyclues
+  <https://www.tinyclues.com/>`_.
 
-The 2011 International Granada sprint was made possible thanks to the support
-of the `PSF <https://www.python.org/psf/>`_ and `tinyclues
-<https://www.tinyclues.com/>`_.
 
 Donating to the project
-.......................
+-----------------------
 
 If you are interested in donating to the project or to one of our code-sprints,
 please donate via the `NumFOCUS Donations Page
 <https://numfocus.org/donate-to-scikit-learn>`_.
 
-.. raw :: html
-
-   <div style="text-align: center;">
-   <a class="btn btn-warning btn-big sk-donate-btn mb-1" href="https://numfocus.org/donate-to-scikit-learn">Help us, <strong>donate!</strong></a>
-   </div>
-   </br>
+.. raw:: html
 
-All donations will be handled by `NumFOCUS
-<https://numfocus.org/>`_, a non-profit-organization which is
-managed by a board of `Scipy community members
-<https://numfocus.org/board.html>`_. NumFOCUS's mission is to foster
-scientific computing software, in particular in Python. As a fiscal home
-of scikit-learn, it ensures that money is available when needed to keep
-the project funded and available while in compliance with tax regulations.
+  <p class="text-center">
+    <a class="btn sk-btn-orange mb-1" href="https://numfocus.org/donate-to-scikit-learn">
+      Help us, <strong>donate!</strong>
+    </a>
+  </p>
 
-The received donations for the scikit-learn project mostly will go towards
-covering travel-expenses for code sprints, as well as towards the organization
-budget of the project [#f1]_.
+All donations will be handled by `NumFOCUS <https://numfocus.org/>`_, a non-profit
+organization which is managed by a board of `Scipy community members
+<https://numfocus.org/board.html>`_. NumFOCUS's mission is to foster scientific
+computing software, in particular in Python. As a fiscal home of scikit-learn, it
+ensures that money is available when needed to keep the project funded and available
+while in compliance with tax regulations.
 
+The received donations for the scikit-learn project mostly will go towards covering
+travel-expenses for code sprints, as well as towards the organization budget of the
+project [#f1]_.
 
 .. rubric:: Notes
 
 .. [#f1] Regarding the organization budget, in particular, we might use some of
-         the donated funds to pay for other project expenses such as DNS,
-         hosting or continuous integration services.
+  the donated funds to pay for other project expenses such as DNS,
+  hosting or continuous integration services.
+
 
 Infrastructure support
 ----------------------
 
-- We would also like to thank `Microsoft Azure
-  <https://azure.microsoft.com/en-us/>`_, `Cirrus Cl <https://cirrus-ci.org>`_,
-  `CircleCl <https://circleci.com/>`_ for free CPU time on their Continuous
-  Integration servers, and `Anaconda Inc. <https://www.anaconda.com>`_ for the
-  storage they provide for our staging and nightly builds.
+We would also like to thank `Microsoft Azure <https://azure.microsoft.com/en-us/>`_,
+`Cirrus Cl <https://cirrus-ci.org>`_, `CircleCl <https://circleci.com/>`_ for free CPU
+time on their Continuous Integration servers, and `Anaconda Inc. <https://www.anaconda.com>`_
+for the storage they provide for our staging and nightly builds.
diff --git a/doc/api/deprecated.rst.template b/doc/api/deprecated.rst.template
new file mode 100644
index 0000000000000..a48f0180f76ed
--- /dev/null
+++ b/doc/api/deprecated.rst.template
@@ -0,0 +1,24 @@
+:html_theme.sidebar_secondary.remove:
+
+.. _api_depr_ref:
+
+Recently Deprecated
+===================
+
+.. currentmodule:: sklearn
+
+{% for ver, objs in DEPRECATED_API_REFERENCE %}
+.. _api_depr_ref-{{ ver|replace(".", "-") }}:
+
+.. rubric:: To be removed in {{ ver }}
+
+.. autosummary::
+  :nosignatures:
+  :toctree: ../modules/generated/
+  :template: base.rst
+
+{% for obj in objs %}
+  {{ obj }}
+{%- endfor %}
+
+{% endfor %}
diff --git a/doc/api/index.rst.template b/doc/api/index.rst.template
new file mode 100644
index 0000000000000..a9f3209d350de
--- /dev/null
+++ b/doc/api/index.rst.template
@@ -0,0 +1,77 @@
+:html_theme.sidebar_secondary.remove:
+
+.. _api_ref:
+
+=============
+API Reference
+=============
+
+This is the class and function reference of scikit-learn. Please refer to the
+:ref:`full user guide <user_guide>` for further details, as the raw specifications of
+classes and functions may not be enough to give full guidelines on their uses. For
+reference on concepts repeated across the API, see :ref:`glossary`.
+
+.. toctree::
+  :maxdepth: 2
+  :hidden:
+
+{% for module, _ in API_REFERENCE %}
+  {{ module }}
+{%- endfor %}
+{%- if DEPRECATED_API_REFERENCE %}
+  deprecated
+{%- endif %}
+
+.. list-table::
+  :header-rows: 1
+  :class: apisearch-table
+
+  * - Object
+    - Description
+
+{% for module, module_info in API_REFERENCE %}
+{% for section in module_info["sections"] %}
+{% for obj in section["autosummary"] %}
+{% set parts = obj.rsplit(".", 1) %}
+{% if parts|length > 1 %}
+{% set full_module = module + "." + parts[0] %}
+{% else %}
+{% set full_module = module %}
+{% endif %}
+  * - :obj:`~{{ module }}.{{ obj }}`
+
+    - .. div:: sk-apisearch-desc
+
+        .. currentmodule:: {{ full_module }}
+
+        .. autoshortsummary:: {{ module }}.{{ obj }}
+
+        .. div:: caption
+
+          :mod:`{{ full_module }}`
+{% endfor %}
+{% endfor %}
+{% endfor %}
+
+{% for ver, objs in DEPRECATED_API_REFERENCE %}
+{% for obj in objs %}
+{% set parts = obj.rsplit(".", 1) %}
+{% if parts|length > 1 %}
+{% set full_module = "sklearn." + parts[0] %}
+{% else %}
+{% set full_module = "sklearn" %}
+{% endif %}
+  * - :obj:`~sklearn.{{ obj }}`
+
+    - .. div:: sk-apisearch-desc
+
+        .. currentmodule:: {{ full_module }}
+
+        .. autoshortsummary:: sklearn.{{ obj }}
+
+        .. div:: caption
+
+          :mod:`{{ full_module }}`
+          :bdg-ref-danger-line:`Deprecated in version {{ ver }} <api_depr_ref-{{ ver|replace(".", "-") }}>`
+{% endfor %}
+{% endfor %}
diff --git a/doc/api/module.rst.template b/doc/api/module.rst.template
new file mode 100644
index 0000000000000..1980f27aad158
--- /dev/null
+++ b/doc/api/module.rst.template
@@ -0,0 +1,46 @@
+:html_theme.sidebar_secondary.remove:
+
+{% if module == "sklearn" -%}
+{%- set module_hook = "sklearn" -%}
+{%- elif module.startswith("sklearn.") -%}
+{%- set module_hook = module[8:] -%}
+{%- else -%}
+{%- set module_hook = None -%}
+{%- endif -%}
+
+{% if module_hook %}
+.. _{{ module_hook }}_ref:
+{% endif %}
+
+{{ module }}
+{{ "=" * module|length }}
+
+.. automodule:: {{ module }}
+
+{% if module_info["description"] %}
+{{ module_info["description"] }}
+{% endif %}
+
+{% for section in module_info["sections"] %}
+{% if section["title"] and module_hook %}
+.. _{{ module_hook }}_ref-{{ section["title"]|lower|replace(" ", "-") }}:
+{% endif %}
+
+{% if section["title"] %}
+{{ section["title"] }}
+{{ "-" * section["title"]|length }}
+{% endif %}
+
+{% if section["description"] %}
+{{ section["description"] }}
+{% endif %}
+
+.. autosummary::
+  :nosignatures:
+  :toctree: ../modules/generated/
+  :template: base.rst
+
+{% for obj in section["autosummary"] %}
+  {{ obj }}
+{%- endfor %}
+{% endfor %}
diff --git a/doc/api_reference.py b/doc/api_reference.py
new file mode 100644
index 0000000000000..39eac12c1c6cf
--- /dev/null
+++ b/doc/api_reference.py
@@ -0,0 +1,1335 @@
+"""Configuration for the API reference documentation."""
+
+
+def _get_guide(*refs, is_developer=False):
+    """Get the rst to refer to user/developer guide.
+
+    `refs` is several references that can be used in the :ref:`...` directive.
+    """
+    if len(refs) == 1:
+        ref_desc = f":ref:`{refs[0]}` section"
+    elif len(refs) == 2:
+        ref_desc = f":ref:`{refs[0]}` and :ref:`{refs[1]}` sections"
+    else:
+        ref_desc = ", ".join(f":ref:`{ref}`" for ref in refs[:-1])
+        ref_desc += f", and :ref:`{refs[-1]}` sections"
+
+    guide_name = "Developer" if is_developer else "User"
+    return f"**{guide_name} guide.** See the {ref_desc} for further details."
+
+
+def _get_submodule(module_name, submodule_name):
+    """Get the submodule docstring and automatically add the hook.
+
+    `module_name` is e.g. `sklearn.feature_extraction`, and `submodule_name` is e.g.
+    `image`, so we get the docstring and hook for `sklearn.feature_extraction.image`
+    submodule. `module_name` is used to reset the current module because autosummary
+    automatically changes the current module.
+    """
+    lines = [
+        f".. automodule:: {module_name}.{submodule_name}",
+        f".. currentmodule:: {module_name}",
+    ]
+    return "\n\n".join(lines)
+
+
+"""
+CONFIGURING API_REFERENCE
+=========================
+
+API_REFERENCE maps each module name to a dictionary that consists of the following
+components:
+
+short_summary (required)
+    The text to be printed on the index page; it has nothing to do the API reference
+    page of each module.
+description (required, `None` if not needed)
+    The additional description for the module to be placed under the module
+    docstring, before the sections start.
+sections (required)
+    A list of sections, each of which consists of:
+    - title (required, `None` if not needed): the section title, commonly it should
+      not be `None` except for the first section of a module,
+    - description (optional): the optional additional description for the section,
+    - autosummary (required): an autosummary block, assuming current module is the
+      current module name.
+
+Essentially, the rendered page would look like the following:
+
+|---------------------------------------------------------------------------------|
+|     {{ module_name }}                                                           |
+|     =================                                                           |
+|     {{ module_docstring }}                                                      |
+|     {{ description }}                                                           |
+|                                                                                 |
+|     {{ section_title_1 }}   <-------------- Optional if one wants the first     |
+|     ---------------------                   section to directly follow          |
+|     {{ section_description_1 }}             without a second-level heading.     |
+|     {{ section_autosummary_1 }}                                                 |
+|                                                                                 |
+|     {{ section_title_2 }}                                                       |
+|     ---------------------                                                       |
+|     {{ section_description_2 }}                                                 |
+|     {{ section_autosummary_2 }}                                                 |
+|                                                                                 |
+|     More sections...                                                            |
+|---------------------------------------------------------------------------------|
+
+Hooks will be automatically generated for each module and each section. For a module,
+e.g., `sklearn.feature_extraction`, the hook would be `feature_extraction_ref`; for a
+section, e.g., "From text" under `sklearn.feature_extraction`, the hook would be
+`feature_extraction_ref-from-text`. However, note that a better way is to refer using
+the :mod: directive, e.g., :mod:`sklearn.feature_extraction` for the module and
+:mod:`sklearn.feature_extraction.text` for the section. Only in case that a section
+is not a particular submodule does the hook become useful, e.g., the "Loaders" section
+under `sklearn.datasets`.
+"""
+
+API_REFERENCE = {
+    "sklearn": {
+        "short_summary": "Settings and information tools.",
+        "description": None,
+        "sections": [
+            {
+                "title": None,
+                "autosummary": [
+                    "config_context",
+                    "get_config",
+                    "set_config",
+                    "show_versions",
+                ],
+            },
+        ],
+    },
+    "sklearn.base": {
+        "short_summary": "Base classes and utility functions.",
+        "description": None,
+        "sections": [
+            {
+                "title": None,
+                "autosummary": [
+                    "BaseEstimator",
+                    "BiclusterMixin",
+                    "ClassNamePrefixFeaturesOutMixin",
+                    "ClassifierMixin",
+                    "ClusterMixin",
+                    "DensityMixin",
+                    "MetaEstimatorMixin",
+                    "OneToOneFeatureMixin",
+                    "OutlierMixin",
+                    "RegressorMixin",
+                    "TransformerMixin",
+                    "clone",
+                    "is_classifier",
+                    "is_clusterer",
+                    "is_regressor",
+                ],
+            }
+        ],
+    },
+    "sklearn.calibration": {
+        "short_summary": "Probability calibration.",
+        "description": _get_guide("calibration"),
+        "sections": [
+            {
+                "title": None,
+                "autosummary": ["CalibratedClassifierCV", "calibration_curve"],
+            },
+            {
+                "title": "Visualization",
+                "autosummary": ["CalibrationDisplay"],
+            },
+        ],
+    },
+    "sklearn.cluster": {
+        "short_summary": "Clustering.",
+        "description": _get_guide("clustering", "biclustering"),
+        "sections": [
+            {
+                "title": None,
+                "autosummary": [
+                    "AffinityPropagation",
+                    "AgglomerativeClustering",
+                    "Birch",
+                    "BisectingKMeans",
+                    "DBSCAN",
+                    "FeatureAgglomeration",
+                    "HDBSCAN",
+                    "KMeans",
+                    "MeanShift",
+                    "MiniBatchKMeans",
+                    "OPTICS",
+                    "SpectralBiclustering",
+                    "SpectralClustering",
+                    "SpectralCoclustering",
+                    "affinity_propagation",
+                    "cluster_optics_dbscan",
+                    "cluster_optics_xi",
+                    "compute_optics_graph",
+                    "dbscan",
+                    "estimate_bandwidth",
+                    "k_means",
+                    "kmeans_plusplus",
+                    "mean_shift",
+                    "spectral_clustering",
+                    "ward_tree",
+                ],
+            },
+        ],
+    },
+    "sklearn.compose": {
+        "short_summary": "Composite estimators.",
+        "description": _get_guide("combining_estimators"),
+        "sections": [
+            {
+                "title": None,
+                "autosummary": [
+                    "ColumnTransformer",
+                    "TransformedTargetRegressor",
+                    "make_column_selector",
+                    "make_column_transformer",
+                ],
+            },
+        ],
+    },
+    "sklearn.covariance": {
+        "short_summary": "Covariance estimation.",
+        "description": _get_guide("covariance"),
+        "sections": [
+            {
+                "title": None,
+                "autosummary": [
+                    "EllipticEnvelope",
+                    "EmpiricalCovariance",
+                    "GraphicalLasso",
+                    "GraphicalLassoCV",
+                    "LedoitWolf",
+                    "MinCovDet",
+                    "OAS",
+                    "ShrunkCovariance",
+                    "empirical_covariance",
+                    "graphical_lasso",
+                    "ledoit_wolf",
+                    "ledoit_wolf_shrinkage",
+                    "oas",
+                    "shrunk_covariance",
+                ],
+            },
+        ],
+    },
+    "sklearn.cross_decomposition": {
+        "short_summary": "Cross decomposition.",
+        "description": _get_guide("cross_decomposition"),
+        "sections": [
+            {
+                "title": None,
+                "autosummary": ["CCA", "PLSCanonical", "PLSRegression", "PLSSVD"],
+            },
+        ],
+    },
+    "sklearn.datasets": {
+        "short_summary": "Datasets.",
+        "description": _get_guide("datasets"),
+        "sections": [
+            {
+                "title": "Loaders",
+                "autosummary": [
+                    "clear_data_home",
+                    "dump_svmlight_file",
+                    "fetch_20newsgroups",
+                    "fetch_20newsgroups_vectorized",
+                    "fetch_california_housing",
+                    "fetch_covtype",
+                    "fetch_kddcup99",
+                    "fetch_lfw_pairs",
+                    "fetch_lfw_people",
+                    "fetch_olivetti_faces",
+                    "fetch_openml",
+                    "fetch_rcv1",
+                    "fetch_species_distributions",
+                    "get_data_home",
+                    "load_breast_cancer",
+                    "load_diabetes",
+                    "load_digits",
+                    "load_files",
+                    "load_iris",
+                    "load_linnerud",
+                    "load_sample_image",
+                    "load_sample_images",
+                    "load_svmlight_file",
+                    "load_svmlight_files",
+                    "load_wine",
+                ],
+            },
+            {
+                "title": "Sample generators",
+                "autosummary": [
+                    "make_biclusters",
+                    "make_blobs",
+                    "make_checkerboard",
+                    "make_circles",
+                    "make_classification",
+                    "make_friedman1",
+                    "make_friedman2",
+                    "make_friedman3",
+                    "make_gaussian_quantiles",
+                    "make_hastie_10_2",
+                    "make_low_rank_matrix",
+                    "make_moons",
+                    "make_multilabel_classification",
+                    "make_regression",
+                    "make_s_curve",
+                    "make_sparse_coded_signal",
+                    "make_sparse_spd_matrix",
+                    "make_sparse_uncorrelated",
+                    "make_spd_matrix",
+                    "make_swiss_roll",
+                ],
+            },
+        ],
+    },
+    "sklearn.decomposition": {
+        "short_summary": "Matrix decomposition.",
+        "description": _get_guide("decompositions"),
+        "sections": [
+            {
+                "title": None,
+                "autosummary": [
+                    "DictionaryLearning",
+                    "FactorAnalysis",
+                    "FastICA",
+                    "IncrementalPCA",
+                    "KernelPCA",
+                    "LatentDirichletAllocation",
+                    "MiniBatchDictionaryLearning",
+                    "MiniBatchNMF",
+                    "MiniBatchSparsePCA",
+                    "NMF",
+                    "PCA",
+                    "SparseCoder",
+                    "SparsePCA",
+                    "TruncatedSVD",
+                    "dict_learning",
+                    "dict_learning_online",
+                    "fastica",
+                    "non_negative_factorization",
+                    "sparse_encode",
+                ],
+            },
+        ],
+    },
+    "sklearn.discriminant_analysis": {
+        "short_summary": "Discriminant analysis.",
+        "description": _get_guide("lda_qda"),
+        "sections": [
+            {
+                "title": None,
+                "autosummary": [
+                    "LinearDiscriminantAnalysis",
+                    "QuadraticDiscriminantAnalysis",
+                ],
+            },
+        ],
+    },
+    "sklearn.dummy": {
+        "short_summary": "Dummy estimators.",
+        "description": _get_guide("model_evaluation"),
+        "sections": [
+            {
+                "title": None,
+                "autosummary": ["DummyClassifier", "DummyRegressor"],
+            },
+        ],
+    },
+    "sklearn.ensemble": {
+        "short_summary": "Ensemble methods.",
+        "description": _get_guide("ensemble"),
+        "sections": [
+            {
+                "title": None,
+                "autosummary": [
+                    "AdaBoostClassifier",
+                    "AdaBoostRegressor",
+                    "BaggingClassifier",
+                    "BaggingRegressor",
+                    "ExtraTreesClassifier",
+                    "ExtraTreesRegressor",
+                    "GradientBoostingClassifier",
+                    "GradientBoostingRegressor",
+                    "HistGradientBoostingClassifier",
+                    "HistGradientBoostingRegressor",
+                    "IsolationForest",
+                    "RandomForestClassifier",
+                    "RandomForestRegressor",
+                    "RandomTreesEmbedding",
+                    "StackingClassifier",
+                    "StackingRegressor",
+                    "VotingClassifier",
+                    "VotingRegressor",
+                ],
+            },
+        ],
+    },
+    "sklearn.exceptions": {
+        "short_summary": "Exceptions and warnings.",
+        "description": None,
+        "sections": [
+            {
+                "title": None,
+                "autosummary": [
+                    "ConvergenceWarning",
+                    "DataConversionWarning",
+                    "DataDimensionalityWarning",
+                    "EfficiencyWarning",
+                    "FitFailedWarning",
+                    "InconsistentVersionWarning",
+                    "NotFittedError",
+                    "UndefinedMetricWarning",
+                ],
+            },
+        ],
+    },
+    "sklearn.experimental": {
+        "short_summary": "Experimental tools.",
+        "description": None,
+        "sections": [
+            {
+                "title": None,
+                "autosummary": ["enable_halving_search_cv", "enable_iterative_imputer"],
+            },
+        ],
+    },
+    "sklearn.feature_extraction": {
+        "short_summary": "Feature extraction.",
+        "description": _get_guide("feature_extraction"),
+        "sections": [
+            {
+                "title": None,
+                "autosummary": ["DictVectorizer", "FeatureHasher"],
+            },
+            {
+                "title": "From images",
+                "description": _get_submodule("sklearn.feature_extraction", "image"),
+                "autosummary": [
+                    "image.PatchExtractor",
+                    "image.extract_patches_2d",
+                    "image.grid_to_graph",
+                    "image.img_to_graph",
+                    "image.reconstruct_from_patches_2d",
+                ],
+            },
+            {
+                "title": "From text",
+                "description": _get_submodule("sklearn.feature_extraction", "text"),
+                "autosummary": [
+                    "text.CountVectorizer",
+                    "text.HashingVectorizer",
+                    "text.TfidfTransformer",
+                    "text.TfidfVectorizer",
+                ],
+            },
+        ],
+    },
+    "sklearn.feature_selection": {
+        "short_summary": "Feature selection.",
+        "description": _get_guide("feature_selection"),
+        "sections": [
+            {
+                "title": None,
+                "autosummary": [
+                    "GenericUnivariateSelect",
+                    "RFE",
+                    "RFECV",
+                    "SelectFdr",
+                    "SelectFpr",
+                    "SelectFromModel",
+                    "SelectFwe",
+                    "SelectKBest",
+                    "SelectPercentile",
+                    "SelectorMixin",
+                    "SequentialFeatureSelector",
+                    "VarianceThreshold",
+                    "chi2",
+                    "f_classif",
+                    "f_regression",
+                    "mutual_info_classif",
+                    "mutual_info_regression",
+                    "r_regression",
+                ],
+            },
+        ],
+    },
+    "sklearn.gaussian_process": {
+        "short_summary": "Gaussian processes.",
+        "description": _get_guide("gaussian_process"),
+        "sections": [
+            {
+                "title": None,
+                "autosummary": [
+                    "GaussianProcessClassifier",
+                    "GaussianProcessRegressor",
+                ],
+            },
+            {
+                "title": "Kernels",
+                "description": _get_submodule("sklearn.gaussian_process", "kernels"),
+                "autosummary": [
+                    "kernels.CompoundKernel",
+                    "kernels.ConstantKernel",
+                    "kernels.DotProduct",
+                    "kernels.ExpSineSquared",
+                    "kernels.Exponentiation",
+                    "kernels.Hyperparameter",
+                    "kernels.Kernel",
+                    "kernels.Matern",
+                    "kernels.PairwiseKernel",
+                    "kernels.Product",
+                    "kernels.RBF",
+                    "kernels.RationalQuadratic",
+                    "kernels.Sum",
+                    "kernels.WhiteKernel",
+                ],
+            },
+        ],
+    },
+    "sklearn.impute": {
+        "short_summary": "Imputation.",
+        "description": _get_guide("impute"),
+        "sections": [
+            {
+                "title": None,
+                "autosummary": [
+                    "IterativeImputer",
+                    "KNNImputer",
+                    "MissingIndicator",
+                    "SimpleImputer",
+                ],
+            },
+        ],
+    },
+    "sklearn.inspection": {
+        "short_summary": "Inspection.",
+        "description": _get_guide("inspection"),
+        "sections": [
+            {
+                "title": None,
+                "autosummary": ["partial_dependence", "permutation_importance"],
+            },
+            {
+                "title": "Plotting",
+                "autosummary": ["DecisionBoundaryDisplay", "PartialDependenceDisplay"],
+            },
+        ],
+    },
+    "sklearn.isotonic": {
+        "short_summary": "Isotonic regression.",
+        "description": _get_guide("isotonic"),
+        "sections": [
+            {
+                "title": None,
+                "autosummary": [
+                    "IsotonicRegression",
+                    "check_increasing",
+                    "isotonic_regression",
+                ],
+            },
+        ],
+    },
+    "sklearn.kernel_approximation": {
+        "short_summary": "Isotonic regression.",
+        "description": _get_guide("kernel_approximation"),
+        "sections": [
+            {
+                "title": None,
+                "autosummary": [
+                    "AdditiveChi2Sampler",
+                    "Nystroem",
+                    "PolynomialCountSketch",
+                    "RBFSampler",
+                    "SkewedChi2Sampler",
+                ],
+            },
+        ],
+    },
+    "sklearn.kernel_ridge": {
+        "short_summary": "Kernel ridge regression.",
+        "description": _get_guide("kernel_ridge"),
+        "sections": [
+            {
+                "title": None,
+                "autosummary": ["KernelRidge"],
+            },
+        ],
+    },
+    "sklearn.linear_model": {
+        "short_summary": "Generalized linear models.",
+        "description": (
+            _get_guide("linear_model")
+            + "\n\nThe following subsections are only rough guidelines: the same "
+            "estimator can fall into multiple categories, depending on its parameters."
+        ),
+        "sections": [
+            {
+                "title": "Linear classifiers",
+                "autosummary": [
+                    "LogisticRegression",
+                    "LogisticRegressionCV",
+                    "PassiveAggressiveClassifier",
+                    "Perceptron",
+                    "RidgeClassifier",
+                    "RidgeClassifierCV",
+                    "SGDClassifier",
+                    "SGDOneClassSVM",
+                ],
+            },
+            {
+                "title": "Classical linear regressors",
+                "autosummary": ["LinearRegression", "Ridge", "RidgeCV", "SGDRegressor"],
+            },
+            {
+                "title": "Regressors with variable selection",
+                "description": (
+                    "The following estimators have built-in variable selection fitting "
+                    "procedures, but any estimator using a L1 or elastic-net penalty "
+                    "also performs variable selection: typically "
+                    ":class:`~linear_model.SGDRegressor` or "
+                    ":class:`~sklearn.linear_model.SGDClassifier` with an appropriate "
+                    "penalty."
+                ),
+                "autosummary": [
+                    "ElasticNet",
+                    "ElasticNetCV",
+                    "Lars",
+                    "LarsCV",
+                    "Lasso",
+                    "LassoCV",
+                    "LassoLars",
+                    "LassoLarsCV",
+                    "LassoLarsIC",
+                    "OrthogonalMatchingPursuit",
+                    "OrthogonalMatchingPursuitCV",
+                ],
+            },
+            {
+                "title": "Bayesian regressors",
+                "autosummary": ["ARDRegression", "BayesianRidge"],
+            },
+            {
+                "title": "Multi-task linear regressors with variable selection",
+                "description": (
+                    "These estimators fit multiple regression problems (or tasks)"
+                    " jointly, while inducing sparse coefficients. While the inferred"
+                    " coefficients may differ between the tasks, they are constrained"
+                    " to agree on the features that are selected (non-zero"
+                    " coefficients)."
+                ),
+                "autosummary": [
+                    "MultiTaskElasticNet",
+                    "MultiTaskElasticNetCV",
+                    "MultiTaskLasso",
+                    "MultiTaskLassoCV",
+                ],
+            },
+            {
+                "title": "Outlier-robust regressors",
+                "description": (
+                    "Any estimator using the Huber loss would also be robust to "
+                    "outliers, e.g., :class:`~linear_model.SGDRegressor` with "
+                    "``loss='huber'``."
+                ),
+                "autosummary": [
+                    "HuberRegressor",
+                    "QuantileRegressor",
+                    "RANSACRegressor",
+                    "TheilSenRegressor",
+                ],
+            },
+            {
+                "title": "Generalized linear models (GLM) for regression",
+                "description": (
+                    "These models allow for response variables to have error "
+                    "distributions other than a normal distribution."
+                ),
+                "autosummary": [
+                    "GammaRegressor",
+                    "PoissonRegressor",
+                    "TweedieRegressor",
+                ],
+            },
+            {
+                "title": "Miscellaneous",
+                "autosummary": [
+                    "PassiveAggressiveRegressor",
+                    "enet_path",
+                    "lars_path",
+                    "lars_path_gram",
+                    "lasso_path",
+                    "orthogonal_mp",
+                    "orthogonal_mp_gram",
+                    "ridge_regression",
+                ],
+            },
+        ],
+    },
+    "sklearn.manifold": {
+        "short_summary": "Manifold learning.",
+        "description": _get_guide("manifold"),
+        "sections": [
+            {
+                "title": None,
+                "autosummary": [
+                    "Isomap",
+                    "LocallyLinearEmbedding",
+                    "MDS",
+                    "SpectralEmbedding",
+                    "TSNE",
+                    "locally_linear_embedding",
+                    "smacof",
+                    "spectral_embedding",
+                    "trustworthiness",
+                ],
+            },
+        ],
+    },
+    "sklearn.metrics": {
+        "short_summary": "Metrics.",
+        "description": _get_guide("model_evaluation", "metrics"),
+        "sections": [
+            {
+                "title": "Model selection interface",
+                "description": _get_guide("scoring_parameter"),
+                "autosummary": [
+                    "check_scoring",
+                    "get_scorer",
+                    "get_scorer_names",
+                    "make_scorer",
+                ],
+            },
+            {
+                "title": "Classification metrics",
+                "description": _get_guide("classification_metrics"),
+                "autosummary": [
+                    "accuracy_score",
+                    "auc",
+                    "average_precision_score",
+                    "balanced_accuracy_score",
+                    "brier_score_loss",
+                    "class_likelihood_ratios",
+                    "classification_report",
+                    "cohen_kappa_score",
+                    "confusion_matrix",
+                    "d2_log_loss_score",
+                    "dcg_score",
+                    "det_curve",
+                    "f1_score",
+                    "fbeta_score",
+                    "hamming_loss",
+                    "hinge_loss",
+                    "jaccard_score",
+                    "log_loss",
+                    "matthews_corrcoef",
+                    "multilabel_confusion_matrix",
+                    "ndcg_score",
+                    "precision_recall_curve",
+                    "precision_recall_fscore_support",
+                    "precision_score",
+                    "recall_score",
+                    "roc_auc_score",
+                    "roc_curve",
+                    "top_k_accuracy_score",
+                    "zero_one_loss",
+                ],
+            },
+            {
+                "title": "Regression metrics",
+                "description": _get_guide("regression_metrics"),
+                "autosummary": [
+                    "d2_absolute_error_score",
+                    "d2_pinball_score",
+                    "d2_tweedie_score",
+                    "explained_variance_score",
+                    "max_error",
+                    "mean_absolute_error",
+                    "mean_absolute_percentage_error",
+                    "mean_gamma_deviance",
+                    "mean_pinball_loss",
+                    "mean_poisson_deviance",
+                    "mean_squared_error",
+                    "mean_squared_log_error",
+                    "mean_tweedie_deviance",
+                    "median_absolute_error",
+                    "r2_score",
+                    "root_mean_squared_error",
+                    "root_mean_squared_log_error",
+                ],
+            },
+            {
+                "title": "Multilabel ranking metrics",
+                "description": _get_guide("multilabel_ranking_metrics"),
+                "autosummary": [
+                    "coverage_error",
+                    "label_ranking_average_precision_score",
+                    "label_ranking_loss",
+                ],
+            },
+            {
+                "title": "Clustering metrics",
+                "description": (
+                    _get_submodule("sklearn.metrics", "cluster")
+                    + "\n\n"
+                    + _get_guide("clustering_evaluation")
+                ),
+                "autosummary": [
+                    "adjusted_mutual_info_score",
+                    "adjusted_rand_score",
+                    "calinski_harabasz_score",
+                    "cluster.contingency_matrix",
+                    "cluster.pair_confusion_matrix",
+                    "completeness_score",
+                    "davies_bouldin_score",
+                    "fowlkes_mallows_score",
+                    "homogeneity_completeness_v_measure",
+                    "homogeneity_score",
+                    "mutual_info_score",
+                    "normalized_mutual_info_score",
+                    "rand_score",
+                    "silhouette_samples",
+                    "silhouette_score",
+                    "v_measure_score",
+                ],
+            },
+            {
+                "title": "Biclustering metrics",
+                "description": _get_guide("biclustering_evaluation"),
+                "autosummary": ["consensus_score"],
+            },
+            {
+                "title": "Distance metrics",
+                "autosummary": ["DistanceMetric"],
+            },
+            {
+                "title": "Pairwise metrics",
+                "description": (
+                    _get_submodule("sklearn.metrics", "pairwise")
+                    + "\n\n"
+                    + _get_guide("metrics")
+                ),
+                "autosummary": [
+                    "pairwise.additive_chi2_kernel",
+                    "pairwise.chi2_kernel",
+                    "pairwise.cosine_distances",
+                    "pairwise.cosine_similarity",
+                    "pairwise.distance_metrics",
+                    "pairwise.euclidean_distances",
+                    "pairwise.haversine_distances",
+                    "pairwise.kernel_metrics",
+                    "pairwise.laplacian_kernel",
+                    "pairwise.linear_kernel",
+                    "pairwise.manhattan_distances",
+                    "pairwise.nan_euclidean_distances",
+                    "pairwise.paired_cosine_distances",
+                    "pairwise.paired_distances",
+                    "pairwise.paired_euclidean_distances",
+                    "pairwise.paired_manhattan_distances",
+                    "pairwise.pairwise_kernels",
+                    "pairwise.polynomial_kernel",
+                    "pairwise.rbf_kernel",
+                    "pairwise.sigmoid_kernel",
+                    "pairwise_distances",
+                    "pairwise_distances_argmin",
+                    "pairwise_distances_argmin_min",
+                    "pairwise_distances_chunked",
+                ],
+            },
+            {
+                "title": "Plotting",
+                "description": _get_guide("visualizations"),
+                "autosummary": [
+                    "ConfusionMatrixDisplay",
+                    "DetCurveDisplay",
+                    "PrecisionRecallDisplay",
+                    "PredictionErrorDisplay",
+                    "RocCurveDisplay",
+                ],
+            },
+        ],
+    },
+    "sklearn.mixture": {
+        "short_summary": "Gaussian mixture models.",
+        "description": _get_guide("mixture"),
+        "sections": [
+            {
+                "title": None,
+                "autosummary": ["BayesianGaussianMixture", "GaussianMixture"],
+            },
+        ],
+    },
+    "sklearn.model_selection": {
+        "short_summary": "Model selection.",
+        "description": _get_guide("cross_validation", "grid_search", "learning_curve"),
+        "sections": [
+            {
+                "title": "Splitters",
+                "autosummary": [
+                    "GroupKFold",
+                    "GroupShuffleSplit",
+                    "KFold",
+                    "LeaveOneGroupOut",
+                    "LeaveOneOut",
+                    "LeavePGroupsOut",
+                    "LeavePOut",
+                    "PredefinedSplit",
+                    "RepeatedKFold",
+                    "RepeatedStratifiedKFold",
+                    "ShuffleSplit",
+                    "StratifiedGroupKFold",
+                    "StratifiedKFold",
+                    "StratifiedShuffleSplit",
+                    "TimeSeriesSplit",
+                    "check_cv",
+                    "train_test_split",
+                ],
+            },
+            {
+                "title": "Hyper-parameter optimizers",
+                "autosummary": [
+                    "GridSearchCV",
+                    "HalvingGridSearchCV",
+                    "HalvingRandomSearchCV",
+                    "ParameterGrid",
+                    "ParameterSampler",
+                    "RandomizedSearchCV",
+                ],
+            },
+            {
+                "title": "Post-fit model tuning",
+                "autosummary": [
+                    "FixedThresholdClassifier",
+                    "TunedThresholdClassifierCV",
+                ],
+            },
+            {
+                "title": "Model validation",
+                "autosummary": [
+                    "cross_val_predict",
+                    "cross_val_score",
+                    "cross_validate",
+                    "learning_curve",
+                    "permutation_test_score",
+                    "validation_curve",
+                ],
+            },
+            {
+                "title": "Visualization",
+                "autosummary": ["LearningCurveDisplay", "ValidationCurveDisplay"],
+            },
+        ],
+    },
+    "sklearn.multiclass": {
+        "short_summary": "Multiclass classification.",
+        "description": _get_guide("multiclass_classification"),
+        "sections": [
+            {
+                "title": None,
+                "autosummary": [
+                    "OneVsOneClassifier",
+                    "OneVsRestClassifier",
+                    "OutputCodeClassifier",
+                ],
+            },
+        ],
+    },
+    "sklearn.multioutput": {
+        "short_summary": "Multioutput regression and classification.",
+        "description": _get_guide(
+            "multilabel_classification",
+            "multiclass_multioutput_classification",
+            "multioutput_regression",
+        ),
+        "sections": [
+            {
+                "title": None,
+                "autosummary": [
+                    "ClassifierChain",
+                    "MultiOutputClassifier",
+                    "MultiOutputRegressor",
+                    "RegressorChain",
+                ],
+            },
+        ],
+    },
+    "sklearn.naive_bayes": {
+        "short_summary": "Naive Bayes.",
+        "description": _get_guide("naive_bayes"),
+        "sections": [
+            {
+                "title": None,
+                "autosummary": [
+                    "BernoulliNB",
+                    "CategoricalNB",
+                    "ComplementNB",
+                    "GaussianNB",
+                    "MultinomialNB",
+                ],
+            },
+        ],
+    },
+    "sklearn.neighbors": {
+        "short_summary": "Nearest neighbors.",
+        "description": _get_guide("neighbors"),
+        "sections": [
+            {
+                "title": None,
+                "autosummary": [
+                    "BallTree",
+                    "KDTree",
+                    "KNeighborsClassifier",
+                    "KNeighborsRegressor",
+                    "KNeighborsTransformer",
+                    "KernelDensity",
+                    "LocalOutlierFactor",
+                    "NearestCentroid",
+                    "NearestNeighbors",
+                    "NeighborhoodComponentsAnalysis",
+                    "RadiusNeighborsClassifier",
+                    "RadiusNeighborsRegressor",
+                    "RadiusNeighborsTransformer",
+                    "kneighbors_graph",
+                    "radius_neighbors_graph",
+                    "sort_graph_by_row_values",
+                ],
+            },
+        ],
+    },
+    "sklearn.neural_network": {
+        "short_summary": "Neural network models.",
+        "description": _get_guide(
+            "neural_networks_supervised", "neural_networks_unsupervised"
+        ),
+        "sections": [
+            {
+                "title": None,
+                "autosummary": ["BernoulliRBM", "MLPClassifier", "MLPRegressor"],
+            },
+        ],
+    },
+    "sklearn.pipeline": {
+        "short_summary": "Pipeline.",
+        "description": _get_guide("combining_estimators"),
+        "sections": [
+            {
+                "title": None,
+                "autosummary": [
+                    "FeatureUnion",
+                    "Pipeline",
+                    "make_pipeline",
+                    "make_union",
+                ],
+            },
+        ],
+    },
+    "sklearn.preprocessing": {
+        "short_summary": "Preprocessing and normalization.",
+        "description": _get_guide("preprocessing"),
+        "sections": [
+            {
+                "title": None,
+                "autosummary": [
+                    "Binarizer",
+                    "FunctionTransformer",
+                    "KBinsDiscretizer",
+                    "KernelCenterer",
+                    "LabelBinarizer",
+                    "LabelEncoder",
+                    "MaxAbsScaler",
+                    "MinMaxScaler",
+                    "MultiLabelBinarizer",
+                    "Normalizer",
+                    "OneHotEncoder",
+                    "OrdinalEncoder",
+                    "PolynomialFeatures",
+                    "PowerTransformer",
+                    "QuantileTransformer",
+                    "RobustScaler",
+                    "SplineTransformer",
+                    "StandardScaler",
+                    "TargetEncoder",
+                    "add_dummy_feature",
+                    "binarize",
+                    "label_binarize",
+                    "maxabs_scale",
+                    "minmax_scale",
+                    "normalize",
+                    "power_transform",
+                    "quantile_transform",
+                    "robust_scale",
+                    "scale",
+                ],
+            },
+        ],
+    },
+    "sklearn.random_projection": {
+        "short_summary": "Random projection.",
+        "description": _get_guide("random_projection"),
+        "sections": [
+            {
+                "title": None,
+                "autosummary": [
+                    "GaussianRandomProjection",
+                    "SparseRandomProjection",
+                    "johnson_lindenstrauss_min_dim",
+                ],
+            },
+        ],
+    },
+    "sklearn.semi_supervised": {
+        "short_summary": "Semi-supervised learning.",
+        "description": _get_guide("semi_supervised"),
+        "sections": [
+            {
+                "title": None,
+                "autosummary": [
+                    "LabelPropagation",
+                    "LabelSpreading",
+                    "SelfTrainingClassifier",
+                ],
+            },
+        ],
+    },
+    "sklearn.svm": {
+        "short_summary": "Support vector machines.",
+        "description": _get_guide("svm"),
+        "sections": [
+            {
+                "title": None,
+                "autosummary": [
+                    "LinearSVC",
+                    "LinearSVR",
+                    "NuSVC",
+                    "NuSVR",
+                    "OneClassSVM",
+                    "SVC",
+                    "SVR",
+                    "l1_min_c",
+                ],
+            },
+        ],
+    },
+    "sklearn.tree": {
+        "short_summary": "Decision trees.",
+        "description": _get_guide("tree"),
+        "sections": [
+            {
+                "title": None,
+                "autosummary": [
+                    "DecisionTreeClassifier",
+                    "DecisionTreeRegressor",
+                    "ExtraTreeClassifier",
+                    "ExtraTreeRegressor",
+                ],
+            },
+            {
+                "title": "Exporting",
+                "autosummary": ["export_graphviz", "export_text"],
+            },
+            {
+                "title": "Plotting",
+                "autosummary": ["plot_tree"],
+            },
+        ],
+    },
+    "sklearn.utils": {
+        "short_summary": "Utilities.",
+        "description": _get_guide("developers-utils", is_developer=True),
+        "sections": [
+            {
+                "title": None,
+                "autosummary": [
+                    "Bunch",
+                    "_safe_indexing",
+                    "as_float_array",
+                    "assert_all_finite",
+                    "deprecated",
+                    "estimator_html_repr",
+                    "gen_batches",
+                    "gen_even_slices",
+                    "indexable",
+                    "murmurhash3_32",
+                    "resample",
+                    "safe_mask",
+                    "safe_sqr",
+                    "shuffle",
+                ],
+            },
+            {
+                "title": "Input and parameter validation",
+                "description": _get_submodule("sklearn.utils", "validation"),
+                "autosummary": [
+                    "check_X_y",
+                    "check_array",
+                    "check_consistent_length",
+                    "check_random_state",
+                    "check_scalar",
+                    "validation.check_is_fitted",
+                    "validation.check_memory",
+                    "validation.check_symmetric",
+                    "validation.column_or_1d",
+                    "validation.has_fit_parameter",
+                ],
+            },
+            {
+                "title": "Meta-estimators",
+                "description": _get_submodule("sklearn.utils", "metaestimators"),
+                "autosummary": ["metaestimators.available_if"],
+            },
+            {
+                "title": "Weight handling based on class labels",
+                "description": _get_submodule("sklearn.utils", "class_weight"),
+                "autosummary": [
+                    "class_weight.compute_class_weight",
+                    "class_weight.compute_sample_weight",
+                ],
+            },
+            {
+                "title": "Dealing with multiclass target in classifiers",
+                "description": _get_submodule("sklearn.utils", "multiclass"),
+                "autosummary": [
+                    "multiclass.is_multilabel",
+                    "multiclass.type_of_target",
+                    "multiclass.unique_labels",
+                ],
+            },
+            {
+                "title": "Optimal mathematical operations",
+                "description": _get_submodule("sklearn.utils", "extmath"),
+                "autosummary": [
+                    "extmath.density",
+                    "extmath.fast_logdet",
+                    "extmath.randomized_range_finder",
+                    "extmath.randomized_svd",
+                    "extmath.safe_sparse_dot",
+                    "extmath.weighted_mode",
+                ],
+            },
+            {
+                "title": "Working with sparse matrices and arrays",
+                "description": _get_submodule("sklearn.utils", "sparsefuncs"),
+                "autosummary": [
+                    "sparsefuncs.incr_mean_variance_axis",
+                    "sparsefuncs.inplace_column_scale",
+                    "sparsefuncs.inplace_csr_column_scale",
+                    "sparsefuncs.inplace_row_scale",
+                    "sparsefuncs.inplace_swap_column",
+                    "sparsefuncs.inplace_swap_row",
+                    "sparsefuncs.mean_variance_axis",
+                ],
+            },
+            {
+                "title": None,
+                "description": _get_submodule("sklearn.utils", "sparsefuncs_fast"),
+                "autosummary": [
+                    "sparsefuncs_fast.inplace_csr_row_normalize_l1",
+                    "sparsefuncs_fast.inplace_csr_row_normalize_l2",
+                ],
+            },
+            {
+                "title": "Working with graphs",
+                "description": _get_submodule("sklearn.utils", "graph"),
+                "autosummary": ["graph.single_source_shortest_path_length"],
+            },
+            {
+                "title": "Random sampling",
+                "description": _get_submodule("sklearn.utils", "random"),
+                "autosummary": ["random.sample_without_replacement"],
+            },
+            {
+                "title": "Auxiliary functions that operate on arrays",
+                "description": _get_submodule("sklearn.utils", "arrayfuncs"),
+                "autosummary": ["arrayfuncs.min_pos"],
+            },
+            {
+                "title": "Metadata routing",
+                "description": (
+                    _get_submodule("sklearn.utils", "metadata_routing")
+                    + "\n\n"
+                    + _get_guide("metadata_routing")
+                ),
+                "autosummary": [
+                    "metadata_routing.MetadataRequest",
+                    "metadata_routing.MetadataRouter",
+                    "metadata_routing.MethodMapping",
+                    "metadata_routing.get_routing_for_object",
+                    "metadata_routing.process_routing",
+                ],
+            },
+            {
+                "title": "Discovering scikit-learn objects",
+                "description": _get_submodule("sklearn.utils", "discovery"),
+                "autosummary": [
+                    "discovery.all_displays",
+                    "discovery.all_estimators",
+                    "discovery.all_functions",
+                ],
+            },
+            {
+                "title": "API compatibility checkers",
+                "description": _get_submodule("sklearn.utils", "estimator_checks"),
+                "autosummary": [
+                    "estimator_checks.check_estimator",
+                    "estimator_checks.parametrize_with_checks",
+                ],
+            },
+            {
+                "title": "Parallel computing",
+                "description": _get_submodule("sklearn.utils", "parallel"),
+                "autosummary": [
+                    "parallel.Parallel",
+                    "parallel.delayed",
+                ],
+            },
+        ],
+    },
+}
+
+
+"""
+CONFIGURING DEPRECATED_API_REFERENCE
+====================================
+
+DEPRECATED_API_REFERENCE maps each deprecation target version to a corresponding
+autosummary block. It will be placed at the bottom of the API index page under the
+"Recently deprecated" section. Essentially, the rendered section would look like the
+following:
+
+|------------------------------------------|
+|     To be removed in {{ version_1 }}     |
+|     --------------------------------     |
+|     {{ autosummary_1 }}                  |
+|                                          |
+|     To be removed in {{ version_2 }}     |
+|     --------------------------------     |
+|     {{ autosummary_2 }}                  |
+|                                          |
+|     More versions...                     |
+|------------------------------------------|
+
+Note that the autosummary here assumes that the current module is `sklearn`, i.e., if
+`sklearn.utils.Memory` is deprecated, one should put `utils.Memory` in the "entries"
+slot of the autosummary block.
+
+Example:
+
+DEPRECATED_API_REFERENCE = {
+    "0.24": [
+        "model_selection.fit_grid_point",
+        "utils.safe_indexing",
+    ],
+}
+"""
+
+DEPRECATED_API_REFERENCE = {
+    "1.7": [
+        "utils.parallel_backend",
+        "utils.register_parallel_backend",
+    ]
+}  # type: ignore
diff --git a/doc/common_pitfalls.rst b/doc/common_pitfalls.rst
index 41eb16665a612..c16385943f9ad 100644
--- a/doc/common_pitfalls.rst
+++ b/doc/common_pitfalls.rst
@@ -1,9 +1,3 @@
-.. Places parent toc into the sidebar
-
-:parenttoc: True
-
-.. include:: includes/big_toc_css.rst
-
 .. _common_pitfalls:
 
 =========================================
@@ -414,43 +408,40 @@ it will allow the estimator RNG to vary for each fold.
     illustration purpose: what matters is what we pass to the
     :class:`~sklearn.ensemble.RandomForestClassifier` estimator.
 
-|details-start|
-**Cloning**
-|details-split|
+.. dropdown:: Cloning
 
-Another subtle side effect of passing `RandomState` instances is how
-:func:`~sklearn.base.clone` will work::
+    Another subtle side effect of passing `RandomState` instances is how
+    :func:`~sklearn.base.clone` will work::
 
-    >>> from sklearn import clone
-    >>> from sklearn.ensemble import RandomForestClassifier
-    >>> import numpy as np
+        >>> from sklearn import clone
+        >>> from sklearn.ensemble import RandomForestClassifier
+        >>> import numpy as np
+
+        >>> rng = np.random.RandomState(0)
+        >>> a = RandomForestClassifier(random_state=rng)
+        >>> b = clone(a)
+
+    Since a `RandomState` instance was passed to `a`, `a` and `b` are not clones
+    in the strict sense, but rather clones in the statistical sense: `a` and `b`
+    will still be different models, even when calling `fit(X, y)` on the same
+    data. Moreover, `a` and `b` will influence each-other since they share the
+    same internal RNG: calling `a.fit` will consume `b`'s RNG, and calling
+    `b.fit` will consume `a`'s RNG, since they are the same. This bit is true for
+    any estimators that share a `random_state` parameter; it is not specific to
+    clones.
+
+    If an integer were passed, `a` and `b` would be exact clones and they would not
+    influence each other.
+
+    .. warning::
+        Even though :func:`~sklearn.base.clone` is rarely used in user code, it is
+        called pervasively throughout scikit-learn codebase: in particular, most
+        meta-estimators that accept non-fitted estimators call
+        :func:`~sklearn.base.clone` internally
+        (:class:`~sklearn.model_selection.GridSearchCV`,
+        :class:`~sklearn.ensemble.StackingClassifier`,
+        :class:`~sklearn.calibration.CalibratedClassifierCV`, etc.).
 
-    >>> rng = np.random.RandomState(0)
-    >>> a = RandomForestClassifier(random_state=rng)
-    >>> b = clone(a)
-
-Since a `RandomState` instance was passed to `a`, `a` and `b` are not clones
-in the strict sense, but rather clones in the statistical sense: `a` and `b`
-will still be different models, even when calling `fit(X, y)` on the same
-data. Moreover, `a` and `b` will influence each-other since they share the
-same internal RNG: calling `a.fit` will consume `b`'s RNG, and calling
-`b.fit` will consume `a`'s RNG, since they are the same. This bit is true for
-any estimators that share a `random_state` parameter; it is not specific to
-clones.
-
-If an integer were passed, `a` and `b` would be exact clones and they would not
-influence each other.
-
-.. warning::
-    Even though :func:`~sklearn.base.clone` is rarely used in user code, it is
-    called pervasively throughout scikit-learn codebase: in particular, most
-    meta-estimators that accept non-fitted estimators call
-    :func:`~sklearn.base.clone` internally
-    (:class:`~sklearn.model_selection.GridSearchCV`,
-    :class:`~sklearn.ensemble.StackingClassifier`,
-    :class:`~sklearn.calibration.CalibratedClassifierCV`, etc.).
-
-|details-end|
 
 CV splitters
 ............
diff --git a/doc/computing.rst b/doc/computing.rst
index 6732b754918b0..9f166432006b2 100644
--- a/doc/computing.rst
+++ b/doc/computing.rst
@@ -1,13 +1,7 @@
-.. Places parent toc into the sidebar
-
-:parenttoc: True
-
 ============================
 Computing with scikit-learn
 ============================
 
-.. include:: includes/big_toc_css.rst
-
 .. toctree::
     :maxdepth: 2
 
diff --git a/doc/computing/computational_performance.rst b/doc/computing/computational_performance.rst
index d6864689502c2..a7b6d3a37001e 100644
--- a/doc/computing/computational_performance.rst
+++ b/doc/computing/computational_performance.rst
@@ -1,7 +1,3 @@
-.. Places parent toc into the sidebar
-
-:parenttoc: True
-
 .. _computational_performance:
 
 .. currentmodule:: sklearn
diff --git a/doc/computing/parallelism.rst b/doc/computing/parallelism.rst
index 53cef5603c5be..5c15cd9db440e 100644
--- a/doc/computing/parallelism.rst
+++ b/doc/computing/parallelism.rst
@@ -1,7 +1,3 @@
-.. Places parent toc into the sidebar
-
-:parenttoc: True
-
 Parallelism, resource management, and configuration
 ===================================================
 
diff --git a/doc/computing/scaling_strategies.rst b/doc/computing/scaling_strategies.rst
index 143643131b0e8..286a1e79d0a8c 100644
--- a/doc/computing/scaling_strategies.rst
+++ b/doc/computing/scaling_strategies.rst
@@ -1,7 +1,3 @@
-.. Places parent toc into the sidebar
-
-:parenttoc: True
-
 .. _scaling_strategies:
 
 Strategies to scale computationally: bigger data
diff --git a/doc/conf.py b/doc/conf.py
index 9d77fc68d0f71..db79bfe3f7865 100644
--- a/doc/conf.py
+++ b/doc/conf.py
@@ -15,7 +15,6 @@
 import sys
 import warnings
 from datetime import datetime
-from io import StringIO
 from pathlib import Path
 
 from sklearn.externals._packaging.version import parse
@@ -25,8 +24,10 @@
 # directory, add these directories to sys.path here. If the directory
 # is relative to the documentation root, use os.path.abspath to make it
 # absolute, like shown here.
+sys.path.insert(0, os.path.abspath("."))
 sys.path.insert(0, os.path.abspath("sphinxext"))
 
+import jinja2
 import sphinx_gallery
 from github_link import make_linkcode_resolve
 from sphinx_gallery.notebook import add_code_cell, add_markdown_cell
@@ -56,14 +57,21 @@
     "sphinx.ext.intersphinx",
     "sphinx.ext.imgconverter",
     "sphinx_gallery.gen_gallery",
-    "sphinx_issues",
-    "add_toctree_functions",
     "sphinx-prompt",
     "sphinx_copybutton",
     "sphinxext.opengraph",
-    "doi_role",
-    "allow_nan_estimators",
     "matplotlib.sphinxext.plot_directive",
+    "sphinxcontrib.sass",
+    "sphinx_remove_toctrees",
+    "sphinx_design",
+    # See sphinxext/
+    "allow_nan_estimators",
+    "autoshortsummary",
+    "doi_role",
+    "dropdown_anchors",
+    "move_gallery_links",
+    "override_pst_pagetoc",
+    "sphinx_issues",
 ]
 
 # Specify how to identify the prompt when copying code snippets
@@ -96,8 +104,12 @@
 plot_html_show_formats = False
 plot_html_show_source_link = False
 
-# this is needed for some reason...
-# see https://github.com/numpy/numpydoc/issues/69
+# We do not need the table of class members because `sphinxext/override_pst_pagetoc.py`
+# will show them in the secondary sidebar
+numpydoc_show_class_members = False
+numpydoc_show_inherited_class_members = False
+
+# We want in-page toc of class members instead of a separate page for each entry
 numpydoc_class_members_toctree = False
 
 
@@ -111,8 +123,6 @@
     extensions.append("sphinx.ext.mathjax")
     mathjax_path = "https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-chtml.js"
 
-autodoc_default_options = {"members": True, "inherited-members": True}
-
 # Add any paths that contain templates here, relative to this directory.
 templates_path = ["templates"]
 
@@ -123,10 +133,10 @@
 source_suffix = ".rst"
 
 # The encoding of source files.
-# source_encoding = 'utf-8'
+source_encoding = "utf-8"
 
 # The main toctree document.
-root_doc = "contents"
+root_doc = "index"
 
 # General information about the project.
 project = "scikit-learn"
@@ -160,7 +170,12 @@
 
 # List of patterns, relative to source directory, that match files and
 # directories to ignore when looking for source files.
-exclude_patterns = ["_build", "templates", "includes", "themes"]
+exclude_patterns = [
+    "_build",
+    "templates",
+    "includes",
+    "**/sg_execution_times.rst",
+]
 
 # The reST default role (used for this markup: `text`) to use for all
 # documents.
@@ -177,9 +192,6 @@
 # output. They are ignored by default.
 # show_authors = False
 
-# The name of the Pygments (syntax highlighting) style to use.
-pygments_style = "sphinx"
-
 # A list of ignored prefixes for module index sorting.
 # modindex_common_prefix = []
 
@@ -188,21 +200,89 @@
 
 # The theme to use for HTML and HTML Help pages.  Major themes that come with
 # Sphinx are currently 'default' and 'sphinxdoc'.
-html_theme = "scikit-learn-modern"
+html_theme = "pydata_sphinx_theme"
 
 # Theme options are theme-specific and customize the look and feel of a theme
 # further.  For a list of options available for each theme, see the
 # documentation.
 html_theme_options = {
-    "legacy_google_analytics": True,
-    "analytics": True,
-    "mathjax_path": mathjax_path,
-    "link_to_live_contributing_page": not parsed_version.is_devrelease,
+    # -- General configuration ------------------------------------------------
+    "sidebar_includehidden": True,
+    "use_edit_page_button": True,
+    "external_links": [],
+    "icon_links_label": "Icon Links",
+    "icon_links": [
+        {
+            "name": "GitHub",
+            "url": "https://github.com/scikit-learn/scikit-learn",
+            "icon": "fa-brands fa-square-github",
+            "type": "fontawesome",
+        },
+    ],
+    "analytics": {
+        "plausible_analytics_domain": "scikit-learn.org",
+        "plausible_analytics_url": "https://views.scientific-python.org/js/script.js",
+    },
+    # If "prev-next" is included in article_footer_items, then setting show_prev_next
+    # to True would repeat prev and next links. See
+    # https://github.com/pydata/pydata-sphinx-theme/blob/b731dc230bc26a3d1d1bb039c56c977a9b3d25d8/src/pydata_sphinx_theme/theme/pydata_sphinx_theme/layout.html#L118-L129
+    "show_prev_next": False,
+    "search_bar_text": "Search the docs ...",
+    "navigation_with_keys": False,
+    "collapse_navigation": False,
+    "navigation_depth": 2,
+    "show_nav_level": 1,
+    "show_toc_level": 1,
+    "navbar_align": "left",
+    "header_links_before_dropdown": 5,
+    "header_dropdown_text": "More",
+    # The switcher requires a JSON file with the list of documentation versions, which
+    # is generated by the script `build_tools/circle/list_versions.py` and placed under
+    # the `js/` static directory; it will then be copied to the `_static` directory in
+    # the built documentation
+    "switcher": {
+        "json_url": "https://scikit-learn.org/dev/_static/versions.json",
+        "version_match": release,
+    },
+    # check_switcher may be set to False if docbuild pipeline fails. See
+    # https://pydata-sphinx-theme.readthedocs.io/en/stable/user_guide/version-dropdown.html#configure-switcher-json-url
+    "check_switcher": True,
+    "pygments_light_style": "tango",
+    "pygments_dark_style": "monokai",
+    "logo": {
+        "alt_text": "scikit-learn homepage",
+        "image_relative": "logos/scikit-learn-logo-small.png",
+        "image_light": "logos/scikit-learn-logo-small.png",
+        "image_dark": "logos/scikit-learn-logo-small.png",
+    },
+    "surface_warnings": True,
+    # -- Template placement in theme layouts ----------------------------------
+    "navbar_start": ["navbar-logo"],
+    # Note that the alignment of navbar_center is controlled by navbar_align
+    "navbar_center": ["navbar-nav"],
+    "navbar_end": ["theme-switcher", "navbar-icon-links", "version-switcher"],
+    # navbar_persistent is persistent right (even when on mobiles)
+    "navbar_persistent": ["search-button"],
+    "article_header_start": ["breadcrumbs"],
+    "article_header_end": [],
+    "article_footer_items": ["prev-next"],
+    "content_footer_items": [],
+    # Use html_sidebars that map page patterns to list of sidebar templates
+    "primary_sidebar_end": [],
+    "footer_start": ["copyright"],
+    "footer_center": [],
+    "footer_end": [],
+    # When specified as a dictionary, the keys should follow glob-style patterns, as in
+    # https://www.sphinx-doc.org/en/master/usage/configuration.html#confval-exclude_patterns
+    # In particular, "**" specifies the default for all pages
+    # Use :html_theme.sidebar_secondary.remove: for file-wide removal
+    "secondary_sidebar_items": {"**": ["page-toc", "sourcelink"]},
+    "show_version_warning_banner": True,
+    "announcement": None,
 }
 
 # Add any paths that contain custom themes here, relative to this directory.
-html_theme_path = ["themes"]
-
+# html_theme_path = ["themes"]
 
 # The name for this set of Sphinx documents.  If None, it defaults to
 # "<project> v<release> documentation".
@@ -211,10 +291,6 @@
 # A shorter title for the navigation bar.  Default is the same as html_title.
 html_short_title = "scikit-learn"
 
-# The name of an image file (relative to this directory) to place at the top
-# of the sidebar.
-html_logo = "logos/scikit-learn-logo-small.png"
-
 # The name of an image file (within the static path) to use as favicon of the
 # docs.  This file should be a Windows icon file (.ico) being 16x16 or 32x32
 # pixels large.
@@ -223,19 +299,77 @@
 # Add any paths that contain custom static files (such as style sheets) here,
 # relative to this directory. They are copied after the builtin static files,
 # so a file named "default.css" will overwrite the builtin "default.css".
-html_static_path = ["images"]
+html_static_path = ["images", "css", "js"]
 
 # If not '', a 'Last updated on:' timestamp is inserted at every page bottom,
 # using the given strftime format.
 # html_last_updated_fmt = '%b %d, %Y'
 
 # Custom sidebar templates, maps document names to template names.
-# html_sidebars = {}
+# Workaround for removing the left sidebar on pages without TOC
+# A better solution would be to follow the merge of:
+# https://github.com/pydata/pydata-sphinx-theme/pull/1682
+html_sidebars = {
+    "install": [],
+    "getting_started": [],
+    "glossary": [],
+    "faq": [],
+    "support": [],
+    "related_projects": [],
+    "roadmap": [],
+    "governance": [],
+    "about": [],
+}
 
 # Additional templates that should be rendered to pages, maps page names to
 # template names.
 html_additional_pages = {"index": "index.html"}
 
+# Additional files to copy
+# html_extra_path = []
+
+# Additional JS files
+html_js_files = [
+    "scripts/dropdown.js",
+    "scripts/version-switcher.js",
+]
+
+# Compile scss files into css files using sphinxcontrib-sass
+sass_src_dir, sass_out_dir = "scss", "css/styles"
+sass_targets = {
+    f"{file.stem}.scss": f"{file.stem}.css"
+    for file in Path(sass_src_dir).glob("*.scss")
+}
+
+# Additional CSS files, should be subset of the values of `sass_targets`
+html_css_files = ["styles/colors.css", "styles/custom.css"]
+
+
+def add_js_css_files(app, pagename, templatename, context, doctree):
+    """Load additional JS and CSS files only for certain pages.
+
+    Note that `html_js_files` and `html_css_files` are included in all pages and
+    should be used for the ones that are used by multiple pages. All page-specific
+    JS and CSS files should be added here instead.
+    """
+    if pagename == "api/index":
+        # External: jQuery and DataTables
+        app.add_js_file("https://code.jquery.com/jquery-3.7.0.js")
+        app.add_js_file("https://cdn.datatables.net/2.0.0/js/dataTables.min.js")
+        app.add_css_file(
+            "https://cdn.datatables.net/2.0.0/css/dataTables.dataTables.min.css"
+        )
+        # Internal: API search intialization and styling
+        app.add_js_file("scripts/api-search.js")
+        app.add_css_file("styles/api-search.css")
+    elif pagename == "index":
+        app.add_css_file("styles/index.css")
+    elif pagename == "install":
+        app.add_css_file("styles/install.css")
+    elif pagename.startswith("modules/generated/"):
+        app.add_css_file("styles/api.css")
+
+
 # If false, no module index is generated.
 html_domain_indices = False
 
@@ -285,6 +419,9 @@
 # redirects dictionary maps from old links to new links
 redirects = {
     "documentation": "index",
+    "contents": "index",
+    "preface": "index",
+    "modules/classes": "api/index",
     "auto_examples/feature_selection/plot_permutation_test_for_classification": (
         "auto_examples/model_selection/plot_permutation_tests_for_classification"
     ),
@@ -301,6 +438,7 @@
     "auto_examples/decomposition/plot_beta_divergence": (
         "auto_examples/applications/plot_topics_extraction_with_nmf_lda"
     ),
+    "auto_examples/svm/plot_svm_nonlinear": "auto_examples/svm/plot_svm_kernels",
     "auto_examples/ensemble/plot_adaboost_hastie_10_2": (
         "auto_examples/ensemble/plot_adaboost_multiclass"
     ),
@@ -315,31 +453,9 @@
 for old_link in redirects:
     html_additional_pages[old_link] = "redirects.html"
 
-# Not showing the search summary makes the search page load faster.
-html_show_search_summary = True
-
-
-# The "summary-anchor" IDs will be overwritten via JavaScript to be unique.
-# See `doc/theme/scikit-learn-modern/static/js/details-permalink.js`.
-rst_prolog = """
-.. |details-start| raw:: html
-
-    <details id="summary-anchor">
-    <summary class="btn btn-light">
-
-.. |details-split| raw:: html
-
-    <span class="tooltiptext">Click for more details</span>
-    <a class="headerlink" href="#summary-anchor" title="Permalink to this heading">¶</a>
-    </summary>
-    <div class="card">
+# See https://github.com/scikit-learn/scikit-learn/pull/22550
+html_context["is_devrelease"] = parsed_version.is_devrelease
 
-.. |details-end| raw:: html
-
-    </div>
-    </details>
-
-"""
 
 # -- Options for LaTeX output ------------------------------------------------
 latex_elements = {
@@ -527,14 +643,16 @@ def reset_sklearn_config(gallery_conf, fname):
     sklearn.set_config(**default_global_config)
 
 
+sg_examples_dir = "../examples"
+sg_gallery_dir = "auto_examples"
 sphinx_gallery_conf = {
     "doc_module": "sklearn",
     "backreferences_dir": os.path.join("modules", "generated"),
     "show_memory": False,
     "reference_url": {"sklearn": None},
-    "examples_dirs": ["../examples"],
-    "gallery_dirs": ["auto_examples"],
-    "subsection_order": SubSectionTitleOrder("../examples"),
+    "examples_dirs": [sg_examples_dir],
+    "gallery_dirs": [sg_gallery_dir],
+    "subsection_order": SubSectionTitleOrder(sg_examples_dir),
     "within_subsection_order": SKExampleTitleSortKey,
     "binder": {
         "org": "scikit-learn",
@@ -548,7 +666,7 @@ def reset_sklearn_config(gallery_conf, fname):
     "inspect_global_variables": False,
     "remove_config_comments": True,
     "plot_gallery": "True",
-    "recommender": {"enable": True, "n_examples": 5, "min_df": 12},
+    "recommender": {"enable": True, "n_examples": 4, "min_df": 12},
     "reset_modules": ("matplotlib", "seaborn", reset_sklearn_config),
 }
 if with_jupyterlite:
@@ -556,6 +674,26 @@ def reset_sklearn_config(gallery_conf, fname):
         "notebook_modification_function": notebook_modification_function
     }
 
+# Secondary sidebar configuration for pages generated by sphinx-gallery
+
+# For the index page of the gallery and each nested section, we hide the secondary
+# sidebar by specifying an empty list (no components), because there is no meaningful
+# in-page toc for these pages, and they are generated so "sourcelink" is not useful
+# either.
+
+# For each example page we keep default ["page-toc", "sourcelink"] specified by the
+# "**" key. "page-toc" is wanted for these pages. "sourcelink" is also necessary since
+# otherwise the secondary sidebar will degenerate when "page-toc" is empty, and the
+# script `sphinxext/move_gallery_links.py` will fail (it assumes the existence of the
+# secondary sidebar). The script will remove "sourcelink" in the end.
+
+html_theme_options["secondary_sidebar_items"][f"{sg_gallery_dir}/index"] = []
+for sub_sg_dir in (Path(".") / sg_examples_dir).iterdir():
+    if sub_sg_dir.is_dir():
+        html_theme_options["secondary_sidebar_items"][
+            f"{sg_gallery_dir}/{sub_sg_dir.name}/index"
+        ] = []
+
 
 # The following dictionary contains the information used to create the
 # thumbnails for the front page of the scikit-learn home page.
@@ -605,73 +743,6 @@ def filter_search_index(app, exception):
         f.write(searchindex_text)
 
 
-def generate_min_dependency_table(app):
-    """Generate min dependency table for docs."""
-    from sklearn._min_dependencies import dependent_packages
-
-    # get length of header
-    package_header_len = max(len(package) for package in dependent_packages) + 4
-    version_header_len = len("Minimum Version") + 4
-    tags_header_len = max(len(tags) for _, tags in dependent_packages.values()) + 4
-
-    output = StringIO()
-    output.write(
-        " ".join(
-            ["=" * package_header_len, "=" * version_header_len, "=" * tags_header_len]
-        )
-    )
-    output.write("\n")
-    dependency_title = "Dependency"
-    version_title = "Minimum Version"
-    tags_title = "Purpose"
-
-    output.write(
-        f"{dependency_title:<{package_header_len}} "
-        f"{version_title:<{version_header_len}} "
-        f"{tags_title}\n"
-    )
-
-    output.write(
-        " ".join(
-            ["=" * package_header_len, "=" * version_header_len, "=" * tags_header_len]
-        )
-    )
-    output.write("\n")
-
-    for package, (version, tags) in dependent_packages.items():
-        output.write(
-            f"{package:<{package_header_len}} {version:<{version_header_len}} {tags}\n"
-        )
-
-    output.write(
-        " ".join(
-            ["=" * package_header_len, "=" * version_header_len, "=" * tags_header_len]
-        )
-    )
-    output.write("\n")
-    output = output.getvalue()
-
-    with (Path(".") / "min_dependency_table.rst").open("w") as f:
-        f.write(output)
-
-
-def generate_min_dependency_substitutions(app):
-    """Generate min dependency substitutions for docs."""
-    from sklearn._min_dependencies import dependent_packages
-
-    output = StringIO()
-
-    for package, (version, _) in dependent_packages.items():
-        package = package.capitalize()
-        output.write(f".. |{package}MinVersion| replace:: {version}")
-        output.write("\n")
-
-    output = output.getvalue()
-
-    with (Path(".") / "min_dependency_substitutions.rst").open("w") as f:
-        f.write(output)
-
-
 # Config for sphinx_issues
 
 # we use the issues path for PRs since the issues URL will forward
@@ -687,10 +758,11 @@ def setup(app):
     # do not run the examples when using linkcheck by using a small priority
     # (default priority is 500 and sphinx-gallery using builder-inited event too)
     app.connect("builder-inited", disable_plot_gallery_for_linkcheck, priority=50)
-    app.connect("builder-inited", generate_min_dependency_table)
-    app.connect("builder-inited", generate_min_dependency_substitutions)
 
-    # to hide/show the prompt in code examples:
+    # triggered just before the HTML for an individual page is created
+    app.connect("html-page-context", add_js_css_files)
+
+    # to hide/show the prompt in code examples
     app.connect("build-finished", make_carousel_thumbs)
     app.connect("build-finished", filter_search_index)
 
@@ -795,6 +867,10 @@ def setup(app):
     "consistently-create-same-random-numpy-array/5837352#comment6712034_5837352",
 ]
 
+# Config for sphinx-remove-toctrees
+
+remove_from_toctrees = ["metadata_routing.rst"]
+
 # Use a browser-like user agent to avoid some "403 Client Error: Forbidden for
 # url" errors. This is taken from the variable navigator.userAgent inside a
 # browser console.
@@ -812,3 +888,78 @@ def setup(app):
     linkcheck_request_headers = {
         "https://github.com/": {"Authorization": f"token {github_token}"},
     }
+
+
+# -- Convert .rst.template files to .rst ---------------------------------------
+
+from api_reference import API_REFERENCE, DEPRECATED_API_REFERENCE
+
+from sklearn._min_dependencies import dependent_packages
+
+# If development build, link to local page in the top navbar; otherwise link to the
+# development version; see https://github.com/scikit-learn/scikit-learn/pull/22550
+if parsed_version.is_devrelease:
+    development_link = "developers/index"
+else:
+    development_link = "https://scikit-learn.org/dev/developers/index.html"
+
+# Define the templates and target files for conversion
+# Each entry is in the format (template name, file name, kwargs for rendering)
+rst_templates = [
+    ("index", "index", {"development_link": development_link}),
+    (
+        "min_dependency_table",
+        "min_dependency_table",
+        {"dependent_packages": dependent_packages},
+    ),
+    (
+        "min_dependency_substitutions",
+        "min_dependency_substitutions",
+        {"dependent_packages": dependent_packages},
+    ),
+    (
+        "api/index",
+        "api/index",
+        {
+            "API_REFERENCE": sorted(API_REFERENCE.items(), key=lambda x: x[0]),
+            "DEPRECATED_API_REFERENCE": sorted(
+                DEPRECATED_API_REFERENCE.items(), key=lambda x: x[0], reverse=True
+            ),
+        },
+    ),
+]
+
+# Convert each module API reference page
+for module in API_REFERENCE:
+    rst_templates.append(
+        (
+            "api/module",
+            f"api/{module}",
+            {"module": module, "module_info": API_REFERENCE[module]},
+        )
+    )
+
+# Convert the deprecated API reference page (if there exists any)
+if DEPRECATED_API_REFERENCE:
+    rst_templates.append(
+        (
+            "api/deprecated",
+            "api/deprecated",
+            {
+                "DEPRECATED_API_REFERENCE": sorted(
+                    DEPRECATED_API_REFERENCE.items(), key=lambda x: x[0], reverse=True
+                )
+            },
+        )
+    )
+
+for rst_template_name, rst_target_name, kwargs in rst_templates:
+    # Read the corresponding template file into jinja2
+    with (Path(".") / f"{rst_template_name}.rst.template").open(
+        "r", encoding="utf-8"
+    ) as f:
+        t = jinja2.Template(f.read())
+
+    # Render the template and write to the target
+    with (Path(".") / f"{rst_target_name}.rst").open("w", encoding="utf-8") as f:
+        f.write(t.render(**kwargs))
diff --git a/doc/contents.rst b/doc/contents.rst
deleted file mode 100644
index a28634621d558..0000000000000
--- a/doc/contents.rst
+++ /dev/null
@@ -1,24 +0,0 @@
-.. include:: includes/big_toc_css.rst
-.. include:: tune_toc.rst
-
-.. Places global toc into the sidebar
-
-:globalsidebartoc: True
-
-=================
-Table Of Contents
-=================
-
-.. Define an order for the Table of Contents:
-
-.. toctree::
-    :maxdepth: 2
-
-    preface
-    tutorial/index
-    getting_started
-    user_guide
-    glossary
-    auto_examples/index
-    modules/classes
-    developers/index
diff --git a/doc/css/.gitkeep b/doc/css/.gitkeep
new file mode 100644
index 0000000000000..e69de29bb2d1d
diff --git a/doc/data_transforms.rst b/doc/data_transforms.rst
index 084214cb094f5..536539ec97007 100644
--- a/doc/data_transforms.rst
+++ b/doc/data_transforms.rst
@@ -1,9 +1,3 @@
-.. Places parent toc into the sidebar
-
-:parenttoc: True
-
-.. include:: includes/big_toc_css.rst
-
 .. _data-transforms:
 
 Dataset transformations
diff --git a/doc/datasets.rst b/doc/datasets.rst
index b9484a02ce84c..ee767e5843256 100644
--- a/doc/datasets.rst
+++ b/doc/datasets.rst
@@ -1,9 +1,3 @@
-.. Places parent toc into the sidebar
-
-:parenttoc: True
-
-.. include:: includes/big_toc_css.rst
-
 .. _datasets:
 
 =========================
diff --git a/doc/datasets/loading_other_datasets.rst b/doc/datasets/loading_other_datasets.rst
index fdd7fd1666cce..004aa66c001e5 100644
--- a/doc/datasets/loading_other_datasets.rst
+++ b/doc/datasets/loading_other_datasets.rst
@@ -1,7 +1,3 @@
-.. Places parent toc into the sidebar
-
-:parenttoc: True
-
 .. _loading_other_datasets:
 
 Loading other datasets
@@ -37,9 +33,9 @@ and pipelines on 2D data.
   if you plan to use ``matplotlib.pyplpt.imshow``, don't forget to scale to the range
   0 - 1 as done in the following example.
 
-.. topic:: Examples:
+.. rubric:: Examples
 
-    * :ref:`sphx_glr_auto_examples_cluster_plot_color_quantization.py`
+* :ref:`sphx_glr_auto_examples_cluster_plot_color_quantization.py`
 
 .. _libsvm_loader:
 
@@ -72,11 +68,10 @@ features::
   ...     "/path/to/test_dataset.txt", n_features=X_train.shape[1])
   ...                                                         # doctest: +SKIP
 
-.. topic:: Related links:
-
- _`Public datasets in svmlight / libsvm format`: https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets
+.. rubric:: Related links
 
- _`Faster API-compatible implementation`: https://github.com/mblondel/svmlight-loader
+- `Public datasets in svmlight / libsvm format`: https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets
+- `Faster API-compatible implementation`: https://github.com/mblondel/svmlight-loader
 
 ..
     For doctests:
@@ -219,11 +214,11 @@ identifies the dataset::
   '969'
 
 
-.. topic:: References:
+.. rubric:: References
 
- * :arxiv:`Vanschoren, van Rijn, Bischl and Torgo. "OpenML: networked science in
-   machine learning" ACM SIGKDD Explorations Newsletter, 15(2), 49-60, 2014.
-   <1407.7722>`
+* :arxiv:`Vanschoren, van Rijn, Bischl and Torgo. "OpenML: networked science in
+  machine learning" ACM SIGKDD Explorations Newsletter, 15(2), 49-60, 2014.
+  <1407.7722>`
 
 .. _openml_parser:
 
diff --git a/doc/datasets/real_world.rst b/doc/datasets/real_world.rst
index 78b09e6f722b0..f05d475b0db78 100644
--- a/doc/datasets/real_world.rst
+++ b/doc/datasets/real_world.rst
@@ -1,7 +1,3 @@
-.. Places parent toc into the sidebar
-
-:parenttoc: True
-
 .. _real_world_datasets:
 
 Real world datasets
diff --git a/doc/datasets/sample_generators.rst b/doc/datasets/sample_generators.rst
index 7dc123f08424c..5b8264c2a22b5 100644
--- a/doc/datasets/sample_generators.rst
+++ b/doc/datasets/sample_generators.rst
@@ -1,7 +1,3 @@
-.. Places parent toc into the sidebar
-
-:parenttoc: True
-
 .. _sample_generators:
 
 Generated datasets
diff --git a/doc/datasets/toy_dataset.rst b/doc/datasets/toy_dataset.rst
index 65fd20abd361d..d7edecddd3510 100644
--- a/doc/datasets/toy_dataset.rst
+++ b/doc/datasets/toy_dataset.rst
@@ -1,7 +1,3 @@
-.. Places parent toc into the sidebar
-
-:parenttoc: True
-
 .. _toy_datasets:
 
 Toy datasets
diff --git a/doc/developers/advanced_installation.rst b/doc/developers/advanced_installation.rst
index ed25d30601e45..88521c6c51867 100644
--- a/doc/developers/advanced_installation.rst
+++ b/doc/developers/advanced_installation.rst
@@ -58,9 +58,9 @@ feature, code or documentation improvement).
    If you plan on submitting a pull-request, you should clone from your fork
    instead.
 
-#. Install a recent version of Python (3.9 is recommended at the time of writing)
-   for instance using Miniforge3_. Miniforge provides a conda-based distribution
-   of Python and the most popular scientific libraries.
+#. Install a recent version of Python (3.9 or later at the time of writing) for
+   instance using Miniforge3_. Miniforge provides a conda-based distribution of
+   Python and the most popular scientific libraries.
 
    If you installed Python with conda, we recommend to create a dedicated
    `conda environment`_ with all the build dependencies of scikit-learn
@@ -68,7 +68,7 @@ feature, code or documentation improvement).
 
    .. prompt:: bash $
 
-     conda create -n sklearn-env -c conda-forge python=3.9 numpy scipy cython meson-python ninja
+     conda create -n sklearn-env -c conda-forge python numpy scipy cython meson-python ninja
 
    It is not always necessary but it is safer to open a new prompt before
    activating the newly created conda environment.
diff --git a/doc/developers/contributing.rst b/doc/developers/contributing.rst
index 9f43d8ed52c38..a9dc7cb305f61 100644
--- a/doc/developers/contributing.rst
+++ b/doc/developers/contributing.rst
@@ -7,12 +7,9 @@ Contributing
 .. currentmodule:: sklearn
 
 This project is a community effort, and everyone is welcome to
-contribute.
-
-The project is hosted on https://github.com/scikit-learn/scikit-learn
-
+contribute. It is hosted on https://github.com/scikit-learn/scikit-learn.
 The decision making process and governance structure of scikit-learn is laid
-out in the governance document: :ref:`governance`.
+out in :ref:`governance`.
 
 Scikit-learn is somewhat :ref:`selective <selectiveness>` when it comes to
 adding new algorithms, and the best way to contribute and to help the project
@@ -70,10 +67,12 @@ link to it from your website, or simply star to say "I use it":
 
 .. raw:: html
 
-   <a class="github-button" href="https://github.com/scikit-learn/scikit-learn"
-   data-icon="octicon-star" data-size="large" data-show-count="true" aria-label="Star
-   scikit-learn/scikit-learn on GitHub">Star</a>
-   <script async defer src="https://buttons.github.io/buttons.js"></script>
+  <p>
+    <object
+      data="https://img.shields.io/github/stars/scikit-learn/scikit-learn?style=for-the-badge&logo=github"
+      type="image/svg+xml">
+    </object>
+  </p>
 
 In case a contribution/issue involves changes to the API principles
 or changes to dependencies or supported versions, it must be backed by a
@@ -82,31 +81,26 @@ or changes to dependencies or supported versions, it must be backed by a
 using the `SLEP template <https://scikit-learn-enhancement-proposals.readthedocs.io/en/latest/slep_template.html>`_
 and follows the decision-making process outlined in :ref:`governance`.
 
-|details-start|
-**Contributing to related projects**
-|details-split|
-
-   Scikit-learn thrives in an ecosystem of several related projects, which also
-   may have relevant issues to work on, including smaller projects such as:
+.. dropdown:: Contributing to related projects
 
-   * `scikit-learn-contrib <https://github.com/search?q=org%3Ascikit-learn-contrib+is%3Aissue+is%3Aopen+sort%3Aupdated-desc&type=Issues>`__
-   * `joblib <https://github.com/joblib/joblib/issues>`__
-   * `sphinx-gallery <https://github.com/sphinx-gallery/sphinx-gallery/issues>`__
-   * `numpydoc <https://github.com/numpy/numpydoc/issues>`__
-   * `liac-arff <https://github.com/renatopp/liac-arff/issues>`__
+  Scikit-learn thrives in an ecosystem of several related projects, which also
+  may have relevant issues to work on, including smaller projects such as:
 
-   and larger projects:
+  * `scikit-learn-contrib <https://github.com/search?q=org%3Ascikit-learn-contrib+is%3Aissue+is%3Aopen+sort%3Aupdated-desc&type=Issues>`__
+  * `joblib <https://github.com/joblib/joblib/issues>`__
+  * `sphinx-gallery <https://github.com/sphinx-gallery/sphinx-gallery/issues>`__
+  * `numpydoc <https://github.com/numpy/numpydoc/issues>`__
+  * `liac-arff <https://github.com/renatopp/liac-arff/issues>`__
 
-   * `numpy <https://github.com/numpy/numpy/issues>`__
-   * `scipy <https://github.com/scipy/scipy/issues>`__
-   * `matplotlib <https://github.com/matplotlib/matplotlib/issues>`__
-   * and so on.
+  and larger projects:
 
-   Look for issues marked "help wanted" or similar.
-   Helping these projects may help Scikit-learn too.
-   See also :ref:`related_projects`.
+  * `numpy <https://github.com/numpy/numpy/issues>`__
+  * `scipy <https://github.com/scipy/scipy/issues>`__
+  * `matplotlib <https://github.com/matplotlib/matplotlib/issues>`__
+  * and so on.
 
-|details-end|
+  Look for issues marked "help wanted" or similar. Helping these projects may help
+  scikit-learn too. See also :ref:`related_projects`.
 
 Submitting a bug report or a feature request
 ============================================
@@ -139,17 +133,15 @@ following rules before submitting:
 How to make a good bug report
 -----------------------------
 
-When you submit an issue to `Github
+When you submit an issue to `GitHub
 <https://github.com/scikit-learn/scikit-learn/issues>`__, please do your best to
 follow these guidelines! This will make it a lot easier to provide you with good
 feedback:
 
 - The ideal bug report contains a :ref:`short reproducible code snippet
-  <minimal_reproducer>`, this way
-  anyone can try to reproduce the bug easily (see `this
-  <https://stackoverflow.com/help/mcve>`_ for more details). If your snippet is
-  longer than around 50 lines, please link to a `gist
-  <https://gist.github.com>`_ or a github repo.
+  <minimal_reproducer>`, this way anyone can try to reproduce the bug easily. If your
+  snippet is longer than around 50 lines, please link to a `Gist
+  <https://gist.github.com>`_ or a GitHub repo.
 
 - If not feasible to include a reproducible snippet, please be specific about
   what **estimators and/or functions are involved and the shape of the data**.
@@ -158,18 +150,18 @@ feedback:
 
 - Please include your **operating system type and version number**, as well as
   your **Python, scikit-learn, numpy, and scipy versions**. This information
-  can be found by running the following code snippet::
+  can be found by running:
 
-    >>> import sklearn
-    >>> sklearn.show_versions()  # doctest: +SKIP
+  .. prompt:: bash
+
+    python -c "import sklearn; sklearn.show_versions()"
 
 - Please ensure all **code snippets and error messages are formatted in
   appropriate code blocks**.  See `Creating and highlighting code blocks
   <https://help.github.com/articles/creating-and-highlighting-code-blocks>`_
   for more details.
 
-If you want to help curate issues, read :ref:`the following
-<bug_triaging>`.
+If you want to help curate issues, read about :ref:`bug_triaging`.
 
 Contributing code
 =================
@@ -245,7 +237,7 @@ how to set up your git repository:
 3. Clone your fork of the scikit-learn repo from your GitHub account to your
    local disk:
 
-   .. prompt:: bash $
+   .. prompt:: bash
 
       git clone git@github.com:YourLogin/scikit-learn.git  # add --depth 1 if your connection is slow
       cd scikit-learn
@@ -255,7 +247,7 @@ how to set up your git repository:
 
 5. Install the development dependencies:
 
-   .. prompt:: bash $
+   .. prompt:: bash
 
         pip install pytest pytest-cov ruff mypy numpydoc black==24.3.0
 
@@ -265,12 +257,14 @@ how to set up your git repository:
    scikit-learn repository, which you can use to keep your repository
    synchronized with the latest changes:
 
-   .. prompt:: bash $
+   .. prompt:: bash
 
         git remote add upstream git@github.com:scikit-learn/scikit-learn.git
 
 7. Check that the `upstream` and `origin` remote aliases are configured correctly
-   by running `git remote -v` which should display::
+   by running `git remote -v` which should display:
+
+   .. code-block:: text
 
         origin	git@github.com:YourLogin/scikit-learn.git (fetch)
         origin	git@github.com:YourLogin/scikit-learn.git (push)
@@ -286,7 +280,7 @@ The next steps now describe the process of modifying code and submitting a PR:
 8. Synchronize your ``main`` branch with the ``upstream/main`` branch,
    more details on `GitHub Docs <https://docs.github.com/en/github/collaborating-with-issues-and-pull-requests/syncing-a-fork>`_:
 
-   .. prompt:: bash $
+   .. prompt:: bash
 
         git checkout main
         git fetch upstream
@@ -294,7 +288,7 @@ The next steps now describe the process of modifying code and submitting a PR:
 
 9. Create a feature branch to hold your development changes:
 
-   .. prompt:: bash $
+   .. prompt:: bash
 
         git checkout -b my_feature
 
@@ -304,7 +298,7 @@ The next steps now describe the process of modifying code and submitting a PR:
 10. (**Optional**) Install `pre-commit <https://pre-commit.com/#install>`_ to
     run code style checks before each commit:
 
-    .. prompt:: bash $
+    .. prompt:: bash
 
           pip install pre-commit
           pre-commit install
@@ -316,7 +310,7 @@ The next steps now describe the process of modifying code and submitting a PR:
     do the version control. When you're done editing, add changed files using
     ``git add`` and then ``git commit``:
 
-    .. prompt:: bash $
+    .. prompt:: bash
 
         git add modified_files
         git commit
@@ -324,7 +318,7 @@ The next steps now describe the process of modifying code and submitting a PR:
     to record your changes in Git, then push the changes to your GitHub
     account with:
 
-    .. prompt:: bash $
+    .. prompt:: bash
 
        git push -u origin my_feature
 
@@ -337,7 +331,7 @@ The next steps now describe the process of modifying code and submitting a PR:
 It is often helpful to keep your local feature branch synchronized with the
 latest changes of the main scikit-learn repository:
 
-.. prompt:: bash $
+.. prompt:: bash
 
     git fetch upstream
     git merge upstream/main
@@ -347,7 +341,7 @@ Subsequently, you might need to solve the conflicts. You can refer to the
 line
 <https://help.github.com/articles/resolving-a-merge-conflict-using-the-command-line/>`_.
 
-.. topic:: Learning git:
+.. topic:: Learning Git
 
     The `Git documentation <https://git-scm.com/documentation>`_ and
     http://try.github.io are excellent resources to get started with git,
@@ -359,19 +353,18 @@ Pull request checklist
 ----------------------
 
 Before a PR can be merged, it needs to be approved by two core developers.
-Please prefix the title of your pull request with ``[MRG]`` if the
-contribution is complete and should be subjected to a detailed review. An
-incomplete contribution -- where you expect to do more work before receiving
-a full review -- should be prefixed ``[WIP]`` (to indicate a work in
-progress) and changed to ``[MRG]`` when it matures. WIPs may be useful to:
+An incomplete contribution -- where you expect to do more work before receiving
+a full review -- should be marked as a `draft pull request
+<https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/proposing-changes-to-your-work-with-pull-requests/changing-the-stage-of-a-pull-request>`__
+and changed to "ready for review" when it matures. Draft PRs may be useful to:
 indicate you are working on something to avoid duplicated work, request
-broad review of functionality or API, or seek collaborators. WIPs often
+broad review of functionality or API, or seek collaborators. Draft PRs often
 benefit from the inclusion of a `task list
 <https://github.com/blog/1375-task-lists-in-gfm-issues-pulls-comments>`_ in
 the PR description.
 
 In order to ease the reviewing process, we recommend that your contribution
-complies with the following rules before marking a PR as ``[MRG]``. The
+complies with the following rules before marking a PR as "ready for review". The
 **bolded** ones are especially important:
 
 1. **Give your pull request a helpful title** that summarizes what your
@@ -418,13 +411,11 @@ complies with the following rules before marking a PR as ``[MRG]``. The
    non-regression tests should fail for the code base in the ``main`` branch
    and pass for the PR code.
 
-
 5. Follow the :ref:`coding-guidelines`.
 
-
-6. When applicable, use the validation tools and scripts in the
-   ``sklearn.utils`` submodule.  A list of utility routines available
-   for developers can be found in the :ref:`developers-utils` page.
+6. When applicable, use the validation tools and scripts in the :mod:`sklearn.utils`
+   module. A list of utility routines available for developers can be found in the
+   :ref:`developers-utils` page.
 
 7. Often pull requests resolve one or more other issues (or pull requests).
    If merging your pull request means that some other issues/PRs should
@@ -433,61 +424,59 @@ complies with the following rules before marking a PR as ``[MRG]``. The
    (e.g., ``Fixes #1234``; multiple issues/PRs are allowed as long as each
    one is preceded by a keyword). Upon merging, those issues/PRs will
    automatically be closed by GitHub. If your pull request is simply
-   related to some other issues/PRs, create a link to them without using
-   the keywords (e.g., ``See also #1234``).
+   related to some other issues/PRs, or it only partially resolves the target
+   issue, create a link to them without using the keywords (e.g., ``Towards #1234``).
 
 8. PRs should often substantiate the change, through benchmarks of
-    performance and efficiency (see :ref:`monitoring_performances`) or through
-    examples of usage. Examples also illustrate the features and intricacies of
-    the library to users. Have a look at other examples in the `examples/
-    <https://github.com/scikit-learn/scikit-learn/tree/main/examples>`_
-    directory for reference. Examples should demonstrate why the new
-    functionality is useful in practice and, if possible, compare it to other
-    methods available in scikit-learn.
+   performance and efficiency (see :ref:`monitoring_performances`) or through
+   examples of usage. Examples also illustrate the features and intricacies of
+   the library to users. Have a look at other examples in the `examples/
+   <https://github.com/scikit-learn/scikit-learn/tree/main/examples>`_
+   directory for reference. Examples should demonstrate why the new
+   functionality is useful in practice and, if possible, compare it to other
+   methods available in scikit-learn.
 
 9. New features have some maintenance overhead. We expect PR authors
-    to take part in the maintenance for the code they submit, at least
-    initially. New features need to be illustrated with narrative
-    documentation in the user guide, with small code snippets.
-    If relevant, please also add references in the literature, with PDF links
-    when possible.
+   to take part in the maintenance for the code they submit, at least
+   initially. New features need to be illustrated with narrative
+   documentation in the user guide, with small code snippets.
+   If relevant, please also add references in the literature, with PDF links
+   when possible.
 
 10. The user guide should also include expected time and space complexity
     of the algorithm and scalability, e.g. "this algorithm can scale to a
     large number of samples > 100000, but does not scale in dimensionality:
-    n_features is expected to be lower than 100".
+    `n_features` is expected to be lower than 100".
 
 You can also check our :ref:`code_review` to get an idea of what reviewers
 will expect.
 
 You can check for common programming errors with the following tools:
 
-* Code with a good unittest coverage (at least 80%, better 100%), check
-  with:
+* Code with a good unit test coverage (at least 80%, better 100%), check with:
 
-  .. prompt:: bash $
+  .. prompt:: bash
 
     pip install pytest pytest-cov
-    pytest --cov sklearn path/to/tests_for_package
+    pytest --cov sklearn path/to/tests
 
-  see also :ref:`testing_coverage`
+  See also :ref:`testing_coverage`.
 
-  Run static analysis with `mypy`:
+* Run static analysis with `mypy`:
 
-  .. prompt:: bash $
+  .. prompt:: bash
 
       mypy sklearn
 
-  must not produce new errors in your pull request. Using `# type: ignore`
+  This must not produce new errors in your pull request. Using `# type: ignore`
   annotation can be a workaround for a few cases that are not supported by
   mypy, in particular,
 
-  - when importing C or Cython modules
-  - on properties with decorators
+  - when importing C or Cython modules,
+  - on properties with decorators.
 
 Bonus points for contributions that include a performance analysis with
 a benchmark script and profiling output (see :ref:`monitoring_performances`).
-
 Also check out the :ref:`performance-howto` guide for more details on
 profiling and Cython optimizations.
 
@@ -498,7 +487,7 @@ profiling and Cython optimizations.
   on all new contributions will get the overall code base quality in the
   right direction.
 
-.. note::
+.. seealso::
 
    For two very well documented and more detailed guides on development
    workflow, please pay a visit to the `Scipy Development Workflow
@@ -550,9 +539,7 @@ Stalled pull requests
 
 As contributing a feature can be a lengthy process, some
 pull requests appear inactive but unfinished. In such a case, taking
-them over is a great service for the project.
-
-A good etiquette to take over is:
+them over is a great service for the project. A good etiquette to take over is:
 
 * **Determine if a PR is stalled**
 
@@ -619,33 +606,32 @@ the contributor become familiar with the contribution workflow, and for the core
 devs to become acquainted with the contributor; besides which, we frequently
 underestimate how easy an issue is to solve!
 
-.. topic:: good first issue tag
+- **Good first issue tag**
 
-    A great way to start contributing to scikit-learn is to pick an item from
-    the list of `good first issues
-    <https://github.com/scikit-learn/scikit-learn/labels/good%20first%20issue>`_
-    in the issue tracker. Resolving these issues allow you to start contributing
-    to the project without much prior knowledge. If you have already contributed
-    to scikit-learn, you should look at Easy issues instead.
+  A great way to start contributing to scikit-learn is to pick an item from
+  the list of `good first issues
+  <https://github.com/scikit-learn/scikit-learn/labels/good%20first%20issue>`_
+  in the issue tracker. Resolving these issues allow you to start contributing
+  to the project without much prior knowledge. If you have already contributed
+  to scikit-learn, you should look at Easy issues instead.
 
-.. topic:: Easy tag
+- **Easy tag**
 
-    If you have already contributed to scikit-learn, another great way to contribute
-    to scikit-learn is to pick an item from the list of `Easy issues
-    <https://github.com/scikit-learn/scikit-learn/labels/Easy>`_ in the issue
-    tracker. Your assistance in this area will be greatly appreciated by the
-    more experienced developers as it helps free up their time to concentrate on
-    other issues.
+  If you have already contributed to scikit-learn, another great way to contribute
+  to scikit-learn is to pick an item from the list of `Easy issues
+  <https://github.com/scikit-learn/scikit-learn/labels/Easy>`_ in the issue
+  tracker. Your assistance in this area will be greatly appreciated by the
+  more experienced developers as it helps free up their time to concentrate on
+  other issues.
 
-.. topic:: help wanted tag
+- **Help wanted tag**
 
-    We often use the help wanted tag to mark issues regardless of difficulty. Additionally,
-    we use the help wanted tag to mark Pull Requests which have been abandoned
-    by their original contributor and are available for someone to pick up where the original
-    contributor left off. The list of issues with the help wanted tag can be found
-    `here <https://github.com/scikit-learn/scikit-learn/labels/help%20wanted>`_.
-
-    Note that not all issues which need contributors will have this tag.
+  We often use the help wanted tag to mark issues regardless of difficulty.
+  Additionally, we use the help wanted tag to mark Pull Requests which have been
+  abandoned by their original contributor and are available for someone to pick up where
+  the original contributor left off. The list of issues with the help wanted tag can be
+  found `here <https://github.com/scikit-learn/scikit-learn/labels/help%20wanted>`_.
+  Note that not all issues which need contributors will have this tag.
 
 .. _contribute_documentation:
 
@@ -654,244 +640,238 @@ Documentation
 
 We are glad to accept any sort of documentation:
 
-* **function/method/class docstrings** (also known as "API documentation") -
-  these describe what the object does and details any parameters, attributes and
-  methods. Docstrings live alongside the code in
-  `sklearn/ <https://github.com/scikit-learn/scikit-learn/tree/main/sklearn>`_.
-* **user guide** - these provide more detailed information about the algorithms
+* **Function/method/class docstrings:** Also known as "API documentation", these
+  describe what the object does and details any parameters, attributes and
+  methods. Docstrings live alongside the code in `sklearn/
+  <https://github.com/scikit-learn/scikit-learn/tree/main/sklearn>`_, and are generated
+  generated according to `doc/api_reference.py
+  <https://github.com/scikit-learn/scikit-learn/blob/main/doc/api_reference.py>`_. To
+  add, update, remove, or deprecate a public API that is listed in :ref:`api_ref`, this
+  is the place to look at.
+* **User guide:** These provide more detailed information about the algorithms
   implemented in scikit-learn and generally live in the root
   `doc/ <https://github.com/scikit-learn/scikit-learn/tree/main/doc>`_ directory
-  and
-  `doc/modules/ <https://github.com/scikit-learn/scikit-learn/tree/main/doc/modules>`_.
-* **tutorials** - these introduce various statistical learning and machine learning
+  and `doc/modules/ <https://github.com/scikit-learn/scikit-learn/tree/main/doc/modules>`_.
+* **Tutorials:** These introduce various statistical learning and machine learning
   concepts and are located in
   `doc/tutorial <https://github.com/scikit-learn/scikit-learn/tree/main/doc/tutorial>`_.
-* **examples** - these provide full code examples that may demonstrate the use
+* **Examples:** These provide full code examples that may demonstrate the use
   of scikit-learn modules, compare different algorithms or discuss their
-  interpretation etc. Examples live in
-  `examples/ <https://github.com/scikit-learn/scikit-learn/tree/main/examples>`_
-* **other reStructuredText documents** - provide various other
-  useful information (e.g., the :ref:`contributing` guide) and live in
+  interpretation, etc. Examples live in
+  `examples/ <https://github.com/scikit-learn/scikit-learn/tree/main/examples>`_.
+* **Other reStructuredText documents:** These provide various other useful information
+  (e.g., the :ref:`contributing` guide) and live in
   `doc/ <https://github.com/scikit-learn/scikit-learn/tree/main/doc>`_.
 
-|details-start|
-**Guidelines for writing docstrings**
-|details-split|
 
-* When documenting the parameters and attributes, here is a list of some
-  well-formatted examples::
+.. dropdown:: Guidelines for writing docstrings
 
-    n_clusters : int, default=3
-        The number of clusters detected by the algorithm.
+  * When documenting the parameters and attributes, here is a list of some
+    well-formatted examples
 
-    some_param : {'hello', 'goodbye'}, bool or int, default=True
-        The parameter description goes here, which can be either a string
-        literal (either `hello` or `goodbye`), a bool, or an int. The default
-        value is True.
+    .. code-block:: text
 
-    array_parameter : {array-like, sparse matrix} of shape (n_samples, n_features) or (n_samples,)
-        This parameter accepts data in either of the mentioned forms, with one
-        of the mentioned shapes. The default value is
-        `np.ones(shape=(n_samples,))`.
+      n_clusters : int, default=3
+          The number of clusters detected by the algorithm.
 
-    list_param : list of int
+      some_param : {"hello", "goodbye"}, bool or int, default=True
+          The parameter description goes here, which can be either a string
+          literal (either `hello` or `goodbye`), a bool, or an int. The default
+          value is True.
 
-    typed_ndarray : ndarray of shape (n_samples,), dtype=np.int32
+      array_parameter : {array-like, sparse matrix} of shape (n_samples, n_features) \
+          or (n_samples,)
+          This parameter accepts data in either of the mentioned forms, with one
+          of the mentioned shapes. The default value is `np.ones(shape=(n_samples,))`.
 
-    sample_weight : array-like of shape (n_samples,), default=None
+      list_param : list of int
 
-    multioutput_array : ndarray of shape (n_samples, n_classes) or list of such arrays
+      typed_ndarray : ndarray of shape (n_samples,), dtype=np.int32
 
-  In general have the following in mind:
+      sample_weight : array-like of shape (n_samples,), default=None
 
-  * Use Python basic types. (``bool`` instead of ``boolean``)
-  * Use parenthesis for defining shapes: ``array-like of shape (n_samples,)``
-    or ``array-like of shape (n_samples, n_features)``
-  * For strings with multiple options, use brackets: ``input: {'log',
-    'squared', 'multinomial'}``
-  * 1D or 2D data can be a subset of ``{array-like, ndarray, sparse matrix,
-    dataframe}``. Note that ``array-like`` can also be a ``list``, while
-    ``ndarray`` is explicitly only a ``numpy.ndarray``.
-  * Specify ``dataframe`` when "frame-like" features are being used, such as
-    the column names.
-  * When specifying the data type of a list, use ``of`` as a delimiter: ``list
-    of int``. When the parameter supports arrays giving details about the
-    shape and/or data type and a list of such arrays, you can use one of
-    ``array-like of shape (n_samples,) or list of such arrays``.
-  * When specifying the dtype of an ndarray, use e.g. ``dtype=np.int32`` after
-    defining the shape: ``ndarray of shape (n_samples,), dtype=np.int32``. You
-    can specify multiple dtype as a set: ``array-like of shape (n_samples,),
-    dtype={np.float64, np.float32}``. If one wants to mention arbitrary
-    precision, use `integral` and `floating` rather than the Python dtype
-    `int` and `float`. When both `int` and `floating` are supported, there is
-    no need to specify the dtype.
-  * When the default is ``None``, ``None`` only needs to be specified at the
-    end with ``default=None``. Be sure to include in the docstring, what it
-    means for the parameter or attribute to be ``None``.
+      multioutput_array : ndarray of shape (n_samples, n_classes) or list of such arrays
 
-* Add "See Also" in docstrings for related classes/functions.
+    In general have the following in mind:
 
-* "See Also" in docstrings should be one line per reference, with a colon and an
-  explanation, for example::
+    * Use Python basic types. (``bool`` instead of ``boolean``)
+    * Use parenthesis for defining shapes: ``array-like of shape (n_samples,)``
+      or ``array-like of shape (n_samples, n_features)``
+    * For strings with multiple options, use brackets: ``input: {'log',
+      'squared', 'multinomial'}``
+    * 1D or 2D data can be a subset of ``{array-like, ndarray, sparse matrix,
+      dataframe}``. Note that ``array-like`` can also be a ``list``, while
+      ``ndarray`` is explicitly only a ``numpy.ndarray``.
+    * Specify ``dataframe`` when "frame-like" features are being used, such as
+      the column names.
+    * When specifying the data type of a list, use ``of`` as a delimiter: ``list
+      of int``. When the parameter supports arrays giving details about the
+      shape and/or data type and a list of such arrays, you can use one of
+      ``array-like of shape (n_samples,) or list of such arrays``.
+    * When specifying the dtype of an ndarray, use e.g. ``dtype=np.int32`` after
+      defining the shape: ``ndarray of shape (n_samples,), dtype=np.int32``. You
+      can specify multiple dtype as a set: ``array-like of shape (n_samples,),
+      dtype={np.float64, np.float32}``. If one wants to mention arbitrary
+      precision, use `integral` and `floating` rather than the Python dtype
+      `int` and `float`. When both `int` and `floating` are supported, there is
+      no need to specify the dtype.
+    * When the default is ``None``, ``None`` only needs to be specified at the
+      end with ``default=None``. Be sure to include in the docstring, what it
+      means for the parameter or attribute to be ``None``.
 
-    See Also
-    --------
-    SelectKBest : Select features based on the k highest scores.
-    SelectFpr : Select features based on a false positive rate test.
+  * Add "See Also" in docstrings for related classes/functions.
 
-* Add one or two snippets of code in "Example" section to show how it can be used.
+  * "See Also" in docstrings should be one line per reference, with a colon and an
+    explanation, for example:
 
-|details-end|
+    .. code-block:: text
 
-|details-start|
-**Guidelines for writing the user guide and other reStructuredText documents**
-|details-split|
+      See Also
+      --------
+      SelectKBest : Select features based on the k highest scores.
+      SelectFpr : Select features based on a false positive rate test.
 
-It is important to keep a good compromise between mathematical and algorithmic
-details, and give intuition to the reader on what the algorithm does.
+  * Add one or two snippets of code in "Example" section to show how it can be used.
 
-* Begin with a concise, hand-waving explanation of what the algorithm/code does on
-  the data.
 
-* Highlight the usefulness of the feature and its recommended application.
-  Consider including the algorithm's complexity
-  (:math:`O\left(g\left(n\right)\right)`) if available, as "rules of thumb" can
-  be very machine-dependent. Only if those complexities are not available, then
-  rules of thumb may be provided instead.
+.. dropdown:: Guidelines for writing the user guide and other reStructuredText documents
 
-* Incorporate a relevant figure (generated from an example) to provide intuitions.
+  It is important to keep a good compromise between mathematical and algorithmic
+  details, and give intuition to the reader on what the algorithm does.
 
-* Include one or two short code examples to demonstrate the feature's usage.
+  * Begin with a concise, hand-waving explanation of what the algorithm/code does on
+    the data.
 
-* Introduce any necessary mathematical equations, followed by references. By
-  deferring the mathematical aspects, the documentation becomes more accessible
-  to users primarily interested in understanding the feature's practical
-  implications rather than its underlying mechanics.
+  * Highlight the usefulness of the feature and its recommended application.
+    Consider including the algorithm's complexity
+    (:math:`O\left(g\left(n\right)\right)`) if available, as "rules of thumb" can
+    be very machine-dependent. Only if those complexities are not available, then
+    rules of thumb may be provided instead.
 
-* When editing reStructuredText (``.rst``) files, try to keep line length under
-  88 characters when possible (exceptions include links and tables).
+  * Incorporate a relevant figure (generated from an example) to provide intuitions.
 
-* In scikit-learn reStructuredText files both single and double backticks
-  surrounding text will render as inline literal (often used for code, e.g.,
-  `list`). This is due to specific configurations we have set. Single
-  backticks should be used nowadays.
+  * Include one or two short code examples to demonstrate the feature's usage.
 
-* Too much information makes it difficult for users to access the content they
-  are interested in. Use dropdowns to factorize it by using the following
-  syntax::
+  * Introduce any necessary mathematical equations, followed by references. By
+    deferring the mathematical aspects, the documentation becomes more accessible
+    to users primarily interested in understanding the feature's practical
+    implications rather than its underlying mechanics.
 
-    |details-start|
-    **Dropdown title**
-    |details-split|
+  * When editing reStructuredText (``.rst``) files, try to keep line length under
+    88 characters when possible (exceptions include links and tables).
 
-    Dropdown content.
+  * In scikit-learn reStructuredText files both single and double backticks
+    surrounding text will render as inline literal (often used for code, e.g.,
+    `list`). This is due to specific configurations we have set. Single
+    backticks should be used nowadays.
 
-    |details-end|
+  * Too much information makes it difficult for users to access the content they
+    are interested in. Use dropdowns to factorize it by using the following syntax
 
-  The snippet above will result in the following dropdown:
+    .. code-block:: rst
 
-  |details-start|
-  **Dropdown title**
-  |details-split|
+      .. dropdown:: Dropdown title
 
-  Dropdown content.
+        Dropdown content.
 
-  |details-end|
+    The snippet above will result in the following dropdown:
 
-* Information that can be hidden by default using dropdowns is:
+    .. dropdown:: Dropdown title
 
-  * low hierarchy sections such as `References`, `Properties`, etc. (see for
-    instance the subsections in :ref:`det_curve`);
+      Dropdown content.
 
-  * in-depth mathematical details;
+  * Information that can be hidden by default using dropdowns is:
 
-  * narrative that is use-case specific;
+    * low hierarchy sections such as `References`, `Properties`, etc. (see for
+      instance the subsections in :ref:`det_curve`);
 
-  * in general, narrative that may only interest users that want to go beyond
-    the pragmatics of a given tool.
+    * in-depth mathematical details;
 
-* Do not use dropdowns for the low level section `Examples`, as it should stay
-  visible to all users. Make sure that the `Examples` section comes right after
-  the main discussion with the least possible folded section in-between.
+    * narrative that is use-case specific;
 
-* Be aware that dropdowns break cross-references. If that makes sense, hide the
-  reference along with the text mentioning it. Else, do not use dropdown.
+    * in general, narrative that may only interest users that want to go beyond
+      the pragmatics of a given tool.
 
-|details-end|
+  * Do not use dropdowns for the low level section `Examples`, as it should stay
+    visible to all users. Make sure that the `Examples` section comes right after
+    the main discussion with the least possible folded section in-between.
 
+  * Be aware that dropdowns break cross-references. If that makes sense, hide the
+    reference along with the text mentioning it. Else, do not use dropdown.
 
-|details-start|
-**Guidelines for writing references**
-|details-split|
 
-* When bibliographic references are available with `arxiv <https://arxiv.org/>`_
-  or `Digital Object Identifier <https://www.doi.org/>`_ identification numbers,
-  use the sphinx directives `:arxiv:` or `:doi:`. For example, see references in
-  :ref:`Spectral Clustering Graphs <spectral_clustering_graph>`.
+.. dropdown:: Guidelines for writing references
 
-* For "References" in docstrings, see the Silhouette Coefficient
-  (:func:`sklearn.metrics.silhouette_score`).
+  * When bibliographic references are available with `arxiv <https://arxiv.org/>`_
+    or `Digital Object Identifier <https://www.doi.org/>`_ identification numbers,
+    use the sphinx directives `:arxiv:` or `:doi:`. For example, see references in
+    :ref:`Spectral Clustering Graphs <spectral_clustering_graph>`.
 
-* To cross-reference to other pages in the scikit-learn documentation use the
-  reStructuredText cross-referencing syntax:
+  * For the "References" section in docstrings, see
+    :func:`sklearn.metrics.silhouette_score` as an example.
 
-  * Section - to link to an arbitrary section in the documentation, use
-    reference labels (see `Sphinx docs
-    <https://www.sphinx-doc.org/en/master/usage/restructuredtext/roles.html#ref-role>`_).
-    For example:
+  * To cross-reference to other pages in the scikit-learn documentation use the
+    reStructuredText cross-referencing syntax:
 
-    .. code-block:: rst
+    * **Section:** to link to an arbitrary section in the documentation, use
+      reference labels (see `Sphinx docs
+      <https://www.sphinx-doc.org/en/master/usage/restructuredtext/roles.html#ref-role>`_).
+      For example:
 
-        .. _my-section:
+      .. code-block:: rst
 
-        My section
-        ----------
+          .. _my-section:
 
-        This is the text of the section.
+          My section
+          ----------
 
-        To refer to itself use :ref:`my-section`.
+          This is the text of the section.
 
-    You should not modify existing sphinx reference labels as this would break
-    existing cross references and external links pointing to specific sections
-    in the scikit-learn documentation.
+          To refer to itself use :ref:`my-section`.
 
-  * Glossary - linking to a term in the :ref:`glossary`:
+      You should not modify existing sphinx reference labels as this would break
+      existing cross references and external links pointing to specific sections
+      in the scikit-learn documentation.
 
-    .. code-block:: rst
+    * **Glossary:** linking to a term in the :ref:`glossary`:
 
-        :term:`cross_validation`
+      .. code-block:: rst
 
-  * Function - to link to the documentation of a function, use the full import
-    path to the function:
+          :term:`cross_validation`
 
-    .. code-block:: rst
+    * **Function:** to link to the documentation of a function, use the full import
+      path to the function:
 
-        :func:`~sklearn.model_selection.cross_val_score`
+      .. code-block:: rst
 
-    However, if there is a `.. currentmodule::` directive above you in the document,
-    you will only need to use the path to the function succeeding the current
-    module specified. For example:
+          :func:`~sklearn.model_selection.cross_val_score`
 
-    .. code-block:: rst
+      However, if there is a `.. currentmodule::` directive above you in the document,
+      you will only need to use the path to the function succeeding the current
+      module specified. For example:
 
-        .. currentmodule:: sklearn.model_selection
+      .. code-block:: rst
 
-        :func:`cross_val_score`
+          .. currentmodule:: sklearn.model_selection
 
-  * Class - to link to documentation of a class, use the full import path to the
-    class, unless there is a 'currentmodule' directive in the document above
-    (see above):
+          :func:`cross_val_score`
 
-    .. code-block:: rst
+    * **Class:** to link to documentation of a class, use the full import path to the
+      class, unless there is a `.. currentmodule::` directive in the document above
+      (see above):
 
-        :class:`~sklearn.preprocessing.StandardScaler`
+      .. code-block:: rst
 
-|details-end|
+          :class:`~sklearn.preprocessing.StandardScaler`
 
 You can edit the documentation using any text editor, and then generate the
 HTML output by following :ref:`building_documentation`. The resulting HTML files
-will be placed in ``_build/html/stable`` and are viewable in a web browser, for
-instance by opening the local ``_build/html/stable/index.html`` file.
+will be placed in ``_build/html/`` and are viewable in a web browser, for instance by
+opening the local ``_build/html/index.html`` file or by running a local server
+
+.. prompt:: bash
+
+  python -m http.server -d _build/html
 
 
 .. _building_documentation:
@@ -902,30 +882,31 @@ Building the documentation
 **Before submitting a pull request check if your modifications have introduced
 new sphinx warnings by building the documentation locally and try to fix them.**
 
-First, make sure you have :ref:`properly installed <install_bleeding_edge>`
-the development version.
+First, make sure you have :ref:`properly installed <install_bleeding_edge>` the
+development version. On top of that, building the documentation requires installing some
+additional packages:
 
 ..
     packaging is not needed once setuptools starts shipping packaging>=17.0
 
-Building the documentation requires installing some additional packages:
-
-.. prompt:: bash $
+.. prompt:: bash
 
     pip install sphinx sphinx-gallery numpydoc matplotlib Pillow pandas \
                 polars scikit-image packaging seaborn sphinx-prompt \
-                sphinxext-opengraph sphinx-copybutton plotly pooch
+                sphinxext-opengraph sphinx-copybutton plotly pooch \
+                pydata-sphinx-theme sphinxcontrib-sass sphinx-design \
+                sphinx-remove-toctrees
 
 To build the documentation, you need to be in the ``doc`` folder:
 
-.. prompt:: bash $
+.. prompt:: bash
 
     cd doc
 
 In the vast majority of cases, you only need to generate the full web site,
 without the example gallery:
 
-.. prompt:: bash $
+.. prompt:: bash
 
     make
 
@@ -934,29 +915,27 @@ and are viewable in a web browser, for instance by opening the local
 ``_build/html/stable/index.html`` file.
 To also generate the example gallery you can use:
 
-.. prompt:: bash $
+.. prompt:: bash
 
     make html
 
-This will run all the examples, which takes a while. If you only want to
-generate a few examples, you can use:
+This will run all the examples, which takes a while. If you only want to generate a few
+examples, which is particularly useful if you are modifying only a few examples, you can
+use:
 
-.. prompt:: bash $
+.. prompt:: bash
 
     EXAMPLES_PATTERN=your_regex_goes_here make html
 
-This is particularly useful if you are modifying a few examples.
-
-Set the environment variable `NO_MATHJAX=1` if you intend to view
-the documentation in an offline setting.
-
-To build the PDF manual, run:
+Set the environment variable `NO_MATHJAX=1` if you intend to view the documentation in
+an offline setting. To build the PDF manual, run:
 
-.. prompt:: bash $
+.. prompt:: bash
 
     make latexpdf
 
-.. warning:: **Sphinx version**
+.. admonition:: Sphinx version
+   :class: warning
 
    While we do our best to have the documentation build under as many
    versions of Sphinx as possible, the different versions tend to
@@ -997,45 +976,36 @@ subpackages. For a more detailed `pytest` workflow, please refer to the
 
 We expect code coverage of new features to be at least around 90%.
 
-|details-start|
-**Writing matplotlib related tests**
-|details-split|
-
-Test fixtures ensure that a set of tests will be executing with the appropriate
-initialization and cleanup. The scikit-learn test suite implements a fixture
-which can be used with ``matplotlib``.
-
-``pyplot``
-    The ``pyplot`` fixture should be used when a test function is dealing with
-    ``matplotlib``. ``matplotlib`` is a soft dependency and is not required.
-    This fixture is in charge of skipping the tests if ``matplotlib`` is not
-    installed. In addition, figures created during the tests will be
-    automatically closed once the test function has been executed.
+.. dropdown:: Writing matplotlib-related tests
 
-To use this fixture in a test function, one needs to pass it as an
-argument::
+  Test fixtures ensure that a set of tests will be executing with the appropriate
+  initialization and cleanup. The scikit-learn test suite implements a ``pyplot``
+  fixture which can be used with ``matplotlib``.
 
-    def test_requiring_mpl_fixture(pyplot):
-        # you can now safely use matplotlib
+  The ``pyplot`` fixture should be used when a test function is dealing with
+  ``matplotlib``. ``matplotlib`` is a soft dependency and is not required.
+  This fixture is in charge of skipping the tests if ``matplotlib`` is not
+  installed. In addition, figures created during the tests will be
+  automatically closed once the test function has been executed.
 
-|details-end|
+  To use this fixture in a test function, one needs to pass it as an
+  argument::
 
-|details-start|
-**Workflow to improve test coverage**
-|details-split|
+      def test_requiring_mpl_fixture(pyplot):
+          # you can now safely use matplotlib
 
-To test code coverage, you need to install the `coverage
-<https://pypi.org/project/coverage/>`_ package in addition to pytest.
+.. dropdown:: Workflow to improve test coverage
 
-1. Run 'make test-coverage'. The output lists for each file the line
-    numbers that are not tested.
+  To test code coverage, you need to install the `coverage
+  <https://pypi.org/project/coverage/>`_ package in addition to `pytest`.
 
-2. Find a low hanging fruit, looking at which lines are not tested,
-    write or adapt a test specifically for these lines.
+  1. Run `make test-coverage`. The output lists for each file the line
+     numbers that are not tested.
 
-3. Loop.
+  2. Find a low hanging fruit, looking at which lines are not tested,
+     write or adapt a test specifically for these lines.
 
-|details-end|
+  3. Loop.
 
 .. _monitoring_performances:
 
@@ -1049,8 +1019,9 @@ When proposing changes to the existing code base, it's important to make sure
 that they don't introduce performance regressions. Scikit-learn uses
 `asv benchmarks <https://github.com/airspeed-velocity/asv>`_ to monitor the
 performance of a selection of common estimators and functions. You can view
-these benchmarks on the `scikit-learn benchmark page <https://scikit-learn.org/scikit-learn-benchmarks>`_.
-The corresponding benchmark suite can be found in the `scikit-learn/asv_benchmarks` directory.
+these benchmarks on the `scikit-learn benchmark page
+<https://scikit-learn.org/scikit-learn-benchmarks>`_.
+The corresponding benchmark suite can be found in the `asv_benchmarks/` directory.
 
 To use all features of asv, you will need either `conda` or `virtualenv`. For
 more details please check the `asv installation webpage
@@ -1058,20 +1029,20 @@ more details please check the `asv installation webpage
 
 First of all you need to install the development version of asv:
 
-.. prompt:: bash $
+.. prompt:: bash
 
     pip install git+https://github.com/airspeed-velocity/asv
 
 and change your directory to `asv_benchmarks/`:
 
-.. prompt:: bash $
+.. prompt:: bash
 
-  cd asv_benchmarks/
+  cd asv_benchmarks
 
 The benchmark suite is configured to run against your local clone of
 scikit-learn. Make sure it is up to date:
 
-.. prompt:: bash $
+.. prompt:: bash
 
   git fetch upstream
 
@@ -1079,20 +1050,20 @@ In the benchmark suite, the benchmarks are organized following the same
 structure as scikit-learn. For example, you can compare the performance of a
 specific estimator between ``upstream/main`` and the branch you are working on:
 
-.. prompt:: bash $
+.. prompt:: bash
 
   asv continuous -b LogisticRegression upstream/main HEAD
 
 The command uses conda by default for creating the benchmark environments. If
 you want to use virtualenv instead, use the `-E` flag:
 
-.. prompt:: bash $
+.. prompt:: bash
 
   asv continuous -E virtualenv -b LogisticRegression upstream/main HEAD
 
 You can also specify a whole module to benchmark:
 
-.. prompt:: bash $
+.. prompt:: bash
 
   asv continuous -b linear_model upstream/main HEAD
 
@@ -1102,7 +1073,7 @@ the `-f` flag.
 
 To run the full benchmark suite, simply remove the `-b` flag :
 
-.. prompt:: bash $
+.. prompt:: bash
 
   asv continuous upstream/main HEAD
 
@@ -1112,14 +1083,14 @@ expression for a more complex subset of benchmarks to run.
 To run the benchmarks without comparing to another branch, use the `run`
 command:
 
-.. prompt:: bash $
+.. prompt:: bash
 
   asv run -b linear_model HEAD^!
 
 You can also run the benchmark suite using the version of scikit-learn already
 installed in your current Python environment:
 
-.. prompt:: bash $
+.. prompt:: bash
 
   asv run --python=same
 
@@ -1128,20 +1099,20 @@ avoid creating a new environment each time you run the benchmarks. By default
 the results are not saved when using an existing installation. To save the
 results you must specify a commit hash:
 
-.. prompt:: bash $
+.. prompt:: bash
 
   asv run --python=same --set-commit-hash=<commit hash>
 
 Benchmarks are saved and organized by machine, environment and commit. To see
 the list of all saved benchmarks:
 
-.. prompt:: bash $
+.. prompt:: bash
 
   asv show
 
 and to see the report of a specific run:
 
-.. prompt:: bash $
+.. prompt:: bash
 
   asv show <commit hash>
 
@@ -1164,11 +1135,11 @@ All issues and pull requests on the
 `GitHub issue tracker <https://github.com/scikit-learn/scikit-learn/issues>`_
 should have (at least) one of the following tags:
 
-:Bug / Crash:
+:Bug:
     Something is happening that clearly shouldn't happen.
     Wrong results as well as unexpected errors from estimators go here.
 
-:Cleanup / Enhancement:
+:Enhancement:
     Improving performance, usability, consistency.
 
 :Documentation:
@@ -1179,7 +1150,7 @@ should have (at least) one of the following tags:
 
 There are four other tags to help new contributors:
 
-:good first issue:
+:Good first issue:
     This issue is ideal for a first contribution to scikit-learn. Ask for help
     if the formulation is unclear. If you have already contributed to
     scikit-learn, look at Easy issues instead.
@@ -1191,7 +1162,7 @@ There are four other tags to help new contributors:
     Might need some knowledge of machine learning or the package,
     but is still approachable for someone new to the project.
 
-:help wanted:
+:Help wanted:
     This tag marks an issue which currently lacks a contributor or a
     PR that needs another contributor to take over the work. These
     issues can range in difficulty, and may not be approachable
@@ -1208,12 +1179,15 @@ Maintaining backwards compatibility
 Deprecation
 -----------
 
-If any publicly accessible method, function, attribute or parameter
-is renamed, we still support the old one for two releases and issue
-a deprecation warning when it is called/passed/accessed.
-E.g., if the function ``zero_one`` is renamed to ``zero_one_loss``,
-we add the decorator ``deprecated`` (from ``sklearn.utils``)
-to ``zero_one`` and call ``zero_one_loss`` from that function::
+If any publicly accessible class, function, method, attribute or parameter is renamed,
+we still support the old one for two releases and issue a deprecation warning when it is
+called, passed, or accessed.
+
+.. rubric:: Deprecating a class or a function
+
+Suppose the function ``zero_one`` is renamed to ``zero_one_loss``, we add the decorator
+:class:`utils.deprecated` to ``zero_one`` and call ``zero_one_loss`` from that
+function::
 
     from ..utils import deprecated
 
@@ -1221,36 +1195,47 @@ to ``zero_one`` and call ``zero_one_loss`` from that function::
         # actual implementation
         pass
 
-    @deprecated("Function 'zero_one' was renamed to 'zero_one_loss' "
-                "in version 0.13 and will be removed in release 0.15. "
-                "Default behavior is changed from 'normalize=False' to "
-                "'normalize=True'")
+    @deprecated(
+        "Function `zero_one` was renamed to `zero_one_loss` in 0.13 and will be "
+        "removed in 0.15. Default behavior is changed from `normalize=False` to "
+        "`normalize=True`"
+    )
     def zero_one(y_true, y_pred, normalize=False):
         return zero_one_loss(y_true, y_pred, normalize)
 
-If an attribute is to be deprecated,
-use the decorator ``deprecated`` on a property. Please note that the
-``deprecated`` decorator should be placed before the ``property``
-decorator for the docstrings to be rendered properly.
-E.g., renaming an attribute ``labels_`` to ``classes_`` can be done as::
+One also needs to move ``zero_one`` from ``API_REFERENCE`` to
+``DEPRECATED_API_REFERENCE`` and add ``zero_one_loss`` to ``API_REFERENCE`` in the
+``doc/api_reference.py`` file to reflect the changes in :ref:`api_ref`.
+
+.. rubric:: Deprecating an attribute or a method
+
+If an attribute or a method is to be deprecated, use the decorator
+:class:`~utils.deprecated` on the property. Please note that the
+:class:`~utils.deprecated` decorator should be placed before the ``property`` decorator
+if there is one, so that the docstrings can be rendered properly. For instance, renaming
+an attribute ``labels_`` to ``classes_`` can be done as::
 
-    @deprecated("Attribute `labels_` was deprecated in version 0.13 and "
-                "will be removed in 0.15. Use `classes_` instead")
+    @deprecated(
+        "Attribute `labels_` was deprecated in 0.13 and will be removed in 0.15. Use "
+        "`classes_` instead"
+    )
     @property
     def labels_(self):
         return self.classes_
 
-If a parameter has to be deprecated, a ``FutureWarning`` warning
-must be raised too.
-In the following example, k is deprecated and renamed to n_clusters::
+.. rubric:: Deprecating a parameter
+
+If a parameter has to be deprecated, a ``FutureWarning`` warning must be raised
+manually. In the following example, ``k`` is deprecated and renamed to n_clusters::
 
     import warnings
 
-    def example_function(n_clusters=8, k='deprecated'):
-        if k != 'deprecated':
-            warnings.warn("'k' was renamed to n_clusters in version 0.13 and "
-                          "will be removed in 0.15.",
-                          FutureWarning)
+    def example_function(n_clusters=8, k="deprecated"):
+        if k != "deprecated":
+            warnings.warn(
+                "`k` was renamed to `n_clusters` in 0.13 and will be removed in 0.15",
+                FutureWarning,
+            )
             n_clusters = k
 
 When the change is in a class, we validate and raise warning in ``fit``::
@@ -1263,10 +1248,11 @@ When the change is in a class, we validate and raise warning in ``fit``::
           self.k = k
 
       def fit(self, X, y):
-          if self.k != 'deprecated':
-              warnings.warn("'k' was renamed to n_clusters in version 0.13 and "
-                            "will be removed in 0.15.",
-                            FutureWarning)
+          if self.k != "deprecated":
+              warnings.warn(
+                  "`k` was renamed to `n_clusters` in 0.13 and will be removed in 0.15.",
+                  FutureWarning,
+              )
               self._n_clusters = self.k
           else:
               self._n_clusters = self.n_clusters
@@ -1280,9 +1266,14 @@ adapt their code to the new behaviour. For example, if the deprecation happened
 in version 0.18-dev, the message should say it happened in version 0.18
 and the old behavior will be removed in version 0.20.
 
+The warning message should also include a brief explanation of the change and point
+users to an alternative.
+
 In addition, a deprecation note should be added in the docstring, recalling the
 same information as the deprecation warning as explained above. Use the
-``.. deprecated::`` directive::
+``.. deprecated::`` directive:
+
+.. code-block:: rst
 
   .. deprecated:: 0.13
      ``k`` was renamed to ``n_clusters`` in version 0.13 and will be removed
@@ -1298,7 +1289,7 @@ Change the default value of a parameter
 ---------------------------------------
 
 If the default value of a parameter needs to be changed, please replace the
-default value with a specific value (e.g., ``warn``) and raise
+default value with a specific value (e.g., ``"warn"``) and raise
 ``FutureWarning`` when users are using the default value. The following
 example assumes that the current version is 0.20 and that we change the
 default value of ``n_clusters`` from 5 (old default for 0.20) to 10
@@ -1306,10 +1297,12 @@ default value of ``n_clusters`` from 5 (old default for 0.20) to 10
 
     import warnings
 
-    def example_function(n_clusters='warn'):
-        if n_clusters == 'warn':
-            warnings.warn("The default value of n_clusters will change from "
-                          "5 to 10 in 0.22.", FutureWarning)
+    def example_function(n_clusters="warn"):
+        if n_clusters == "warn":
+            warnings.warn(
+                "The default value of `n_clusters` will change from 5 to 10 in 0.22.",
+                FutureWarning,
+            )
             n_clusters = 5
 
 When the change is in a class, we validate and raise warning in ``fit``::
@@ -1317,22 +1310,26 @@ When the change is in a class, we validate and raise warning in ``fit``::
   import warnings
 
   class ExampleEstimator:
-      def __init__(self, n_clusters='warn'):
+      def __init__(self, n_clusters="warn"):
           self.n_clusters = n_clusters
 
       def fit(self, X, y):
-          if self.n_clusters == 'warn':
-            warnings.warn("The default value of n_clusters will change from "
-                          "5 to 10 in 0.22.", FutureWarning)
-            self._n_clusters = 5
+          if self.n_clusters == "warn":
+              warnings.warn(
+                  "The default value of `n_clusters` will change from 5 to 10 in 0.22.",
+                  FutureWarning,
+              )
+              self._n_clusters = 5
 
 Similar to deprecations, the warning message should always give both the
 version in which the change happened and the version in which the old behavior
 will be removed.
 
 The parameter description in the docstring needs to be updated accordingly by adding
-a `versionchanged` directive with the old and new default value, pointing to the
-version when the change will be effective::
+a ``versionchanged`` directive with the old and new default value, pointing to the
+version when the change will be effective:
+
+.. code-block:: rst
 
     .. versionchanged:: 0.22
        The default value for `n_clusters` will change from 5 to 10 in version 0.22.
@@ -1342,12 +1339,11 @@ not in other cases. The warning should be caught in all other tests
 (using e.g., ``@pytest.mark.filterwarnings``), and there should be no warning
 in the examples.
 
-.. currentmodule:: sklearn
-
 .. _code_review:
 
 Code Review Guidelines
 ======================
+
 Reviewing code contributed to the project as PRs is a crucial component of
 scikit-learn development. We encourage anyone to start reviewing code of other
 developers. The code review process is often highly educational for everybody
@@ -1365,95 +1361,87 @@ up this process by providing your feedback.
   retraction. Regarding docs: typos, grammar issues and disambiguations are
   better addressed immediately.
 
-|details-start|
-**Important aspects to be covered in any code review**
-|details-split|
+.. dropdown:: Important aspects to be covered in any code review
 
-Here are a few important aspects that need to be covered in any code review,
-from high-level questions to a more detailed check-list.
+  Here are a few important aspects that need to be covered in any code review,
+  from high-level questions to a more detailed check-list.
 
-- Do we want this in the library? Is it likely to be used? Do you, as
-  a scikit-learn user, like the change and intend to use it? Is it in
-  the scope of scikit-learn? Will the cost of maintaining a new
-  feature be worth its benefits?
+  - Do we want this in the library? Is it likely to be used? Do you, as
+    a scikit-learn user, like the change and intend to use it? Is it in
+    the scope of scikit-learn? Will the cost of maintaining a new
+    feature be worth its benefits?
 
-- Is the code consistent with the API of scikit-learn? Are public
-  functions/classes/parameters well named and intuitively designed?
+  - Is the code consistent with the API of scikit-learn? Are public
+    functions/classes/parameters well named and intuitively designed?
 
-- Are all public functions/classes and their parameters, return types, and
-  stored attributes named according to scikit-learn conventions and documented clearly?
+  - Are all public functions/classes and their parameters, return types, and
+    stored attributes named according to scikit-learn conventions and documented clearly?
 
-- Is any new functionality described in the user-guide and illustrated with examples?
+  - Is any new functionality described in the user-guide and illustrated with examples?
 
-- Is every public function/class tested? Are a reasonable set of
-  parameters, their values, value types, and combinations tested? Do
-  the tests validate that the code is correct, i.e. doing what the
-  documentation says it does? If the change is a bug-fix, is a
-  non-regression test included? Look at `this
-  <https://jeffknupp.com/blog/2013/12/09/improve-your-python-understanding-unit-testing>`__
-  to get started with testing in Python.
+  - Is every public function/class tested? Are a reasonable set of
+    parameters, their values, value types, and combinations tested? Do
+    the tests validate that the code is correct, i.e. doing what the
+    documentation says it does? If the change is a bug-fix, is a
+    non-regression test included? Look at `this
+    <https://jeffknupp.com/blog/2013/12/09/improve-your-python-understanding-unit-testing>`__
+    to get started with testing in Python.
 
-- Do the tests pass in the continuous integration build? If
-  appropriate, help the contributor understand why tests failed.
+  - Do the tests pass in the continuous integration build? If
+    appropriate, help the contributor understand why tests failed.
 
-- Do the tests cover every line of code (see the coverage report in the build
-  log)? If not, are the lines missing coverage good exceptions?
+  - Do the tests cover every line of code (see the coverage report in the build
+    log)? If not, are the lines missing coverage good exceptions?
 
-- Is the code easy to read and low on redundancy? Should variable names be
-  improved for clarity or consistency? Should comments be added? Should comments
-  be removed as unhelpful or extraneous?
+  - Is the code easy to read and low on redundancy? Should variable names be
+    improved for clarity or consistency? Should comments be added? Should comments
+    be removed as unhelpful or extraneous?
 
-- Could the code easily be rewritten to run much more efficiently for
-  relevant settings?
+  - Could the code easily be rewritten to run much more efficiently for
+    relevant settings?
 
-- Is the code backwards compatible with previous versions? (or is a
-  deprecation cycle necessary?)
+  - Is the code backwards compatible with previous versions? (or is a
+    deprecation cycle necessary?)
 
-- Will the new code add any dependencies on other libraries? (this is
-  unlikely to be accepted)
+  - Will the new code add any dependencies on other libraries? (this is
+    unlikely to be accepted)
 
-- Does the documentation render properly (see the
-  :ref:`contribute_documentation` section for more details), and are the plots
-  instructive?
+  - Does the documentation render properly (see the
+    :ref:`contribute_documentation` section for more details), and are the plots
+    instructive?
 
-:ref:`saved_replies` includes some frequent comments that reviewers may make.
-
-|details-end|
+  :ref:`saved_replies` includes some frequent comments that reviewers may make.
 
 .. _communication:
 
-|details-start|
-**Communication Guidelines**
-|details-split|
-
-Reviewing open pull requests (PRs) helps move the project forward. It is a
-great way to get familiar with the codebase and should motivate the
-contributor to keep involved in the project. [1]_
+.. dropdown:: Communication Guidelines
 
-- Every PR, good or bad, is an act of generosity. Opening with a positive
-  comment will help the author feel rewarded, and your subsequent remarks may
-  be heard more clearly. You may feel good also.
-- Begin if possible with the large issues, so the author knows they've been
-  understood. Resist the temptation to immediately go line by line, or to open
-  with small pervasive issues.
-- Do not let perfect be the enemy of the good. If you find yourself making
-  many small suggestions that don't fall into the :ref:`code_review`, consider
-  the following approaches:
+  Reviewing open pull requests (PRs) helps move the project forward. It is a
+  great way to get familiar with the codebase and should motivate the
+  contributor to keep involved in the project. [1]_
 
-  - refrain from submitting these;
-  - prefix them as "Nit" so that the contributor knows it's OK not to address;
-  - follow up in a subsequent PR, out of courtesy, you may want to let the
-    original contributor know.
+  - Every PR, good or bad, is an act of generosity. Opening with a positive
+    comment will help the author feel rewarded, and your subsequent remarks may
+    be heard more clearly. You may feel good also.
+  - Begin if possible with the large issues, so the author knows they've been
+    understood. Resist the temptation to immediately go line by line, or to open
+    with small pervasive issues.
+  - Do not let perfect be the enemy of the good. If you find yourself making
+    many small suggestions that don't fall into the :ref:`code_review`, consider
+    the following approaches:
 
-- Do not rush, take the time to make your comments clear and justify your
-  suggestions.
-- You are the face of the project. Bad days occur to everyone, in that
-  occasion you deserve a break: try to take your time and stay offline.
+    - refrain from submitting these;
+    - prefix them as "Nit" so that the contributor knows it's OK not to address;
+    - follow up in a subsequent PR, out of courtesy, you may want to let the
+      original contributor know.
 
-.. [1] Adapted from the numpy `communication guidelines
-       <https://numpy.org/devdocs/dev/reviewer_guidelines.html#communication-guidelines>`_.
+  - Do not rush, take the time to make your comments clear and justify your
+    suggestions.
+  - You are the face of the project. Bad days occur to everyone, in that
+    occasion you deserve a break: try to take your time and stay offline.
 
-|details-end|
+  .. [1] Adapted from the numpy `communication guidelines
+        <https://numpy.org/devdocs/dev/reviewer_guidelines.html#communication-guidelines>`_.
 
 Reading the existing code base
 ==============================
@@ -1474,9 +1462,9 @@ make this task easier and faster (in no particular order).
   relevant, and which are not. In scikit-learn **a lot** of input checking
   is performed, especially at the beginning of the :term:`fit` methods.
   Sometimes, only a very small portion of the code is doing the actual job.
-  For example looking at the ``fit()`` method of
+  For example looking at the :meth:`~linear_model.LinearRegression.fit` method of
   :class:`~linear_model.LinearRegression`, what you're looking for
-  might just be the call the ``scipy.linalg.lstsq``, but it is buried into
+  might just be the call the :func:`scipy.linalg.lstsq`, but it is buried into
   multiple lines of input checking and the handling of different kinds of
   parameters.
 - Due to the use of `Inheritance
@@ -1506,7 +1494,7 @@ make this task easier and faster (in no particular order).
     IDE goes a long way towards digesting the code base. Being able to quickly
     jump (or *peek*) to a function/class/attribute definition helps a lot.
     So does being able to quickly see where a given name is used in a file.
-  - `git <https://git-scm.com/book/en>`_ also has some built-in killer
+  - `Git <https://git-scm.com/book/en>`_ also has some built-in killer
     features. It is often useful to understand how a file changed over time,
     using e.g. ``git blame`` (`manual
     <https://git-scm.com/docs/git-blame>`_). This can also be done directly
@@ -1518,7 +1506,7 @@ make this task easier and faster (in no particular order).
 - Configure `git blame` to ignore the commit that migrated the code style to
   `black`.
 
-  .. prompt:: bash $
+  .. prompt:: bash
 
       git config blame.ignoreRevsFile .git-blame-ignore-revs
 
diff --git a/doc/developers/cython.rst b/doc/developers/cython.rst
index e98501879d50e..82022ddcbcc56 100644
--- a/doc/developers/cython.rst
+++ b/doc/developers/cython.rst
@@ -141,3 +141,16 @@ must be ``cimported`` from this module and not from the OpenMP library directly:
 
 The parallel loop, `prange`, is already protected by cython and can be used directly
 from `cython.parallel`.
+
+Types
+~~~~~
+
+Cython code requires to use explicit types. This is one of the reasons you get a
+performance boost. In order to avoid code duplication, we have a central place
+for the most used types in
+`sklearn/utils/_typedefs.pyd <https://github.com/scikit-learn/scikit-learn/blob/main/sklearn/utils/_typedefs.pyd>`_.
+Ideally you start by having a look there and `cimport` types you need, for example
+
+.. code-block:: cython
+
+    from sklear.utils._typedefs cimport float32, float64
diff --git a/doc/developers/develop.rst b/doc/developers/develop.rst
index f22c217203ac2..97cb156da5812 100644
--- a/doc/developers/develop.rst
+++ b/doc/developers/develop.rst
@@ -54,7 +54,7 @@ multiple interfaces):
 
 :Transformer:
 
-    For modifying the data in a supervised or unsupervised way (e.g. by adding, changing, 
+    For modifying the data in a supervised or unsupervised way (e.g. by adding, changing,
     or removing columns, but not by adding or removing rows). Implements::
 
       new_data = transformer.transform(data)
@@ -282,12 +282,16 @@ the correct interface more easily.
     in the scikit-learn-contrib
     `project template <https://github.com/scikit-learn-contrib/project-template/blob/master/skltemplate/_template.py>`__.
 
+    It is particularly important to notice that mixins should be "on the left" while
+    the ``BaseEstimator`` should be "on the right" in the inheritance list for proper
+    MRO.
+
       >>> import numpy as np
       >>> from sklearn.base import BaseEstimator, ClassifierMixin
       >>> from sklearn.utils.validation import check_X_y, check_array, check_is_fitted
       >>> from sklearn.utils.multiclass import unique_labels
       >>> from sklearn.metrics import euclidean_distances
-      >>> class TemplateClassifier(BaseEstimator, ClassifierMixin):
+      >>> class TemplateClassifier(ClassifierMixin, BaseEstimator):
       ...
       ...     def __init__(self, demo_param='demo'):
       ...         self.demo_param = demo_param
@@ -349,7 +353,7 @@ The parameter `deep` will control whether or not the parameters of the
     subestimator__intercept_scaling -> 1
     subestimator__l1_ratio -> None
     subestimator__max_iter -> 100
-    subestimator__multi_class -> auto
+    subestimator__multi_class -> deprecated
     subestimator__n_jobs -> None
     subestimator__penalty -> l2
     subestimator__random_state -> None
diff --git a/doc/developers/index.rst b/doc/developers/index.rst
index c2cc35928cbf9..cca77b6a015c9 100644
--- a/doc/developers/index.rst
+++ b/doc/developers/index.rst
@@ -1,16 +1,9 @@
-.. Places parent toc into the sidebar
-
-:parenttoc: True
-
 .. _developers_guide:
 
 =================
 Developer's Guide
 =================
 
-.. include:: ../includes/big_toc_css.rst
-.. include:: ../tune_toc.rst
-
 .. toctree::
 
    contributing
diff --git a/doc/developers/maintainer.rst b/doc/developers/maintainer.rst
index e82a7993997b2..ffc9b73156fa8 100644
--- a/doc/developers/maintainer.rst
+++ b/doc/developers/maintainer.rst
@@ -1,6 +1,5 @@
-Maintainer / core-developer information
-========================================
-
+Maintainer/Core-Developer Information
+======================================
 
 Releasing
 ---------
@@ -105,14 +104,13 @@ in the description of the Pull Request to track progress.
 This PR will be used to push commits related to the release as explained in
 :ref:`making_a_release`.
 
-You can also create a second PR from main and targeting main to increment
-the ``__version__`` variable in `sklearn/__init__.py` to increment the dev
-version. This means while we're in the release candidate period, the latest
-stable is two versions behind the main branch, instead of one. In this PR
-targeting main you should also include a new file for the matching version
-under the ``doc/whats_new/`` folder so PRs that target the next version can
-contribute their changelog entries to this file in parallel to the release
-process.
+You can also create a second PR from main and targeting main to increment the
+``__version__`` variable in `sklearn/__init__.py` and in `pyproject.toml` to increment
+the dev version. This means while we're in the release candidate period, the latest
+stable is two versions behind the main branch, instead of one. In this PR targeting
+main you should also include a new file for the matching version under the
+``doc/whats_new/`` folder so PRs that target the next version can contribute their
+changelog entries to this file in parallel to the release process.
 
 Minor version release (also known as bug-fix release)
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -212,8 +210,8 @@ Making a release
      the old entries (two years or three releases are typically good
      enough) and to update the on-going development entry.
 
-2. On the branch for releasing, update the version number in
-   ``sklearn/__init__.py``, the ``__version__``.
+2. On the branch for releasing, update the version number in ``sklearn/__init__.py``,
+   the ``__version__`` variable, and in `pyproject.toml`.
 
    For major releases, please add a 0 at the end: `0.99.0` instead of `0.99`.
 
diff --git a/doc/dispatching.rst b/doc/dispatching.rst
index d42fdcc86f9e8..101e493ee96b7 100644
--- a/doc/dispatching.rst
+++ b/doc/dispatching.rst
@@ -1,9 +1,3 @@
-.. Places parent toc into the sidebar
-
-:parenttoc: True
-
-.. include:: includes/big_toc_css.rst
-
 ===========
 Dispatching
 ===========
diff --git a/doc/faq.rst b/doc/faq.rst
index 8ddf0c4c238f6..81f03b49bc7c9 100644
--- a/doc/faq.rst
+++ b/doc/faq.rst
@@ -1,3 +1,32 @@
+.. raw:: html
+
+  <style>
+    /* h3 headings on this page are the questions; make them rubric-like */
+    h3 {
+      font-size: 1rem;
+      font-weight: bold;
+      padding-bottom: 0.2rem;
+      margin: 2rem 0 1.15rem 0;
+      border-bottom: 1px solid var(--pst-color-border);
+    }
+
+    /* Increase top margin for first question in each section */
+    h2 + section > h3 {
+      margin-top: 2.5rem;
+    }
+
+    /* Make the headerlinks a bit more visible */
+    h3 > a.headerlink {
+      font-size: 0.9rem;
+    }
+
+    /* Remove the backlink decoration on the titles */
+    h2 > a.toc-backref,
+    h3 > a.toc-backref {
+      text-decoration: none;
+    }
+  </style>
+
 .. _faq:
 
 ==========================
@@ -9,8 +38,9 @@ Frequently Asked Questions
 Here we try to give some answers to questions that regularly pop up on the mailing list.
 
 .. contents:: Table of Contents
-   :local:
-   :depth: 2
+  :local:
+  :depth: 2
+
 
 About the project
 -----------------
@@ -323,12 +353,14 @@ Using scikit-learn
 
 What's the best way to get help on scikit-learn usage?
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-**For general machine learning questions**, please use
-`Cross Validated <https://stats.stackexchange.com/>`_ with the ``[machine-learning]`` tag.
 
-**For scikit-learn usage questions**, please use `Stack Overflow <https://stackoverflow.com/questions/tagged/scikit-learn>`_
-with the ``[scikit-learn]`` and ``[python]`` tags. You can alternatively use the `mailing list
-<https://mail.python.org/mailman/listinfo/scikit-learn>`_.
+* General machine learning questions: use `Cross Validated
+  <https://stats.stackexchange.com/>`_ with the ``[machine-learning]`` tag.
+
+* scikit-learn usage questions: use `Stack Overflow
+  <https://stackoverflow.com/questions/tagged/scikit-learn>`_ with the
+  ``[scikit-learn]`` and ``[python]`` tags. You can alternatively use the `mailing list
+  <https://mail.python.org/mailman/listinfo/scikit-learn>`_.
 
 Please make sure to include a minimal reproduction code snippet (ideally shorter
 than 10 lines) that highlights your problem on a toy dataset (for instance from
diff --git a/doc/governance.rst b/doc/governance.rst
index 33afd7dde8ddb..d6b07afe4eeb4 100644
--- a/doc/governance.rst
+++ b/doc/governance.rst
@@ -58,56 +58,47 @@ members and recant their rights until they become active again. The list of
 members, active and emeritus (with dates at which they became active) is public
 on the scikit-learn website.
 
-The following teams form the core contributors group.
-
-
-Contributor Experience Team
-~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-The contributor experience team improves the experience of contributors by
-helping with the triage of issues and pull requests, as well as noticing any
-repeating patterns where people might struggle, and to help with improving
-those aspects of the project.
-
-To this end, they have the required permissions on github to label and close
-issues. :ref:`Their work <bug_triaging>` is crucial to improve the
-communication in the project and limit the crowding of the issue tracker.
-
-.. _communication_team:
-
-Communication team
-~~~~~~~~~~~~~~~~~~
-
-Members of the communication team help with outreach and communication
-for scikit-learn. The goal of the team is to develop public awareness of
-scikit-learn, of its features and usage, as well as branding.
-
-For this, they can operate the scikit-learn accounts on various social networks
-and produce materials. They also have the required rights to our blog
-repository and other relevant accounts and platforms.
-
-Documentation team
-~~~~~~~~~~~~~~~~~~
-
-Members of the documentation team engage with the documentation of the project
-among other things. They might also be involved in other aspects of the
-project, but their reviews on documentation contributions are considered
-authoritative, and can merge such contributions.
-
-To this end, they have permissions to merge pull requests in scikit-learn's
-repository.
-
-Maintainers
-~~~~~~~~~~~
-
-Maintainers are community members who have shown that they are dedicated to the
-continued development of the project through ongoing engagement with the
-community. They have shown they can be trusted to maintain scikit-learn with
-care. Being a maintainer allows contributors to more easily carry on with their
-project related activities by giving them direct access to the project's
-repository. Maintainers are expected to review code contributions, merge
-approved pull requests, cast votes for and against merging a pull-request,
-and to be involved in deciding major changes to the API.
+The following teams form the core contributors group:
+
+* **Contributor Experience Team**
+  The contributor experience team improves the experience of contributors by
+  helping with the triage of issues and pull requests, as well as noticing any
+  repeating patterns where people might struggle, and to help with improving
+  those aspects of the project.
+
+  To this end, they have the required permissions on github to label and close
+  issues. :ref:`Their work <bug_triaging>` is crucial to improve the
+  communication in the project and limit the crowding of the issue tracker.
+
+  .. _communication_team:
+
+* **Communication Team**
+  Members of the communication team help with outreach and communication
+  for scikit-learn. The goal of the team is to develop public awareness of
+  scikit-learn, of its features and usage, as well as branding.
+
+  For this, they can operate the scikit-learn accounts on various social networks
+  and produce materials. They also have the required rights to our blog
+  repository and other relevant accounts and platforms.
+
+* **Documentation Team**
+  Members of the documentation team engage with the documentation of the project
+  among other things. They might also be involved in other aspects of the
+  project, but their reviews on documentation contributions are considered
+  authoritative, and can merge such contributions.
+
+  To this end, they have permissions to merge pull requests in scikit-learn's
+  repository.
+
+* **Maintainers Team**
+  Maintainers are community members who have shown that they are dedicated to the
+  continued development of the project through ongoing engagement with the
+  community. They have shown they can be trusted to maintain scikit-learn with
+  care. Being a maintainer allows contributors to more easily carry on with their
+  project related activities by giving them direct access to the project's
+  repository. Maintainers are expected to review code contributions, merge
+  approved pull requests, cast votes for and against merging a pull-request,
+  and to be involved in deciding major changes to the API.
 
 Technical Committee
 -------------------
diff --git a/doc/images/Tidelift-logo-on-light.svg b/doc/images/Tidelift-logo-on-light.svg
new file mode 100644
index 0000000000000..af12d68417235
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@@ -0,0 +1,33 @@
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+	.st0{fill:#4B5168;}
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+</style>
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diff --git a/doc/images/ml_map.png b/doc/images/ml_map.png
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font-size="16px" text-anchor="middle">predicting...</text></switch></g></g><g><path d="M 756 275 L 732.81 305.92" fill="none" stroke="#009900" stroke-width="3" stroke-miterlimit="10" pointer-events="stroke"/><path d="M 728.01 312.32 L 729.61 303.52 L 736.01 308.32 Z" fill="#009900" stroke="#009900" stroke-width="3" stroke-miterlimit="10" pointer-events="all"/></g><g><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 1px; height: 1px; padding-top: 293px; margin-left: 747px;"><div data-drawio-colors="color: #009900; background-color: rgb(255, 255, 255); " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 12px; font-family: 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style="display: inline-block; font-size: 16px; font-family: Georgia; color: rgb(0, 0, 0); line-height: 1.2; pointer-events: all; white-space: normal; overflow-wrap: normal;"><div><span style="font-size: 10px;">do you have</span></div><div>labeled</div><div>data</div></div></div></div></foreignObject><text x="636" y="445" fill="rgb(0, 0, 0)" font-family="Georgia" font-size="16px" text-anchor="middle">do you hav...</text></switch></g></g><g><path d="M 697.03 374.14 L 671.88 406.86" fill="none" stroke="#009900" stroke-width="3" stroke-miterlimit="10" pointer-events="stroke"/><path d="M 667.01 413.2 L 668.71 404.42 L 675.06 409.3 Z" fill="#009900" stroke="#009900" stroke-width="3" stroke-miterlimit="10" pointer-events="all"/></g><g><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 1px; height: 1px; padding-top: 394px; margin-left: 686px;"><div data-drawio-colors="color: #009900; background-color: rgb(255, 255, 255); " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 12px; font-family: Georgia; color: rgb(0, 153, 0); line-height: 1.2; pointer-events: all; font-weight: bold; background-color: rgb(255, 255, 255); white-space: nowrap;">YES</div></div></div></foreignObject><text x="686" y="398" fill="#009900" font-family="Georgia" font-size="12px" text-anchor="middle" font-weight="bold">YES</text></switch></g></g><g><ellipse cx="800" cy="450" rx="40" ry="35" fill="#cce5ff" stroke="#3399ff" stroke-width="5" pointer-events="all"/></g><g><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 78px; height: 1px; padding-top: 450px; margin-left: 761px;"><div data-drawio-colors="color: rgb(0, 0, 0); " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 16px; font-family: Georgia; color: rgb(0, 0, 0); line-height: 1.2; pointer-events: all; white-space: normal; overflow-wrap: normal;"><div><span style="font-size: 10px;">predicting a</span></div><div>quantity</div></div></div></div></foreignObject><text x="800" y="455" fill="rgb(0, 0, 0)" font-family="Georgia" font-size="16px" text-anchor="middle">predicting...</text></switch></g></g><g><path d="M 754.97 374.14 L 767.67 415.02" fill="none" stroke="#ff3333" stroke-width="3" stroke-miterlimit="10" pointer-events="stroke"/><path d="M 770.04 422.66 L 763.85 416.21 L 771.49 413.83 Z" fill="#ff3333" stroke="#ff3333" stroke-width="3" stroke-miterlimit="10" pointer-events="all"/></g><g><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 1px; height: 1px; padding-top: 396px; margin-left: 765px;"><div data-drawio-colors="color: #FF3333; background-color: rgb(255, 255, 255); " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 12px; font-family: Georgia; color: rgb(255, 51, 51); line-height: 1.2; pointer-events: all; font-weight: bold; background-color: rgb(255, 255, 255); white-space: nowrap;">NO</div></div></div></foreignObject><text x="765" y="399" fill="#FF3333" font-family="Georgia" font-size="12px" text-anchor="middle" font-weight="bold">NO</text></switch></g></g><g><ellipse cx="756" cy="560" rx="40" ry="35" fill="#cce5ff" stroke="#3399ff" stroke-width="5" pointer-events="all"/></g><g><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 78px; height: 1px; padding-top: 560px; margin-left: 717px;"><div data-drawio-colors="color: rgb(0, 0, 0); " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 16px; font-family: Georgia; color: rgb(0, 0, 0); line-height: 1.2; pointer-events: all; white-space: normal; overflow-wrap: normal;"><div><span style="font-size: 10px;">just</span></div><div>looking</div></div></div></div></foreignObject><text x="756" y="565" fill="rgb(0, 0, 0)" font-family="Georgia" font-size="16px" text-anchor="middle">just...</text></switch></g></g><g><path d="M 800 485 L 788.18 524.97" fill="none" stroke="#ff3333" stroke-width="3" stroke-miterlimit="10" pointer-events="stroke"/><path d="M 785.92 532.65 L 784.35 523.84 L 792.02 526.11 Z" fill="#ff3333" stroke="#ff3333" stroke-width="3" stroke-miterlimit="10" pointer-events="all"/></g><g><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 1px; height: 1px; padding-top: 508px; margin-left: 797px;"><div data-drawio-colors="color: #FF3333; background-color: rgb(255, 255, 255); " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 12px; font-family: Georgia; color: rgb(255, 51, 51); line-height: 1.2; pointer-events: all; font-weight: bold; background-color: rgb(255, 255, 255); white-space: nowrap;">NO</div></div></div></foreignObject><text x="797" y="512" fill="#FF3333" font-family="Georgia" font-size="12px" text-anchor="middle" font-weight="bold">NO</text></switch></g></g><g><ellipse cx="760" cy="670" rx="40" ry="35" fill="#cce5ff" stroke="#3399ff" stroke-width="5" pointer-events="all"/></g><g><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 78px; height: 1px; padding-top: 670px; margin-left: 721px;"><div data-drawio-colors="color: rgb(0, 0, 0); " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 16px; font-family: Georgia; color: rgb(0, 0, 0); line-height: 1.2; pointer-events: all; white-space: normal; overflow-wrap: normal;"><div><span style="font-size: 10px;">predicting</span></div><div>structure</div></div></div></div></foreignObject><text x="760" y="675" fill="rgb(0, 0, 0)" font-family="Georgia" font-size="16px" text-anchor="middle">predicting...</text></switch></g></g><g><path d="M 756 595 L 758.87 623.7" fill="none" stroke="#ff3333" stroke-width="3" stroke-miterlimit="10" pointer-events="stroke"/><path d="M 759.67 631.66 L 754.89 624.1 L 762.85 623.3 Z" fill="#ff3333" stroke="#ff3333" stroke-width="3" stroke-miterlimit="10" pointer-events="all"/></g><g><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 1px; height: 1px; padding-top: 611px; margin-left: 761px;"><div data-drawio-colors="color: #FF3333; background-color: rgb(255, 255, 255); " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 12px; font-family: Georgia; color: rgb(255, 51, 51); line-height: 1.2; pointer-events: all; font-weight: bold; background-color: rgb(255, 255, 255); white-space: nowrap;">NO</div></div></div></foreignObject><text x="761" y="615" fill="#FF3333" font-family="Georgia" font-size="12px" text-anchor="middle" font-weight="bold">NO</text></switch></g></g><g><path d="M 807.03 154.14 L 764.04 196.99" fill="none" stroke="#ff9933" stroke-width="3" stroke-miterlimit="10" pointer-events="stroke"/><path d="M 758.38 202.63 L 761.22 194.15 L 766.87 199.82 Z" fill="#ff9933" stroke="#ff9933" stroke-width="3" stroke-miterlimit="10" pointer-events="all"/></g><g><ellipse cx="626" cy="660" rx="40" ry="35" fill="#cce5ff" stroke="#3399ff" stroke-width="5" pointer-events="all"/></g><g><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 78px; height: 1px; padding-top: 660px; margin-left: 587px;"><div data-drawio-colors="color: rgb(0, 0, 0); " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 16px; font-family: Georgia; color: rgb(0, 0, 0); line-height: 1.2; pointer-events: all; white-space: normal; overflow-wrap: normal;"><div><span style="background-color: initial;">tough</span><br /></div><div><span style="background-color: initial;">luck</span></div></div></div></div></foreignObject><text x="626" y="665" fill="rgb(0, 0, 0)" font-family="Georgia" font-size="16px" text-anchor="middle">tough...</text></switch></g></g><g><path d="M 720 670 L 677.16 662.07" fill="none" stroke="#ff9933" stroke-width="3" stroke-miterlimit="10" pointer-events="stroke"/><path d="M 669.3 660.61 L 677.89 658.13 L 676.44 666 Z" fill="#ff9933" stroke="#ff9933" stroke-width="3" stroke-miterlimit="10" pointer-events="all"/></g><g><ellipse cx="516" cy="330" rx="40" ry="35" fill="#cce5ff" stroke="#3399ff" stroke-width="5" pointer-events="all"/></g><g><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 78px; height: 1px; padding-top: 330px; margin-left: 477px;"><div data-drawio-colors="color: rgb(0, 0, 0); " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 16px; font-family: Georgia; color: rgb(0, 0, 0); line-height: 1.2; pointer-events: all; white-space: normal; overflow-wrap: normal;"><font style="font-size: 16px;">&lt;100K</font><div style=""><span style=""><font style="font-size: 10px;">samples</font></span></div></div></div></div></foreignObject><text x="516" y="335" fill="rgb(0, 0, 0)" font-family="Georgia" font-size="16px" text-anchor="middle">&lt;100K...</text></switch></g></g><g><path d="M 607.03 415.86 L 553.02 362.14" fill="none" stroke="#009900" stroke-width="3" stroke-miterlimit="10" pointer-events="stroke"/><path d="M 547.34 356.5 L 555.84 359.31 L 550.2 364.98 Z" fill="#009900" stroke="#009900" stroke-width="3" stroke-miterlimit="10" pointer-events="all"/></g><g><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 1px; height: 1px; padding-top: 393px; margin-left: 582px;"><div data-drawio-colors="color: #009900; background-color: rgb(255, 255, 255); " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 12px; font-family: Georgia; color: rgb(0, 153, 0); line-height: 1.2; pointer-events: all; font-weight: bold; background-color: rgb(255, 255, 255); white-space: nowrap;">YES</div></div></div></foreignObject><text x="582" y="397" fill="#009900" font-family="Georgia" font-size="12px" text-anchor="middle" font-weight="bold">YES</text></switch></g></g><a xlink:href="../../modules/sgd.html#classification"><g><rect x="436" y="185" width="80" height="50" rx="7.5" ry="7.5" fill="#ccffe6" stroke="#00cc66" stroke-width="5" pointer-events="all"/></g><g><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 78px; height: 1px; padding-top: 210px; margin-left: 437px;"><div data-drawio-colors="color: rgb(0, 0, 0); " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 16px; font-family: Georgia; color: rgb(0, 0, 0); line-height: 1.2; pointer-events: all; white-space: normal; overflow-wrap: normal;">SGD<div>Classifier</div></div></div></div></foreignObject><text x="476" y="215" fill="rgb(0, 0, 0)" font-family="Georgia" font-size="16px" text-anchor="middle">SGD...</text></switch></g></g></a><g><path d="M 516 295 L 499.59 245.77" fill="none" stroke="#ff3333" stroke-width="3" stroke-miterlimit="10" pointer-events="stroke"/><path d="M 497.06 238.18 L 503.39 244.51 L 495.8 247.04 Z" fill="#ff3333" stroke="#ff3333" stroke-width="3" stroke-miterlimit="10" pointer-events="all"/></g><g><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 1px; height: 1px; padding-top: 271px; margin-left: 507px;"><div data-drawio-colors="color: #FF3333; background-color: #FFCCCC; " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 12px; font-family: Georgia; color: rgb(255, 51, 51); line-height: 1.2; pointer-events: all; font-weight: bold; background-color: rgb(255, 204, 204); white-space: nowrap;">NO</div></div></div></foreignObject><text x="507" y="275" fill="#FF3333" font-family="Georgia" font-size="12px" text-anchor="middle" font-weight="bold">NO</text></switch></g></g><a xlink:href="../../modules/svm.html#classification"><g><rect x="356" y="335" width="60" height="50" rx="7.5" ry="7.5" fill="#ccffe6" stroke="#00cc66" stroke-width="5" pointer-events="all"/></g><g><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 58px; height: 1px; padding-top: 360px; margin-left: 357px;"><div data-drawio-colors="color: rgb(0, 0, 0); " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 16px; font-family: Georgia; color: rgb(0, 0, 0); line-height: 1.2; pointer-events: all; white-space: normal; overflow-wrap: normal;">Linear<div>SVC</div></div></div></div></foreignObject><text x="386" y="365" fill="rgb(0, 0, 0)" font-family="Georgia" font-size="16px" text-anchor="middle">Linear...</text></switch></g></g></a><g><path d="M 476 330 L 426.16 354.92" fill="none" stroke="#009900" stroke-width="3" stroke-miterlimit="10" pointer-events="stroke"/><path d="M 419 358.5 L 424.37 351.34 L 427.94 358.5 Z" fill="#009900" stroke="#009900" stroke-width="3" stroke-miterlimit="10" pointer-events="all"/></g><g><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 1px; height: 1px; padding-top: 345px; margin-left: 454px;"><div data-drawio-colors="color: #009900; background-color: #FFCCCC; " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 12px; font-family: Georgia; color: rgb(0, 153, 0); line-height: 1.2; pointer-events: all; font-weight: bold; background-color: rgb(255, 204, 204); white-space: nowrap;">YES</div></div></div></foreignObject><text x="454" y="348" fill="#009900" font-family="Georgia" font-size="12px" text-anchor="middle" font-weight="bold">YES</text></switch></g></g><g><ellipse cx="236" cy="330" rx="40" ry="35" fill="#cce5ff" stroke="#3399ff" stroke-width="5" pointer-events="all"/></g><g><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 78px; height: 1px; padding-top: 330px; margin-left: 197px;"><div data-drawio-colors="color: rgb(0, 0, 0); " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 16px; font-family: Georgia; color: rgb(0, 0, 0); line-height: 1.2; pointer-events: all; white-space: normal; overflow-wrap: normal;"><div><span style="background-color: initial;">text</span><br /></div><div>data</div></div></div></div></foreignObject><text x="236" y="335" fill="rgb(0, 0, 0)" font-family="Georgia" font-size="16px" text-anchor="middle">text...</text></switch></g></g><g><path d="M 356 347.5 L 287.09 332.43" fill="none" stroke="#ff9933" stroke-width="3" stroke-miterlimit="10" pointer-events="stroke"/><path d="M 279.28 330.72 L 287.95 328.52 L 286.24 336.33 Z" fill="#ff9933" stroke="#ff9933" stroke-width="3" stroke-miterlimit="10" pointer-events="all"/></g><g><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 1px; height: 1px; padding-top: 344px; margin-left: 321px;"><div data-drawio-colors="color: #CC6600; background-color: #FFCCCC; " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 12px; font-family: Helvetica; color: rgb(204, 102, 0); line-height: 1.2; pointer-events: all; font-weight: bold; background-color: rgb(255, 204, 204); white-space: nowrap;"><span style="color: rgb(77, 81, 86); font-family: &quot;Google Sans&quot;, arial, sans-serif; font-size: 16px; font-weight: 400; text-align: start; text-wrap: wrap;">😭</span></div></div></div></foreignObject><text x="321" y="348" fill="#CC6600" font-family="Helvetica" font-size="12px" text-anchor="middle" font-weight="bold">😭</text></switch></g></g><a xlink:href="../../modules/kernel_approximation.html"><g><rect x="256" y="125" width="120" height="50" rx="7.5" ry="7.5" fill="#ccffe6" stroke="#00cc66" stroke-width="5" pointer-events="all"/></g><g><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 118px; height: 1px; padding-top: 150px; margin-left: 257px;"><div data-drawio-colors="color: rgb(0, 0, 0); " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 16px; font-family: Georgia; color: rgb(0, 0, 0); line-height: 1.2; pointer-events: all; white-space: normal; overflow-wrap: normal;">Kernel<div>Approximation</div></div></div></div></foreignObject><text x="316" y="155" fill="rgb(0, 0, 0)" font-family="Georgia" font-size="16px" text-anchor="middle">Kernel...</text></switch></g></g></a><g><path d="M 436 197.5 L 385.81 168.22" fill="none" stroke="#ff9933" stroke-width="3" stroke-miterlimit="10" pointer-events="stroke"/><path d="M 378.9 164.19 L 387.82 164.77 L 383.79 171.68 Z" fill="#ff9933" stroke="#ff9933" stroke-width="3" stroke-miterlimit="10" pointer-events="all"/></g><g><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 1px; height: 1px; padding-top: 186px; margin-left: 408px;"><div data-drawio-colors="color: #CC6600; background-color: #FFCCCC; " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 12px; font-family: Helvetica; color: rgb(204, 102, 0); line-height: 1.2; pointer-events: all; font-weight: bold; background-color: rgb(255, 204, 204); white-space: nowrap;"><span style="color: rgb(77, 81, 86); font-family: &quot;Google Sans&quot;, arial, sans-serif; font-size: 16px; font-weight: 400; text-align: start; text-wrap: wrap;">😭</span></div></div></div></foreignObject><text x="408" y="189" fill="#CC6600" font-family="Helvetica" font-size="12px" text-anchor="middle" font-weight="bold">😭</text></switch></g></g><a xlink:href="../../modules/neighbors.html"><g><rect x="216" y="205" width="100" height="50" rx="7.5" ry="7.5" fill="#ccffe6" stroke="#00cc66" stroke-width="5" pointer-events="all"/></g><g><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 98px; height: 1px; padding-top: 230px; margin-left: 217px;"><div data-drawio-colors="color: rgb(0, 0, 0); " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 16px; font-family: Georgia; color: rgb(0, 0, 0); line-height: 1.2; pointer-events: all; white-space: normal; overflow-wrap: normal;">KNeighbors<div>Classifier</div></div></div></div></foreignObject><text x="266" y="235" fill="rgb(0, 0, 0)" font-family="Georgia" font-size="16px" text-anchor="middle">KNeighbors...</text></switch></g></g></a><g><path d="M 236 295 L 239.59 266.27" fill="none" stroke="#ff3333" stroke-width="3" stroke-miterlimit="10" pointer-events="stroke"/><path d="M 240.58 258.33 L 243.56 266.76 L 235.62 265.77 Z" fill="#ff3333" stroke="#ff3333" stroke-width="3" stroke-miterlimit="10" pointer-events="all"/></g><g><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 1px; height: 1px; padding-top: 280px; margin-left: 237px;"><div data-drawio-colors="color: #FF3333; background-color: #FFCCCC; " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 12px; font-family: Georgia; color: rgb(255, 51, 51); line-height: 1.2; pointer-events: all; font-weight: bold; background-color: rgb(255, 204, 204); white-space: nowrap;">NO</div></div></div></foreignObject><text x="237" y="284" fill="#FF3333" font-family="Georgia" font-size="12px" text-anchor="middle" font-weight="bold">NO</text></switch></g></g><a xlink:href="../../modules/svm.html#classification"><g><rect x="57.49" y="180" width="90" height="30" rx="4.5" ry="4.5" fill="#ccffe6" stroke="#00cc66" stroke-width="5" pointer-events="all"/></g><g><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 88px; height: 1px; padding-top: 195px; margin-left: 58px;"><div data-drawio-colors="color: rgb(0, 0, 0); " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 16px; font-family: Georgia; color: rgb(0, 0, 0); line-height: 1.2; pointer-events: all; white-space: normal; overflow-wrap: normal;">SVC</div></div></div></foreignObject><text x="102" y="200" fill="rgb(0, 0, 0)" font-family="Georgia" font-size="16px" text-anchor="middle">SVC</text></switch></g></g></a><a xlink:href="../../modules/ensemble.html"><g><rect x="57.49" y="210" width="90" height="50" rx="7.5" ry="7.5" fill="#ccffe6" stroke="#00cc66" stroke-width="5" pointer-events="all"/></g><g><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 88px; height: 1px; padding-top: 235px; margin-left: 58px;"><div data-drawio-colors="color: rgb(0, 0, 0); " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 16px; font-family: Georgia; color: rgb(0, 0, 0); line-height: 1.2; pointer-events: all; white-space: normal; overflow-wrap: normal;">Ensemble<div>Classifiers</div></div></div></div></foreignObject><text x="102" y="240" fill="rgb(0, 0, 0)" font-family="Georgia" font-size="16px" text-anchor="middle">Ensemble...</text></switch></g></g></a><g><path d="M 216 230 L 158.78 223.74" fill="none" stroke="#ff9933" stroke-width="3" stroke-miterlimit="10" pointer-events="stroke"/><path d="M 150.82 222.86 L 159.21 219.76 L 158.34 227.71 Z" fill="#ff9933" stroke="#ff9933" stroke-width="3" stroke-miterlimit="10" pointer-events="all"/></g><g><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 1px; height: 1px; padding-top: 231px; margin-left: 187px;"><div data-drawio-colors="color: #CC6600; background-color: #FFCCCC; " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 12px; font-family: Helvetica; color: rgb(204, 102, 0); line-height: 1.2; pointer-events: all; font-weight: bold; background-color: rgb(255, 204, 204); white-space: nowrap;"><span style="color: rgb(77, 81, 86); font-family: &quot;Google Sans&quot;, arial, sans-serif; font-size: 16px; font-weight: 400; text-align: start; text-wrap: wrap;">😭</span></div></div></div></foreignObject><text x="187" y="235" fill="#CC6600" font-family="Helvetica" font-size="12px" text-anchor="middle" font-weight="bold">😭</text></switch></g></g><a xlink:href="../../modules/naive_bayes.html"><g><rect x="72.49" y="295" width="60" height="50" rx="7.5" ry="7.5" fill="#ccffe6" stroke="#00cc66" stroke-width="5" pointer-events="all"/></g><g><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 58px; height: 1px; padding-top: 320px; margin-left: 73px;"><div data-drawio-colors="color: rgb(0, 0, 0); " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 16px; font-family: Georgia; color: rgb(0, 0, 0); line-height: 1.2; pointer-events: all; white-space: normal; overflow-wrap: normal;">Naive<div>Bayes</div></div></div></div></foreignObject><text x="102" y="325" fill="rgb(0, 0, 0)" font-family="Georgia" font-size="16px" text-anchor="middle">Naive...</text></switch></g></g></a><g><path d="M 196 330 L 143.71 321.77" fill="none" stroke="#009900" stroke-width="3" stroke-miterlimit="10" pointer-events="stroke"/><path d="M 135.8 320.52 L 144.33 317.81 L 143.08 325.72 Z" fill="#009900" stroke="#009900" stroke-width="3" stroke-miterlimit="10" pointer-events="all"/></g><g><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 1px; height: 1px; padding-top: 329px; margin-left: 170px;"><div data-drawio-colors="color: #009900; background-color: #FFCCCC; " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 12px; font-family: Georgia; color: rgb(0, 153, 0); line-height: 1.2; pointer-events: all; font-weight: bold; background-color: rgb(255, 204, 204); white-space: nowrap;">YES</div></div></div></foreignObject><text x="170" y="333" fill="#009900" font-family="Georgia" font-size="12px" text-anchor="middle" font-weight="bold">YES</text></switch></g></g><g><rect x="36" y="115" width="170" height="40" fill="none" stroke="none" pointer-events="all"/></g><g><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 168px; height: 1px; padding-top: 135px; margin-left: 37px;"><div data-drawio-colors="color: rgb(0, 0, 0); " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 24px; font-family: Helvetica; color: rgb(0, 0, 0); line-height: 1.2; pointer-events: all; font-weight: bold; white-space: normal; overflow-wrap: normal;"><span style="font-size: 24px;"><font style="font-size: 24px;" face="Georgia">classification</font></span></div></div></div></foreignObject><text x="121" y="142" fill="rgb(0, 0, 0)" font-family="Helvetica" font-size="24px" text-anchor="middle" font-weight="bold">classification</text></switch></g></g><g><ellipse cx="426" cy="520" rx="40" ry="35" fill="#cce5ff" stroke="#3399ff" stroke-width="5" pointer-events="all"/></g><g><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 78px; height: 1px; padding-top: 520px; margin-left: 387px;"><div data-drawio-colors="color: rgb(0, 0, 0); " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 12px; font-family: Georgia; color: rgb(0, 0, 0); line-height: 1.2; pointer-events: all; white-space: normal; overflow-wrap: normal;"><div style="font-size: 12px;"><font style="font-size: 12px;"><span style="background-color: initial; font-size: 12px;">number of</span><br style="font-size: 12px;" /></font></div><div style="font-size: 12px;"><font style="font-size: 12px;">categories</font></div><div style="font-size: 12px;"><font style="font-size: 12px;">known</font></div></div></div></div></foreignObject><text x="426" y="524" fill="rgb(0, 0, 0)" font-family="Georgia" font-size="12px" text-anchor="middle">number of...</text></switch></g></g><g><path d="M 596 440 L 465.52 491.68" fill="none" stroke="#ff3333" stroke-width="3" stroke-miterlimit="10" pointer-events="stroke"/><path d="M 458.08 494.63 L 464.05 487.96 L 466.99 495.4 Z" fill="#ff3333" stroke="#ff3333" stroke-width="3" stroke-miterlimit="10" pointer-events="all"/></g><g><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 1px; height: 1px; padding-top: 466px; margin-left: 540px;"><div data-drawio-colors="color: #FF3333; background-color: #E5CCFF; " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 12px; font-family: Georgia; color: rgb(255, 51, 51); line-height: 1.2; pointer-events: all; font-weight: bold; background-color: rgb(229, 204, 255); white-space: nowrap;">NO</div></div></div></foreignObject><text x="540" y="469" fill="#FF3333" font-family="Georgia" font-size="12px" text-anchor="middle" font-weight="bold">NO</text></switch></g></g><g><ellipse cx="496" cy="620" rx="40" ry="35" fill="#cce5ff" stroke="#3399ff" stroke-width="5" pointer-events="all"/></g><g><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 78px; height: 1px; padding-top: 620px; margin-left: 457px;"><div data-drawio-colors="color: rgb(0, 0, 0); " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 16px; font-family: Georgia; color: rgb(0, 0, 0); line-height: 1.2; pointer-events: all; white-space: normal; overflow-wrap: normal;"><font style="font-size: 16px;">&lt;10K</font><div style=""><span style=""><font style="font-size: 10px;">samples</font></span></div></div></div></div></foreignObject><text x="496" y="625" fill="rgb(0, 0, 0)" font-family="Georgia" font-size="16px" text-anchor="middle">&lt;10K...</text></switch></g></g><g><ellipse cx="286" cy="570" rx="40" ry="35" fill="#cce5ff" stroke="#3399ff" stroke-width="5" pointer-events="all"/></g><g><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 78px; height: 1px; padding-top: 570px; margin-left: 247px;"><div data-drawio-colors="color: rgb(0, 0, 0); " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 16px; font-family: Georgia; color: rgb(0, 0, 0); line-height: 1.2; pointer-events: all; white-space: normal; overflow-wrap: normal;"><font style="font-size: 16px;">&lt;10K</font><div style=""><span style=""><font style="font-size: 10px;">samples</font></span></div></div></div></div></foreignObject><text x="286" y="575" fill="rgb(0, 0, 0)" font-family="Georgia" font-size="16px" text-anchor="middle">&lt;10K...</text></switch></g></g><g><path d="M 536 620 L 586.05 633.01" fill="none" stroke="#ff3333" stroke-width="3" stroke-miterlimit="10" pointer-events="stroke"/><path d="M 593.79 635.02 L 585.04 636.88 L 587.05 629.13 Z" fill="#ff3333" stroke="#ff3333" stroke-width="3" stroke-miterlimit="10" pointer-events="all"/></g><g><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 1px; height: 1px; padding-top: 626px; margin-left: 563px;"><div data-drawio-colors="color: #FF3333; background-color: #E5CCFF; " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 12px; font-family: Georgia; color: rgb(255, 51, 51); line-height: 1.2; pointer-events: all; font-weight: bold; background-color: rgb(229, 204, 255); white-space: nowrap;">NO</div></div></div></foreignObject><text x="563" y="629" fill="#FF3333" font-family="Georgia" font-size="12px" text-anchor="middle" font-weight="bold">NO</text></switch></g></g><g><path d="M 454.97 544.14 L 464.45 584.81" fill="none" stroke="#ff3333" stroke-width="3" stroke-miterlimit="10" pointer-events="stroke"/><path d="M 466.27 592.6 L 460.56 585.71 L 468.35 583.9 Z" fill="#ff3333" stroke="#ff3333" stroke-width="3" stroke-miterlimit="10" pointer-events="all"/></g><g><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 1px; height: 1px; padding-top: 566px; margin-left: 463px;"><div data-drawio-colors="color: #FF3333; background-color: #E5CCFF; " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 12px; font-family: Georgia; color: rgb(255, 51, 51); line-height: 1.2; pointer-events: all; font-weight: bold; background-color: rgb(229, 204, 255); white-space: nowrap;">NO</div></div></div></foreignObject><text x="463" y="570" fill="#FF3333" font-family="Georgia" font-size="12px" text-anchor="middle" font-weight="bold">NO</text></switch></g></g><g><path d="M 386 520 L 325.63 541.98" fill="none" stroke="#009900" stroke-width="3" stroke-miterlimit="10" pointer-events="stroke"/><path d="M 318.12 544.71 L 324.27 538.22 L 327 545.74 Z" fill="#009900" stroke="#009900" stroke-width="3" stroke-miterlimit="10" pointer-events="all"/></g><g><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 1px; height: 1px; padding-top: 534px; margin-left: 359px;"><div data-drawio-colors="color: #009900; background-color: #E5CCFF; " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 12px; font-family: Georgia; color: rgb(0, 153, 0); line-height: 1.2; pointer-events: all; font-weight: bold; background-color: rgb(229, 204, 255); white-space: nowrap;">YES</div></div></div></foreignObject><text x="359" y="537" fill="#009900" font-family="Georgia" font-size="12px" text-anchor="middle" font-weight="bold">YES</text></switch></g></g><a xlink:href="../../modules/clustering.html#mean-shift"><g><rect x="326" y="655" width="90" height="30" rx="4.5" ry="4.5" fill="#ccffe6" 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xlink:href="../../modules/mixture.html#bgmm"><g><rect x="326" y="685" width="90" height="30" rx="4.5" ry="4.5" fill="#ccffe6" stroke="#00cc66" stroke-width="5" pointer-events="all"/></g><g><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 88px; height: 1px; padding-top: 700px; margin-left: 327px;"><div data-drawio-colors="color: rgb(0, 0, 0); " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 16px; font-family: Georgia; color: rgb(0, 0, 0); line-height: 1.2; pointer-events: all; white-space: normal; overflow-wrap: normal;">VBGMM</div></div></div></foreignObject><text x="371" y="705" fill="rgb(0, 0, 0)" font-family="Georgia" font-size="16px" text-anchor="middle">VBGMM</text></switch></g></g></a><g><path d="M 467.03 644.14 L 425.5 671.29" fill="none" stroke="#009900" stroke-width="3" stroke-miterlimit="10" pointer-events="stroke"/><path d="M 418.81 675.66 L 423.31 667.94 L 427.69 674.64 Z" fill="#009900" stroke="#009900" stroke-width="3" stroke-miterlimit="10" pointer-events="all"/></g><g><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 1px; height: 1px; padding-top: 660px; margin-left: 449px;"><div data-drawio-colors="color: #009900; background-color: #E5CCFF; " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 12px; font-family: Georgia; color: rgb(0, 153, 0); line-height: 1.2; pointer-events: all; font-weight: bold; background-color: rgb(229, 204, 255); white-space: nowrap;">YES</div></div></div></foreignObject><text x="449" y="664" fill="#009900" font-family="Georgia" font-size="12px" text-anchor="middle" font-weight="bold">YES</text></switch></g></g><a xlink:href="../../modules/clustering.html#mini-batch-k-means"><g><rect x="191" y="645" width="90" height="50" rx="7.5" ry="7.5" fill="#ccffe6" stroke="#00cc66" stroke-width="5" pointer-events="all"/></g><g><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 88px; height: 1px; padding-top: 670px; margin-left: 192px;"><div data-drawio-colors="color: rgb(0, 0, 0); " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 16px; font-family: Georgia; color: rgb(0, 0, 0); line-height: 1.2; pointer-events: all; white-space: normal; overflow-wrap: normal;">MiniBatch<div>KMeans</div></div></div></div></foreignObject><text x="236" y="675" fill="rgb(0, 0, 0)" font-family="Georgia" font-size="16px" text-anchor="middle">MiniBatch...</text></switch></g></g></a><g><path d="M 257.03 594.14 L 240.34 634.51" fill="none" stroke="#ff3333" stroke-width="3" stroke-miterlimit="10" pointer-events="stroke"/><path d="M 237.28 641.9 L 236.64 632.98 L 244.04 636.04 Z" fill="#ff3333" stroke="#ff3333" stroke-width="3" stroke-miterlimit="10" pointer-events="all"/></g><g><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 1px; height: 1px; padding-top: 617px; margin-left: 252px;"><div data-drawio-colors="color: #FF3333; background-color: #E5CCFF; " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 12px; font-family: Georgia; color: rgb(255, 51, 51); line-height: 1.2; pointer-events: all; font-weight: bold; background-color: rgb(229, 204, 255); white-space: nowrap;">NO</div></div></div></foreignObject><text x="252" y="620" fill="#FF3333" font-family="Georgia" font-size="12px" text-anchor="middle" font-weight="bold">NO</text></switch></g></g><g><rect x="38.98" y="605" width="138.51" height="40" fill="none" stroke="none" pointer-events="all"/></g><g><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 137px; height: 1px; padding-top: 625px; margin-left: 40px;"><div data-drawio-colors="color: rgb(0, 0, 0); " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 24px; font-family: Helvetica; color: rgb(0, 0, 0); line-height: 1.2; pointer-events: all; font-weight: bold; white-space: normal; overflow-wrap: normal;"><span style="font-size: 24px;"><font style="font-size: 24px;" face="Georgia">clustering</font></span></div></div></div></foreignObject><text x="108" y="632" fill="rgb(0, 0, 0)" font-family="Helvetica" font-size="24px" text-anchor="middle" font-weight="bold">clustering</text></switch></g></g><a xlink:href="../../modules/clustering.html#k-means"><g><rect x="177.49" y="465" width="78.51" height="30" rx="4.5" ry="4.5" fill="#ccffe6" stroke="#00cc66" stroke-width="5" pointer-events="all"/></g><g><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 77px; height: 1px; padding-top: 480px; margin-left: 178px;"><div data-drawio-colors="color: rgb(0, 0, 0); " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 16px; font-family: Georgia; color: rgb(0, 0, 0); line-height: 1.2; pointer-events: all; white-space: normal; overflow-wrap: normal;">KMeans</div></div></div></foreignObject><text x="217" y="485" fill="rgb(0, 0, 0)" font-family="Georgia" font-size="16px" text-anchor="middle">KMeans</text></switch></g></g></a><g><path d="M 257.03 545.86 L 240.65 505.52" fill="none" stroke="#009900" stroke-width="3" stroke-miterlimit="10" pointer-events="stroke"/><path d="M 237.63 498.11 L 244.35 504.01 L 236.94 507.02 Z" fill="#009900" stroke="#009900" stroke-width="3" stroke-miterlimit="10" pointer-events="all"/></g><g><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 1px; height: 1px; padding-top: 527px; margin-left: 248px;"><div data-drawio-colors="color: #009900; background-color: #E5CCFF; " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 12px; font-family: Georgia; color: rgb(0, 153, 0); line-height: 1.2; pointer-events: all; font-weight: bold; background-color: rgb(229, 204, 255); white-space: nowrap;">YES</div></div></div></foreignObject><text x="248" y="531" fill="#009900" font-family="Georgia" font-size="12px" text-anchor="middle" font-weight="bold">YES</text></switch></g></g><a xlink:href="../../modules/clustering.html#spectral-clustering"><g><rect x="36" y="505" width="90" height="50" rx="7.5" ry="7.5" fill="#ccffe6" stroke="#00cc66" stroke-width="5" pointer-events="all"/></g><g><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 88px; height: 1px; padding-top: 530px; margin-left: 37px;"><div data-drawio-colors="color: rgb(0, 0, 0); " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 16px; font-family: Georgia; color: rgb(0, 0, 0); line-height: 1.2; pointer-events: all; white-space: normal; overflow-wrap: normal;"><div>Spectral</div><div>Clustering</div></div></div></div></foreignObject><text x="81" y="535" fill="rgb(0, 0, 0)" font-family="Georgia" font-size="16px" text-anchor="middle">Spectral...</text></switch></g></g></a><a xlink:href="../../modules/mixture.html"><g><rect x="36" y="555" width="90" height="30" rx="4.5" ry="4.5" fill="#ccffe6" stroke="#00cc66" stroke-width="5" pointer-events="all"/></g><g><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 88px; height: 1px; padding-top: 570px; margin-left: 37px;"><div data-drawio-colors="color: rgb(0, 0, 0); " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 16px; font-family: Georgia; color: rgb(0, 0, 0); line-height: 1.2; pointer-events: all; white-space: normal; overflow-wrap: normal;">GMM</div></div></div></foreignObject><text x="81" y="575" fill="rgb(0, 0, 0)" font-family="Georgia" font-size="16px" text-anchor="middle">GMM</text></switch></g></g></a><g><path d="M 177.49 487.5 L 135.81 511.78" fill="none" stroke="#ff9933" stroke-width="3" stroke-miterlimit="10" pointer-events="stroke"/><path d="M 128.9 515.81 L 133.8 508.33 L 137.82 515.24 Z" fill="#ff9933" stroke="#ff9933" stroke-width="3" stroke-miterlimit="10" pointer-events="all"/></g><g><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 1px; height: 1px; padding-top: 505px; margin-left: 156px;"><div data-drawio-colors="color: #CC6600; background-color: #E5CCFF; " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 12px; font-family: Helvetica; color: rgb(204, 102, 0); line-height: 1.2; pointer-events: all; font-weight: bold; background-color: rgb(229, 204, 255); white-space: nowrap;"><span style="color: rgb(77, 81, 86); font-family: &quot;Google Sans&quot;, arial, sans-serif; font-size: 16px; font-weight: 400; text-align: start; text-wrap: wrap;">😭</span></div></div></div></foreignObject><text x="156" y="508" fill="#CC6600" font-family="Helvetica" font-size="12px" text-anchor="middle" font-weight="bold">😭</text></switch></g></g><g><ellipse cx="926" cy="370" rx="40" ry="35" fill="#cce5ff" stroke="#3399ff" stroke-width="5" pointer-events="all"/></g><g><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 78px; height: 1px; padding-top: 370px; margin-left: 887px;"><div data-drawio-colors="color: rgb(0, 0, 0); " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 16px; font-family: Georgia; color: rgb(0, 0, 0); line-height: 1.2; pointer-events: all; white-space: normal; overflow-wrap: normal;"><font style="font-size: 16px;">&lt;100K</font><div style=""><span style=""><font style="font-size: 10px;">samples</font></span></div></div></div></div></foreignObject><text x="926" y="375" fill="rgb(0, 0, 0)" font-family="Georgia" font-size="16px" text-anchor="middle">&lt;100K...</text></switch></g></g><g><path d="M 828.97 425.86 L 877.89 377.94" fill="none" stroke="#009900" stroke-width="3" stroke-miterlimit="10" pointer-events="stroke"/><path d="M 883.6 372.35 L 880.69 380.8 L 875.09 375.09 Z" fill="#009900" stroke="#009900" stroke-width="3" stroke-miterlimit="10" pointer-events="all"/></g><g><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 1px; height: 1px; padding-top: 402px; margin-left: 852px;"><div data-drawio-colors="color: #009900; background-color: rgb(255, 255, 255); " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 12px; font-family: Georgia; color: rgb(0, 153, 0); line-height: 1.2; pointer-events: all; font-weight: bold; background-color: rgb(255, 255, 255); white-space: nowrap;">YES</div></div></div></foreignObject><text x="852" y="406" fill="#009900" font-family="Georgia" font-size="12px" text-anchor="middle" font-weight="bold">YES</text></switch></g></g><g><ellipse cx="1076" cy="380" rx="40" ry="35" fill="#cce5ff" stroke="#3399ff" stroke-width="5" pointer-events="all"/></g><g><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 78px; height: 1px; padding-top: 380px; margin-left: 1037px;"><div data-drawio-colors="color: rgb(0, 0, 0); " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 12px; font-family: Georgia; color: rgb(0, 0, 0); line-height: 1.2; pointer-events: all; white-space: normal; overflow-wrap: normal;"><div style="font-size: 12px;">few features</div><div style="font-size: 12px;">should be</div><div style="font-size: 12px;">important</div></div></div></div></foreignObject><text x="1076" y="384" fill="rgb(0, 0, 0)" font-family="Georgia" font-size="12px" text-anchor="middle">few features...</text></switch></g></g><g><path d="M 966 370 L 1024.76 378.39" fill="none" stroke="#009900" stroke-width="3" stroke-miterlimit="10" pointer-events="stroke"/><path d="M 1032.68 379.53 L 1024.19 382.35 L 1025.33 374.43 Z" fill="#009900" stroke="#009900" stroke-width="3" stroke-miterlimit="10" pointer-events="all"/></g><g><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 1px; height: 1px; padding-top: 373px; margin-left: 996px;"><div data-drawio-colors="color: #009900; background-color: #CCE5FF; " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 12px; font-family: Georgia; color: rgb(0, 153, 0); line-height: 1.2; pointer-events: all; font-weight: bold; background-color: rgb(204, 229, 255); white-space: nowrap;">YES</div></div></div></foreignObject><text x="996" y="377" fill="#009900" font-family="Georgia" font-size="12px" text-anchor="middle" font-weight="bold">YES</text></switch></g></g><a xlink:href="../../modules/sgd.html#regression"><g><rect x="976" y="260" width="90" height="50" rx="7.5" ry="7.5" fill="#ccffe6" stroke="#00cc66" stroke-width="5" pointer-events="all"/></g><g><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 88px; height: 1px; padding-top: 285px; margin-left: 977px;"><div data-drawio-colors="color: rgb(0, 0, 0); " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 16px; font-family: Georgia; color: rgb(0, 0, 0); line-height: 1.2; pointer-events: all; white-space: normal; overflow-wrap: normal;"><div>SGD</div><div>Regressor</div></div></div></div></foreignObject><text x="1021" y="290" fill="rgb(0, 0, 0)" font-family="Georgia" font-size="16px" text-anchor="middle">SGD...</text></switch></g></g></a><g><path d="M 954.97 345.86 L 989.74 317.22" fill="none" stroke="#ff3333" stroke-width="3" stroke-miterlimit="10" pointer-events="stroke"/><path d="M 995.91 312.13 L 992.28 320.31 L 987.19 314.13 Z" fill="#ff3333" stroke="#ff3333" stroke-width="3" stroke-miterlimit="10" pointer-events="all"/></g><g><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 1px; height: 1px; padding-top: 331px; margin-left: 972px;"><div data-drawio-colors="color: #FF3333; background-color: #CCE5FF; " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 12px; font-family: Georgia; color: rgb(255, 51, 51); line-height: 1.2; pointer-events: all; font-weight: bold; background-color: rgb(204, 229, 255); white-space: nowrap;">NO</div></div></div></foreignObject><text x="972" y="335" fill="#FF3333" font-family="Georgia" font-size="12px" text-anchor="middle" font-weight="bold">NO</text></switch></g></g><a xlink:href="../../modules/linear_model.html#lasso"><g><rect x="1106" y="230" width="90" height="30" rx="4.5" ry="4.5" fill="#ccffe6" stroke="#00cc66" stroke-width="5" pointer-events="all"/></g><g><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 88px; height: 1px; padding-top: 245px; margin-left: 1107px;"><div data-drawio-colors="color: rgb(0, 0, 0); " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 16px; font-family: Georgia; color: rgb(0, 0, 0); line-height: 1.2; pointer-events: all; white-space: normal; overflow-wrap: 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rgb(0, 0, 0); line-height: 1.2; pointer-events: all; white-space: normal; overflow-wrap: normal;">ElasticNet</div></div></div></foreignObject><text x="1151" y="280" fill="rgb(0, 0, 0)" font-family="Georgia" font-size="16px" text-anchor="middle">ElasticNet</text></switch></g></g></a><g><path d="M 1104.97 355.86 L 1124.68 300.69" fill="none" stroke="#009900" stroke-width="3" stroke-miterlimit="10" pointer-events="stroke"/><path d="M 1127.37 293.16 L 1128.45 302.04 L 1120.91 299.35 Z" fill="#009900" stroke="#009900" stroke-width="3" stroke-miterlimit="10" pointer-events="all"/></g><g><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 1px; height: 1px; padding-top: 329px; margin-left: 1114px;"><div data-drawio-colors="color: #009900; background-color: #CCE5FF; " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 12px; font-family: Georgia; color: rgb(0, 153, 0); line-height: 1.2; pointer-events: all; font-weight: bold; background-color: rgb(204, 229, 255); white-space: nowrap;">YES</div></div></div></foreignObject><text x="1114" y="333" fill="#009900" font-family="Georgia" font-size="12px" text-anchor="middle" font-weight="bold">YES</text></switch></g></g><a xlink:href="../../modules/linear_model.html#ridge-regression"><g><rect x="1176" y="395" width="140" height="30" rx="4.5" ry="4.5" fill="#ccffe6" stroke="#00cc66" stroke-width="5" pointer-events="all"/></g><g><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 138px; height: 1px; padding-top: 410px; margin-left: 1177px;"><div data-drawio-colors="color: rgb(0, 0, 0); " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 16px; font-family: Georgia; color: rgb(0, 0, 0); line-height: 1.2; pointer-events: all; white-space: normal; overflow-wrap: normal;">RidgeRegression</div></div></div></foreignObject><text x="1246" y="415" fill="rgb(0, 0, 0)" font-family="Georgia" font-size="16px" text-anchor="middle">RidgeRegression</text></switch></g></g></a><a xlink:href="../../modules/svm.html#regression"><g><rect x="1176" y="425" width="140" height="30" rx="4.5" ry="4.5" fill="#ccffe6" stroke="#00cc66" stroke-width="5" pointer-events="all"/></g><g><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 138px; height: 1px; padding-top: 440px; margin-left: 1177px;"><div data-drawio-colors="color: rgb(0, 0, 0); " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 16px; font-family: Georgia; color: rgb(0, 0, 0); line-height: 1.2; pointer-events: all; white-space: normal; overflow-wrap: normal;">SVR<font style="font-size: 12px;">(kernel="linear")</font></div></div></div></foreignObject><text x="1246" y="445" fill="rgb(0, 0, 0)" font-family="Georgia" font-size="16px" text-anchor="middle">SVR(kernel="linea...</text></switch></g></g></a><g><path d="M 1116 380 L 1165.84 404.92" fill="none" stroke="#ff3333" stroke-width="3" stroke-miterlimit="10" pointer-events="stroke"/><path d="M 1173 408.5 L 1164.06 408.5 L 1167.63 401.34 Z" fill="#ff3333" stroke="#ff3333" stroke-width="3" stroke-miterlimit="10" pointer-events="all"/></g><g><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 1px; height: 1px; padding-top: 391px; margin-left: 1142px;"><div data-drawio-colors="color: #FF3333; background-color: #CCE5FF; " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 12px; font-family: Georgia; color: rgb(255, 51, 51); line-height: 1.2; pointer-events: all; font-weight: bold; background-color: rgb(204, 229, 255); white-space: nowrap;">NO</div></div></div></foreignObject><text x="1142" y="395" fill="#FF3333" font-family="Georgia" font-size="12px" text-anchor="middle" font-weight="bold">NO</text></switch></g></g><a xlink:href="../../modules/svm.html#regression"><g><rect x="1266" y="255" width="120" height="30" rx="4.5" ry="4.5" fill="#ccffe6" stroke="#00cc66" stroke-width="5" pointer-events="all"/></g><g><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 118px; height: 1px; padding-top: 270px; margin-left: 1267px;"><div data-drawio-colors="color: rgb(0, 0, 0); " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 16px; font-family: Georgia; color: rgb(0, 0, 0); line-height: 1.2; pointer-events: all; white-space: normal; overflow-wrap: normal;">SVR<font style="font-size: 12px;">(kernel="rbf")</font></div></div></div></foreignObject><text x="1326" y="275" fill="rgb(0, 0, 0)" font-family="Georgia" font-size="16px" text-anchor="middle">SVR(kernel="rbf...</text></switch></g></g></a><a xlink:href="../../modules/ensemble.html"><g><rect x="1266" y="285" width="120" height="50" rx="7.5" ry="7.5" fill="#ccffe6" stroke="#00cc66" stroke-width="5" pointer-events="all"/></g><g><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 118px; height: 1px; padding-top: 310px; margin-left: 1267px;"><div data-drawio-colors="color: rgb(0, 0, 0); " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 16px; font-family: Georgia; color: rgb(0, 0, 0); line-height: 1.2; pointer-events: all; white-space: normal; overflow-wrap: normal;">Ensemble<div>Regressors</div></div></div></div></foreignObject><text x="1326" y="315" fill="rgb(0, 0, 0)" font-family="Georgia" font-size="16px" text-anchor="middle">Ensemble...</text></switch></g></g></a><g><path d="M 1281 395 L 1293.25 346.01" fill="none" stroke="#ff9933" stroke-width="3" stroke-miterlimit="10" pointer-events="stroke"/><path d="M 1295.19 338.25 L 1297.13 346.99 L 1289.37 345.04 Z" fill="#ff9933" stroke="#ff9933" stroke-width="3" stroke-miterlimit="10" pointer-events="all"/></g><g><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 1px; height: 1px; padding-top: 370px; margin-left: 1285px;"><div data-drawio-colors="color: #CC6600; background-color: #CCE5FF; " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 12px; font-family: Helvetica; color: rgb(204, 102, 0); line-height: 1.2; pointer-events: all; font-weight: bold; background-color: rgb(204, 229, 255); white-space: nowrap;"><span style="color: rgb(77, 81, 86); font-family: &quot;Google Sans&quot;, arial, sans-serif; font-size: 16px; font-weight: 400; text-align: start; text-wrap: wrap;">😭</span></div></div></div></foreignObject><text x="1285" y="373" fill="#CC6600" font-family="Helvetica" font-size="12px" text-anchor="middle" font-weight="bold">😭</text></switch></g></g><g><rect x="886" y="205" width="140" height="40" fill="none" stroke="none" pointer-events="all"/></g><g><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 138px; height: 1px; padding-top: 225px; margin-left: 887px;"><div data-drawio-colors="color: rgb(0, 0, 0); " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 24px; font-family: Helvetica; color: rgb(0, 0, 0); line-height: 1.2; pointer-events: all; font-weight: bold; white-space: normal; overflow-wrap: normal;"><span style="font-size: 24px;"><font style="font-size: 24px;" face="Georgia">regression</font></span></div></div></div></foreignObject><text x="956" y="232" fill="rgb(0, 0, 0)" font-family="Helvetica" font-size="24px" text-anchor="middle" font-weight="bold">regression</text></switch></g></g><a xlink:href="../../modules/decomposition.html#principal-component-analysis-pca"><g><rect x="901" y="535" width="110" height="50" rx="7.5" ry="7.5" fill="#ccffe6" stroke="#00cc66" stroke-width="5" pointer-events="all"/></g><g><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 108px; height: 1px; padding-top: 560px; margin-left: 902px;"><div data-drawio-colors="color: rgb(0, 0, 0); " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 16px; font-family: Georgia; color: rgb(0, 0, 0); line-height: 1.2; pointer-events: all; white-space: normal; overflow-wrap: normal;"><div><span style="background-color: initial;">Ramdomized</span><br /></div><div><span style="background-color: initial;">PCA</span></div></div></div></div></foreignObject><text x="956" y="565" fill="rgb(0, 0, 0)" font-family="Georgia" font-size="16px" text-anchor="middle">Ramdomized...</text></switch></g></g></a><g><path d="M 796 560 L 889.65 560" fill="none" stroke="#009900" stroke-width="3" stroke-miterlimit="10" pointer-events="stroke"/><path d="M 897.65 560 L 889.65 564 L 889.65 556 Z" fill="#009900" stroke="#009900" stroke-width="3" stroke-miterlimit="10" pointer-events="all"/></g><g><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 1px; height: 1px; padding-top: 559px; margin-left: 839px;"><div data-drawio-colors="color: #009900; background-color: rgb(255, 255, 255); " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 12px; font-family: Georgia; color: rgb(0, 153, 0); line-height: 1.2; pointer-events: all; font-weight: bold; background-color: rgb(255, 255, 255); white-space: nowrap;">YES</div></div></div></foreignObject><text x="839" y="563" fill="#009900" font-family="Georgia" font-size="12px" text-anchor="middle" font-weight="bold">YES</text></switch></g></g><g><ellipse cx="941" cy="680" rx="40" ry="35" fill="#cce5ff" stroke="#3399ff" stroke-width="5" pointer-events="all"/></g><g><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 78px; height: 1px; padding-top: 680px; margin-left: 902px;"><div data-drawio-colors="color: rgb(0, 0, 0); " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 16px; font-family: Georgia; color: rgb(0, 0, 0); line-height: 1.2; pointer-events: all; white-space: normal; overflow-wrap: normal;"><font style="font-size: 16px;">&lt;10K</font><div style=""><span style=""><font style="font-size: 10px;">samples</font></span></div></div></div></div></foreignObject><text x="941" y="685" fill="rgb(0, 0, 0)" font-family="Georgia" font-size="16px" text-anchor="middle">&lt;10K...</text></switch></g></g><g><path d="M 956 585 L 943.75 633.99" fill="none" stroke="#ff9933" stroke-width="3" stroke-miterlimit="10" pointer-events="stroke"/><path d="M 941.81 641.75 L 939.87 633.01 L 947.63 634.96 Z" fill="#ff9933" stroke="#ff9933" stroke-width="3" stroke-miterlimit="10" pointer-events="all"/></g><g><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 1px; height: 1px; padding-top: 613px; margin-left: 953px;"><div data-drawio-colors="color: #CC6600; background-color: #FFFFCC; " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 12px; font-family: Helvetica; color: rgb(204, 102, 0); line-height: 1.2; pointer-events: all; font-weight: bold; background-color: rgb(255, 255, 204); white-space: nowrap;"><span style="color: rgb(77, 81, 86); font-family: &quot;Google Sans&quot;, arial, sans-serif; font-size: 16px; font-weight: 400; text-align: start; text-wrap: wrap;">😭</span></div></div></div></foreignObject><text x="953" y="616" fill="#CC6600" font-family="Helvetica" font-size="12px" text-anchor="middle" font-weight="bold">😭</text></switch></g></g><a xlink:href="../../modules/kernel_approximation.html"><g><rect x="1056" y="675" width="120" height="50" rx="7.5" ry="7.5" fill="#ccffe6" stroke="#00cc66" stroke-width="5" pointer-events="all"/></g><g><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 118px; height: 1px; padding-top: 700px; margin-left: 1057px;"><div data-drawio-colors="color: rgb(0, 0, 0); " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 16px; font-family: Georgia; color: rgb(0, 0, 0); line-height: 1.2; pointer-events: all; white-space: normal; overflow-wrap: normal;">Kernel<div>Approximation</div></div></div></div></foreignObject><text x="1116" y="705" fill="rgb(0, 0, 0)" font-family="Georgia" font-size="16px" text-anchor="middle">Kernel...</text></switch></g></g></a><g><path d="M 981 680 L 1044.7 686.37" fill="none" stroke="#ff3333" stroke-width="3" stroke-miterlimit="10" pointer-events="stroke"/><path d="M 1052.66 687.17 L 1044.3 690.35 L 1045.1 682.39 Z" fill="#ff3333" stroke="#ff3333" stroke-width="3" stroke-miterlimit="10" pointer-events="all"/></g><g><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 1px; height: 1px; padding-top: 682px; margin-left: 1013px;"><div data-drawio-colors="color: #FF3333; background-color: #FFFFCC; " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 12px; font-family: Georgia; color: rgb(255, 51, 51); line-height: 1.2; pointer-events: all; font-weight: bold; background-color: rgb(255, 255, 204); white-space: nowrap;">NO</div></div></div></foreignObject><text x="1013" y="686" fill="#FF3333" font-family="Georgia" font-size="12px" text-anchor="middle" font-weight="bold">NO</text></switch></g></g><a xlink:href="../../modules/manifold.html#isomap"><g><rect x="1066" y="555" width="100" height="30" rx="4.5" ry="4.5" fill="#ccffe6" stroke="#00cc66" stroke-width="5" pointer-events="all"/></g><g><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 98px; height: 1px; padding-top: 570px; margin-left: 1067px;"><div data-drawio-colors="color: rgb(0, 0, 0); " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 16px; font-family: Georgia; color: rgb(0, 0, 0); line-height: 1.2; pointer-events: all; white-space: normal; overflow-wrap: normal;">IsoMap</div></div></div></foreignObject><text x="1116" y="575" fill="rgb(0, 0, 0)" font-family="Georgia" font-size="16px" text-anchor="middle">IsoMap</text></switch></g></g></a><a xlink:href="../../modules/manifold.html#spectral-embedding"><g><rect x="1066" y="585" width="100" height="50" rx="7.5" ry="7.5" fill="#ccffe6" stroke="#00cc66" stroke-width="5" pointer-events="all"/></g><g><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 98px; height: 1px; padding-top: 610px; margin-left: 1067px;"><div data-drawio-colors="color: rgb(0, 0, 0); " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 16px; font-family: Georgia; color: rgb(0, 0, 0); line-height: 1.2; pointer-events: all; white-space: normal; overflow-wrap: normal;"><div>Spectral</div><div>Embedding</div></div></div></div></foreignObject><text x="1116" y="615" fill="rgb(0, 0, 0)" font-family="Georgia" font-size="16px" text-anchor="middle">Spectral...</text></switch></g></g></a><g><path d="M 969.97 655.86 L 1055.27 626.23" fill="none" stroke="#009900" stroke-width="3" stroke-miterlimit="10" pointer-events="stroke"/><path d="M 1062.83 623.6 L 1056.59 630 L 1053.96 622.45 Z" fill="#009900" stroke="#009900" stroke-width="3" stroke-miterlimit="10" pointer-events="all"/></g><g><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 1px; height: 1px; padding-top: 641px; margin-left: 1010px;"><div data-drawio-colors="color: #009900; background-color: #FFFFCC; " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 12px; font-family: Georgia; color: rgb(0, 153, 0); line-height: 1.2; pointer-events: all; font-weight: bold; background-color: rgb(255, 255, 204); white-space: nowrap;">YES</div></div></div></foreignObject><text x="1010" y="645" fill="#009900" font-family="Georgia" font-size="12px" text-anchor="middle" font-weight="bold">YES</text></switch></g></g><a 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font-family="Georgia" font-size="16px" text-anchor="middle">LLE</text></switch></g></g></a><g><path d="M 1166 610 L 1234.99 627.25" fill="none" stroke="#ff9933" stroke-width="3" stroke-miterlimit="10" pointer-events="stroke"/><path d="M 1242.75 629.19 L 1234.01 631.13 L 1235.96 623.37 Z" fill="#ff9933" stroke="#ff9933" stroke-width="3" stroke-miterlimit="10" pointer-events="all"/></g><g><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 1px; height: 1px; padding-top: 617px; margin-left: 1201px;"><div data-drawio-colors="color: #CC6600; background-color: #FFFFCC; " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 12px; font-family: Helvetica; color: rgb(204, 102, 0); line-height: 1.2; pointer-events: all; font-weight: bold; background-color: rgb(255, 255, 204); white-space: nowrap;"><span style="color: rgb(77, 81, 86); font-family: &quot;Google Sans&quot;, arial, sans-serif; font-size: 16px; font-weight: 400; text-align: start; text-wrap: wrap;">😭</span></div></div></div></foreignObject><text x="1201" y="620" fill="#CC6600" font-family="Helvetica" font-size="12px" text-anchor="middle" font-weight="bold">😭</text></switch></g></g><g><rect x="1196" y="667" width="210" height="65" fill="none" stroke="none" pointer-events="all"/></g><g><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 208px; height: 1px; padding-top: 700px; margin-left: 1197px;"><div data-drawio-colors="color: rgb(0, 0, 0); " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 24px; font-family: Helvetica; color: rgb(0, 0, 0); line-height: 1.2; pointer-events: all; font-weight: bold; white-space: normal; overflow-wrap: normal;"><span style="font-size: 24px;"><font style="font-size: 24px;" face="Georgia">dimensionality</font></span><div><span style="font-size: 24px;"><font style="font-size: 24px;" face="Georgia">reduction</font></span></div></div></div></div></foreignObject><text x="1301" y="707" fill="rgb(0, 0, 0)" font-family="Helvetica" font-size="24px" text-anchor="middle" font-weight="bold">dimensionality...</text></switch></g></g><g><image x="1165.5" y="14.5" width="166.92" height="90" xlink:href="data:image/svg+xml;base64,<svg xmlns="http://www.w3.org/2000/svg" width="1251" viewBox="0 0 1251 675" height="675"><path fill="#f89939" d="m959.940063 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y="370" x="688" font-size="103.85775" font-family="Helvetica" fill="#fff">scikit</text><path fill="none" d="m1015.055969 620.905518h1464.444031v193.333557h-1464.444031z"/></svg>" preserveAspectRatio="none"/></g><g><rect x="953.5" y="65" width="375" height="90" fill="none" stroke="none" pointer-events="all"/></g><g><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe flex-start; width: 373px; height: 1px; padding-top: 110px; margin-left: 956px;"><div data-drawio-colors="color: rgb(0, 0, 0); " style="box-sizing: border-box; font-size: 0px; text-align: left;"><div style="display: inline-block; font-size: 32px; font-family: Helvetica; color: rgb(0, 0, 0); line-height: 1.2; pointer-events: all; font-weight: bold; white-space: normal; overflow-wrap: normal;"><span style="font-size: 32px;"><font style="font-size: 32px;" face="Georgia">scikit-learn</font></span><div style="font-size: 32px;"><span style="font-size: 32px;"><font style="font-size: 32px;" face="Georgia">algorithm cheat sheet</font></span></div></div></div></div></foreignObject><text x="956" y="120" fill="rgb(0, 0, 0)" font-family="Helvetica" font-size="32px" font-weight="bold">scikit-learn...</text></switch></g></g></g><switch><g requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility"/><a transform="translate(0,-5)" xlink:href="https://www.drawio.com/doc/faq/svg-export-text-problems" target="_blank"><text text-anchor="middle" font-size="10px" x="50%" y="100%">Text is not SVG - cannot display</text></a></switch></svg>
diff --git a/doc/includes/big_toc_css.rst b/doc/includes/big_toc_css.rst
deleted file mode 100644
index a8ba83e99c5b8..0000000000000
--- a/doc/includes/big_toc_css.rst
+++ /dev/null
@@ -1,40 +0,0 @@
-..  
-    File to ..include in a document with a big table of content, to give
-    it 'style'
-
-.. raw:: html
-
-  <style type="text/css">
-    div.body div.toctree-wrapper ul {
-        padding-left: 0;
-    }
-
-    div.body li.toctree-l1 {
-        padding: 0 0 0.5em 0;
-        list-style-type: none;
-        font-size: 150%;
-        font-weight: bold;
-    }
-
-    div.body li.toctree-l2 {
-        font-size: 70%;
-        list-style-type: square;
-        font-weight: normal;
-        margin-left: 40px;
-    }
-
-    div.body li.toctree-l3 {
-        font-size: 85%;
-        list-style-type: circle;
-        font-weight: normal;
-        margin-left: 40px;
-    }
-
-    div.body li.toctree-l4 {
-        margin-left: 40px;
-    }
- 
-  </style>
-
-
-
diff --git a/doc/includes/bigger_toc_css.rst b/doc/includes/bigger_toc_css.rst
deleted file mode 100644
index d866bd145d883..0000000000000
--- a/doc/includes/bigger_toc_css.rst
+++ /dev/null
@@ -1,60 +0,0 @@
-..  
-    File to ..include in a document with a very big table of content, to 
-    give it 'style'
-
-.. raw:: html
-
-  <style type="text/css">
-    div.bodywrapper blockquote {
-        margin: 0 ;
-    }
-
-    div.toctree-wrapper ul {
-	margin: 0 ;
-	padding-left: 0px ;
-    }
-
-    li.toctree-l1 {
-        padding: 0 ;
-        list-style-type: none;
-        font-size: 150% ;
-	font-family: Arial, sans-serif;
-	background-color: #BED4EB;
-	font-weight: normal;
-	color: #212224;
-	margin-left : 0;
-	font-weight: bold;
-        }
-
-    li.toctree-l1 a {
-        padding: 0 0 0 10px ;
-    }
- 
-    li.toctree-l2 {
-        padding: 0.25em 0 0.25em 0 ;
-        list-style-type: none;
-	background-color: #FFFFFF;
-        font-size: 90% ;
-	font-weight: bold;
-        }
-
-    li.toctree-l2 ul {
-	padding-left: 40px ;
-    }
-
-    li.toctree-l3 {
-        font-size: 70% ;
-        list-style-type: none;
-	font-weight: normal;
-        }
-
-    li.toctree-l4 {
-        font-size: 85% ;
-        list-style-type: none;
-	font-weight: normal;
-        }
- 
-  </style>
-
-
-
diff --git a/doc/index.rst.template b/doc/index.rst.template
new file mode 100644
index 0000000000000..df058f5fb6185
--- /dev/null
+++ b/doc/index.rst.template
@@ -0,0 +1,25 @@
+.. title:: Index
+
+.. Define the overall structure, that affects the prev-next buttons and the order
+   of the sections in the top navbar.
+
+.. toctree::
+   :hidden:
+   :maxdepth: 2
+
+   Install <install>
+   user_guide
+   API <api/index>
+   auto_examples/index
+   Community <https://blog.scikit-learn.org/>
+   getting_started
+   Tutorials <tutorial/index>
+   whats_new
+   Glossary <glossary>
+   Development <{{ development_link }}>
+   FAQ <faq>
+   support
+   related_projects
+   roadmap
+   Governance <governance>
+   about
diff --git a/doc/inspection.rst b/doc/inspection.rst
index dcf169377e320..88c5ba6f2e678 100644
--- a/doc/inspection.rst
+++ b/doc/inspection.rst
@@ -1,9 +1,3 @@
-.. Places parent toc into the sidebar
-
-:parenttoc: True
-
-.. include:: includes/big_toc_css.rst
-
 .. _inspection:
 
 Inspection
@@ -21,9 +15,9 @@ predictions from a model and what affects them. This can be used to
 evaluate assumptions and biases of a model, design a better model, or
 to diagnose issues with model performance.
 
-.. topic:: Examples:
+.. rubric:: Examples
 
-   * :ref:`sphx_glr_auto_examples_inspection_plot_linear_model_coefficient_interpretation.py`
+* :ref:`sphx_glr_auto_examples_inspection_plot_linear_model_coefficient_interpretation.py`
 
 .. toctree::
 
diff --git a/doc/install.rst b/doc/install.rst
index c4a3548016021..be924b012ce65 100644
--- a/doc/install.rst
+++ b/doc/install.rst
@@ -6,21 +6,21 @@ Installing scikit-learn
 
 There are different ways to install scikit-learn:
 
-  * :ref:`Install the latest official release <install_official_release>`. This
-    is the best approach for most users. It will provide a stable version
-    and pre-built packages are available for most platforms.
+* :ref:`Install the latest official release <install_official_release>`. This
+  is the best approach for most users. It will provide a stable version
+  and pre-built packages are available for most platforms.
 
-  * Install the version of scikit-learn provided by your
-    :ref:`operating system or Python distribution <install_by_distribution>`.
-    This is a quick option for those who have operating systems or Python
-    distributions that distribute scikit-learn.
-    It might not provide the latest release version.
+* Install the version of scikit-learn provided by your
+  :ref:`operating system or Python distribution <install_by_distribution>`.
+  This is a quick option for those who have operating systems or Python
+  distributions that distribute scikit-learn.
+  It might not provide the latest release version.
 
-  * :ref:`Building the package from source
-    <install_bleeding_edge>`. This is best for users who want the
-    latest-and-greatest features and aren't afraid of running
-    brand-new code. This is also needed for users who wish to contribute to the
-    project.
+* :ref:`Building the package from source
+  <install_bleeding_edge>`. This is best for users who want the
+  latest-and-greatest features and aren't afraid of running
+  brand-new code. This is also needed for users who wish to contribute to the
+  project.
 
 
 .. _install_official_release:
@@ -28,117 +28,132 @@ There are different ways to install scikit-learn:
 Installing the latest release
 =============================
 
-.. This quickstart installation is a hack of the awesome
-   https://spacy.io/usage/#quickstart page.
-   See the original javascript implementation
-   https://github.com/ines/quickstart
-
-
-.. raw:: html
-
-  <div class="install">
-       <strong>Operating System</strong>
-          <input type="radio" name="os" id="quickstart-win" checked>
-          <label for="quickstart-win">Windows</label>
-          <input type="radio" name="os" id="quickstart-mac">
-          <label for="quickstart-mac">macOS</label>
-          <input type="radio" name="os" id="quickstart-lin">
-          <label for="quickstart-lin">Linux</label><br />
-       <strong>Packager</strong>
-          <input type="radio" name="packager" id="quickstart-pip" checked>
-          <label for="quickstart-pip">pip</label>
-          <input type="radio" name="packager" id="quickstart-conda">
-          <label for="quickstart-conda">conda</label><br />
-          <input type="checkbox" name="config" id="quickstart-venv">
-          <label for="quickstart-venv"></label>
-       </span>
-
-.. raw:: html
-
-       <div>
-         <span class="sk-expandable" data-packager="pip" data-os="windows">Install the 64bit version of Python 3, for instance from <a href="https://www.python.org/">https://www.python.org</a>.</span
-         ><span class="sk-expandable" data-packager="pip" data-os="mac">Install Python 3 using <a href="https://brew.sh/">homebrew</a> (<code>brew install python</code>) or by manually installing the package from <a href="https://www.python.org">https://www.python.org</a>.</span
-         ><span class="sk-expandable" data-packager="pip" data-os="linux">Install python3 and python3-pip using the package manager of the Linux Distribution.</span
-         ><span class="sk-expandable" data-packager="conda"
-            >Install conda using the <a href="https://docs.conda.io/projects/conda/en/latest/user-guide/install/">Anaconda or miniconda</a>
-             installers or the <a href="https://github.com/conda-forge/miniforge#miniforge">miniforge</a> installers
-             (no administrator permission required for any of those).</span>
-       </div>
-
-Then run:
-
-.. raw:: html
-
-  <div class="highlight">
-    <pre class="sk-expandable" data-packager="pip" data-os="linux" data-venv="no"
-    ><span>pip3 install -U scikit-learn</span></pre>
-
-    <pre class="sk-expandable" data-packager="pip" data-os="windows" data-venv="no"
-    ><span>pip install -U scikit-learn</span></pre>
-
-    <pre class="sk-expandable" data-packager="pip" data-os="mac" data-venv="no"
-    ><span>pip install -U scikit-learn</span></pre>
-
-    <pre class="sk-expandable" data-packager="pip" data-os="linux" data-venv=""
-    ><span>python3 -m venv sklearn-venv</span>
-  <span>source sklearn-venv/bin/activate</span>
-  <span>pip3 install -U scikit-learn</span></pre>
-
-    <pre class="sk-expandable" data-packager="pip" data-os="windows" data-venv=""
-    ><span>python -m venv sklearn-venv</span>
-  <span>sklearn-venv\Scripts\activate</span>
-  <span>pip install -U scikit-learn</span></pre>
-
-    <pre class="sk-expandable" data-packager="pip" data-os="mac" data-venv=""
-    ><span>python -m venv sklearn-venv</span>
-  <span>source sklearn-venv/bin/activate</span>
-  <span>pip install -U scikit-learn</span></pre>
-
-    <pre class="sk-expandable" data-packager="conda"
-    ><span>conda create -n sklearn-env -c conda-forge scikit-learn</span>
-  <span>conda activate sklearn-env</span></pre>
-  </div>
-
-In order to check your installation you can use
-
-.. raw:: html
-
-  <div class="highlight">
-    <pre class="sk-expandable" data-packager="pip" data-os="linux" data-venv="no"
-    ><span>python3 -m pip show scikit-learn  # to see which version and where scikit-learn is installed</span>
-  <span>python3 -m pip freeze  # to see all packages installed in the active virtualenv</span>
-  <span>python3 -c "import sklearn; sklearn.show_versions()"</span></pre>
-
-    <pre class="sk-expandable" data-packager="pip" data-os="windows" data-venv="no"
-    ><span>python -m pip show scikit-learn  # to see which version and where scikit-learn is installed</span>
-  <span>python -m pip freeze  # to see all packages installed in the active virtualenv</span>
-  <span>python -c "import sklearn; sklearn.show_versions()"</span></pre>
-
-    <pre class="sk-expandable" data-packager="pip" data-os="mac" data-venv="no"
-    ><span>python -m pip show scikit-learn  # to see which version and where scikit-learn is installed</span>
-  <span>python -m pip freeze  # to see all packages installed in the active virtualenv</span>
-  <span>python -c "import sklearn; sklearn.show_versions()"</span></pre>
-
-    <pre class="sk-expandable" data-packager="pip" data-venv=""
-    ><span>python -m pip show scikit-learn  # to see which version and where scikit-learn is installed</span>
-  <span>python -m pip freeze  # to see all packages installed in the active virtualenv</span>
-  <span>python -c "import sklearn; sklearn.show_versions()"</span></pre>
-
-    <pre class="sk-expandable" data-packager="conda"
-    ><span>conda list scikit-learn  # to see which scikit-learn version is installed</span>
-  <span>conda list  # to see all packages installed in the active conda environment</span>
-  <span>python -c "import sklearn; sklearn.show_versions()"</span></pre>
-  </div>
-
-Note that in order to avoid potential conflicts with other packages it is
-strongly recommended to use a `virtual environment (venv)
-<https://docs.python.org/3/tutorial/venv.html>`_ or a `conda environment
-<https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html>`_.
-
-Using such an isolated environment makes it possible to install a specific
-version of scikit-learn with pip or conda and its dependencies independently of
-any previously installed Python packages. In particular under Linux is it
-discouraged to install pip packages alongside the packages managed by the
+.. `scss/install.scss` overrides some default sphinx-design styling for the tabs
+
+.. div:: install-instructions
+
+  .. tab-set::
+
+    .. tab-item:: pip
+      :class-label: tab-6
+      :sync: packager-pip
+
+      .. tab-set::
+
+        .. tab-item:: Windows
+          :class-label: tab-4
+          :sync: os-windows
+
+          Install the 64-bit version of Python 3, for instance from the
+          `official website <https://www.python.org/downloads/windows/>`__.
+
+          Now create a `virtual environment (venv)
+          <https://docs.python.org/3/tutorial/venv.html>`_ and install scikit-learn.
+          Note that the virtual environment is optional but strongly recommended, in
+          order to avoid potential conflicts with other packages.
+
+          .. prompt:: powershell
+
+            python -m venv sklearn-env
+            sklearn-env\Scripts\activate  # activate
+            pip install -U scikit-learn
+
+          In order to check your installation, you can use:
+
+          .. prompt:: powershell
+
+            python -m pip show scikit-learn  # show scikit-learn version and location
+            python -m pip freeze             # show all installed packages in the environment
+            python -c "import sklearn; sklearn.show_versions()"
+
+        .. tab-item:: macOS
+          :class-label: tab-4
+          :sync: os-macos
+
+          Install Python 3 using `homebrew <https://brew.sh/>`_ (`brew install python`)
+          or by manually installing the package from the `official website
+          <https://www.python.org/downloads/macos/>`__.
+
+          Now create a `virtual environment (venv)
+          <https://docs.python.org/3/tutorial/venv.html>`_ and install scikit-learn.
+          Note that the virtual environment is optional but strongly recommended, in
+          order to avoid potential conflicts with other packges.
+
+          .. prompt:: bash
+
+            python -m venv sklearn-env
+            source sklearn-env/bin/activate  # activate
+            pip install -U scikit-learn
+
+          In order to check your installation, you can use:
+
+          .. prompt:: bash
+
+            python -m pip show scikit-learn  # show scikit-learn version and location
+            python -m pip freeze             # show all installed packages in the environment
+            python -c "import sklearn; sklearn.show_versions()"
+
+        .. tab-item:: Linux
+          :class-label: tab-4
+          :sync: os-linux
+
+          Python 3 is usually installed by default on most Linux distributions. To
+          check if you have it installed, try:
+
+          .. prompt:: bash
+
+            python3 --version
+            pip3 --version
+
+          If you don't have Python 3 installed, please install `python3` and
+          `python3-pip` from your distribution's package manager.
+
+          Now create a `virtual environment (venv)
+          <https://docs.python.org/3/tutorial/venv.html>`_ and install scikit-learn.
+          Note that the virtual environment is optional but strongly recommended, in
+          order to avoid potential conflicts with other packages.
+
+          .. prompt:: bash
+
+            python3 -m venv sklearn-env
+            source sklearn-env/bin/activate  # activate
+            pip3 install -U scikit-learn
+
+          In order to check your installation, you can use:
+
+          .. prompt:: bash
+
+            python3 -m pip show scikit-learn  # show scikit-learn version and location
+            python3 -m pip freeze             # show all installed packages in the environment
+            python3 -c "import sklearn; sklearn.show_versions()"
+
+    .. tab-item:: conda
+      :class-label: tab-6
+      :sync: packager-conda
+
+      Install conda using the `Anaconda or miniconda installers
+      <https://docs.conda.io/projects/conda/en/latest/user-guide/install/>`__
+      or the `miniforge installers
+      <https://github.com/conda-forge/miniforge#miniforge>`__ (no administrator
+      permission required for any of those). Then run:
+
+      .. prompt:: bash
+
+        conda create -n sklearn-env -c conda-forge scikit-learn
+        conda activate sklearn-env
+
+      In order to check your installation, you can use:
+
+      .. prompt:: bash
+
+        conda list scikit-learn  # show scikit-learn version and location
+        conda list               # show all installed packages in the environment
+        python -c "import sklearn; sklearn.show_versions()"
+
+Using an isolated environment such as pip venv or conda makes it possible to
+install a specific version of scikit-learn with pip or conda and its dependencies
+independently of any previously installed Python packages. In particular under Linux
+it is discouraged to install pip packages alongside the packages managed by the
 package manager of the distribution (apt, dnf, pacman...).
 
 Note that you should always remember to activate the environment of your choice
@@ -150,11 +165,10 @@ and NumPy and SciPy are not recompiled from source, which can happen when using
 particular configurations of operating system and hardware (such as Linux on
 a Raspberry Pi).
 
-
-Scikit-learn plotting capabilities (i.e., functions start with "plot\_"
-and classes end with "Display") require Matplotlib. The examples require
+Scikit-learn plotting capabilities (i.e., functions starting with `plot\_`
+and classes ending with `Display`) require Matplotlib. The examples require
 Matplotlib and some examples require scikit-image, pandas, or seaborn. The
-minimum version of Scikit-learn dependencies are listed below along with its
+minimum version of scikit-learn dependencies are listed below along with its
 purpose.
 
 .. include:: min_dependency_table.rst
@@ -164,10 +178,10 @@ purpose.
     Scikit-learn 0.20 was the last version to support Python 2.7 and Python 3.4.
     Scikit-learn 0.21 supported Python 3.5-3.7.
     Scikit-learn 0.22 supported Python 3.5-3.8.
-    Scikit-learn 0.23 - 0.24 require Python 3.6 or newer.
+    Scikit-learn 0.23-0.24 required Python 3.6 or newer.
     Scikit-learn 1.0 supported Python 3.7-3.10.
-    Scikit-learn 1.1 and later requires Python 3.8 or newer.
-
+    Scikit-learn 1.1, 1.2 and 1.3 support Python 3.8-3.12
+    Scikit-learn 1.4 requires Python 3.9 or newer.
 
 .. _install_by_distribution:
 
@@ -192,7 +206,7 @@ Alpine Linux's package is provided through the `official repositories
 ``py3-scikit-learn`` for Python.
 It can be installed by typing the following command:
 
-.. prompt:: bash $
+.. prompt:: bash
 
   sudo apk add py3-scikit-learn
 
@@ -205,7 +219,7 @@ Arch Linux's package is provided through the `official repositories
 ``python-scikit-learn`` for Python.
 It can be installed by typing the following command:
 
-.. prompt:: bash $
+.. prompt:: bash
 
   sudo pacman -S python-scikit-learn
 
@@ -220,7 +234,7 @@ Note that scikit-learn requires Python 3, hence the need to use the `python3-`
 suffixed package names.
 Packages can be installed using ``apt-get``:
 
-.. prompt:: bash $
+.. prompt:: bash
 
   sudo apt-get install python3-sklearn python3-sklearn-lib python3-sklearn-doc
 
@@ -232,7 +246,7 @@ The Fedora package is called ``python3-scikit-learn`` for the python 3 version,
 the only one available in Fedora.
 It can be installed using ``dnf``:
 
-.. prompt:: bash $
+.. prompt:: bash
 
   sudo dnf install python3-scikit-learn
 
@@ -240,10 +254,8 @@ It can be installed using ``dnf``:
 NetBSD
 ------
 
-scikit-learn is available via `pkgsrc-wip
-<http://pkgsrc-wip.sourceforge.net/>`_:
-
-    https://pkgsrc.se/math/py-scikit-learn
+scikit-learn is available via `pkgsrc-wip <http://pkgsrc-wip.sourceforge.net/>`_:
+https://pkgsrc.se/math/py-scikit-learn
 
 
 MacPorts for Mac OSX
@@ -254,7 +266,7 @@ where ``XY`` denotes the Python version.
 It can be installed by typing the following
 command:
 
-.. prompt:: bash $
+.. prompt:: bash
 
   sudo port install py39-scikit-learn
 
@@ -276,7 +288,7 @@ Intel Extension for Scikit-learn
 Intel maintains an optimized x86_64 package, available in PyPI (via `pip`),
 and in the `main`, `conda-forge` and `intel` conda channels:
 
-.. prompt:: bash $
+.. prompt:: bash
 
   conda install scikit-learn-intelex
 
@@ -302,7 +314,7 @@ with `scikit-learn-intelex`, please report the issue on their
 
 
 WinPython for Windows
------------------------
+---------------------
 
 The `WinPython <https://winpython.github.io/>`_ project distributes
 scikit-learn as an additional plugin.
@@ -311,6 +323,10 @@ scikit-learn as an additional plugin.
 Troubleshooting
 ===============
 
+If you encounter unexpected failures when installing scikit-learn, you may submit
+an issue to the `issue tracker <https://github.com/scikit-learn/scikit-learn/issues>`_.
+Before that, please also make sure to check the following common issues.
+
 .. _windows_longpath:
 
 Error caused by file path length limit on Windows
@@ -340,6 +356,6 @@ using the ``regedit`` tool:
 
 #. Reinstall scikit-learn (ignoring the previous broken installation):
 
-.. prompt:: bash $
+   .. prompt:: powershell
 
-    pip install --exists-action=i scikit-learn
+      pip install --exists-action=i scikit-learn
diff --git a/doc/js/scripts/api-search.js b/doc/js/scripts/api-search.js
new file mode 100644
index 0000000000000..2148e0c429aaa
--- /dev/null
+++ b/doc/js/scripts/api-search.js
@@ -0,0 +1,12 @@
+/**
+ * This script is for initializing the search table on the API index page. See
+ * DataTables documentation for more information: https://datatables.net/
+ */
+
+document.addEventListener("DOMContentLoaded", function () {
+  new DataTable("table.apisearch-table", {
+    order: [], // Keep original order
+    lengthMenu: [10, 25, 50, 100, { label: "All", value: -1 }],
+    pageLength: -1, // Show all entries by default
+  });
+});
diff --git a/doc/js/scripts/dropdown.js b/doc/js/scripts/dropdown.js
new file mode 100644
index 0000000000000..ec2e6d9419a28
--- /dev/null
+++ b/doc/js/scripts/dropdown.js
@@ -0,0 +1,61 @@
+/**
+ * This script is used to add the functionality of collapsing/expanding all dropdowns
+ * on the page to the sphinx-design dropdowns. This is because some browsers cannot
+ * search into collapsed <details> (such as Firefox).
+ *
+ * The reason why the buttons are added to the page with JS (dynamic) instead of with
+ * sphinx (static) is that the button will not work without JS activated, so we do not
+ * want them to show up in that case.
+ */
+
+function addToggleAllButtons() {
+  // Get all sphinx-design dropdowns
+  const allDropdowns = document.querySelectorAll("details.sd-dropdown");
+
+  function collapseAll() {
+    // Function to collapse all dropdowns on the page
+    console.log("[SK] Collapsing all dropdowns...");
+    allDropdowns.forEach((dropdown) => {
+      dropdown.removeAttribute("open");
+    });
+  }
+
+  function expandAll() {
+    // Function to expand all dropdowns on the page
+    console.log("[SK] Expanding all dropdowns...");
+    allDropdowns.forEach((dropdown) => {
+      dropdown.setAttribute("open", "");
+    });
+  }
+
+  const buttonConfigs = new Map([
+    ["up", { desc: "Collapse", action: collapseAll }],
+    ["down", { desc: "Expand", action: expandAll }],
+  ]);
+
+  allDropdowns.forEach((dropdown) => {
+    // Get the summary element of the dropdown, where we will place the buttons
+    const summaryTitle = dropdown.querySelector("summary.sd-summary-title");
+    for (const [direction, config] of buttonConfigs) {
+      // Button with icon inside
+      var newButton = document.createElement("button");
+      var newIcon = document.createElement("i");
+      newIcon.classList.add("fa-solid", `fa-angles-${direction}`);
+      newButton.appendChild(newIcon);
+      // Class for styling; `sd-summary-up/down` is implemented by sphinx-design;
+      // `sk-toggle-all` is implemented by us
+      newButton.classList.add(`sd-summary-${direction}`, `sk-toggle-all`);
+      // Bootstrap tooltip configurations
+      newButton.setAttribute("data-bs-toggle", "tooltip");
+      newButton.setAttribute("data-bs-placement", "top");
+      newButton.setAttribute("data-bs-offset", "0,10");
+      newButton.setAttribute("data-bs-title", `${config.desc} all dropdowns`);
+      // Assign the collapse/expand action to the button
+      newButton.onclick = config.action;
+      // Append the button to the summary element
+      summaryTitle.appendChild(newButton);
+    }
+  });
+}
+
+document.addEventListener("DOMContentLoaded", addToggleAllButtons);
diff --git a/doc/js/scripts/vendor/svg-pan-zoom.min.js b/doc/js/scripts/vendor/svg-pan-zoom.min.js
new file mode 100644
index 0000000000000..bde44a689bfe1
--- /dev/null
+++ b/doc/js/scripts/vendor/svg-pan-zoom.min.js
@@ -0,0 +1,31 @@
+/**
+ * svg-pan-zoom v3.6.2
+ *
+ * https://github.com/bumbu/svg-pan-zoom
+ *
+ * Copyright 2009-2010 Andrea Leofreddi <a.leofreddi@vleo.net>
+ * All rights reserved.
+ *
+ * Redistribution and use in source and binary forms, with or without modification,
+ * are permitted provided that the following conditions are met:
+ *
+ * * Redistributions of source code must retain the above copyright notice, this
+ *   list of conditions and the following disclaimer.
+ *
+ * * Redistributions in binary form must reproduce the above copyright notice, this
+ *   list of conditions and the following disclaimer in the documentation and/or
+ *   other materials provided with the distribution.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+ * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+ * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+ * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR
+ * ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+ * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+ * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
+ * ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+ * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *
+ */
+!function s(r,a,l){function u(e,t){if(!a[e]){if(!r[e]){var o="function"==typeof require&&require;if(!t&&o)return o(e,!0);if(h)return h(e,!0);var n=new Error("Cannot find module '"+e+"'");throw n.code="MODULE_NOT_FOUND",n}var i=a[e]={exports:{}};r[e][0].call(i.exports,function(t){return u(r[e][1][t]||t)},i,i.exports,s,r,a,l)}return a[e].exports}for(var h="function"==typeof require&&require,t=0;t<l.length;t++)u(l[t]);return u}({1:[function(t,e,o){var s=t("./svg-utilities");e.exports={enable:function(t){var e=t.svg.querySelector("defs");if(e||(e=document.createElementNS(s.svgNS,"defs"),t.svg.appendChild(e)),!e.querySelector("style#svg-pan-zoom-controls-styles")){var o=document.createElementNS(s.svgNS,"style");o.setAttribute("id","svg-pan-zoom-controls-styles"),o.setAttribute("type","text/css"),o.textContent=".svg-pan-zoom-control { cursor: pointer; fill: black; fill-opacity: 0.333; } .svg-pan-zoom-control:hover { fill-opacity: 0.8; } .svg-pan-zoom-control-background { fill: white; 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M170.337,20.14h-10.336v28.43h-7.266V20.14h-10.383v-6.117h27.984V20.14z"),i.setAttribute("class","svg-pan-zoom-control-element"),e.appendChild(i),e},_createZoomOut:function(t){var e=document.createElementNS(s.svgNS,"g");e.setAttribute("id","svg-pan-zoom-zoom-out"),e.setAttribute("transform","translate(30.5 70) scale(0.015)"),e.setAttribute("class","svg-pan-zoom-control"),e.addEventListener("click",function(){t.getPublicInstance().zoomOut()},!1),e.addEventListener("touchstart",function(){t.getPublicInstance().zoomOut()},!1);var o=document.createElementNS(s.svgNS,"rect");o.setAttribute("x","0"),o.setAttribute("y","0"),o.setAttribute("width","1500"),o.setAttribute("height","1400"),o.setAttribute("class","svg-pan-zoom-control-background"),e.appendChild(o);var n=document.createElementNS(s.svgNS,"path");return n.setAttribute("d","M1280 576v128q0 26 -19 45t-45 19h-896q-26 0 -45 -19t-19 -45v-128q0 -26 19 -45t45 -19h896q26 0 45 19t19 45zM1536 1120v-960q0 -119 -84.5 -203.5t-203.5 -84.5h-960q-119 0 -203.5 84.5t-84.5 203.5v960q0 119 84.5 203.5t203.5 84.5h960q119 0 203.5 -84.5 t84.5 -203.5z"),n.setAttribute("class","svg-pan-zoom-control-element"),e.appendChild(n),e},disable:function(t){t.controlIcons&&(t.controlIcons.parentNode.removeChild(t.controlIcons),t.controlIcons=null)}}},{"./svg-utilities":5}],2:[function(t,e,o){function n(t,e){this.init(t,e)}var i=t("./svg-utilities"),r=t("./utilities");n.prototype.init=function(t,e){this.viewport=t,this.options=e,this.originalState={zoom:1,x:0,y:0},this.activeState={zoom:1,x:0,y:0},this.updateCTMCached=r.proxy(this.updateCTM,this),this.requestAnimationFrame=r.createRequestAnimationFrame(this.options.refreshRate),this.viewBox={x:0,y:0,width:0,height:0},this.cacheViewBox();var o=this.processCTM();this.setCTM(o),this.updateCTM()},n.prototype.cacheViewBox=function(){var t=this.options.svg.getAttribute("viewBox");if(t){var e=t.split(/[\s\,]/).filter(function(t){return t}).map(parseFloat);this.viewBox.x=e[0],this.viewBox.y=e[1],this.viewBox.width=e[2],this.viewBox.height=e[3];var o=Math.min(this.options.width/this.viewBox.width,this.options.height/this.viewBox.height);this.activeState.zoom=o,this.activeState.x=(this.options.width-this.viewBox.width*o)/2,this.activeState.y=(this.options.height-this.viewBox.height*o)/2,this.updateCTMOnNextFrame(),this.options.svg.removeAttribute("viewBox")}else this.simpleViewBoxCache()},n.prototype.simpleViewBoxCache=function(){var t=this.viewport.getBBox();this.viewBox.x=t.x,this.viewBox.y=t.y,this.viewBox.width=t.width,this.viewBox.height=t.height},n.prototype.getViewBox=function(){return r.extend({},this.viewBox)},n.prototype.processCTM=function(){var t,e=this.getCTM();(this.options.fit||this.options.contain)&&(t=this.options.fit?Math.min(this.options.width/this.viewBox.width,this.options.height/this.viewBox.height):Math.max(this.options.width/this.viewBox.width,this.options.height/this.viewBox.height),e.a=t,e.d=t,e.e=-this.viewBox.x*t,e.f=-this.viewBox.y*t);if(this.options.center){var o=.5*(this.options.width-(this.viewBox.width+2*this.viewBox.x)*e.a),n=.5*(this.options.height-(this.viewBox.height+2*this.viewBox.y)*e.a);e.e=o,e.f=n}return this.originalState.zoom=e.a,this.originalState.x=e.e,this.originalState.y=e.f,e},n.prototype.getOriginalState=function(){return r.extend({},this.originalState)},n.prototype.getState=function(){return r.extend({},this.activeState)},n.prototype.getZoom=function(){return this.activeState.zoom},n.prototype.getRelativeZoom=function(){return this.activeState.zoom/this.originalState.zoom},n.prototype.computeRelativeZoom=function(t){return t/this.originalState.zoom},n.prototype.getPan=function(){return{x:this.activeState.x,y:this.activeState.y}},n.prototype.getCTM=function(){var t=this.options.svg.createSVGMatrix();return t.a=this.activeState.zoom,t.b=0,t.c=0,t.d=this.activeState.zoom,t.e=this.activeState.x,t.f=this.activeState.y,t},n.prototype.setCTM=function(t){var e=this.isZoomDifferent(t),o=this.isPanDifferent(t);if(e||o){if(e&&(!1===this.options.beforeZoom(this.getRelativeZoom(),this.computeRelativeZoom(t.a))?(t.a=t.d=this.activeState.zoom,e=!1):(this.updateCache(t),this.options.onZoom(this.getRelativeZoom()))),o){var n=this.options.beforePan(this.getPan(),{x:t.e,y:t.f}),i=!1,s=!1;!1===n?(t.e=this.getPan().x,t.f=this.getPan().y,i=s=!0):r.isObject(n)&&(!1===n.x?(t.e=this.getPan().x,i=!0):r.isNumber(n.x)&&(t.e=n.x),!1===n.y?(t.f=this.getPan().y,s=!0):r.isNumber(n.y)&&(t.f=n.y)),i&&s||!this.isPanDifferent(t)?o=!1:(this.updateCache(t),this.options.onPan(this.getPan()))}(e||o)&&this.updateCTMOnNextFrame()}},n.prototype.isZoomDifferent=function(t){return this.activeState.zoom!==t.a},n.prototype.isPanDifferent=function(t){return this.activeState.x!==t.e||this.activeState.y!==t.f},n.prototype.updateCache=function(t){this.activeState.zoom=t.a,this.activeState.x=t.e,this.activeState.y=t.f},n.prototype.pendingUpdate=!1,n.prototype.updateCTMOnNextFrame=function(){this.pendingUpdate||(this.pendingUpdate=!0,this.requestAnimationFrame.call(window,this.updateCTMCached))},n.prototype.updateCTM=function(){var t=this.getCTM();i.setCTM(this.viewport,t,this.defs),this.pendingUpdate=!1,this.options.onUpdatedCTM&&this.options.onUpdatedCTM(t)},e.exports=function(t,e){return new n(t,e)}},{"./svg-utilities":5,"./utilities":7}],3:[function(t,e,o){var n,i=t("./svg-pan-zoom.js");n=window,document,"function"==typeof define&&define.amd?define("svg-pan-zoom",function(){return i}):void 0!==e&&e.exports&&(e.exports=i,n.svgPanZoom=i)},{"./svg-pan-zoom.js":4}],4:[function(t,e,o){function i(t,e){this.init(t,e)}var n=t("./uniwheel"),s=t("./control-icons"),r=t("./utilities"),a=t("./svg-utilities"),l=t("./shadow-viewport"),u={viewportSelector:".svg-pan-zoom_viewport",panEnabled:!0,controlIconsEnabled:!1,zoomEnabled:!0,dblClickZoomEnabled:!0,mouseWheelZoomEnabled:!0,preventMouseEventsDefault:!0,zoomScaleSensitivity:.1,minZoom:.5,maxZoom:10,fit:!0,contain:!1,center:!0,refreshRate:"auto",beforeZoom:null,onZoom:null,beforePan:null,onPan:null,customEventsHandler:null,eventsListenerElement:null,onUpdatedCTM:null},h={passive:!0};i.prototype.init=function(t,e){var o=this;this.svg=t,this.defs=t.querySelector("defs"),a.setupSvgAttributes(this.svg),this.options=r.extend(r.extend({},u),e),this.state="none";var n=a.getBoundingClientRectNormalized(t);this.width=n.width,this.height=n.height,this.viewport=l(a.getOrCreateViewport(this.svg,this.options.viewportSelector),{svg:this.svg,width:this.width,height:this.height,fit:this.options.fit,contain:this.options.contain,center:this.options.center,refreshRate:this.options.refreshRate,beforeZoom:function(t,e){if(o.viewport&&o.options.beforeZoom)return o.options.beforeZoom(t,e)},onZoom:function(t){if(o.viewport&&o.options.onZoom)return o.options.onZoom(t)},beforePan:function(t,e){if(o.viewport&&o.options.beforePan)return o.options.beforePan(t,e)},onPan:function(t){if(o.viewport&&o.options.onPan)return o.options.onPan(t)},onUpdatedCTM:function(t){if(o.viewport&&o.options.onUpdatedCTM)return o.options.onUpdatedCTM(t)}});var i=this.getPublicInstance();i.setBeforeZoom(this.options.beforeZoom),i.setOnZoom(this.options.onZoom),i.setBeforePan(this.options.beforePan),i.setOnPan(this.options.onPan),i.setOnUpdatedCTM(this.options.onUpdatedCTM),this.options.controlIconsEnabled&&s.enable(this),this.lastMouseWheelEventTime=Date.now(),this.setupHandlers()},i.prototype.setupHandlers=function(){var o=this,n=null;if(this.eventListeners={mousedown:function(t){var e=o.handleMouseDown(t,n);return n=t,e},touchstart:function(t){var e=o.handleMouseDown(t,n);return n=t,e},mouseup:function(t){return o.handleMouseUp(t)},touchend:function(t){return o.handleMouseUp(t)},mousemove:function(t){return o.handleMouseMove(t)},touchmove:function(t){return o.handleMouseMove(t)},mouseleave:function(t){return o.handleMouseUp(t)},touchleave:function(t){return o.handleMouseUp(t)},touchcancel:function(t){return o.handleMouseUp(t)}},null!=this.options.customEventsHandler){this.options.customEventsHandler.init({svgElement:this.svg,eventsListenerElement:this.options.eventsListenerElement,instance:this.getPublicInstance()});var t=this.options.customEventsHandler.haltEventListeners;if(t&&t.length)for(var e=t.length-1;0<=e;e--)this.eventListeners.hasOwnProperty(t[e])&&delete this.eventListeners[t[e]]}for(var i in this.eventListeners)(this.options.eventsListenerElement||this.svg).addEventListener(i,this.eventListeners[i],!this.options.preventMouseEventsDefault&&h);this.options.mouseWheelZoomEnabled&&(this.options.mouseWheelZoomEnabled=!1,this.enableMouseWheelZoom())},i.prototype.enableMouseWheelZoom=function(){if(!this.options.mouseWheelZoomEnabled){var e=this;this.wheelListener=function(t){return e.handleMouseWheel(t)};var t=!this.options.preventMouseEventsDefault;n.on(this.options.eventsListenerElement||this.svg,this.wheelListener,t),this.options.mouseWheelZoomEnabled=!0}},i.prototype.disableMouseWheelZoom=function(){if(this.options.mouseWheelZoomEnabled){var t=!this.options.preventMouseEventsDefault;n.off(this.options.eventsListenerElement||this.svg,this.wheelListener,t),this.options.mouseWheelZoomEnabled=!1}},i.prototype.handleMouseWheel=function(t){if(this.options.zoomEnabled&&"none"===this.state){this.options.preventMouseEventsDefault&&(t.preventDefault?t.preventDefault():t.returnValue=!1);var e=t.deltaY||1,o=Date.now()-this.lastMouseWheelEventTime,n=3+Math.max(0,30-o);this.lastMouseWheelEventTime=Date.now(),"deltaMode"in t&&0===t.deltaMode&&t.wheelDelta&&(e=0===t.deltaY?0:Math.abs(t.wheelDelta)/t.deltaY),e=-.3<e&&e<.3?e:(0<e?1:-1)*Math.log(Math.abs(e)+10)/n;var i=this.svg.getScreenCTM().inverse(),s=a.getEventPoint(t,this.svg).matrixTransform(i),r=Math.pow(1+this.options.zoomScaleSensitivity,-1*e);this.zoomAtPoint(r,s)}},i.prototype.zoomAtPoint=function(t,e,o){var n=this.viewport.getOriginalState();o?(t=Math.max(this.options.minZoom*n.zoom,Math.min(this.options.maxZoom*n.zoom,t)),t/=this.getZoom()):this.getZoom()*t<this.options.minZoom*n.zoom?t=this.options.minZoom*n.zoom/this.getZoom():this.getZoom()*t>this.options.maxZoom*n.zoom&&(t=this.options.maxZoom*n.zoom/this.getZoom());var i=this.viewport.getCTM(),s=e.matrixTransform(i.inverse()),r=this.svg.createSVGMatrix().translate(s.x,s.y).scale(t).translate(-s.x,-s.y),a=i.multiply(r);a.a!==i.a&&this.viewport.setCTM(a)},i.prototype.zoom=function(t,e){this.zoomAtPoint(t,a.getSvgCenterPoint(this.svg,this.width,this.height),e)},i.prototype.publicZoom=function(t,e){e&&(t=this.computeFromRelativeZoom(t)),this.zoom(t,e)},i.prototype.publicZoomAtPoint=function(t,e,o){if(o&&(t=this.computeFromRelativeZoom(t)),"SVGPoint"!==r.getType(e)){if(!("x"in e&&"y"in e))throw new Error("Given point is invalid");e=a.createSVGPoint(this.svg,e.x,e.y)}this.zoomAtPoint(t,e,o)},i.prototype.getZoom=function(){return this.viewport.getZoom()},i.prototype.getRelativeZoom=function(){return this.viewport.getRelativeZoom()},i.prototype.computeFromRelativeZoom=function(t){return t*this.viewport.getOriginalState().zoom},i.prototype.resetZoom=function(){var t=this.viewport.getOriginalState();this.zoom(t.zoom,!0)},i.prototype.resetPan=function(){this.pan(this.viewport.getOriginalState())},i.prototype.reset=function(){this.resetZoom(),this.resetPan()},i.prototype.handleDblClick=function(t){var e;if((this.options.preventMouseEventsDefault&&(t.preventDefault?t.preventDefault():t.returnValue=!1),this.options.controlIconsEnabled)&&-1<(t.target.getAttribute("class")||"").indexOf("svg-pan-zoom-control"))return!1;e=t.shiftKey?1/(2*(1+this.options.zoomScaleSensitivity)):2*(1+this.options.zoomScaleSensitivity);var o=a.getEventPoint(t,this.svg).matrixTransform(this.svg.getScreenCTM().inverse());this.zoomAtPoint(e,o)},i.prototype.handleMouseDown=function(t,e){this.options.preventMouseEventsDefault&&(t.preventDefault?t.preventDefault():t.returnValue=!1),r.mouseAndTouchNormalize(t,this.svg),this.options.dblClickZoomEnabled&&r.isDblClick(t,e)?this.handleDblClick(t):(this.state="pan",this.firstEventCTM=this.viewport.getCTM(),this.stateOrigin=a.getEventPoint(t,this.svg).matrixTransform(this.firstEventCTM.inverse()))},i.prototype.handleMouseMove=function(t){if(this.options.preventMouseEventsDefault&&(t.preventDefault?t.preventDefault():t.returnValue=!1),"pan"===this.state&&this.options.panEnabled){var e=a.getEventPoint(t,this.svg).matrixTransform(this.firstEventCTM.inverse()),o=this.firstEventCTM.translate(e.x-this.stateOrigin.x,e.y-this.stateOrigin.y);this.viewport.setCTM(o)}},i.prototype.handleMouseUp=function(t){this.options.preventMouseEventsDefault&&(t.preventDefault?t.preventDefault():t.returnValue=!1),"pan"===this.state&&(this.state="none")},i.prototype.fit=function(){var t=this.viewport.getViewBox(),e=Math.min(this.width/t.width,this.height/t.height);this.zoom(e,!0)},i.prototype.contain=function(){var t=this.viewport.getViewBox(),e=Math.max(this.width/t.width,this.height/t.height);this.zoom(e,!0)},i.prototype.center=function(){var t=this.viewport.getViewBox(),e=.5*(this.width-(t.width+2*t.x)*this.getZoom()),o=.5*(this.height-(t.height+2*t.y)*this.getZoom());this.getPublicInstance().pan({x:e,y:o})},i.prototype.updateBBox=function(){this.viewport.simpleViewBoxCache()},i.prototype.pan=function(t){var e=this.viewport.getCTM();e.e=t.x,e.f=t.y,this.viewport.setCTM(e)},i.prototype.panBy=function(t){var e=this.viewport.getCTM();e.e+=t.x,e.f+=t.y,this.viewport.setCTM(e)},i.prototype.getPan=function(){var t=this.viewport.getState();return{x:t.x,y:t.y}},i.prototype.resize=function(){var t=a.getBoundingClientRectNormalized(this.svg);this.width=t.width,this.height=t.height;var e=this.viewport;e.options.width=this.width,e.options.height=this.height,e.processCTM(),this.options.controlIconsEnabled&&(this.getPublicInstance().disableControlIcons(),this.getPublicInstance().enableControlIcons())},i.prototype.destroy=function(){var e=this;for(var t in this.beforeZoom=null,this.onZoom=null,this.beforePan=null,this.onPan=null,(this.onUpdatedCTM=null)!=this.options.customEventsHandler&&this.options.customEventsHandler.destroy({svgElement:this.svg,eventsListenerElement:this.options.eventsListenerElement,instance:this.getPublicInstance()}),this.eventListeners)(this.options.eventsListenerElement||this.svg).removeEventListener(t,this.eventListeners[t],!this.options.preventMouseEventsDefault&&h);this.disableMouseWheelZoom(),this.getPublicInstance().disableControlIcons(),this.reset(),c=c.filter(function(t){return t.svg!==e.svg}),delete this.options,delete this.viewport,delete this.publicInstance,delete this.pi,this.getPublicInstance=function(){return null}},i.prototype.getPublicInstance=function(){var o=this;return this.publicInstance||(this.publicInstance=this.pi={enablePan:function(){return o.options.panEnabled=!0,o.pi},disablePan:function(){return o.options.panEnabled=!1,o.pi},isPanEnabled:function(){return!!o.options.panEnabled},pan:function(t){return o.pan(t),o.pi},panBy:function(t){return o.panBy(t),o.pi},getPan:function(){return o.getPan()},setBeforePan:function(t){return o.options.beforePan=null===t?null:r.proxy(t,o.publicInstance),o.pi},setOnPan:function(t){return o.options.onPan=null===t?null:r.proxy(t,o.publicInstance),o.pi},enableZoom:function(){return o.options.zoomEnabled=!0,o.pi},disableZoom:function(){return o.options.zoomEnabled=!1,o.pi},isZoomEnabled:function(){return!!o.options.zoomEnabled},enableControlIcons:function(){return o.options.controlIconsEnabled||(o.options.controlIconsEnabled=!0,s.enable(o)),o.pi},disableControlIcons:function(){return o.options.controlIconsEnabled&&(o.options.controlIconsEnabled=!1,s.disable(o)),o.pi},isControlIconsEnabled:function(){return!!o.options.controlIconsEnabled},enableDblClickZoom:function(){return o.options.dblClickZoomEnabled=!0,o.pi},disableDblClickZoom:function(){return 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diff --git a/doc/js/scripts/version-switcher.js b/doc/js/scripts/version-switcher.js
new file mode 100644
index 0000000000000..c88c45b16ee93
--- /dev/null
+++ b/doc/js/scripts/version-switcher.js
@@ -0,0 +1,40 @@
+/**
+ * Adds the link to available documentation page as the last entry in the version
+ * switcher dropdown. Since other entries in the dropdown are also added dynamically,
+ * we only add the link when the user clicks on some version switcher button to make
+ * sure that this entry is the last one.
+ */
+
+function addVersionSwitcherAvailDocsLink() {
+  var availDocsLinkAdded = false;
+
+  // There can be multiple version switcher buttons because there is at least one for
+  // laptop size and one for mobile size (in the sidebar)
+  document
+    .querySelectorAll(".version-switcher__button")
+    .forEach(function (btn) {
+      btn.addEventListener("click", function () {
+        if (!availDocsLinkAdded) {
+          // All version switcher dropdowns are updated once any button is clicked
+          document
+            .querySelectorAll(".version-switcher__menu")
+            .forEach(function (menu) {
+              var availDocsLink = document.createElement("a");
+              availDocsLink.setAttribute(
+                "href",
+                "https://scikit-learn.org/dev/versions.html"
+              );
+              availDocsLink.innerHTML = "More";
+              // We use the same class as the last entry to be safe
+              availDocsLink.className = menu.lastChild.className;
+              availDocsLink.classList.add("sk-avail-docs-link");
+              menu.appendChild(availDocsLink);
+            });
+          // Set the flag so we do not add again
+          availDocsLinkAdded = true;
+        }
+      });
+    });
+}
+
+document.addEventListener("DOMContentLoaded", addVersionSwitcherAvailDocsLink);
diff --git a/doc/authors.rst b/doc/maintainers.rst
similarity index 100%
rename from doc/authors.rst
rename to doc/maintainers.rst
diff --git a/doc/authors_emeritus.rst b/doc/maintainers_emeritus.rst
similarity index 100%
rename from doc/authors_emeritus.rst
rename to doc/maintainers_emeritus.rst
diff --git a/doc/make.bat b/doc/make.bat
index b7e269a6a7836..2a32bcb678f62 100644
--- a/doc/make.bat
+++ b/doc/make.bat
@@ -29,8 +29,30 @@ if "%1" == "help" (
 )
 
 if "%1" == "clean" (
-	for /d %%i in (%BUILDDIR%\*) do rmdir /q /s %%i
-	del /q /s %BUILDDIR%\*
+	if exist %BUILDDIR%\ (
+		for /d %%i in (%BUILDDIR%\*) do rmdir /q /s "%%i"
+		del /q /s %BUILDDIR%\*
+		echo. Removed %BUILDDIR%\*
+	)
+	if exist auto_examples\ (
+		rmdir /q /s auto_examples
+		echo. Removed auto_examples\
+	)
+	if exist generated\ (
+		for /d %%i in (generated\*) do rmdir /q /s "%%i"
+		del /q /s generated\*
+		echo. Removed generated\*
+	)
+	if exist modules\generated\ (
+		rmdir /q /s modules\generated
+		echo. Removed modules\generated\
+	)
+	if exist css\styles\ (
+		rmdir /q /s css\styles
+		echo. Removed css\styles\
+	)
+	for %%i in (api\*.rst) do del /q "%%i"
+	echo. Removed api\*.rst
 	goto end
 )
 
@@ -46,6 +68,7 @@ if "%1" == "html-noplot" (
 	%SPHINXBUILD% -D plot_gallery=0 -b html %ALLSPHINXOPTS% %BUILDDIR%/html
 	echo.
 	echo.Build finished. The HTML pages are in %BUILDDIR%/html
+	goto end
 )
 
 if "%1" == "dirhtml" (
diff --git a/doc/metadata_routing.rst b/doc/metadata_routing.rst
index d319b311dddd7..27000a192ab21 100644
--- a/doc/metadata_routing.rst
+++ b/doc/metadata_routing.rst
@@ -277,6 +277,8 @@ Meta-estimators and functions supporting metadata routing:
 - :class:`sklearn.calibration.CalibratedClassifierCV`
 - :class:`sklearn.compose.ColumnTransformer`
 - :class:`sklearn.covariance.GraphicalLassoCV`
+- :class:`sklearn.ensemble.StackingClassifier`
+- :class:`sklearn.ensemble.StackingRegressor`
 - :class:`sklearn.ensemble.VotingClassifier`
 - :class:`sklearn.ensemble.VotingRegressor`
 - :class:`sklearn.ensemble.BaggingClassifier`
@@ -290,6 +292,7 @@ Meta-estimators and functions supporting metadata routing:
 - :class:`sklearn.linear_model.LogisticRegressionCV`
 - :class:`sklearn.linear_model.MultiTaskElasticNetCV`
 - :class:`sklearn.linear_model.MultiTaskLassoCV`
+- :class:`sklearn.linear_model.OrthogonalMatchingPursuitCV`
 - :class:`sklearn.linear_model.RANSACRegressor`
 - :class:`sklearn.linear_model.RidgeClassifierCV`
 - :class:`sklearn.linear_model.RidgeCV`
@@ -300,13 +303,13 @@ Meta-estimators and functions supporting metadata routing:
 - :func:`sklearn.model_selection.cross_validate`
 - :func:`sklearn.model_selection.cross_val_score`
 - :func:`sklearn.model_selection.cross_val_predict`
+- :class:`sklearn.model_selection.learning_curve`
 - :class:`sklearn.multiclass.OneVsOneClassifier`
 - :class:`sklearn.multiclass.OneVsRestClassifier`
 - :class:`sklearn.multiclass.OutputCodeClassifier`
 - :class:`sklearn.multioutput.ClassifierChain`
 - :class:`sklearn.multioutput.MultiOutputClassifier`
 - :class:`sklearn.multioutput.MultiOutputRegressor`
-- :class:`sklearn.linear_model.OrthogonalMatchingPursuitCV`
 - :class:`sklearn.multioutput.RegressorChain`
 - :class:`sklearn.pipeline.FeatureUnion`
 - :class:`sklearn.pipeline.Pipeline`
@@ -316,14 +319,9 @@ Meta-estimators and tools not supporting metadata routing yet:
 - :class:`sklearn.compose.TransformedTargetRegressor`
 - :class:`sklearn.ensemble.AdaBoostClassifier`
 - :class:`sklearn.ensemble.AdaBoostRegressor`
-- :class:`sklearn.ensemble.StackingClassifier`
-- :class:`sklearn.ensemble.StackingRegressor`
 - :class:`sklearn.feature_selection.RFE`
 - :class:`sklearn.feature_selection.RFECV`
 - :class:`sklearn.feature_selection.SequentialFeatureSelector`
-- :class:`sklearn.impute.IterativeImputer`
-- :class:`sklearn.linear_model.RANSACRegressor`
-- :class:`sklearn.model_selection.learning_curve`
 - :class:`sklearn.model_selection.permutation_test_score`
 - :class:`sklearn.model_selection.validation_curve`
 - :class:`sklearn.semi_supervised.SelfTrainingClassifier`
diff --git a/doc/min_dependency_substitutions.rst.template b/doc/min_dependency_substitutions.rst.template
new file mode 100644
index 0000000000000..946de84902b3b
--- /dev/null
+++ b/doc/min_dependency_substitutions.rst.template
@@ -0,0 +1,3 @@
+{% for package, (version, _) in dependent_packages.items() -%}
+.. |{{ package|capitalize }}MinVersion| replace:: {{ version }}
+{% endfor %}
diff --git a/doc/min_dependency_table.rst.template b/doc/min_dependency_table.rst.template
new file mode 100644
index 0000000000000..fbe58633e913a
--- /dev/null
+++ b/doc/min_dependency_table.rst.template
@@ -0,0 +1,13 @@
+.. list-table::
+  :header-rows: 1
+
+  * - Dependency
+    - Minimum Version
+    - Purpose
+
+  {% for package, (version, tags) in dependent_packages.items() -%}
+  * - {{ package }}
+    - {{ version }}
+    - {{ tags }}
+
+  {% endfor %}
diff --git a/doc/model_persistence.rst b/doc/model_persistence.rst
index 0f775c774465a..cd5347d302123 100644
--- a/doc/model_persistence.rst
+++ b/doc/model_persistence.rst
@@ -1,183 +1,353 @@
-.. Places parent toc into the sidebar
-
-:parenttoc: True
-
-.. _model_persistence:
-
-=================
-Model persistence
-=================
-
-After training a scikit-learn model, it is desirable to have a way to persist
-the model for future use without having to retrain. The following sections give
-you some hints on how to persist a scikit-learn model.
-
-Python specific serialization
------------------------------
-
-It is possible to save a model in scikit-learn by using Python's built-in
-persistence model, namely `pickle
-<https://docs.python.org/3/library/pickle.html>`_::
-
-  >>> from sklearn import svm
-  >>> from sklearn import datasets
-  >>> clf = svm.SVC()
-  >>> X, y= datasets.load_iris(return_X_y=True)
-  >>> clf.fit(X, y)
-  SVC()
-
-  >>> import pickle
-  >>> s = pickle.dumps(clf)
-  >>> clf2 = pickle.loads(s)
-  >>> clf2.predict(X[0:1])
-  array([0])
-  >>> y[0]
-  0
-
-In the specific case of scikit-learn, it may be better to use joblib's
-replacement of pickle (``dump`` & ``load``), which is more efficient on
-objects that carry large numpy arrays internally as is often the case for
-fitted scikit-learn estimators, but can only pickle to the disk and not to a
-string::
-
-  >>> from joblib import dump, load
-  >>> dump(clf, 'filename.joblib') # doctest: +SKIP
-
-Later you can load back the pickled model (possibly in another Python process)
-with::
-
-  >>> clf = load('filename.joblib') # doctest:+SKIP
-
-.. note::
-
-   ``dump`` and ``load`` functions also accept file-like object
-   instead of filenames. More information on data persistence with Joblib is
-   available `here
-   <https://joblib.readthedocs.io/en/latest/persistence.html>`_.
-
-|details-start|
-**InconsistentVersionWarning**
-|details-split|
-
-When an estimator is unpickled with a scikit-learn version that is inconsistent
-with the version the estimator was pickled with, a
-:class:`~sklearn.exceptions.InconsistentVersionWarning` is raised. This warning
-can be caught to obtain the original version the estimator was pickled with::
-
-  from sklearn.exceptions import InconsistentVersionWarning
-  warnings.simplefilter("error", InconsistentVersionWarning)
-
-  try:
-      est = pickle.loads("model_from_prevision_version.pickle")
-  except InconsistentVersionWarning as w:
-      print(w.original_sklearn_version)
-
-|details-end|
-
-.. _persistence_limitations:
-
-Security & maintainability limitations
-......................................
-
-pickle (and joblib by extension), has some issues regarding maintainability
-and security. Because of this,
-
-* Never unpickle untrusted data as it could lead to malicious code being
-  executed upon loading.
-* While models saved using one version of scikit-learn might load in
-  other versions, this is entirely unsupported and inadvisable. It should
-  also be kept in mind that operations performed on such data could give
-  different and unexpected results.
-
-In order to rebuild a similar model with future versions of scikit-learn,
-additional metadata should be saved along the pickled model:
-
-* The training data, e.g. a reference to an immutable snapshot
-* The python source code used to generate the model
-* The versions of scikit-learn and its dependencies
-* The cross validation score obtained on the training data
-
-This should make it possible to check that the cross-validation score is in the
-same range as before.
-
-Aside for a few exceptions, pickled models should be portable across
-architectures assuming the same versions of dependencies and Python are used.
-If you encounter an estimator that is not portable please open an issue on
-GitHub. Pickled models are often deployed in production using containers, like
-Docker, in order to freeze the environment and dependencies.
-
-If you want to know more about these issues and explore other possible
-serialization methods, please refer to this
-`talk by Alex Gaynor
-<https://pyvideo.org/video/2566/pickles-are-for-delis-not-software>`_.
-
-
-A more secure format: `skops`
-.............................
-
-`skops <https://skops.readthedocs.io/en/stable/>`__ provides a more secure
-format via the :mod:`skops.io` module. It avoids using :mod:`pickle` and only
-loads files which have types and references to functions which are trusted
-either by default or by the user.
-
-|details-start|
-**Using skops**
-|details-split|
-
-The API is very similar to ``pickle``, and
-you can persist your models as explain in the `docs
-<https://skops.readthedocs.io/en/stable/persistence.html>`__ using
-:func:`skops.io.dump` and :func:`skops.io.dumps`::
-
-    import skops.io as sio
-    obj = sio.dumps(clf)
-
-And you can load them back using :func:`skops.io.load` and
-:func:`skops.io.loads`. However, you need to specify the types which are
-trusted by you. You can get existing unknown types in a dumped object / file
-using :func:`skops.io.get_untrusted_types`, and after checking its contents,
-pass it to the load function::
-
-    unknown_types = sio.get_untrusted_types(data=obj)
-    clf = sio.loads(obj, trusted=unknown_types)
-
-If you trust the source of the file / object, you can pass ``trusted=True``::
-
-    clf = sio.loads(obj, trusted=True)
-
-Please report issues and feature requests related to this format on the `skops
-issue tracker <https://github.com/skops-dev/skops/issues>`__.
-
-|details-end|
-
-Interoperable formats
----------------------
-
-For reproducibility and quality control needs, when different architectures
-and environments should be taken into account, exporting the model in
-`Open Neural Network
-Exchange <https://onnx.ai/>`_ format or `Predictive Model Markup Language
-(PMML) <https://dmg.org/pmml/v4-4-1/GeneralStructure.html>`_ format
-might be a better approach than using `pickle` alone.
-These are helpful where you may want to use your model for prediction in a
-different environment from where the model was trained.
-
-ONNX is a binary serialization of the model. It has been developed to improve
-the usability of the interoperable representation of data models.
-It aims to facilitate the conversion of the data
-models between different machine learning frameworks, and to improve their
-portability on different computing architectures. More details are available
-from the `ONNX tutorial <https://onnx.ai/get-started.html>`_.
-To convert scikit-learn model to ONNX a specific tool `sklearn-onnx
-<http://onnx.ai/sklearn-onnx/>`_ has been developed.
-
-PMML is an implementation of the `XML
-<https://en.wikipedia.org/wiki/XML>`_ document standard
-defined to represent data models together with the data used to generate them.
-Being human and machine readable,
-PMML is a good option for model validation on different platforms and
-long term archiving. On the other hand, as XML in general, its verbosity does
-not help in production when performance is critical.
-To convert scikit-learn model to PMML you can use for example `sklearn2pmml
-<https://github.com/jpmml/sklearn2pmml>`_ distributed under the Affero GPLv3
-license.
+.. _model_persistence:
+
+=================
+Model persistence
+=================
+
+After training a scikit-learn model, it is desirable to have a way to persist
+the model for future use without having to retrain. Based on your use-case,
+there are a few different ways to persist a scikit-learn model, and here we
+help you decide which one suits you best. In order to make a decision, you need
+to answer the following questions:
+
+1. Do you need the Python object after persistence, or do you only need to
+   persist in order to serve the model and get predictions out of it?
+
+If you only need to serve the model and no further investigation on the Python
+object itself is required, then :ref:`ONNX <onnx_persistence>` might be the
+best fit for you. Note that not all models are supported by ONNX.
+
+In case ONNX is not suitable for your use-case, the next question is:
+
+2. Do you absolutely trust the source of the model, or are there any security
+   concerns regarding where the persisted model comes from?
+
+If you have security concerns, then you should consider using :ref:`skops.io
+<skops_persistence>` which gives you back the Python object, but unlike
+`pickle` based persistence solutions, loading the persisted model doesn't
+automatically allow arbitrary code execution. Note that this requires manual
+investigation of the persisted file, which :mod:`skops.io` allows you to do.
+
+The other solutions assume you absolutely trust the source of the file to be
+loaded, as they are all susceptible to arbitrary code execution upon loading
+the persisted file since they all use the pickle protocol under the hood.
+
+3. Do you care about the performance of loading the model, and sharing it
+   between processes where a memory mapped object on disk is beneficial?
+
+If yes, then you can consider using :ref:`joblib <pickle_persistence>`. If this
+is not a major concern for you, then you can use the built-in :mod:`pickle`
+module.
+
+4. Did you try :mod:`pickle` or :mod:`joblib` and found that the model cannot
+   be persisted? It can happen for instance when you have user defined
+   functions in your model.
+
+If yes, then you can use `cloudpickle`_ which can serialize certain objects
+which cannot be serialized by :mod:`pickle` or :mod:`joblib`.
+
+
+Workflow Overview
+-----------------
+
+In a typical workflow, the first step is to train the model using scikit-learn
+and scikit-learn compatible libraries. Note that support for scikit-learn and
+third party estimators varies across the different persistence methods.
+
+Train and Persist the Model
+...........................
+
+Creating an appropriate model depends on your use-case. As an example, here we
+train a :class:`sklearn.ensemble.HistGradientBoostingClassifier` on the iris
+dataset::
+
+  >>> from sklearn import ensemble
+  >>> from sklearn import datasets
+  >>> clf = ensemble.HistGradientBoostingClassifier()
+  >>> X, y = datasets.load_iris(return_X_y=True)
+  >>> clf.fit(X, y)
+  HistGradientBoostingClassifier()
+
+Once the model is trained, you can persist it using your desired method, and
+then you can load the model in a separate environment and get predictions from
+it given input data. Here there are two major paths depending on how you
+persist and plan to serve the model:
+
+- :ref:`ONNX <onnx_persistence>`: You need an `ONNX` runtime and an environment
+  with appropriate dependencies installed to load the model and use the runtime
+  to get predictions. This environment can be minimal and does not necessarily
+  even require Python to be installed to load the model and compute
+  predictions. Also note that `onnxruntime` typically requires much less RAM
+  than Python to to compute predictions from small models.
+
+- :mod:`skops.io`, :mod:`pickle`, :mod:`joblib`, `cloudpickle`_: You need a
+  Python environment with the appropriate dependencies installed to load the
+  model and get predictions from it. This environment should have the same
+  **packages** and the same **versions** as the environment where the model was
+  trained. Note that none of these methods support loading a model trained with
+  a different version of scikit-learn, and possibly different versions of other
+  dependencies such as `numpy` and `scipy`. Another concern would be running
+  the persisted model on a different hardware, and in most cases you should be
+  able to load your persisted model on a different hardware.
+
+
+.. _onnx_persistence:
+
+ONNX
+----
+
+`ONNX`, or `Open Neural Network Exchange <https://onnx.ai/>`__ format is best
+suitable in use-cases where one needs to persist the model and then use the
+persisted artifact to get predictions without the need to load the Python
+object itself. It is also useful in cases where the serving environment needs
+to be lean and minimal, since the `ONNX` runtime does not require `python`.
+
+`ONNX` is a binary serialization of the model. It has been developed to improve
+the usability of the interoperable representation of data models. It aims to
+facilitate the conversion of the data models between different machine learning
+frameworks, and to improve their portability on different computing
+architectures. More details are available from the `ONNX tutorial
+<https://onnx.ai/get-started.html>`__. To convert scikit-learn model to `ONNX`
+`sklearn-onnx <http://onnx.ai/sklearn-onnx/>`__ has been developed. However,
+not all scikit-learn models are supported, and it is limited to the core
+scikit-learn and does not support most third party estimators. One can write a
+custom converter for third party or custom estimators, but the documentation to
+do that is sparse and it might be challenging to do so.
+
+.. dropdown:: Using ONNX
+
+  To convert the model to `ONNX` format, you need to give the converter some
+  information about the input as well, about which you can read more `here
+  <http://onnx.ai/sklearn-onnx/index.html>`__::
+
+      from skl2onnx import to_onnx
+      onx = to_onnx(clf, X[:1].astype(numpy.float32), target_opset=12)
+      with open("filename.onnx", "wb") as f:
+          f.write(onx.SerializeToString())
+
+  You can load the model in Python and use the `ONNX` runtime to get
+  predictions::
+
+      from onnxruntime import InferenceSession
+      with open("filename.onnx", "rb") as f:
+          onx = f.read()
+      sess = InferenceSession(onx, providers=["CPUExecutionProvider"])
+      pred_ort = sess.run(None, {"X": X_test.astype(numpy.float32)})[0]
+
+.. _skops_persistence:
+
+`skops.io`
+----------
+
+:mod:`skops.io` avoids using :mod:`pickle` and only loads files which have types
+and references to functions which are trusted either by default or by the user.
+Therefore it provides a more secure format than :mod:`pickle`, :mod:`joblib`,
+and `cloudpickle`_.
+
+
+.. dropdown:: Using skops
+
+  The API is very similar to :mod:`pickle`, and you can persist your models as
+  explained in the `documentation
+  <https://skops.readthedocs.io/en/stable/persistence.html>`__ using
+  :func:`skops.io.dump` and :func:`skops.io.dumps`::
+
+      import skops.io as sio
+      obj = sio.dump(clf, "filename.skops")
+
+  And you can load them back using :func:`skops.io.load` and
+  :func:`skops.io.loads`. However, you need to specify the types which are
+  trusted by you. You can get existing unknown types in a dumped object / file
+  using :func:`skops.io.get_untrusted_types`, and after checking its contents,
+  pass it to the load function::
+
+      unknown_types = sio.get_untrusted_types(file="filename.skops")
+      # investigate the contents of unknown_types, and only load if you trust
+      # everything you see.
+      clf = sio.load("filename.skops", trusted=unknown_types)
+
+  Please report issues and feature requests related to this format on the `skops
+  issue tracker <https://github.com/skops-dev/skops/issues>`__.
+
+
+.. _pickle_persistence:
+
+`pickle`, `joblib`, and `cloudpickle`
+-------------------------------------
+
+These three modules / packages, use the `pickle` protocol under the hood, but
+come with slight variations:
+
+- :mod:`pickle` is a module from the Python Standard Library. It can serialize
+  and  deserialize any Python object, including custom Python classes and
+  objects.
+- :mod:`joblib` is more efficient than `pickle` when working with large machine
+  learning models or large numpy arrays.
+- `cloudpickle`_ can serialize certain objects which cannot be serialized by
+  :mod:`pickle` or :mod:`joblib`, such as user defined functions and lambda
+  functions. This can happen for instance, when using a
+  :class:`~sklearn.preprocessing.FunctionTransformer` and using a custom
+  function to transform the data.
+
+.. dropdown:: Using `pickle`, `joblib`, or `cloudpickle`
+
+  Depending on your use-case, you can choose one of these three methods to
+  persist and load your scikit-learn model, and they all follow the same API::
+
+      # Here you can replace pickle with joblib or cloudpickle
+      from pickle import dump
+      with open("filename.pkl", "wb") as f:
+          dump(clf, f, protocol=5)
+
+  Using `protocol=5` is recommended to reduce memory usage and make it faster to
+  store and load any large NumPy array stored as a fitted attribute in the model.
+  You can alternatively pass `protocol=pickle.HIGHEST_PROTOCOL` which is
+  equivalent to `protocol=5` in Python 3.8 and later (at the time of writing).
+
+  And later when needed, you can load the same object from the persisted file::
+
+      # Here you can replace pickle with joblib or cloudpickle
+      from pickle import load
+      with open("filename.pkl", "rb") as f:
+          clf = load(f)
+
+.. _persistence_limitations:
+
+Security & Maintainability Limitations
+--------------------------------------
+
+:mod:`pickle` (and :mod:`joblib` and :mod:`clouldpickle` by extension), has
+many documented security vulnerabilities by design and should only be used if
+the artifact, i.e. the pickle-file, is coming from a trusted and verified
+source. You should never load a pickle file from an untrusted source, similarly
+to how you should never execute code from an untrusted source.
+
+Also note that arbitrary computations can be represented using the `ONNX`
+format, and it is therefore recommended to serve models using `ONNX` in a
+sandboxed environment to safeguard against computational and memory exploits.
+
+Also note that there are no supported ways to load a model trained with a
+different version of scikit-learn. While using :mod:`skops.io`, :mod:`joblib`,
+:mod:`pickle`, or `cloudpickle`_, models saved using one version of
+scikit-learn might load in other versions, however, this is entirely
+unsupported and inadvisable. It should also be kept in mind that operations
+performed on such data could give different and unexpected results, or even
+crash your Python process.
+
+In order to rebuild a similar model with future versions of scikit-learn,
+additional metadata should be saved along the pickled model:
+
+* The training data, e.g. a reference to an immutable snapshot
+* The Python source code used to generate the model
+* The versions of scikit-learn and its dependencies
+* The cross validation score obtained on the training data
+
+This should make it possible to check that the cross-validation score is in the
+same range as before.
+
+Aside for a few exceptions, persisted models should be portable across
+operating systems and hardware architectures assuming the same versions of
+dependencies and Python are used. If you encounter an estimator that is not
+portable, please open an issue on GitHub. Persisted models are often deployed
+in production using containers like Docker, in order to freeze the environment
+and dependencies.
+
+If you want to know more about these issues, please refer to these talks:
+
+- `Adrin Jalali: Let's exploit pickle, and skops to the rescue! | PyData
+  Amsterdam 2023 <https://www.youtube.com/watch?v=9w_H5OSTO9A>`__.
+- `Alex Gaynor: Pickles are for Delis, not Software - PyCon 2014
+  <https://pyvideo.org/video/2566/pickles-are-for-delis-not-software>`__.
+
+
+.. _serving_environment:
+
+Replicating the training environment in production
+..................................................
+
+If the versions of the dependencies used may differ from training to
+production, it may result in unexpected behaviour and errors while using the
+trained model. To prevent such situations it is recommended to use the same
+dependencies and versions in both the training and production environment.
+These transitive dependencies can be pinned with the help of package management
+tools like `pip`, `mamba`, `conda`, `poetry`, `conda-lock`, `pixi`, etc.
+
+It is not always possible to load an model trained with older versions of the
+scikit-learn library and its dependencies in an updated software environment.
+Instead, you might need to retrain the model with the new versions of the all
+the libraries. So when training a model, it is important to record the training
+recipe (e.g. a Python script) and training set information, and metadata about
+all the dependencies to be able to automatically reconstruct the same training
+environment for the updated software.
+
+.. dropdown:: InconsistentVersionWarning
+
+  When an estimator is loaded with a scikit-learn version that is inconsistent
+  with the version the estimator was pickled with, a
+  :class:`~sklearn.exceptions.InconsistentVersionWarning` is raised. This warning
+  can be caught to obtain the original version the estimator was pickled with::
+
+    from sklearn.exceptions import InconsistentVersionWarning
+    warnings.simplefilter("error", InconsistentVersionWarning)
+
+    try:
+        with open("model_from_prevision_version.pickle", "rb") as f:
+            est = pickle.load(f)
+    except InconsistentVersionWarning as w:
+        print(w.original_sklearn_version)
+
+
+Serving the model artifact
+..........................
+
+The last step after training a scikit-learn model is serving the model.
+Once the trained model is successfully loaded, it can be served to manage
+different prediction requests. This can involve deploying the model as a
+web service using containerization, or other model deployment strategies,
+according to the specifications.
+
+
+Summarizing the key points
+--------------------------
+
+Based on the different approaches for model persistence, the key points for
+each approach can be summarized as follows:
+
+* `ONNX`: It provides a uniform format for persisting any machine learning or
+  deep learning model (other than scikit-learn) and is useful for model
+  inference (predictions). It can however, result in compatibility issues with
+  different frameworks.
+* :mod:`skops.io`: Trained scikit-learn models can be easily shared and put
+  into production using :mod:`skops.io`. It is more secure compared to
+  alternate approaches based on :mod:`pickle` because it does not load
+  arbitrary code unless explicitly asked for by the user. Such code needs to be
+  packaged and importable in the target Python environment.
+* :mod:`joblib`: Efficient memory mapping techniques make it faster when using
+  the same persisted model in multiple Python processes when using
+  `mmap_mode="r"`. It also gives easy shortcuts to compress and decompress the
+  persisted object without the need for extra code. However, it may trigger the
+  execution of malicious code when loading a model from an untrusted source as
+  any other pickle-based persistence mechanism.
+* :mod:`pickle`: It is native to Python and most Python objects can be
+  serialized and deserialized using :mod:`pickle`, including custom Python
+  classes and functions as long as they are defined in a package that can be
+  imported in the target environment. While :mod:`pickle` can be used to easily
+  save and load scikit-learn models, it may trigger the execution of malicious
+  code while loading a model from an untrusted source. :mod:`pickle` can also
+  be very efficient memorywise if the model was persisted with `protocol=5` but
+  it does not support memory mapping.
+* `cloudpickle`_: It has comparable loading efficiency as :mod:`pickle` and
+  :mod:`joblib` (without memory mapping), but offers additional flexibility to
+  serialize custom Python code such as lambda expressions and interactively
+  defined functions and classes. It might be a last resort to persist pipelines
+  with custom Python components such as a
+  :class:`sklearn.preprocessing.FunctionTransformer` that wraps a function
+  defined in the training script itself or more generally outside of any
+  importable Python package. Note that `cloudpickle`_ offers no forward
+  compatibility guarantees and you might need the same version of
+  `cloudpickle`_ to load the persisted model along with the same version of all
+  the libraries used to define the model. As the other pickle-based persistence
+  mechanisms, it may trigger the execution of malicious code while loading
+  a model from an untrusted source.
+
+.. _cloudpickle: https://github.com/cloudpipe/cloudpickle
diff --git a/doc/model_selection.rst b/doc/model_selection.rst
index 25cd2b655ccc5..b78c9ff4c3aa8 100644
--- a/doc/model_selection.rst
+++ b/doc/model_selection.rst
@@ -1,9 +1,3 @@
-.. Places parent toc into the sidebar
-
-:parenttoc: True
-
-.. include:: includes/big_toc_css.rst
-
 .. _model_selection:
 
 Model selection and evaluation
@@ -14,5 +8,6 @@ Model selection and evaluation
 
     modules/cross_validation
     modules/grid_search
+    modules/classification_threshold
     modules/model_evaluation
     modules/learning_curve
diff --git a/doc/modules/array_api.rst b/doc/modules/array_api.rst
index 6037d644d3f7d..9b58cde56b09a 100644
--- a/doc/modules/array_api.rst
+++ b/doc/modules/array_api.rst
@@ -1,7 +1,3 @@
-.. Places parent toc into the sidebar
-
-:parenttoc: True
-
 .. _array_api:
 
 ================================
@@ -95,6 +91,7 @@ Estimators
 
 - :class:`decomposition.PCA` (with `svd_solver="full"`,
   `svd_solver="randomized"` and `power_iteration_normalizer="QR"`)
+- :class:`linear_model.Ridge` (with `solver="svd"`)
 - :class:`discriminant_analysis.LinearDiscriminantAnalysis` (with `solver="svd"`)
 - :class:`preprocessing.KernelCenterer`
 - :class:`preprocessing.MaxAbsScaler`
@@ -105,6 +102,9 @@ Metrics
 -------
 
 - :func:`sklearn.metrics.accuracy_score`
+- :func:`sklearn.metrics.mean_absolute_error`
+- :func:`sklearn.metrics.mean_tweedie_deviance`
+- :func:`sklearn.metrics.pairwise.cosine_similarity`
 - :func:`sklearn.metrics.r2_score`
 - :func:`sklearn.metrics.zero_one_loss`
 
diff --git a/doc/modules/biclustering.rst b/doc/modules/biclustering.rst
index 2189e85e0f0ef..503a535c408f0 100644
--- a/doc/modules/biclustering.rst
+++ b/doc/modules/biclustering.rst
@@ -147,21 +147,21 @@ Then the rows of :math:`Z` are clustered using :ref:`k-means
 and the remaining ``n_columns`` labels provide the column partitioning.
 
 
-.. topic:: Examples:
+.. rubric:: Examples
 
- * :ref:`sphx_glr_auto_examples_bicluster_plot_spectral_coclustering.py`: A simple example
-   showing how to generate a data matrix with biclusters and apply
-   this method to it.
+* :ref:`sphx_glr_auto_examples_bicluster_plot_spectral_coclustering.py`: A simple example
+  showing how to generate a data matrix with biclusters and apply
+  this method to it.
 
- * :ref:`sphx_glr_auto_examples_bicluster_plot_bicluster_newsgroups.py`: An example of finding
-   biclusters in the twenty newsgroup dataset.
+* :ref:`sphx_glr_auto_examples_bicluster_plot_bicluster_newsgroups.py`: An example of finding
+  biclusters in the twenty newsgroup dataset.
 
 
-.. topic:: References:
+.. rubric:: References
 
- * Dhillon, Inderjit S, 2001. :doi:`Co-clustering documents and words using
-   bipartite spectral graph partitioning
-   <10.1145/502512.502550>`
+* Dhillon, Inderjit S, 2001. :doi:`Co-clustering documents and words using
+  bipartite spectral graph partitioning
+  <10.1145/502512.502550>`
 
 
 .. _spectral_biclustering:
@@ -234,17 +234,17 @@ Similarly, projecting the columns to :math:`A^{\top} * U` and
 clustering this :math:`n \times q` matrix yields the column labels.
 
 
-.. topic:: Examples:
+.. rubric:: Examples
 
- * :ref:`sphx_glr_auto_examples_bicluster_plot_spectral_biclustering.py`: a simple example
-   showing how to generate a checkerboard matrix and bicluster it.
+* :ref:`sphx_glr_auto_examples_bicluster_plot_spectral_biclustering.py`: a simple example
+  showing how to generate a checkerboard matrix and bicluster it.
 
 
-.. topic:: References:
+.. rubric:: References
 
- * Kluger, Yuval, et. al., 2003. :doi:`Spectral biclustering of microarray
-   data: coclustering genes and conditions
-   <10.1101/gr.648603>`
+* Kluger, Yuval, et. al., 2003. :doi:`Spectral biclustering of microarray
+  data: coclustering genes and conditions
+  <10.1101/gr.648603>`
 
 
 .. _biclustering_evaluation:
@@ -298,8 +298,8 @@ are totally dissimilar. The maximum score, 1, occurs when both sets
 are identical.
 
 
-.. topic:: References:
+.. rubric:: References
 
- * Hochreiter, Bodenhofer, et. al., 2010. `FABIA: factor analysis
-   for bicluster acquisition
-   <https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2881408/>`__.
+* Hochreiter, Bodenhofer, et. al., 2010. `FABIA: factor analysis
+  for bicluster acquisition
+  <https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2881408/>`__.
diff --git a/doc/modules/calibration.rst b/doc/modules/calibration.rst
index c0a6edb837b2f..a2bfa152d2b26 100644
--- a/doc/modules/calibration.rst
+++ b/doc/modules/calibration.rst
@@ -262,51 +262,51 @@ probabilities, the calibrated probabilities for each class
 are predicted separately. As those probabilities do not necessarily sum to
 one, a postprocessing is performed to normalize them.
 
-.. topic:: Examples:
-
-   * :ref:`sphx_glr_auto_examples_calibration_plot_calibration_curve.py`
-   * :ref:`sphx_glr_auto_examples_calibration_plot_calibration_multiclass.py`
-   * :ref:`sphx_glr_auto_examples_calibration_plot_calibration.py`
-   * :ref:`sphx_glr_auto_examples_calibration_plot_compare_calibration.py`
-
-.. topic:: References:
-
-    .. [1] Allan H. Murphy (1973).
-           :doi:`"A New Vector Partition of the Probability Score"
-           <10.1175/1520-0450(1973)012%3C0595:ANVPOT%3E2.0.CO;2>`
-           Journal of Applied Meteorology and Climatology
-
-    .. [2] `On the combination of forecast probabilities for
-           consecutive precipitation periods.
-           <https://journals.ametsoc.org/waf/article/5/4/640/40179>`_
-           Wea. Forecasting, 5, 640–650., Wilks, D. S., 1990a
-
-    .. [3] `Predicting Good Probabilities with Supervised Learning
-           <https://www.cs.cornell.edu/~alexn/papers/calibration.icml05.crc.rev3.pdf>`_,
-           A. Niculescu-Mizil & R. Caruana, ICML 2005
-
-
-    .. [4] `Probabilistic Outputs for Support Vector Machines and Comparisons
-           to Regularized Likelihood Methods.
-           <https://www.cs.colorado.edu/~mozer/Teaching/syllabi/6622/papers/Platt1999.pdf>`_
-           J. Platt, (1999)
-
-    .. [5] `Transforming Classifier Scores into Accurate Multiclass
-           Probability Estimates.
-           <https://dl.acm.org/doi/pdf/10.1145/775047.775151>`_
-           B. Zadrozny & C. Elkan, (KDD 2002)
-
-    .. [6] `Predicting accurate probabilities with a ranking loss.
-           <https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4180410/>`_
-           Menon AK, Jiang XJ, Vembu S, Elkan C, Ohno-Machado L.
-           Proc Int Conf Mach Learn. 2012;2012:703-710
-
-    .. [7] `Beyond sigmoids: How to obtain well-calibrated probabilities from
-           binary classifiers with beta calibration
-           <https://projecteuclid.org/euclid.ejs/1513306867>`_
-           Kull, M., Silva Filho, T. M., & Flach, P. (2017).
-
-    .. [8] Mario V. Wüthrich, Michael Merz (2023).
-           :doi:`"Statistical Foundations of Actuarial Learning and its Applications"
-           <10.1007/978-3-031-12409-9>`
-           Springer Actuarial
+.. rubric:: Examples
+
+* :ref:`sphx_glr_auto_examples_calibration_plot_calibration_curve.py`
+* :ref:`sphx_glr_auto_examples_calibration_plot_calibration_multiclass.py`
+* :ref:`sphx_glr_auto_examples_calibration_plot_calibration.py`
+* :ref:`sphx_glr_auto_examples_calibration_plot_compare_calibration.py`
+
+.. rubric:: References
+
+.. [1] Allan H. Murphy (1973).
+       :doi:`"A New Vector Partition of the Probability Score"
+       <10.1175/1520-0450(1973)012%3C0595:ANVPOT%3E2.0.CO;2>`
+       Journal of Applied Meteorology and Climatology
+
+.. [2] `On the combination of forecast probabilities for
+       consecutive precipitation periods.
+       <https://journals.ametsoc.org/waf/article/5/4/640/40179>`_
+       Wea. Forecasting, 5, 640–650., Wilks, D. S., 1990a
+
+.. [3] `Predicting Good Probabilities with Supervised Learning
+       <https://www.cs.cornell.edu/~alexn/papers/calibration.icml05.crc.rev3.pdf>`_,
+       A. Niculescu-Mizil & R. Caruana, ICML 2005
+
+
+.. [4] `Probabilistic Outputs for Support Vector Machines and Comparisons
+       to Regularized Likelihood Methods.
+       <https://www.cs.colorado.edu/~mozer/Teaching/syllabi/6622/papers/Platt1999.pdf>`_
+       J. Platt, (1999)
+
+.. [5] `Transforming Classifier Scores into Accurate Multiclass
+       Probability Estimates.
+       <https://dl.acm.org/doi/pdf/10.1145/775047.775151>`_
+       B. Zadrozny & C. Elkan, (KDD 2002)
+
+.. [6] `Predicting accurate probabilities with a ranking loss.
+       <https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4180410/>`_
+       Menon AK, Jiang XJ, Vembu S, Elkan C, Ohno-Machado L.
+       Proc Int Conf Mach Learn. 2012;2012:703-710
+
+.. [7] `Beyond sigmoids: How to obtain well-calibrated probabilities from
+       binary classifiers with beta calibration
+       <https://projecteuclid.org/euclid.ejs/1513306867>`_
+       Kull, M., Silva Filho, T. M., & Flach, P. (2017).
+
+.. [8] Mario V. Wüthrich, Michael Merz (2023).
+       :doi:`"Statistical Foundations of Actuarial Learning and its Applications"
+       <10.1007/978-3-031-12409-9>`
+       Springer Actuarial
diff --git a/doc/modules/classes.rst b/doc/modules/classes.rst
deleted file mode 100644
index 370b4cd284b50..0000000000000
--- a/doc/modules/classes.rst
+++ /dev/null
@@ -1,1905 +0,0 @@
-.. _api_ref:
-
-=============
-API Reference
-=============
-
-This is the class and function reference of scikit-learn. Please refer to
-the :ref:`full user guide <user_guide>` for further details, as the class and
-function raw specifications may not be enough to give full guidelines on their
-uses.
-For reference on concepts repeated across the API, see :ref:`glossary`.
-
-:mod:`sklearn`: Settings and information tools
-==============================================
-
-.. automodule:: sklearn
-    :no-members:
-    :no-inherited-members:
-
-.. currentmodule:: sklearn
-
-.. autosummary::
-   :toctree: generated/
-   :template: function.rst
-
-   config_context
-   get_config
-   set_config
-   show_versions
-
-:mod:`sklearn.base`: Base classes and utility functions
-=======================================================
-
-.. automodule:: sklearn.base
-    :no-members:
-    :no-inherited-members:
-
-Base classes
-------------
-.. currentmodule:: sklearn
-
-.. autosummary::
-   :nosignatures:
-   :toctree: generated/
-   :template: class.rst
-
-   base.BaseEstimator
-   base.BiclusterMixin
-   base.ClassifierMixin
-   base.ClusterMixin
-   base.DensityMixin
-   base.RegressorMixin
-   base.TransformerMixin
-   base.MetaEstimatorMixin
-   base.OneToOneFeatureMixin
-   base.OutlierMixin
-   base.ClassNamePrefixFeaturesOutMixin
-   feature_selection.SelectorMixin
-
-Functions
----------
-.. currentmodule:: sklearn
-
-.. autosummary::
-   :toctree: generated/
-   :template: function.rst
-
-   base.clone
-   base.is_classifier
-   base.is_regressor
-
-.. _calibration_ref:
-
-:mod:`sklearn.calibration`: Probability Calibration
-===================================================
-
-.. automodule:: sklearn.calibration
-   :no-members:
-   :no-inherited-members:
-
-**User guide:** See the :ref:`calibration` section for further details.
-
-.. currentmodule:: sklearn
-
-.. autosummary::
-   :toctree: generated/
-   :template: class.rst
-
-   calibration.CalibratedClassifierCV
-
-
-.. autosummary::
-   :toctree: generated/
-   :template: function.rst
-
-   calibration.calibration_curve
-
-.. _cluster_ref:
-
-:mod:`sklearn.cluster`: Clustering
-==================================
-
-.. automodule:: sklearn.cluster
-   :no-members:
-   :no-inherited-members:
-
-**User guide:** See the :ref:`clustering` and :ref:`biclustering` sections for
-further details.
-
-Classes
--------
-.. currentmodule:: sklearn
-
-.. autosummary::
-   :toctree: generated/
-   :template: class.rst
-
-   cluster.AffinityPropagation
-   cluster.AgglomerativeClustering
-   cluster.Birch
-   cluster.DBSCAN
-   cluster.HDBSCAN
-   cluster.FeatureAgglomeration
-   cluster.KMeans
-   cluster.BisectingKMeans
-   cluster.MiniBatchKMeans
-   cluster.MeanShift
-   cluster.OPTICS
-   cluster.SpectralClustering
-   cluster.SpectralBiclustering
-   cluster.SpectralCoclustering
-
-Functions
----------
-.. autosummary::
-   :toctree: generated/
-   :template: function.rst
-
-   cluster.affinity_propagation
-   cluster.cluster_optics_dbscan
-   cluster.cluster_optics_xi
-   cluster.compute_optics_graph
-   cluster.dbscan
-   cluster.estimate_bandwidth
-   cluster.k_means
-   cluster.kmeans_plusplus
-   cluster.mean_shift
-   cluster.spectral_clustering
-   cluster.ward_tree
-
-.. _compose_ref:
-
-:mod:`sklearn.compose`: Composite Estimators
-============================================
-
-.. automodule:: sklearn.compose
-   :no-members:
-   :no-inherited-members:
-
-**User guide:** See the :ref:`combining_estimators` section for further
-details.
-
-.. currentmodule:: sklearn
-
-.. autosummary::
-    :toctree: generated/
-    :template: class.rst
-
-    compose.ColumnTransformer
-    compose.TransformedTargetRegressor
-
-.. autosummary::
-   :toctree: generated/
-   :template: function.rst
-
-   compose.make_column_transformer
-   compose.make_column_selector
-
-.. _covariance_ref:
-
-:mod:`sklearn.covariance`: Covariance Estimators
-================================================
-
-.. automodule:: sklearn.covariance
-   :no-members:
-   :no-inherited-members:
-
-**User guide:** See the :ref:`covariance` section for further details.
-
-.. currentmodule:: sklearn
-
-.. autosummary::
-   :toctree: generated/
-   :template: class.rst
-
-   covariance.EmpiricalCovariance
-   covariance.EllipticEnvelope
-   covariance.GraphicalLasso
-   covariance.GraphicalLassoCV
-   covariance.LedoitWolf
-   covariance.MinCovDet
-   covariance.OAS
-   covariance.ShrunkCovariance
-
-.. autosummary::
-   :toctree: generated/
-   :template: function.rst
-
-   covariance.empirical_covariance
-   covariance.graphical_lasso
-   covariance.ledoit_wolf
-   covariance.ledoit_wolf_shrinkage
-   covariance.oas
-   covariance.shrunk_covariance
-
-.. _cross_decomposition_ref:
-
-:mod:`sklearn.cross_decomposition`: Cross decomposition
-=======================================================
-
-.. automodule:: sklearn.cross_decomposition
-   :no-members:
-   :no-inherited-members:
-
-**User guide:** See the :ref:`cross_decomposition` section for further details.
-
-.. currentmodule:: sklearn
-
-.. autosummary::
-   :toctree: generated/
-   :template: class.rst
-
-   cross_decomposition.CCA
-   cross_decomposition.PLSCanonical
-   cross_decomposition.PLSRegression
-   cross_decomposition.PLSSVD
-
-.. _datasets_ref:
-
-:mod:`sklearn.datasets`: Datasets
-=================================
-
-.. automodule:: sklearn.datasets
-   :no-members:
-   :no-inherited-members:
-
-**User guide:** See the :ref:`datasets` section for further details.
-
-Loaders
--------
-
-.. currentmodule:: sklearn
-
-.. autosummary::
-   :toctree: generated/
-   :template: function.rst
-
-   datasets.clear_data_home
-   datasets.dump_svmlight_file
-   datasets.fetch_20newsgroups
-   datasets.fetch_20newsgroups_vectorized
-   datasets.fetch_california_housing
-   datasets.fetch_covtype
-   datasets.fetch_kddcup99
-   datasets.fetch_lfw_pairs
-   datasets.fetch_lfw_people
-   datasets.fetch_olivetti_faces
-   datasets.fetch_openml
-   datasets.fetch_rcv1
-   datasets.fetch_species_distributions
-   datasets.get_data_home
-   datasets.load_breast_cancer
-   datasets.load_diabetes
-   datasets.load_digits
-   datasets.load_files
-   datasets.load_iris
-   datasets.load_linnerud
-   datasets.load_sample_image
-   datasets.load_sample_images
-   datasets.load_svmlight_file
-   datasets.load_svmlight_files
-   datasets.load_wine
-
-Samples generator
------------------
-
-.. currentmodule:: sklearn
-
-.. autosummary::
-   :toctree: generated/
-   :template: function.rst
-
-   datasets.make_biclusters
-   datasets.make_blobs
-   datasets.make_checkerboard
-   datasets.make_circles
-   datasets.make_classification
-   datasets.make_friedman1
-   datasets.make_friedman2
-   datasets.make_friedman3
-   datasets.make_gaussian_quantiles
-   datasets.make_hastie_10_2
-   datasets.make_low_rank_matrix
-   datasets.make_moons
-   datasets.make_multilabel_classification
-   datasets.make_regression
-   datasets.make_s_curve
-   datasets.make_sparse_coded_signal
-   datasets.make_sparse_spd_matrix
-   datasets.make_sparse_uncorrelated
-   datasets.make_spd_matrix
-   datasets.make_swiss_roll
-
-
-.. _decomposition_ref:
-
-:mod:`sklearn.decomposition`: Matrix Decomposition
-==================================================
-
-.. automodule:: sklearn.decomposition
-   :no-members:
-   :no-inherited-members:
-
-**User guide:** See the :ref:`decompositions` section for further details.
-
-.. currentmodule:: sklearn
-
-.. autosummary::
-   :toctree: generated/
-   :template: class.rst
-
-   decomposition.DictionaryLearning
-   decomposition.FactorAnalysis
-   decomposition.FastICA
-   decomposition.IncrementalPCA
-   decomposition.KernelPCA
-   decomposition.LatentDirichletAllocation
-   decomposition.MiniBatchDictionaryLearning
-   decomposition.MiniBatchSparsePCA
-   decomposition.NMF
-   decomposition.MiniBatchNMF
-   decomposition.PCA
-   decomposition.SparsePCA
-   decomposition.SparseCoder
-   decomposition.TruncatedSVD
-
-.. autosummary::
-   :toctree: generated/
-   :template: function.rst
-
-   decomposition.dict_learning
-   decomposition.dict_learning_online
-   decomposition.fastica
-   decomposition.non_negative_factorization
-   decomposition.sparse_encode
-
-.. _lda_ref:
-
-:mod:`sklearn.discriminant_analysis`: Discriminant Analysis
-===========================================================
-
-.. automodule:: sklearn.discriminant_analysis
-   :no-members:
-   :no-inherited-members:
-
-**User guide:** See the :ref:`lda_qda` section for further details.
-
-.. currentmodule:: sklearn
-
-.. autosummary::
-   :toctree: generated/
-   :template: class.rst
-
-   discriminant_analysis.LinearDiscriminantAnalysis
-   discriminant_analysis.QuadraticDiscriminantAnalysis
-
-.. _dummy_ref:
-
-:mod:`sklearn.dummy`: Dummy estimators
-======================================
-
-.. automodule:: sklearn.dummy
-   :no-members:
-   :no-inherited-members:
-
-**User guide:** See the :ref:`model_evaluation` section for further details.
-
-.. currentmodule:: sklearn
-
-.. autosummary::
-   :toctree: generated/
-   :template: class.rst
-
-   dummy.DummyClassifier
-   dummy.DummyRegressor
-
-.. autosummary::
-   :toctree: generated/
-   :template: function.rst
-
-.. _ensemble_ref:
-
-:mod:`sklearn.ensemble`: Ensemble Methods
-=========================================
-
-.. automodule:: sklearn.ensemble
-   :no-members:
-   :no-inherited-members:
-
-**User guide:** See the :ref:`ensemble` section for further details.
-
-.. currentmodule:: sklearn
-
-.. autosummary::
-   :toctree: generated/
-   :template: class.rst
-
-   ensemble.AdaBoostClassifier
-   ensemble.AdaBoostRegressor
-   ensemble.BaggingClassifier
-   ensemble.BaggingRegressor
-   ensemble.ExtraTreesClassifier
-   ensemble.ExtraTreesRegressor
-   ensemble.GradientBoostingClassifier
-   ensemble.GradientBoostingRegressor
-   ensemble.IsolationForest
-   ensemble.RandomForestClassifier
-   ensemble.RandomForestRegressor
-   ensemble.RandomTreesEmbedding
-   ensemble.StackingClassifier
-   ensemble.StackingRegressor
-   ensemble.VotingClassifier
-   ensemble.VotingRegressor
-   ensemble.HistGradientBoostingRegressor
-   ensemble.HistGradientBoostingClassifier
-
-
-.. autosummary::
-   :toctree: generated/
-   :template: function.rst
-
-
-.. _exceptions_ref:
-
-:mod:`sklearn.exceptions`: Exceptions and warnings
-==================================================
-
-.. automodule:: sklearn.exceptions
-   :no-members:
-   :no-inherited-members:
-
-.. currentmodule:: sklearn
-
-.. autosummary::
-   :toctree: generated/
-   :template: class.rst
-
-   exceptions.ConvergenceWarning
-   exceptions.DataConversionWarning
-   exceptions.DataDimensionalityWarning
-   exceptions.EfficiencyWarning
-   exceptions.FitFailedWarning
-   exceptions.InconsistentVersionWarning
-   exceptions.NotFittedError
-   exceptions.UndefinedMetricWarning
-
-
-:mod:`sklearn.experimental`: Experimental
-=========================================
-
-.. automodule:: sklearn.experimental
-   :no-members:
-   :no-inherited-members:
-
-.. currentmodule:: sklearn
-
-.. autosummary::
-   :toctree: generated/
-
-   experimental.enable_iterative_imputer
-   experimental.enable_halving_search_cv
-
-
-.. _feature_extraction_ref:
-
-:mod:`sklearn.feature_extraction`: Feature Extraction
-=====================================================
-
-.. automodule:: sklearn.feature_extraction
-   :no-members:
-   :no-inherited-members:
-
-**User guide:** See the :ref:`feature_extraction` section for further details.
-
-.. currentmodule:: sklearn
-
-.. autosummary::
-   :toctree: generated/
-   :template: class.rst
-
-   feature_extraction.DictVectorizer
-   feature_extraction.FeatureHasher
-
-From images
------------
-
-.. automodule:: sklearn.feature_extraction.image
-   :no-members:
-   :no-inherited-members:
-
-.. currentmodule:: sklearn
-
-.. autosummary::
-   :toctree: generated/
-   :template: function.rst
-
-   feature_extraction.image.extract_patches_2d
-   feature_extraction.image.grid_to_graph
-   feature_extraction.image.img_to_graph
-   feature_extraction.image.reconstruct_from_patches_2d
-
-   :template: class.rst
-
-   feature_extraction.image.PatchExtractor
-
-.. _text_feature_extraction_ref:
-
-From text
----------
-
-.. automodule:: sklearn.feature_extraction.text
-   :no-members:
-   :no-inherited-members:
-
-.. currentmodule:: sklearn
-
-.. autosummary::
-   :toctree: generated/
-   :template: class.rst
-
-   feature_extraction.text.CountVectorizer
-   feature_extraction.text.HashingVectorizer
-   feature_extraction.text.TfidfTransformer
-   feature_extraction.text.TfidfVectorizer
-
-
-.. _feature_selection_ref:
-
-:mod:`sklearn.feature_selection`: Feature Selection
-===================================================
-
-.. automodule:: sklearn.feature_selection
-   :no-members:
-   :no-inherited-members:
-
-**User guide:** See the :ref:`feature_selection` section for further details.
-
-.. currentmodule:: sklearn
-
-.. autosummary::
-   :toctree: generated/
-   :template: class.rst
-
-   feature_selection.GenericUnivariateSelect
-   feature_selection.SelectPercentile
-   feature_selection.SelectKBest
-   feature_selection.SelectFpr
-   feature_selection.SelectFdr
-   feature_selection.SelectFromModel
-   feature_selection.SelectFwe
-   feature_selection.SequentialFeatureSelector
-   feature_selection.RFE
-   feature_selection.RFECV
-   feature_selection.VarianceThreshold
-
-.. autosummary::
-   :toctree: generated/
-   :template: function.rst
-
-   feature_selection.chi2
-   feature_selection.f_classif
-   feature_selection.f_regression
-   feature_selection.r_regression
-   feature_selection.mutual_info_classif
-   feature_selection.mutual_info_regression
-
-
-.. _gaussian_process_ref:
-
-:mod:`sklearn.gaussian_process`: Gaussian Processes
-===================================================
-
-.. automodule:: sklearn.gaussian_process
-   :no-members:
-   :no-inherited-members:
-
-**User guide:** See the :ref:`gaussian_process` section for further details.
-
-.. currentmodule:: sklearn
-
-.. autosummary::
-  :toctree: generated/
-  :template: class.rst
-
-  gaussian_process.GaussianProcessClassifier
-  gaussian_process.GaussianProcessRegressor
-
-Kernels
--------
-
-.. automodule:: sklearn.gaussian_process.kernels
-   :no-members:
-   :no-inherited-members:
-
-.. currentmodule:: sklearn
-
-.. autosummary::
-  :toctree: generated/
-  :template: class_with_call.rst
-
-  gaussian_process.kernels.CompoundKernel
-  gaussian_process.kernels.ConstantKernel
-  gaussian_process.kernels.DotProduct
-  gaussian_process.kernels.ExpSineSquared
-  gaussian_process.kernels.Exponentiation
-  gaussian_process.kernels.Hyperparameter
-  gaussian_process.kernels.Kernel
-  gaussian_process.kernels.Matern
-  gaussian_process.kernels.PairwiseKernel
-  gaussian_process.kernels.Product
-  gaussian_process.kernels.RBF
-  gaussian_process.kernels.RationalQuadratic
-  gaussian_process.kernels.Sum
-  gaussian_process.kernels.WhiteKernel
-
-
-.. _impute_ref:
-
-:mod:`sklearn.impute`: Impute
-=============================
-
-.. automodule:: sklearn.impute
-   :no-members:
-   :no-inherited-members:
-
-**User guide:** See the :ref:`Impute` section for further details.
-
-.. currentmodule:: sklearn
-
-.. autosummary::
-   :toctree: generated/
-   :template: class.rst
-
-   impute.SimpleImputer
-   impute.IterativeImputer
-   impute.MissingIndicator
-   impute.KNNImputer
-
-
-.. _inspection_ref:
-
-:mod:`sklearn.inspection`: Inspection
-=====================================
-
-.. automodule:: sklearn.inspection
-   :no-members:
-   :no-inherited-members:
-
-.. currentmodule:: sklearn
-
-.. autosummary::
-   :toctree: generated/
-   :template: function.rst
-
-   inspection.h_statistic
-   inspection.partial_dependence
-   inspection.permutation_importance
-
-Plotting
---------
-
-.. currentmodule:: sklearn
-
-.. autosummary::
-   :toctree: generated/
-   :template: display_only_from_estimator.rst
-
-   inspection.DecisionBoundaryDisplay
-   inspection.PartialDependenceDisplay
-
-.. _isotonic_ref:
-
-:mod:`sklearn.isotonic`: Isotonic regression
-============================================
-
-.. automodule:: sklearn.isotonic
-   :no-members:
-   :no-inherited-members:
-
-**User guide:** See the :ref:`isotonic` section for further details.
-
-.. currentmodule:: sklearn
-
-.. autosummary::
-   :toctree: generated/
-   :template: class.rst
-
-   isotonic.IsotonicRegression
-
-.. autosummary::
-   :toctree: generated/
-   :template: function.rst
-
-   isotonic.check_increasing
-   isotonic.isotonic_regression
-
-
-.. _kernel_approximation_ref:
-
-:mod:`sklearn.kernel_approximation`: Kernel Approximation
-=========================================================
-
-.. automodule:: sklearn.kernel_approximation
-   :no-members:
-   :no-inherited-members:
-
-**User guide:** See the :ref:`kernel_approximation` section for further details.
-
-.. currentmodule:: sklearn
-
-.. autosummary::
-   :toctree: generated/
-   :template: class.rst
-
-   kernel_approximation.AdditiveChi2Sampler
-   kernel_approximation.Nystroem
-   kernel_approximation.PolynomialCountSketch
-   kernel_approximation.RBFSampler
-   kernel_approximation.SkewedChi2Sampler
-
-.. _kernel_ridge_ref:
-
-:mod:`sklearn.kernel_ridge`: Kernel Ridge Regression
-====================================================
-
-.. automodule:: sklearn.kernel_ridge
-   :no-members:
-   :no-inherited-members:
-
-**User guide:** See the :ref:`kernel_ridge` section for further details.
-
-.. currentmodule:: sklearn
-
-.. autosummary::
-   :toctree: generated/
-   :template: class.rst
-
-   kernel_ridge.KernelRidge
-
-.. _linear_model_ref:
-
-:mod:`sklearn.linear_model`: Linear Models
-==========================================
-
-.. automodule:: sklearn.linear_model
-   :no-members:
-   :no-inherited-members:
-
-**User guide:** See the :ref:`linear_model` section for further details.
-
-The following subsections are only rough guidelines: the same estimator can
-fall into multiple categories, depending on its parameters.
-
-.. currentmodule:: sklearn
-
-Linear classifiers
-------------------
-.. autosummary::
-   :toctree: generated/
-   :template: class.rst
-
-   linear_model.LogisticRegression
-   linear_model.LogisticRegressionCV
-   linear_model.PassiveAggressiveClassifier
-   linear_model.Perceptron
-   linear_model.RidgeClassifier
-   linear_model.RidgeClassifierCV
-   linear_model.SGDClassifier
-   linear_model.SGDOneClassSVM
-
-Classical linear regressors
----------------------------
-
-.. autosummary::
-   :toctree: generated/
-   :template: class.rst
-
-   linear_model.LinearRegression
-   linear_model.Ridge
-   linear_model.RidgeCV
-   linear_model.SGDRegressor
-
-Regressors with variable selection
-----------------------------------
-
-The following estimators have built-in variable selection fitting
-procedures, but any estimator using a L1 or elastic-net penalty also
-performs variable selection: typically :class:`~linear_model.SGDRegressor`
-or :class:`~sklearn.linear_model.SGDClassifier` with an appropriate penalty.
-
-.. autosummary::
-   :toctree: generated/
-   :template: class.rst
-
-   linear_model.ElasticNet
-   linear_model.ElasticNetCV
-   linear_model.Lars
-   linear_model.LarsCV
-   linear_model.Lasso
-   linear_model.LassoCV
-   linear_model.LassoLars
-   linear_model.LassoLarsCV
-   linear_model.LassoLarsIC
-   linear_model.OrthogonalMatchingPursuit
-   linear_model.OrthogonalMatchingPursuitCV
-
-Bayesian regressors
--------------------
-
-.. autosummary::
-   :toctree: generated/
-   :template: class.rst
-
-   linear_model.ARDRegression
-   linear_model.BayesianRidge
-
-Multi-task linear regressors with variable selection
-----------------------------------------------------
-
-These estimators fit multiple regression problems (or tasks) jointly, while
-inducing sparse coefficients. While the inferred coefficients may differ
-between the tasks, they are constrained to agree on the features that are
-selected (non-zero coefficients).
-
-.. autosummary::
-   :toctree: generated/
-   :template: class.rst
-
-   linear_model.MultiTaskElasticNet
-   linear_model.MultiTaskElasticNetCV
-   linear_model.MultiTaskLasso
-   linear_model.MultiTaskLassoCV
-
-Outlier-robust regressors
--------------------------
-
-Any estimator using the Huber loss would also be robust to outliers, e.g.
-:class:`~linear_model.SGDRegressor` with ``loss='huber'``.
-
-.. autosummary::
-   :toctree: generated/
-   :template: class.rst
-
-   linear_model.HuberRegressor
-   linear_model.QuantileRegressor
-   linear_model.RANSACRegressor
-   linear_model.TheilSenRegressor
-
-Generalized linear models (GLM) for regression
-----------------------------------------------
-
-These models allow for response variables to have error distributions other
-than a normal distribution:
-
-.. autosummary::
-   :toctree: generated/
-   :template: class.rst
-
-   linear_model.PoissonRegressor
-   linear_model.TweedieRegressor
-   linear_model.GammaRegressor
-
-
-Miscellaneous
--------------
-
-.. autosummary::
-   :toctree: generated/
-   :template: classes.rst
-
-   linear_model.PassiveAggressiveRegressor
-
-.. autosummary::
-   :toctree: generated/
-   :template: function.rst
-
-   linear_model.enet_path
-   linear_model.lars_path
-   linear_model.lars_path_gram
-   linear_model.lasso_path
-   linear_model.orthogonal_mp
-   linear_model.orthogonal_mp_gram
-   linear_model.ridge_regression
-
-
-.. _manifold_ref:
-
-:mod:`sklearn.manifold`: Manifold Learning
-==========================================
-
-.. automodule:: sklearn.manifold
-   :no-members:
-   :no-inherited-members:
-
-**User guide:** See the :ref:`manifold` section for further details.
-
-.. currentmodule:: sklearn
-
-.. autosummary::
-    :toctree: generated
-    :template: class.rst
-
-    manifold.Isomap
-    manifold.LocallyLinearEmbedding
-    manifold.MDS
-    manifold.SpectralEmbedding
-    manifold.TSNE
-
-.. autosummary::
-    :toctree: generated
-    :template: function.rst
-
-    manifold.locally_linear_embedding
-    manifold.smacof
-    manifold.spectral_embedding
-    manifold.trustworthiness
-
-
-.. _metrics_ref:
-
-:mod:`sklearn.metrics`: Metrics
-===============================
-
-See the :ref:`model_evaluation` section and the :ref:`metrics` section of the
-user guide for further details.
-
-.. automodule:: sklearn.metrics
-   :no-members:
-   :no-inherited-members:
-
-.. currentmodule:: sklearn
-
-Model Selection Interface
--------------------------
-See the :ref:`scoring_parameter` section of the user guide for further
-details.
-
-.. autosummary::
-   :toctree: generated/
-   :template: function.rst
-
-   metrics.check_scoring
-   metrics.get_scorer
-   metrics.get_scorer_names
-   metrics.make_scorer
-
-Classification metrics
-----------------------
-
-See the :ref:`classification_metrics` section of the user guide for further
-details.
-
-.. autosummary::
-   :toctree: generated/
-   :template: function.rst
-
-   metrics.accuracy_score
-   metrics.auc
-   metrics.average_precision_score
-   metrics.balanced_accuracy_score
-   metrics.brier_score_loss
-   metrics.class_likelihood_ratios
-   metrics.classification_report
-   metrics.cohen_kappa_score
-   metrics.confusion_matrix
-   metrics.dcg_score
-   metrics.det_curve
-   metrics.f1_score
-   metrics.fbeta_score
-   metrics.hamming_loss
-   metrics.hinge_loss
-   metrics.jaccard_score
-   metrics.log_loss
-   metrics.matthews_corrcoef
-   metrics.multilabel_confusion_matrix
-   metrics.ndcg_score
-   metrics.precision_recall_curve
-   metrics.precision_recall_fscore_support
-   metrics.precision_score
-   metrics.recall_score
-   metrics.roc_auc_score
-   metrics.roc_curve
-   metrics.top_k_accuracy_score
-   metrics.zero_one_loss
-
-Regression metrics
-------------------
-
-See the :ref:`regression_metrics` section of the user guide for further
-details.
-
-.. autosummary::
-   :toctree: generated/
-   :template: function.rst
-
-   metrics.explained_variance_score
-   metrics.max_error
-   metrics.mean_absolute_error
-   metrics.mean_squared_error
-   metrics.mean_squared_log_error
-   metrics.median_absolute_error
-   metrics.mean_absolute_percentage_error
-   metrics.r2_score
-   metrics.root_mean_squared_log_error
-   metrics.root_mean_squared_error
-   metrics.mean_poisson_deviance
-   metrics.mean_gamma_deviance
-   metrics.mean_tweedie_deviance
-   metrics.d2_tweedie_score
-   metrics.mean_pinball_loss
-   metrics.d2_pinball_score
-   metrics.d2_absolute_error_score
-
-Multilabel ranking metrics
---------------------------
-See the :ref:`multilabel_ranking_metrics` section of the user guide for further
-details.
-
-.. autosummary::
-   :toctree: generated/
-   :template: function.rst
-
-   metrics.coverage_error
-   metrics.label_ranking_average_precision_score
-   metrics.label_ranking_loss
-
-
-Clustering metrics
-------------------
-
-See the :ref:`clustering_evaluation` section of the user guide for further
-details.
-
-.. automodule:: sklearn.metrics.cluster
-   :no-members:
-   :no-inherited-members:
-
-.. currentmodule:: sklearn
-
-.. autosummary::
-   :toctree: generated/
-   :template: function.rst
-
-   metrics.adjusted_mutual_info_score
-   metrics.adjusted_rand_score
-   metrics.calinski_harabasz_score
-   metrics.davies_bouldin_score
-   metrics.completeness_score
-   metrics.cluster.contingency_matrix
-   metrics.cluster.pair_confusion_matrix
-   metrics.fowlkes_mallows_score
-   metrics.homogeneity_completeness_v_measure
-   metrics.homogeneity_score
-   metrics.mutual_info_score
-   metrics.normalized_mutual_info_score
-   metrics.rand_score
-   metrics.silhouette_score
-   metrics.silhouette_samples
-   metrics.v_measure_score
-
-Biclustering metrics
---------------------
-
-See the :ref:`biclustering_evaluation` section of the user guide for
-further details.
-
-.. currentmodule:: sklearn
-
-.. autosummary::
-   :toctree: generated/
-   :template: function.rst
-
-   metrics.consensus_score
-
-Distance metrics
-----------------
-
-.. currentmodule:: sklearn
-
-.. autosummary::
-   :toctree: generated/
-   :template: class.rst
-
-   metrics.DistanceMetric
-
-Pairwise metrics
-----------------
-
-See the :ref:`metrics` section of the user guide for further details.
-
-.. automodule:: sklearn.metrics.pairwise
-   :no-members:
-   :no-inherited-members:
-
-.. currentmodule:: sklearn
-
-.. autosummary::
-   :toctree: generated/
-   :template: function.rst
-
-   metrics.pairwise.additive_chi2_kernel
-   metrics.pairwise.chi2_kernel
-   metrics.pairwise.cosine_similarity
-   metrics.pairwise.cosine_distances
-   metrics.pairwise.distance_metrics
-   metrics.pairwise.euclidean_distances
-   metrics.pairwise.haversine_distances
-   metrics.pairwise.kernel_metrics
-   metrics.pairwise.laplacian_kernel
-   metrics.pairwise.linear_kernel
-   metrics.pairwise.manhattan_distances
-   metrics.pairwise.nan_euclidean_distances
-   metrics.pairwise.pairwise_kernels
-   metrics.pairwise.polynomial_kernel
-   metrics.pairwise.rbf_kernel
-   metrics.pairwise.sigmoid_kernel
-   metrics.pairwise.paired_euclidean_distances
-   metrics.pairwise.paired_manhattan_distances
-   metrics.pairwise.paired_cosine_distances
-   metrics.pairwise.paired_distances
-   metrics.pairwise_distances
-   metrics.pairwise_distances_argmin
-   metrics.pairwise_distances_argmin_min
-   metrics.pairwise_distances_chunked
-
-
-Plotting
---------
-
-See the :ref:`visualizations` section of the user guide for further details.
-
-.. currentmodule:: sklearn
-
-.. autosummary::
-   :toctree: generated/
-   :template: display_all_class_methods.rst
-
-   metrics.ConfusionMatrixDisplay
-   metrics.DetCurveDisplay
-   metrics.PrecisionRecallDisplay
-   metrics.PredictionErrorDisplay
-   metrics.RocCurveDisplay
-   calibration.CalibrationDisplay
-
-.. _mixture_ref:
-
-:mod:`sklearn.mixture`: Gaussian Mixture Models
-===============================================
-
-.. automodule:: sklearn.mixture
-   :no-members:
-   :no-inherited-members:
-
-**User guide:** See the :ref:`mixture` section for further details.
-
-.. currentmodule:: sklearn
-
-.. autosummary::
-   :toctree: generated/
-   :template: class.rst
-
-   mixture.BayesianGaussianMixture
-   mixture.GaussianMixture
-
-.. _modelselection_ref:
-
-:mod:`sklearn.model_selection`: Model Selection
-===============================================
-
-.. automodule:: sklearn.model_selection
-   :no-members:
-   :no-inherited-members:
-
-**User guide:** See the :ref:`cross_validation`, :ref:`grid_search` and
-:ref:`learning_curve` sections for further details.
-
-Splitter Classes
-----------------
-
-.. currentmodule:: sklearn
-
-.. autosummary::
-   :toctree: generated/
-   :template: class.rst
-
-   model_selection.GroupKFold
-   model_selection.GroupShuffleSplit
-   model_selection.KFold
-   model_selection.LeaveOneGroupOut
-   model_selection.LeavePGroupsOut
-   model_selection.LeaveOneOut
-   model_selection.LeavePOut
-   model_selection.PredefinedSplit
-   model_selection.RepeatedKFold
-   model_selection.RepeatedStratifiedKFold
-   model_selection.ShuffleSplit
-   model_selection.StratifiedKFold
-   model_selection.StratifiedShuffleSplit
-   model_selection.StratifiedGroupKFold
-   model_selection.TimeSeriesSplit
-
-Splitter Functions
-------------------
-
-.. currentmodule:: sklearn
-
-.. autosummary::
-   :toctree: generated/
-   :template: function.rst
-
-   model_selection.check_cv
-   model_selection.train_test_split
-
-.. _hyper_parameter_optimizers:
-
-Hyper-parameter optimizers
---------------------------
-
-.. currentmodule:: sklearn
-
-.. autosummary::
-   :toctree: generated/
-   :template: class.rst
-
-   model_selection.GridSearchCV
-   model_selection.HalvingGridSearchCV
-   model_selection.ParameterGrid
-   model_selection.ParameterSampler
-   model_selection.RandomizedSearchCV
-   model_selection.HalvingRandomSearchCV
-
-
-Model validation
-----------------
-
-.. currentmodule:: sklearn
-
-.. autosummary::
-   :toctree: generated/
-   :template: function.rst
-
-   model_selection.cross_validate
-   model_selection.cross_val_predict
-   model_selection.cross_val_score
-   model_selection.learning_curve
-   model_selection.permutation_test_score
-   model_selection.validation_curve
-
-Visualization
--------------
-
-.. currentmodule:: sklearn
-
-.. autosummary::
-   :toctree: generated/
-   :template: display_only_from_estimator.rst
-
-   model_selection.LearningCurveDisplay
-   model_selection.ValidationCurveDisplay
-
-.. _multiclass_ref:
-
-:mod:`sklearn.multiclass`: Multiclass classification
-====================================================
-
-.. automodule:: sklearn.multiclass
-   :no-members:
-   :no-inherited-members:
-
-**User guide:** See the :ref:`multiclass_classification` section for further details.
-
-.. currentmodule:: sklearn
-
-.. autosummary::
-    :toctree: generated/
-    :template: class.rst
-
-    multiclass.OneVsRestClassifier
-    multiclass.OneVsOneClassifier
-    multiclass.OutputCodeClassifier
-
-.. _multioutput_ref:
-
-:mod:`sklearn.multioutput`: Multioutput regression and classification
-=====================================================================
-
-.. automodule:: sklearn.multioutput
-   :no-members:
-   :no-inherited-members:
-
-**User guide:** See the :ref:`multilabel_classification`,
-:ref:`multiclass_multioutput_classification`, and
-:ref:`multioutput_regression` sections for further details.
-
-.. currentmodule:: sklearn
-
-.. autosummary::
-    :toctree: generated
-    :template: class.rst
-
-    multioutput.ClassifierChain
-    multioutput.MultiOutputRegressor
-    multioutput.MultiOutputClassifier
-    multioutput.RegressorChain
-
-.. _naive_bayes_ref:
-
-:mod:`sklearn.naive_bayes`: Naive Bayes
-=======================================
-
-.. automodule:: sklearn.naive_bayes
-   :no-members:
-   :no-inherited-members:
-
-**User guide:** See the :ref:`naive_bayes` section for further details.
-
-.. currentmodule:: sklearn
-
-.. autosummary::
-   :toctree: generated/
-   :template: class.rst
-
-   naive_bayes.BernoulliNB
-   naive_bayes.CategoricalNB
-   naive_bayes.ComplementNB
-   naive_bayes.GaussianNB
-   naive_bayes.MultinomialNB
-
-
-.. _neighbors_ref:
-
-:mod:`sklearn.neighbors`: Nearest Neighbors
-===========================================
-
-.. automodule:: sklearn.neighbors
-   :no-members:
-   :no-inherited-members:
-
-**User guide:** See the :ref:`neighbors` section for further details.
-
-.. currentmodule:: sklearn
-
-.. autosummary::
-   :toctree: generated/
-   :template: class.rst
-
-   neighbors.BallTree
-   neighbors.KDTree
-   neighbors.KernelDensity
-   neighbors.KNeighborsClassifier
-   neighbors.KNeighborsRegressor
-   neighbors.KNeighborsTransformer
-   neighbors.LocalOutlierFactor
-   neighbors.RadiusNeighborsClassifier
-   neighbors.RadiusNeighborsRegressor
-   neighbors.RadiusNeighborsTransformer
-   neighbors.NearestCentroid
-   neighbors.NearestNeighbors
-   neighbors.NeighborhoodComponentsAnalysis
-
-.. autosummary::
-   :toctree: generated/
-   :template: function.rst
-
-   neighbors.kneighbors_graph
-   neighbors.radius_neighbors_graph
-   neighbors.sort_graph_by_row_values
-
-.. _neural_network_ref:
-
-:mod:`sklearn.neural_network`: Neural network models
-====================================================
-
-.. automodule:: sklearn.neural_network
-   :no-members:
-   :no-inherited-members:
-
-**User guide:** See the :ref:`neural_networks_supervised` and :ref:`neural_networks_unsupervised` sections for further details.
-
-.. currentmodule:: sklearn
-
-.. autosummary::
-   :toctree: generated/
-   :template: class.rst
-
-   neural_network.BernoulliRBM
-   neural_network.MLPClassifier
-   neural_network.MLPRegressor
-
-.. _pipeline_ref:
-
-:mod:`sklearn.pipeline`: Pipeline
-=================================
-
-.. automodule:: sklearn.pipeline
-   :no-members:
-   :no-inherited-members:
-
-**User guide:** See the :ref:`combining_estimators` section for further
-details.
-
-.. currentmodule:: sklearn
-
-.. autosummary::
-   :toctree: generated/
-   :template: class.rst
-
-   pipeline.FeatureUnion
-   pipeline.Pipeline
-
-.. autosummary::
-   :toctree: generated/
-   :template: function.rst
-
-   pipeline.make_pipeline
-   pipeline.make_union
-
-.. _preprocessing_ref:
-
-:mod:`sklearn.preprocessing`: Preprocessing and Normalization
-=============================================================
-
-.. automodule:: sklearn.preprocessing
-   :no-members:
-   :no-inherited-members:
-
-**User guide:** See the :ref:`preprocessing` section for further details.
-
-.. currentmodule:: sklearn
-
-.. autosummary::
-   :toctree: generated/
-   :template: class.rst
-
-   preprocessing.Binarizer
-   preprocessing.FunctionTransformer
-   preprocessing.KBinsDiscretizer
-   preprocessing.KernelCenterer
-   preprocessing.LabelBinarizer
-   preprocessing.LabelEncoder
-   preprocessing.MultiLabelBinarizer
-   preprocessing.MaxAbsScaler
-   preprocessing.MinMaxScaler
-   preprocessing.Normalizer
-   preprocessing.OneHotEncoder
-   preprocessing.OrdinalEncoder
-   preprocessing.PolynomialFeatures
-   preprocessing.PowerTransformer
-   preprocessing.QuantileTransformer
-   preprocessing.RobustScaler
-   preprocessing.SplineTransformer
-   preprocessing.StandardScaler
-   preprocessing.TargetEncoder
-
-.. autosummary::
-   :toctree: generated/
-   :template: function.rst
-
-   preprocessing.add_dummy_feature
-   preprocessing.binarize
-   preprocessing.label_binarize
-   preprocessing.maxabs_scale
-   preprocessing.minmax_scale
-   preprocessing.normalize
-   preprocessing.quantile_transform
-   preprocessing.robust_scale
-   preprocessing.scale
-   preprocessing.power_transform
-
-
-.. _random_projection_ref:
-
-:mod:`sklearn.random_projection`: Random projection
-===================================================
-
-.. automodule:: sklearn.random_projection
-   :no-members:
-   :no-inherited-members:
-
-**User guide:** See the :ref:`random_projection` section for further details.
-
-.. currentmodule:: sklearn
-
-.. autosummary::
-   :toctree: generated/
-   :template: class.rst
-
-   random_projection.GaussianRandomProjection
-   random_projection.SparseRandomProjection
-
-.. autosummary::
-   :toctree: generated/
-   :template: function.rst
-
-   random_projection.johnson_lindenstrauss_min_dim
-
-
-.. _semi_supervised_ref:
-
-:mod:`sklearn.semi_supervised`: Semi-Supervised Learning
-========================================================
-
-.. automodule:: sklearn.semi_supervised
-   :no-members:
-   :no-inherited-members:
-
-**User guide:** See the :ref:`semi_supervised` section for further details.
-
-.. currentmodule:: sklearn
-
-.. autosummary::
-   :toctree: generated/
-   :template: class.rst
-
-   semi_supervised.LabelPropagation
-   semi_supervised.LabelSpreading
-   semi_supervised.SelfTrainingClassifier
-
-
-.. _svm_ref:
-
-:mod:`sklearn.svm`: Support Vector Machines
-===========================================
-
-.. automodule:: sklearn.svm
-   :no-members:
-   :no-inherited-members:
-
-**User guide:** See the :ref:`svm` section for further details.
-
-Estimators
-----------
-
-.. currentmodule:: sklearn
-
-.. autosummary::
-   :toctree: generated/
-   :template: class.rst
-
-   svm.LinearSVC
-   svm.LinearSVR
-   svm.NuSVC
-   svm.NuSVR
-   svm.OneClassSVM
-   svm.SVC
-   svm.SVR
-
-.. autosummary::
-   :toctree: generated/
-   :template: function.rst
-
-   svm.l1_min_c
-
-.. _tree_ref:
-
-:mod:`sklearn.tree`: Decision Trees
-===================================
-
-.. automodule:: sklearn.tree
-   :no-members:
-   :no-inherited-members:
-
-**User guide:** See the :ref:`tree` section for further details.
-
-.. currentmodule:: sklearn
-
-.. autosummary::
-   :toctree: generated/
-   :template: class.rst
-
-   tree.DecisionTreeClassifier
-   tree.DecisionTreeRegressor
-   tree.ExtraTreeClassifier
-   tree.ExtraTreeRegressor
-
-.. autosummary::
-   :toctree: generated/
-   :template: function.rst
-
-   tree.export_graphviz
-   tree.export_text
-
-Plotting
---------
-
-.. currentmodule:: sklearn
-
-.. autosummary::
-   :toctree: generated/
-   :template: function.rst
-
-   tree.plot_tree
-
-.. _utils_ref:
-
-:mod:`sklearn.utils`: Utilities
-===============================
-
-.. automodule:: sklearn.utils
-   :no-members:
-   :no-inherited-members:
-
-**Developer guide:** See the :ref:`developers-utils` page for further details.
-
-.. currentmodule:: sklearn
-
-.. autosummary::
-   :toctree: generated/
-   :template: class.rst
-
-   utils.Bunch
-
-.. autosummary::
-   :toctree: generated/
-   :template: function.rst
-
-   utils.as_float_array
-   utils.assert_all_finite
-   utils.deprecated
-   utils.estimator_html_repr
-   utils.gen_batches
-   utils.gen_even_slices
-   utils.indexable
-   utils.murmurhash3_32
-   utils.resample
-   utils._safe_indexing
-   utils.safe_mask
-   utils.safe_sqr
-   utils.shuffle
-
-Input and parameter validation
-------------------------------
-
-.. automodule:: sklearn.utils.validation
-   :no-members:
-   :no-inherited-members:
-
-.. currentmodule:: sklearn
-
-.. autosummary::
-   :toctree: generated/
-   :template: function.rst
-
-   utils.check_X_y
-   utils.check_array
-   utils.check_scalar
-   utils.check_consistent_length
-   utils.check_random_state
-   utils.validation.check_is_fitted
-   utils.validation.check_memory
-   utils.validation.check_symmetric
-   utils.validation.column_or_1d
-   utils.validation.has_fit_parameter
-
-Utilities used in meta-estimators
----------------------------------
-
-.. automodule:: sklearn.utils.metaestimators
-   :no-members:
-   :no-inherited-members:
-
-.. currentmodule:: sklearn
-
-.. autosummary::
-   :toctree: generated/
-   :template: function.rst
-
-   utils.metaestimators.available_if
-
-Utilities to handle weights based on class labels
--------------------------------------------------
-
-.. automodule:: sklearn.utils.class_weight
-   :no-members:
-   :no-inherited-members:
-
-.. currentmodule:: sklearn
-
-.. autosummary::
-   :toctree: generated/
-   :template: function.rst
-
-   utils.class_weight.compute_class_weight
-   utils.class_weight.compute_sample_weight
-
-Utilities to deal with multiclass target in classifiers
--------------------------------------------------------
-
-.. automodule:: sklearn.utils.multiclass
-   :no-members:
-   :no-inherited-members:
-
-.. currentmodule:: sklearn
-
-.. autosummary::
-   :toctree: generated/
-   :template: function.rst
-
-   utils.multiclass.type_of_target
-   utils.multiclass.is_multilabel
-   utils.multiclass.unique_labels
-
-Utilities for optimal mathematical operations
----------------------------------------------
-
-.. automodule:: sklearn.utils.extmath
-   :no-members:
-   :no-inherited-members:
-
-.. currentmodule:: sklearn
-
-.. autosummary::
-   :toctree: generated/
-   :template: function.rst
-
-   utils.extmath.safe_sparse_dot
-   utils.extmath.randomized_range_finder
-   utils.extmath.randomized_svd
-   utils.extmath.fast_logdet
-   utils.extmath.density
-   utils.extmath.weighted_mode
-
-Utilities to work with sparse matrices and arrays
--------------------------------------------------
-
-.. automodule:: sklearn.utils.sparsefuncs
-   :no-members:
-   :no-inherited-members:
-
-.. currentmodule:: sklearn
-
-.. autosummary::
-   :toctree: generated/
-   :template: function.rst
-
-   utils.sparsefuncs.incr_mean_variance_axis
-   utils.sparsefuncs.inplace_column_scale
-   utils.sparsefuncs.inplace_row_scale
-   utils.sparsefuncs.inplace_swap_row
-   utils.sparsefuncs.inplace_swap_column
-   utils.sparsefuncs.mean_variance_axis
-   utils.sparsefuncs.inplace_csr_column_scale
-
-.. automodule:: sklearn.utils.sparsefuncs_fast
-   :no-members:
-   :no-inherited-members:
-
-.. currentmodule:: sklearn
-
-.. autosummary::
-   :toctree: generated/
-   :template: function.rst
-
-   utils.sparsefuncs_fast.inplace_csr_row_normalize_l1
-   utils.sparsefuncs_fast.inplace_csr_row_normalize_l2
-
-Utilities to work with graphs
------------------------------
-
-.. automodule:: sklearn.utils.graph
-   :no-members:
-   :no-inherited-members:
-
-.. currentmodule:: sklearn
-
-.. autosummary::
-   :toctree: generated/
-   :template: function.rst
-
-   utils.graph.single_source_shortest_path_length
-
-Utilities for random sampling
------------------------------
-
-.. automodule:: sklearn.utils.random
-   :no-members:
-   :no-inherited-members:
-
-.. currentmodule:: sklearn
-
-.. autosummary::
-   :toctree: generated/
-   :template: function.rst
-
-   utils.random.sample_without_replacement
-
-
-Utilities to operate on arrays
-------------------------------
-
-.. automodule:: sklearn.utils.arrayfuncs
-   :no-members:
-   :no-inherited-members:
-
-.. currentmodule:: sklearn
-
-.. autosummary::
-   :toctree: generated/
-   :template: function.rst
-
-   utils.arrayfuncs.min_pos
-
-Metadata routing
-----------------
-
-.. automodule:: sklearn.utils.metadata_routing
-   :no-members:
-   :no-inherited-members:
-
-.. currentmodule:: sklearn
-
-.. autosummary::
-   :toctree: generated/
-   :template: function.rst
-
-   utils.metadata_routing.get_routing_for_object
-   utils.metadata_routing.process_routing
-
-.. autosummary::
-   :toctree: generated/
-   :template: class.rst
-
-   utils.metadata_routing.MetadataRouter
-   utils.metadata_routing.MetadataRequest
-   utils.metadata_routing.MethodMapping
-
-Scikit-learn object discovery
------------------------------
-
-.. automodule:: sklearn.utils.discovery
-   :no-members:
-   :no-inherited-members:
-
-.. currentmodule:: sklearn
-
-.. autosummary::
-   :toctree: generated/
-   :template: function.rst
-
-   utils.discovery.all_estimators
-   utils.discovery.all_displays
-   utils.discovery.all_functions
-
-Scikit-learn compatibility checker
-----------------------------------
-
-.. automodule:: sklearn.utils.estimator_checks
-   :no-members:
-   :no-inherited-members:
-
-.. currentmodule:: sklearn
-
-.. autosummary::
-   :toctree: generated/
-   :template: function.rst
-
-   utils.estimator_checks.check_estimator
-   utils.estimator_checks.parametrize_with_checks
-
-Utilities for parallel computing
---------------------------------
-
-.. automodule:: sklearn.utils.parallel
-   :no-members:
-   :no-inherited-members:
-
-.. currentmodule:: sklearn
-
-.. autosummary::
-   :toctree: generated/
-   :template: function.rst
-
-   utils.parallel.delayed
-   utils.parallel_backend
-   utils.register_parallel_backend
-
-.. autosummary::
-   :toctree: generated/
-   :template: class.rst
-
-   utils.parallel.Parallel
-
-
-Recently deprecated
-===================
diff --git a/doc/modules/classification_threshold.rst b/doc/modules/classification_threshold.rst
new file mode 100644
index 0000000000000..236c0736f7d23
--- /dev/null
+++ b/doc/modules/classification_threshold.rst
@@ -0,0 +1,157 @@
+.. currentmodule:: sklearn.model_selection
+
+.. _TunedThresholdClassifierCV:
+
+==================================================
+Tuning the decision threshold for class prediction
+==================================================
+
+Classification is best divided into two parts:
+
+* the statistical problem of learning a model to predict, ideally, class probabilities;
+* the decision problem to take concrete action based on those probability predictions.
+
+Let's take a straightforward example related to weather forecasting: the first point is
+related to answering "what is the chance that it will rain tomorrow?" while the second
+point is related to answering "should I take an umbrella tomorrow?".
+
+When it comes to the scikit-learn API, the first point is addressed providing scores
+using :term:`predict_proba` or :term:`decision_function`. The former returns conditional
+probability estimates :math:`P(y|X)` for each class, while the latter returns a decision
+score for each class.
+
+The decision corresponding to the labels are obtained with :term:`predict`. In binary
+classification, a decision rule or action is then defined by thresholding the scores,
+leading to the prediction of a single class label for each sample. For binary
+classification in scikit-learn, class labels predictions are obtained by hard-coded
+cut-off rules: a positive class is predicted when the conditional probability
+:math:`P(y|X)` is greater than 0.5 (obtained with :term:`predict_proba`) or if the
+decision score is greater than 0 (obtained with :term:`decision_function`).
+
+Here, we show an example that illustrates the relation between conditional
+probability estimates :math:`P(y|X)` and class labels::
+
+    >>> from sklearn.datasets import make_classification
+    >>> from sklearn.tree import DecisionTreeClassifier
+    >>> X, y = make_classification(random_state=0)
+    >>> classifier = DecisionTreeClassifier(max_depth=2, random_state=0).fit(X, y)
+    >>> classifier.predict_proba(X[:4])
+    array([[0.94     , 0.06     ],
+           [0.94     , 0.06     ],
+           [0.0416..., 0.9583...],
+           [0.0416..., 0.9583...]])
+    >>> classifier.predict(X[:4])
+    array([0, 0, 1, 1])
+
+While these hard-coded rules might at first seem reasonable as default behavior, they
+are most certainly not ideal for most use cases. Let's illustrate with an example.
+
+Consider a scenario where a predictive model is being deployed to assist
+physicians in detecting tumors. In this setting, physicians will most likely be
+interested in identifying all patients with cancer and not missing anyone with cancer so
+that they can provide them with the right treatment. In other words, physicians
+prioritize achieving a high recall rate. This emphasis on recall comes, of course, with
+the trade-off of potentially more false-positive predictions, reducing the precision of
+the model. That is a risk physicians are willing to take because the cost of a missed
+cancer is much higher than the cost of further diagnostic tests. Consequently, when it
+comes to deciding whether to classify a patient as having cancer or not, it may be more
+beneficial to classify them as positive for cancer when the conditional probability
+estimate is much lower than 0.5.
+
+Post-tuning the decision threshold
+==================================
+
+One solution to address the problem stated in the introduction is to tune the decision
+threshold of the classifier once the model has been trained. The
+:class:`~sklearn.model_selection.TunedThresholdClassifierCV` tunes this threshold using
+an internal cross-validation. The optimum threshold is chosen to maximize a given
+metric.
+
+The following image illustrates the tuning of the decision threshold for a gradient
+boosting classifier. While the vanilla and tuned classifiers provide the same
+:term:`predict_proba` outputs and thus the same Receiver Operating Characteristic (ROC)
+and Precision-Recall curves, the class label predictions differ because of the tuned
+decision threshold. The vanilla classifier predicts the class of interest for a
+conditional probability greater than 0.5 while the tuned classifier predicts the class
+of interest for a very low probability (around 0.02). This decision threshold optimizes
+a utility metric defined by the business (in this case an insurance company).
+
+.. figure:: ../auto_examples/model_selection/images/sphx_glr_plot_cost_sensitive_learning_002.png
+   :target: ../auto_examples/model_selection/plot_cost_sensitive_learning.html
+   :align: center
+
+Options to tune the decision threshold
+--------------------------------------
+
+The decision threshold can be tuned through different strategies controlled by the
+parameter `scoring`.
+
+One way to tune the threshold is by maximizing a pre-defined scikit-learn metric. These
+metrics can be found by calling the function :func:`~sklearn.metrics.get_scorer_names`.
+By default, the balanced accuracy is the metric used but be aware that one should choose
+a meaningful metric for their use case.
+
+.. note::
+
+    It is important to notice that these metrics come with default parameters, notably
+    the label of the class of interest (i.e. `pos_label`). Thus, if this label is not
+    the right one for your application, you need to define a scorer and pass the right
+    `pos_label` (and additional parameters) using the
+    :func:`~sklearn.metrics.make_scorer`. Refer to :ref:`scoring` to get
+    information to define your own scoring function. For instance, we show how to pass
+    the information to the scorer that the label of interest is `0` when maximizing the
+    :func:`~sklearn.metrics.f1_score`::
+
+        >>> from sklearn.linear_model import LogisticRegression
+        >>> from sklearn.model_selection import TunedThresholdClassifierCV
+        >>> from sklearn.metrics import make_scorer, f1_score
+        >>> X, y = make_classification(
+        ...   n_samples=1_000, weights=[0.1, 0.9], random_state=0)
+        >>> pos_label = 0
+        >>> scorer = make_scorer(f1_score, pos_label=pos_label)
+        >>> base_model = LogisticRegression()
+        >>> model = TunedThresholdClassifierCV(base_model, scoring=scorer)
+        >>> scorer(model.fit(X, y), X, y)
+        0.88...
+        >>> # compare it with the internal score found by cross-validation
+        >>> model.best_score_
+        0.86...
+
+Important notes regarding the internal cross-validation
+-------------------------------------------------------
+
+By default :class:`~sklearn.model_selection.TunedThresholdClassifierCV` uses a 5-fold
+stratified cross-validation to tune the decision threshold. The parameter `cv` allows to
+control the cross-validation strategy. It is possible to bypass cross-validation by
+setting `cv="prefit"` and providing a fitted classifier. In this case, the decision
+threshold is tuned on the data provided to the `fit` method.
+
+However, you should be extremely careful when using this option. You should never use
+the same data for training the classifier and tuning the decision threshold due to the
+risk of overfitting. Refer to the following example section for more details (cf.
+:ref:`TunedThresholdClassifierCV_no_cv`). If you have limited resources, consider using
+a float number for `cv` to limit to an internal single train-test split.
+
+The option `cv="prefit"` should only be used when the provided classifier was already
+trained, and you just want to find the best decision threshold using a new validation
+set.
+
+.. _FixedThresholdClassifier:
+
+Manually setting the decision threshold
+---------------------------------------
+
+The previous sections discussed strategies to find an optimal decision threshold. It is
+also possible to manually set the decision threshold using the class
+:class:`~sklearn.model_selection.FixedThresholdClassifier`. In case that you don't want
+to refit the model when calling `fit`, you can set the parameter `prefit=True`.
+
+Examples
+--------
+
+- See the example entitled
+  :ref:`sphx_glr_auto_examples_model_selection_plot_tuned_decision_threshold.py`,
+  to get insights on the post-tuning of the decision threshold.
+- See the example entitled
+  :ref:`sphx_glr_auto_examples_model_selection_plot_cost_sensitive_learning.py`,
+  to learn about cost-sensitive learning and decision threshold tuning.
diff --git a/doc/modules/clustering.rst b/doc/modules/clustering.rst
index 1d9fa51b6c834..2de39d0317bf5 100644
--- a/doc/modules/clustering.rst
+++ b/doc/modules/clustering.rst
@@ -241,13 +241,13 @@ K-means can be used for vector quantization. This is achieved using the
 performing vector quantization on an image refer to
 :ref:`sphx_glr_auto_examples_cluster_plot_color_quantization.py`.
 
-.. topic:: Examples:
+.. rubric:: Examples
 
- * :ref:`sphx_glr_auto_examples_cluster_plot_cluster_iris.py`: Example usage of
-   :class:`KMeans` using the iris dataset
+* :ref:`sphx_glr_auto_examples_cluster_plot_cluster_iris.py`: Example usage of
+  :class:`KMeans` using the iris dataset
 
- * :ref:`sphx_glr_auto_examples_text_plot_document_clustering.py`: Document clustering
-   using :class:`KMeans` and :class:`MiniBatchKMeans` based on sparse data
+* :ref:`sphx_glr_auto_examples_text_plot_document_clustering.py`: Document clustering
+  using :class:`KMeans` and :class:`MiniBatchKMeans` based on sparse data
 
 Low-level parallelism
 ---------------------
@@ -257,24 +257,20 @@ chunks of data (256 samples) are processed in parallel, which in addition
 yields a low memory footprint. For more details on how to control the number of
 threads, please refer to our :ref:`parallelism` notes.
 
-.. topic:: Examples:
+.. rubric:: Examples
 
-  * :ref:`sphx_glr_auto_examples_cluster_plot_kmeans_assumptions.py`: Demonstrating
-    when k-means performs intuitively and when it does not
-  * :ref:`sphx_glr_auto_examples_cluster_plot_kmeans_digits.py`: Clustering
-    handwritten digits
+* :ref:`sphx_glr_auto_examples_cluster_plot_kmeans_assumptions.py`: Demonstrating when
+  k-means performs intuitively and when it does not
+* :ref:`sphx_glr_auto_examples_cluster_plot_kmeans_digits.py`: Clustering handwritten digits
 
+.. dropdown:: References
 
-|details-start|
-**References**
-|details-split|
+  * `"k-means++: The advantages of careful seeding"
+    <http://ilpubs.stanford.edu:8090/778/1/2006-13.pdf>`_
+    Arthur, David, and Sergei Vassilvitskii,
+    *Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete
+    algorithms*, Society for Industrial and Applied Mathematics (2007)
 
-* `"k-means++: The advantages of careful seeding"
-  <http://ilpubs.stanford.edu:8090/778/1/2006-13.pdf>`_ Arthur, David, and
-  Sergei Vassilvitskii, *Proceedings of the eighteenth annual ACM-SIAM symposium
-  on Discrete algorithms*, Society for Industrial and Applied Mathematics (2007)
-
-|details-end|
 
 .. _mini_batch_kmeans:
 
@@ -310,24 +306,22 @@ small, as shown in the example and cited reference.
    :scale: 100
 
 
-.. topic:: Examples:
+.. rubric:: Examples
 
- * :ref:`sphx_glr_auto_examples_cluster_plot_mini_batch_kmeans.py`: Comparison of
-   :class:`KMeans` and :class:`MiniBatchKMeans`
+* :ref:`sphx_glr_auto_examples_cluster_plot_mini_batch_kmeans.py`: Comparison of
+  :class:`KMeans` and :class:`MiniBatchKMeans`
 
- * :ref:`sphx_glr_auto_examples_text_plot_document_clustering.py`: Document clustering
-   using :class:`KMeans` and :class:`MiniBatchKMeans` based on sparse data
+* :ref:`sphx_glr_auto_examples_text_plot_document_clustering.py`: Document clustering
+  using :class:`KMeans` and :class:`MiniBatchKMeans` based on sparse data
 
-|details-start|
-**References**
-|details-split|
+* :ref:`sphx_glr_auto_examples_cluster_plot_dict_face_patches.py`
 
-* `"Web Scale K-Means clustering"
-  <https://www.eecs.tufts.edu/~dsculley/papers/fastkmeans.pdf>`_
-  D. Sculley, *Proceedings of the 19th international conference on World
-  wide web* (2010)
+.. dropdown:: References
 
-|details-end|
+  * `"Web Scale K-Means clustering"
+    <https://www.eecs.tufts.edu/~dsculley/papers/fastkmeans.pdf>`_
+    D. Sculley, *Proceedings of the 19th international conference on World
+    wide web* (2010)
 
 .. _affinity_propagation:
 
@@ -364,55 +358,50 @@ convergence. Further, the memory complexity is of the order
 sparse similarity matrix is used. This makes Affinity Propagation most
 appropriate for small to medium sized datasets.
 
-|details-start|
-**Algorithm description**
-|details-split|
-
-The messages sent between points belong to one of two categories. The first is
-the responsibility :math:`r(i, k)`, which is the accumulated evidence that
-sample :math:`k` should be the exemplar for sample :math:`i`. The second is the
-availability :math:`a(i, k)` which is the accumulated evidence that sample
-:math:`i` should choose sample :math:`k` to be its exemplar, and considers the
-values for all other samples that :math:`k` should be an exemplar. In this way,
-exemplars are chosen by samples if they are (1) similar enough to many samples
-and (2) chosen by many samples to be representative of themselves.
+.. dropdown:: Algorithm description
 
-More formally, the responsibility of a sample :math:`k` to be the exemplar of
-sample :math:`i` is given by:
+  The messages sent between points belong to one of two categories. The first is
+  the responsibility :math:`r(i, k)`, which is the accumulated evidence that
+  sample :math:`k` should be the exemplar for sample :math:`i`. The second is the
+  availability :math:`a(i, k)` which is the accumulated evidence that sample
+  :math:`i` should choose sample :math:`k` to be its exemplar, and considers the
+  values for all other samples that :math:`k` should be an exemplar. In this way,
+  exemplars are chosen by samples if they are (1) similar enough to many samples
+  and (2) chosen by many samples to be representative of themselves.
 
-.. math::
+  More formally, the responsibility of a sample :math:`k` to be the exemplar of
+  sample :math:`i` is given by:
 
-    r(i, k) \leftarrow s(i, k) - max [ a(i, k') + s(i, k') \forall k' \neq k ]
+  .. math::
 
-Where :math:`s(i, k)` is the similarity between samples :math:`i` and :math:`k`.
-The availability of sample :math:`k` to be the exemplar of sample :math:`i` is
-given by:
-
-.. math::
+      r(i, k) \leftarrow s(i, k) - max [ a(i, k') + s(i, k') \forall k' \neq k ]
 
-    a(i, k) \leftarrow min [0, r(k, k) + \sum_{i'~s.t.~i' \notin \{i, k\}}{r(i',
-    k)}]
+  Where :math:`s(i, k)` is the similarity between samples :math:`i` and :math:`k`.
+  The availability of sample :math:`k` to be the exemplar of sample :math:`i` is
+  given by:
 
-To begin with, all values for :math:`r` and :math:`a` are set to zero, and the
-calculation of each iterates until convergence. As discussed above, in order to
-avoid numerical oscillations when updating the messages, the damping factor
-:math:`\lambda` is introduced to iteration process:
+  .. math::
 
-.. math:: r_{t+1}(i, k) = \lambda\cdot r_{t}(i, k) + (1-\lambda)\cdot r_{t+1}(i, k)
-.. math:: a_{t+1}(i, k) = \lambda\cdot a_{t}(i, k) + (1-\lambda)\cdot a_{t+1}(i, k)
+      a(i, k) \leftarrow min [0, r(k, k) + \sum_{i'~s.t.~i' \notin \{i, k\}}{r(i',
+      k)}]
 
-where :math:`t` indicates the iteration times.
+  To begin with, all values for :math:`r` and :math:`a` are set to zero, and the
+  calculation of each iterates until convergence. As discussed above, in order to
+  avoid numerical oscillations when updating the messages, the damping factor
+  :math:`\lambda` is introduced to iteration process:
 
-|details-end|
+  .. math:: r_{t+1}(i, k) = \lambda\cdot r_{t}(i, k) + (1-\lambda)\cdot r_{t+1}(i, k)
+  .. math:: a_{t+1}(i, k) = \lambda\cdot a_{t}(i, k) + (1-\lambda)\cdot a_{t+1}(i, k)
 
+  where :math:`t` indicates the iteration times.
 
-.. topic:: Examples:
 
-  * :ref:`sphx_glr_auto_examples_cluster_plot_affinity_propagation.py`: Affinity
-    Propagation on a synthetic 2D datasets with 3 classes.
+.. rubric:: Examples
 
-  * :ref:`sphx_glr_auto_examples_applications_plot_stock_market.py` Affinity
-    Propagation on Financial time series to find groups of companies
+* :ref:`sphx_glr_auto_examples_cluster_plot_affinity_propagation.py`: Affinity
+  Propagation on a synthetic 2D datasets with 3 classes
+* :ref:`sphx_glr_auto_examples_applications_plot_stock_market.py` Affinity Propagation
+  on financial time series to find groups of companies
 
 
 .. _mean_shift:
@@ -425,43 +414,40 @@ for centroids to be the mean of the points within a given region. These
 candidates are then filtered in a post-processing stage to eliminate
 near-duplicates to form the final set of centroids.
 
-|details-start|
-**Mathematical details**
-|details-split|
+.. dropdown:: Mathematical details
 
-The position of centroid candidates is iteratively adjusted using a technique
-called hill climbing, which finds local maxima of the estimated probability
-density. Given a candidate centroid :math:`x` for iteration :math:`t`, the
-candidate is updated according to the following equation:
+  The position of centroid candidates is iteratively adjusted using a technique
+  called hill climbing, which finds local maxima of the estimated probability
+  density. Given a candidate centroid :math:`x` for iteration :math:`t`, the
+  candidate is updated according to the following equation:
 
-.. math::
+  .. math::
 
-    x^{t+1} = x^t + m(x^t)
+      x^{t+1} = x^t + m(x^t)
 
-Where :math:`m` is the *mean shift* vector that is computed for each centroid
-that points towards a region of the maximum increase in the density of points.
-To compute :math:`m` we define :math:`N(x)` as the neighborhood of samples
-within a given distance around :math:`x`. Then :math:`m` is computed using the
-following equation, effectively updating a centroid to be the mean of the
-samples within its neighborhood:
+  Where :math:`m` is the *mean shift* vector that is computed for each centroid
+  that points towards a region of the maximum increase in the density of points.
+  To compute :math:`m` we define :math:`N(x)` as the neighborhood of samples
+  within a given distance around :math:`x`. Then :math:`m` is computed using the
+  following equation, effectively updating a centroid to be the mean of the
+  samples within its neighborhood:
 
-.. math::
+  .. math::
 
-    m(x) = \frac{1}{|N(x)|} \sum_{x_j \in N(x)}x_j - x
+      m(x) = \frac{1}{|N(x)|} \sum_{x_j \in N(x)}x_j - x
 
-In general, the equation for :math:`m` depends on a kernel used for density
-estimation. The generic formula is:
+  In general, the equation for :math:`m` depends on a kernel used for density
+  estimation. The generic formula is:
 
-.. math::
+  .. math::
 
-    m(x) = \frac{\sum_{x_j \in N(x)}K(x_j - x)x_j}{\sum_{x_j \in N(x)}K(x_j -
-    x)} - x
+      m(x) = \frac{\sum_{x_j \in N(x)}K(x_j - x)x_j}{\sum_{x_j \in N(x)}K(x_j -
+      x)} - x
 
-In our implementation, :math:`K(x)` is equal to 1 if :math:`x` is small enough
-and is equal to 0 otherwise. Effectively :math:`K(y - x)` indicates whether
-:math:`y` is in the neighborhood of :math:`x`.
+  In our implementation, :math:`K(x)` is equal to 1 if :math:`x` is small enough
+  and is equal to 0 otherwise. Effectively :math:`K(y - x)` indicates whether
+  :math:`y` is in the neighborhood of :math:`x`.
 
-|details-end|
 
 The algorithm automatically sets the number of clusters, instead of relying on a
 parameter ``bandwidth``, which dictates the size of the region to search through.
@@ -483,21 +469,17 @@ given sample.
    :scale: 50
 
 
-.. topic:: Examples:
+.. rubric:: Examples
 
-  * :ref:`sphx_glr_auto_examples_cluster_plot_mean_shift.py`: Mean Shift
-    clustering on a synthetic 2D datasets with 3 classes.
+* :ref:`sphx_glr_auto_examples_cluster_plot_mean_shift.py`: Mean Shift clustering
+  on a synthetic 2D datasets with 3 classes.
 
+.. dropdown:: References
 
-|details-start|
-**References**
-|details-split|
+  * :doi:`"Mean shift: A robust approach toward feature space analysis"
+    <10.1109/34.1000236>` D. Comaniciu and P. Meer, *IEEE Transactions on Pattern
+    Analysis and Machine Intelligence* (2002)
 
-* :doi:`"Mean shift: A robust approach toward feature space analysis"
-  <10.1109/34.1000236>` D. Comaniciu and P. Meer, *IEEE Transactions on Pattern
-  Analysis and Machine Intelligence* (2002)
-
-|details-end|
 
 .. _spectral_clustering:
 
@@ -547,13 +529,13 @@ computed using a function of a gradient of the image.
 
     See the examples for such an application.
 
-.. topic:: Examples:
+.. rubric:: Examples
 
-  * :ref:`sphx_glr_auto_examples_cluster_plot_segmentation_toy.py`: Segmenting
-    objects from a noisy background using spectral clustering.
+* :ref:`sphx_glr_auto_examples_cluster_plot_segmentation_toy.py`: Segmenting objects
+  from a noisy background using spectral clustering.
+* :ref:`sphx_glr_auto_examples_cluster_plot_coin_segmentation.py`: Spectral clustering
+  to split the image of coins in regions.
 
-  * :ref:`sphx_glr_auto_examples_cluster_plot_coin_segmentation.py`: Spectral
-    clustering to split the image of coins in regions.
 
 .. |coin_kmeans| image:: ../auto_examples/cluster/images/sphx_glr_plot_coin_segmentation_001.png
   :target: ../auto_examples/cluster/plot_coin_segmentation.html
@@ -588,18 +570,15 @@ below.
 |coin_kmeans|                          |coin_discretize|                  |coin_cluster_qr|
 ================================  ================================  ================================
 
-|details-start|
-**References**
-|details-split|
+.. dropdown:: References
 
-* `"Multiclass spectral clustering"
-  <https://people.eecs.berkeley.edu/~jordan/courses/281B-spring04/readings/yu-shi.pdf>`_
-  Stella X. Yu, Jianbo Shi, 2003
+  * `"Multiclass spectral clustering"
+    <https://people.eecs.berkeley.edu/~jordan/courses/281B-spring04/readings/yu-shi.pdf>`_
+    Stella X. Yu, Jianbo Shi, 2003
 
-* :doi:`"Simple, direct, and efficient multi-way spectral clustering"<10.1093/imaiai/iay008>`
-  Anil Damle, Victor Minden, Lexing Ying, 2019
+  * :doi:`"Simple, direct, and efficient multi-way spectral clustering"<10.1093/imaiai/iay008>`
+    Anil Damle, Victor Minden, Lexing Ying, 2019
 
-|details-end|
 
 .. _spectral_clustering_graph:
 
@@ -615,28 +594,25 @@ graph, and SpectralClustering is initialized with `affinity='precomputed'`::
     ...                         assign_labels='discretize')
     >>> sc.fit_predict(adjacency_matrix)  # doctest: +SKIP
 
-|details-start|
-**References**
-|details-split|
+.. dropdown:: References
 
-* :doi:`"A Tutorial on Spectral Clustering" <10.1007/s11222-007-9033-z>` Ulrike
-  von Luxburg, 2007
+  * :doi:`"A Tutorial on Spectral Clustering" <10.1007/s11222-007-9033-z>` Ulrike
+    von Luxburg, 2007
 
-* :doi:`"Normalized cuts and image segmentation" <10.1109/34.868688>` Jianbo
-  Shi, Jitendra Malik, 2000
+  * :doi:`"Normalized cuts and image segmentation" <10.1109/34.868688>` Jianbo
+    Shi, Jitendra Malik, 2000
 
-* `"A Random Walks View of Spectral Segmentation"
-  <https://citeseerx.ist.psu.edu/doc_view/pid/84a86a69315e994cfd1e0c7debb86d62d7bd1f44>`_
-  Marina Meila, Jianbo Shi, 2001
+  * `"A Random Walks View of Spectral Segmentation"
+    <https://citeseerx.ist.psu.edu/doc_view/pid/84a86a69315e994cfd1e0c7debb86d62d7bd1f44>`_
+    Marina Meila, Jianbo Shi, 2001
 
-* `"On Spectral Clustering: Analysis and an algorithm"
-  <https://citeseerx.ist.psu.edu/doc_view/pid/796c5d6336fc52aa84db575fb821c78918b65f58>`_
-  Andrew Y. Ng, Michael I. Jordan, Yair Weiss, 2001
+  * `"On Spectral Clustering: Analysis and an algorithm"
+    <https://citeseerx.ist.psu.edu/doc_view/pid/796c5d6336fc52aa84db575fb821c78918b65f58>`_
+    Andrew Y. Ng, Michael I. Jordan, Yair Weiss, 2001
 
-* :arxiv:`"Preconditioned Spectral Clustering for Stochastic Block Partition
-  Streaming Graph Challenge" <1708.07481>` David Zhuzhunashvili, Andrew Knyazev
+  * :arxiv:`"Preconditioned Spectral Clustering for Stochastic Block Partition
+    Streaming Graph Challenge" <1708.07481>` David Zhuzhunashvili, Andrew Knyazev
 
-|details-end|
 
 .. _hierarchical_clustering:
 
@@ -697,10 +673,13 @@ while not robust to noisy data, can be computed very efficiently and can
 therefore be useful to provide hierarchical clustering of larger datasets.
 Single linkage can also perform well on non-globular data.
 
-.. topic:: Examples:
+.. rubric:: Examples
+
+* :ref:`sphx_glr_auto_examples_cluster_plot_digits_linkage.py`: exploration of the
+  different linkage strategies in a real dataset.
 
-  * :ref:`sphx_glr_auto_examples_cluster_plot_digits_linkage.py`: exploration of
-    the different linkage strategies in a real dataset.
+  * :ref:`sphx_glr_auto_examples_cluster_plot_linkage_comparison.py`: exploration of
+    the different linkage strategies in toy datasets.
 
 
 Visualization of cluster hierarchy
@@ -714,6 +693,10 @@ of the data, though more so in the case of small sample sizes.
     :target: ../auto_examples/cluster/plot_agglomerative_dendrogram.html
     :scale: 42
 
+.. rubric:: Examples
+
+* :ref:`sphx_glr_auto_examples_cluster_plot_agglomerative_dendrogram.py`
+
 
 Adding connectivity constraints
 -------------------------------
@@ -781,20 +764,20 @@ enable only merging of neighboring pixels on an image, as in the
     :target: ../auto_examples/cluster/plot_agglomerative_clustering.html
     :scale: 38
 
-.. topic:: Examples:
+.. rubric:: Examples
 
-  * :ref:`sphx_glr_auto_examples_cluster_plot_coin_ward_segmentation.py`: Ward
-    clustering to split the image of coins in regions.
+* :ref:`sphx_glr_auto_examples_cluster_plot_coin_ward_segmentation.py`: Ward
+  clustering to split the image of coins in regions.
 
-  * :ref:`sphx_glr_auto_examples_cluster_plot_ward_structured_vs_unstructured.py`: Example
-    of Ward algorithm on a swiss-roll, comparison of structured approaches
-    versus unstructured approaches.
+* :ref:`sphx_glr_auto_examples_cluster_plot_ward_structured_vs_unstructured.py`: Example
+  of Ward algorithm on a swiss-roll, comparison of structured approaches
+  versus unstructured approaches.
 
-  * :ref:`sphx_glr_auto_examples_cluster_plot_feature_agglomeration_vs_univariate_selection.py`: Example
-    of dimensionality reduction with feature agglomeration based on Ward
-    hierarchical clustering.
+* :ref:`sphx_glr_auto_examples_cluster_plot_feature_agglomeration_vs_univariate_selection.py`: Example
+  of dimensionality reduction with feature agglomeration based on Ward
+  hierarchical clustering.
 
-  * :ref:`sphx_glr_auto_examples_cluster_plot_agglomerative_clustering.py`
+* :ref:`sphx_glr_auto_examples_cluster_plot_agglomerative_clustering.py`
 
 
 Varying the metric
@@ -828,9 +811,9 @@ each class.
     :target: ../auto_examples/cluster/plot_agglomerative_clustering_metrics.html
     :scale: 32
 
-.. topic:: Examples:
+.. rubric:: Examples
 
-  * :ref:`sphx_glr_auto_examples_cluster_plot_agglomerative_clustering_metrics.py`
+* :ref:`sphx_glr_auto_examples_cluster_plot_agglomerative_clustering_metrics.py`
 
 
 Bisecting K-Means
@@ -874,26 +857,23 @@ Difference between Bisecting K-Means and regular K-Means can be seen on example
 While the regular K-Means algorithm tends to create non-related clusters,
 clusters from Bisecting K-Means are well ordered and create quite a visible hierarchy.
 
-|details-start|
-**References**
-|details-split|
-
-* `"A Comparison of Document Clustering Techniques"
-  <http://www.philippe-fournier-viger.com/spmf/bisectingkmeans.pdf>`_ Michael
-  Steinbach, George Karypis and Vipin Kumar, Department of Computer Science and
-  Egineering, University of Minnesota (June 2000)
-* `"Performance Analysis of K-Means and Bisecting K-Means Algorithms in Weblog
-  Data"
-  <https://ijeter.everscience.org/Manuscripts/Volume-4/Issue-8/Vol-4-issue-8-M-23.pdf>`_
-  K.Abirami and Dr.P.Mayilvahanan, International Journal of Emerging
-  Technologies in Engineering Research (IJETER) Volume 4, Issue 8, (August 2016)
-* `"Bisecting K-means Algorithm Based on K-valued Self-determining and
-  Clustering Center Optimization"
-  <http://www.jcomputers.us/vol13/jcp1306-01.pdf>`_ Jian Di, Xinyue Gou School
-  of Control and Computer Engineering,North China Electric Power University,
-  Baoding, Hebei, China (August 2017)
-
-|details-end|
+.. dropdown:: References
+
+  * `"A Comparison of Document Clustering Techniques"
+    <http://www.philippe-fournier-viger.com/spmf/bisectingkmeans.pdf>`_ Michael
+    Steinbach, George Karypis and Vipin Kumar, Department of Computer Science and
+    Egineering, University of Minnesota (June 2000)
+  * `"Performance Analysis of K-Means and Bisecting K-Means Algorithms in Weblog
+    Data"
+    <https://ijeter.everscience.org/Manuscripts/Volume-4/Issue-8/Vol-4-issue-8-M-23.pdf>`_
+    K.Abirami and Dr.P.Mayilvahanan, International Journal of Emerging
+    Technologies in Engineering Research (IJETER) Volume 4, Issue 8, (August 2016)
+  * `"Bisecting K-means Algorithm Based on K-valued Self-determining and
+    Clustering Center Optimization"
+    <http://www.jcomputers.us/vol13/jcp1306-01.pdf>`_ Jian Di, Xinyue Gou School
+    of Control and Computer Engineering,North China Electric Power University,
+    Baoding, Hebei, China (August 2017)
+
 
 .. _dbscan:
 
@@ -947,79 +927,68 @@ samples that are still part of a cluster. Moreover, the outliers are indicated
 by black points below.
 
 .. |dbscan_results| image:: ../auto_examples/cluster/images/sphx_glr_plot_dbscan_002.png
-        :target: ../auto_examples/cluster/plot_dbscan.html
-        :scale: 50
+    :target: ../auto_examples/cluster/plot_dbscan.html
+    :scale: 50
 
 .. centered:: |dbscan_results|
 
-.. topic:: Examples:
-
-    * :ref:`sphx_glr_auto_examples_cluster_plot_dbscan.py`
+.. rubric:: Examples
 
-|details-start|
-**Implementation**
-|details-split|
+* :ref:`sphx_glr_auto_examples_cluster_plot_dbscan.py`
 
-The DBSCAN algorithm is deterministic, always generating the same clusters when
-given the same data in the same order.  However, the results can differ when
-data is provided in a different order. First, even though the core samples will
-always be assigned to the same clusters, the labels of those clusters will
-depend on the order in which those samples are encountered in the data. Second
-and more importantly, the clusters to which non-core samples are assigned can
-differ depending on the data order.  This would happen when a non-core sample
-has a distance lower than ``eps`` to two core samples in different clusters. By
-the triangular inequality, those two core samples must be more distant than
-``eps`` from each other, or they would be in the same cluster. The non-core
-sample is assigned to whichever cluster is generated first in a pass through the
-data, and so the results will depend on the data ordering.
+.. dropdown:: Implementation
 
-The current implementation uses ball trees and kd-trees to determine the
-neighborhood of points, which avoids calculating the full distance matrix (as
-was done in scikit-learn versions before 0.14). The possibility to use custom
-metrics is retained; for details, see :class:`NearestNeighbors`.
+  The DBSCAN algorithm is deterministic, always generating the same clusters when
+  given the same data in the same order.  However, the results can differ when
+  data is provided in a different order. First, even though the core samples will
+  always be assigned to the same clusters, the labels of those clusters will
+  depend on the order in which those samples are encountered in the data. Second
+  and more importantly, the clusters to which non-core samples are assigned can
+  differ depending on the data order.  This would happen when a non-core sample
+  has a distance lower than ``eps`` to two core samples in different clusters. By
+  the triangular inequality, those two core samples must be more distant than
+  ``eps`` from each other, or they would be in the same cluster. The non-core
+  sample is assigned to whichever cluster is generated first in a pass through the
+  data, and so the results will depend on the data ordering.
 
-|details-end|
+  The current implementation uses ball trees and kd-trees to determine the
+  neighborhood of points, which avoids calculating the full distance matrix (as
+  was done in scikit-learn versions before 0.14). The possibility to use custom
+  metrics is retained; for details, see :class:`NearestNeighbors`.
 
-|details-start|
-**Memory consumption for large sample sizes**
-|details-split|
+.. dropdown:: Memory consumption for large sample sizes
 
-This implementation is by default not memory efficient because it constructs a
-full pairwise similarity matrix in the case where kd-trees or ball-trees cannot
-be used (e.g., with sparse matrices). This matrix will consume :math:`n^2`
-floats. A couple of mechanisms for getting around this are:
+  This implementation is by default not memory efficient because it constructs a
+  full pairwise similarity matrix in the case where kd-trees or ball-trees cannot
+  be used (e.g., with sparse matrices). This matrix will consume :math:`n^2`
+  floats. A couple of mechanisms for getting around this are:
 
-- Use :ref:`OPTICS <optics>` clustering in conjunction with the `extract_dbscan`
-  method. OPTICS clustering also calculates the full pairwise matrix, but only
-  keeps one row in memory at a time (memory complexity n).
+  - Use :ref:`OPTICS <optics>` clustering in conjunction with the `extract_dbscan`
+    method. OPTICS clustering also calculates the full pairwise matrix, but only
+    keeps one row in memory at a time (memory complexity n).
 
-- A sparse radius neighborhood graph (where missing entries are presumed to be
-  out of eps) can be precomputed in a memory-efficient way and dbscan can be run
-  over this with ``metric='precomputed'``.  See
-  :meth:`sklearn.neighbors.NearestNeighbors.radius_neighbors_graph`.
+  - A sparse radius neighborhood graph (where missing entries are presumed to be
+    out of eps) can be precomputed in a memory-efficient way and dbscan can be run
+    over this with ``metric='precomputed'``.  See
+    :meth:`sklearn.neighbors.NearestNeighbors.radius_neighbors_graph`.
 
-- The dataset can be compressed, either by removing exact duplicates if these
-  occur in your data, or by using BIRCH. Then you only have a relatively small
-  number of representatives for a large number of points. You can then provide a
-  ``sample_weight`` when fitting DBSCAN.
+  - The dataset can be compressed, either by removing exact duplicates if these
+    occur in your data, or by using BIRCH. Then you only have a relatively small
+    number of representatives for a large number of points. You can then provide a
+    ``sample_weight`` when fitting DBSCAN.
 
-|details-end|
-
-|details-start|
-**References**
-|details-split|
+.. dropdown:: References
 
 * `A Density-Based Algorithm for Discovering Clusters in Large Spatial
   Databases with Noise <https://www.aaai.org/Papers/KDD/1996/KDD96-037.pdf>`_
   Ester, M., H. P. Kriegel, J. Sander, and X. Xu, In Proceedings of the 2nd
   International Conference on Knowledge Discovery and Data Mining, Portland, OR,
-  AAAI Press, pp. 226–231. 1996
+  AAAI Press, pp. 226-231. 1996
 
 * :doi:`DBSCAN revisited, revisited: why and how you should (still) use DBSCAN.
   <10.1145/3068335>` Schubert, E., Sander, J., Ester, M., Kriegel, H. P., & Xu,
   X. (2017). In ACM Transactions on Database Systems (TODS), 42(3), 19.
 
-|details-end|
 
 .. _hdbscan:
 
@@ -1039,10 +1008,10 @@ scales by building an alternative representation of the clustering problem.
   This implementation is adapted from the original implementation of HDBSCAN,
   `scikit-learn-contrib/hdbscan <https://github.com/scikit-learn-contrib/hdbscan>`_ based on [LJ2017]_.
 
-.. topic:: Examples:
+.. rubric:: Examples
+
+* :ref:`sphx_glr_auto_examples_cluster_plot_hdbscan.py`
 
-    * :ref:`sphx_glr_auto_examples_cluster_plot_hdbscan.py`
-  
 Mutual Reachability Graph
 -------------------------
 
@@ -1102,11 +1071,11 @@ it relies solely on the choice of `min_samples`, which tends to be a more robust
 hyperparameter.
 
 .. |hdbscan_ground_truth| image:: ../auto_examples/cluster/images/sphx_glr_plot_hdbscan_005.png
-        :target: ../auto_examples/cluster/plot_hdbscan.html
-        :scale: 75
+    :target: ../auto_examples/cluster/plot_hdbscan.html
+    :scale: 75
 .. |hdbscan_results| image:: ../auto_examples/cluster/images/sphx_glr_plot_hdbscan_007.png
-        :target: ../auto_examples/cluster/plot_hdbscan.html
-        :scale: 75
+    :target: ../auto_examples/cluster/plot_hdbscan.html
+    :scale: 75
 
 .. centered:: |hdbscan_ground_truth|
 .. centered:: |hdbscan_results|
@@ -1117,19 +1086,19 @@ than `minimum_cluster_size` many samples are considered noise. In practice, one
 can set `minimum_cluster_size = min_samples` to couple the parameters and
 simplify the hyperparameter space.
 
-.. topic:: References:
+.. rubric:: References
 
- .. [CM2013] Campello, R.J.G.B., Moulavi, D., Sander, J. (2013). Density-Based
-   Clustering Based on Hierarchical Density Estimates. In: Pei, J., Tseng, V.S.,
-   Cao, L., Motoda, H., Xu, G. (eds) Advances in Knowledge Discovery and Data
-   Mining. PAKDD 2013. Lecture Notes in Computer Science(), vol 7819. Springer,
-   Berlin, Heidelberg. :doi:`Density-Based Clustering Based on Hierarchical
-   Density Estimates <10.1007/978-3-642-37456-2_14>`
+.. [CM2013] Campello, R.J.G.B., Moulavi, D., Sander, J. (2013). Density-Based
+  Clustering Based on Hierarchical Density Estimates. In: Pei, J., Tseng, V.S.,
+  Cao, L., Motoda, H., Xu, G. (eds) Advances in Knowledge Discovery and Data
+  Mining. PAKDD 2013. Lecture Notes in Computer Science(), vol 7819. Springer,
+  Berlin, Heidelberg. :doi:`Density-Based Clustering Based on Hierarchical
+  Density Estimates <10.1007/978-3-642-37456-2_14>`
 
- .. [LJ2017] L. McInnes and J. Healy, (2017). Accelerated Hierarchical Density
-   Based Clustering. In: IEEE International Conference on Data Mining Workshops
-   (ICDMW), 2017, pp. 33-42. :doi:`Accelerated Hierarchical Density Based
-   Clustering <10.1109/ICDMW.2017.12>`
+.. [LJ2017] L. McInnes and J. Healy, (2017). Accelerated Hierarchical Density
+  Based Clustering. In: IEEE International Conference on Data Mining Workshops
+  (ICDMW), 2017, pp. 33-42. :doi:`Accelerated Hierarchical Density Based
+  Clustering <10.1109/ICDMW.2017.12>`
 
 .. _optics:
 
@@ -1175,58 +1144,48 @@ the linear segment clusters of the reachability plot. Note that the blue and
 red clusters are adjacent in the reachability plot, and can be hierarchically
 represented as children of a larger parent cluster.
 
-.. topic:: Examples:
+.. rubric:: Examples
 
-  * :ref:`sphx_glr_auto_examples_cluster_plot_optics.py`
+* :ref:`sphx_glr_auto_examples_cluster_plot_optics.py`
 
 
-|details-start|
-**Comparison with DBSCAN**
-|details-split|
+.. dropdown:: Comparison with DBSCAN
 
-The results from OPTICS ``cluster_optics_dbscan`` method and DBSCAN are very
-similar, but not always identical; specifically, labeling of periphery and noise
-points. This is in part because the first samples of each dense area processed
-by OPTICS have a large reachability value while being close to other points in
-their area, and will thus sometimes be marked as noise rather than periphery.
-This affects adjacent points when they are considered as candidates for being
-marked as either periphery or noise.
+  The results from OPTICS ``cluster_optics_dbscan`` method and DBSCAN are very
+  similar, but not always identical; specifically, labeling of periphery and noise
+  points. This is in part because the first samples of each dense area processed
+  by OPTICS have a large reachability value while being close to other points in
+  their area, and will thus sometimes be marked as noise rather than periphery.
+  This affects adjacent points when they are considered as candidates for being
+  marked as either periphery or noise.
 
-Note that for any single value of ``eps``, DBSCAN will tend to have a shorter
-run time than OPTICS; however, for repeated runs at varying ``eps`` values, a
-single run of OPTICS may require less cumulative runtime than DBSCAN. It is also
-important to note that OPTICS' output is close to DBSCAN's only if ``eps`` and
-``max_eps`` are close.
+  Note that for any single value of ``eps``, DBSCAN will tend to have a shorter
+  run time than OPTICS; however, for repeated runs at varying ``eps`` values, a
+  single run of OPTICS may require less cumulative runtime than DBSCAN. It is also
+  important to note that OPTICS' output is close to DBSCAN's only if ``eps`` and
+  ``max_eps`` are close.
 
-|details-end|
+.. dropdown:: Computational Complexity
 
-|details-start|
-**Computational Complexity**
-|details-split|
+  Spatial indexing trees are used to avoid calculating the full distance matrix,
+  and allow for efficient memory usage on large sets of samples. Different
+  distance metrics can be supplied via the ``metric`` keyword.
 
-Spatial indexing trees are used to avoid calculating the full distance matrix,
-and allow for efficient memory usage on large sets of samples. Different
-distance metrics can be supplied via the ``metric`` keyword.
+  For large datasets, similar (but not identical) results can be obtained via
+  :class:`HDBSCAN`. The HDBSCAN implementation is multithreaded, and has better
+  algorithmic runtime complexity than OPTICS, at the cost of worse memory scaling.
+  For extremely large datasets that exhaust system memory using HDBSCAN, OPTICS
+  will maintain :math:`n` (as opposed to :math:`n^2`) memory scaling; however,
+  tuning of the ``max_eps`` parameter will likely need to be used to give a
+  solution in a reasonable amount of wall time.
 
-For large datasets, similar (but not identical) results can be obtained via
-:class:`HDBSCAN`. The HDBSCAN implementation is multithreaded, and has better
-algorithmic runtime complexity than OPTICS, at the cost of worse memory scaling.
-For extremely large datasets that exhaust system memory using HDBSCAN, OPTICS
-will maintain :math:`n` (as opposed to :math:`n^2`) memory scaling; however,
-tuning of the ``max_eps`` parameter will likely need to be used to give a
-solution in a reasonable amount of wall time.
 
-|details-end|
+.. dropdown:: References
 
-|details-start|
-**References**
-|details-split|
+  * "OPTICS: ordering points to identify the clustering structure." Ankerst,
+    Mihael, Markus M. Breunig, Hans-Peter Kriegel, and Jörg Sander. In ACM Sigmod
+    Record, vol. 28, no. 2, pp. 49-60. ACM, 1999.
 
-* "OPTICS: ordering points to identify the clustering structure." Ankerst,
-  Mihael, Markus M. Breunig, Hans-Peter Kriegel, and Jörg Sander. In ACM Sigmod
-  Record, vol. 28, no. 2, pp. 49-60. ACM, 1999.
-
-|details-end|
 
 .. _birch:
 
@@ -1262,75 +1221,60 @@ If ``n_clusters`` is set to None, the subclusters from the leaves are directly
 read off, otherwise a global clustering step labels these subclusters into global
 clusters (labels) and the samples are mapped to the global label of the nearest subcluster.
 
-|details-start|
-**Algorithm description**
-|details-split|
-
-- A new sample is inserted into the root of the CF Tree which is a CF Node. It
-  is then merged with the subcluster of the root, that has the smallest radius
-  after merging, constrained by the threshold and branching factor conditions.
-  If the subcluster has any child node, then this is done repeatedly till it
-  reaches a leaf. After finding the nearest subcluster in the leaf, the
-  properties of this subcluster and the parent subclusters are recursively
-  updated.
-
-- If the radius of the subcluster obtained by merging the new sample and the
-  nearest subcluster is greater than the square of the threshold and if the
-  number of subclusters is greater than the branching factor, then a space is
-  temporarily allocated to this new sample. The two farthest subclusters are
-  taken and the subclusters are divided into two groups on the basis of the
-  distance between these subclusters.
-
-- If this split node has a parent subcluster and there is room for a new
-  subcluster, then the parent is split into two. If there is no room, then this
-  node is again split into two and the process is continued recursively, till it
-  reaches the root.
-
-|details-end|
-
-|details-start|
-**BIRCH or MiniBatchKMeans?**
-|details-split|
-
-- BIRCH does not scale very well to high dimensional data. As a rule of thumb if
-  ``n_features`` is greater than twenty, it is generally better to use MiniBatchKMeans.
-- If the number of instances of data needs to be reduced, or if one wants a
-  large number of subclusters either as a preprocessing step or otherwise,
-  BIRCH is more useful than MiniBatchKMeans.
-
-.. image:: ../auto_examples/cluster/images/sphx_glr_plot_birch_vs_minibatchkmeans_001.png
+.. dropdown:: Algorithm description
+
+  - A new sample is inserted into the root of the CF Tree which is a CF Node. It
+    is then merged with the subcluster of the root, that has the smallest radius
+    after merging, constrained by the threshold and branching factor conditions.
+    If the subcluster has any child node, then this is done repeatedly till it
+    reaches a leaf. After finding the nearest subcluster in the leaf, the
+    properties of this subcluster and the parent subclusters are recursively
+    updated.
+
+  - If the radius of the subcluster obtained by merging the new sample and the
+    nearest subcluster is greater than the square of the threshold and if the
+    number of subclusters is greater than the branching factor, then a space is
+    temporarily allocated to this new sample. The two farthest subclusters are
+    taken and the subclusters are divided into two groups on the basis of the
+    distance between these subclusters.
+
+  - If this split node has a parent subcluster and there is room for a new
+    subcluster, then the parent is split into two. If there is no room, then this
+    node is again split into two and the process is continued recursively, till it
+    reaches the root.
+
+.. dropdown:: BIRCH or MiniBatchKMeans?
+
+  - BIRCH does not scale very well to high dimensional data. As a rule of thumb if
+    ``n_features`` is greater than twenty, it is generally better to use MiniBatchKMeans.
+  - If the number of instances of data needs to be reduced, or if one wants a
+    large number of subclusters either as a preprocessing step or otherwise,
+    BIRCH is more useful than MiniBatchKMeans.
+
+  .. image:: ../auto_examples/cluster/images/sphx_glr_plot_birch_vs_minibatchkmeans_001.png
     :target: ../auto_examples/cluster/plot_birch_vs_minibatchkmeans.html
 
-|details-end|
-
-|details-start|
-**How to use partial_fit?**
-|details-split|
-
-To avoid the computation of global clustering, for every call of ``partial_fit``
-the user is advised
+.. dropdown:: How to use partial_fit?
 
-1. To set ``n_clusters=None`` initially
-2. Train all data by multiple calls to partial_fit.
-3. Set ``n_clusters`` to a required value using
-   ``brc.set_params(n_clusters=n_clusters)``.
-4. Call ``partial_fit`` finally with no arguments, i.e. ``brc.partial_fit()``
-   which performs the global clustering.
+  To avoid the computation of global clustering, for every call of ``partial_fit``
+  the user is advised:
 
-|details-end|
+  1. To set ``n_clusters=None`` initially.
+  2. Train all data by multiple calls to partial_fit.
+  3. Set ``n_clusters`` to a required value using
+     ``brc.set_params(n_clusters=n_clusters)``.
+  4. Call ``partial_fit`` finally with no arguments, i.e. ``brc.partial_fit()``
+     which performs the global clustering.
 
-|details-start|
-**References**
-|details-split|
+.. dropdown:: References
 
-* Tian Zhang, Raghu Ramakrishnan, Maron Livny BIRCH: An efficient data
-  clustering method for large databases.
-  https://www.cs.sfu.ca/CourseCentral/459/han/papers/zhang96.pdf
+  * Tian Zhang, Raghu Ramakrishnan, Maron Livny BIRCH: An efficient data
+    clustering method for large databases.
+    https://www.cs.sfu.ca/CourseCentral/459/han/papers/zhang96.pdf
 
-* Roberto Perdisci JBirch - Java implementation of BIRCH clustering algorithm
-  https://code.google.com/archive/p/jbirch
+  * Roberto Perdisci JBirch - Java implementation of BIRCH clustering algorithm
+    https://code.google.com/archive/p/jbirch
 
-|details-end|
 
 
 .. _clustering_evaluation:
@@ -1453,64 +1397,53 @@ will not necessarily be close to zero.::
     ground truth clustering resulting in a high proportion of pair labels that
     agree, which leads subsequently to a high score.
 
-.. topic:: Examples:
-
-  * :ref:`sphx_glr_auto_examples_cluster_plot_adjusted_for_chance_measures.py`:
-    Analysis of the impact of the dataset size on the value of clustering measures
-    for random assignments.
-
+.. rubric:: Examples
 
-|details-start|
-**Mathematical formulation**
-|details-split|
+* :ref:`sphx_glr_auto_examples_cluster_plot_adjusted_for_chance_measures.py`:
+  Analysis of the impact of the dataset size on the value of
+  clustering measures for random assignments.
 
-If C is a ground truth class assignment and K the clustering, let us define
-:math:`a` and :math:`b` as:
+.. dropdown:: Mathematical formulation
 
-- :math:`a`, the number of pairs of elements that are in the same set in C and
-  in the same set in K
+  If C is a ground truth class assignment and K the clustering, let us define
+  :math:`a` and :math:`b` as:
 
-- :math:`b`, the number of pairs of elements that are in different sets in C and
-  in different sets in K
+  - :math:`a`, the number of pairs of elements that are in the same set in C and
+    in the same set in K
 
-The unadjusted Rand index is then given by:
+  - :math:`b`, the number of pairs of elements that are in different sets in C and
+    in different sets in K
 
-.. math:: \text{RI} = \frac{a + b}{C_2^{n_{samples}}}
+  The unadjusted Rand index is then given by:
 
-where :math:`C_2^{n_{samples}}` is the total number of possible pairs in the
-dataset. It does not matter if the calculation is performed on ordered pairs or
-unordered pairs as long as the calculation is performed consistently.
+  .. math:: \text{RI} = \frac{a + b}{C_2^{n_{samples}}}
 
-However, the Rand index does not guarantee that random label assignments will
-get a value close to zero (esp. if the number of clusters is in the same order
-of magnitude as the number of samples).
+  where :math:`C_2^{n_{samples}}` is the total number of possible pairs in the
+  dataset. It does not matter if the calculation is performed on ordered pairs or
+  unordered pairs as long as the calculation is performed consistently.
 
-To counter this effect we can discount the expected RI :math:`E[\text{RI}]` of
-random labelings by defining the adjusted Rand index as follows:
+  However, the Rand index does not guarantee that random label assignments will
+  get a value close to zero (esp. if the number of clusters is in the same order
+  of magnitude as the number of samples).
 
-.. math:: \text{ARI} = \frac{\text{RI} - E[\text{RI}]}{\max(\text{RI}) - E[\text{RI}]}
+  To counter this effect we can discount the expected RI :math:`E[\text{RI}]` of
+  random labelings by defining the adjusted Rand index as follows:
 
-|details-end|
+  .. math:: \text{ARI} = \frac{\text{RI} - E[\text{RI}]}{\max(\text{RI}) - E[\text{RI}]}
 
-|details-start|
-**References**
-|details-split|
+.. dropdown:: References
 
-* `Comparing Partitions
-  <https://link.springer.com/article/10.1007%2FBF01908075>`_ L. Hubert and P.
-  Arabie, Journal of Classification 1985
+  * `Comparing Partitions
+    <https://link.springer.com/article/10.1007%2FBF01908075>`_ L. Hubert and P.
+    Arabie, Journal of Classification 1985
 
-* `Properties of the Hubert-Arabie adjusted Rand index
-  <https://psycnet.apa.org/record/2004-17801-007>`_ D. Steinley, Psychological
-  Methods 2004
+  * `Properties of the Hubert-Arabie adjusted Rand index
+    <https://psycnet.apa.org/record/2004-17801-007>`_ D. Steinley, Psychological
+    Methods 2004
 
-* `Wikipedia entry for the Rand index
-  <https://en.wikipedia.org/wiki/Rand_index>`_
+  * `Wikipedia entry for the Rand index
+    <https://en.wikipedia.org/wiki/Rand_index#Adjusted_Rand_index>`_
 
-* `Wikipedia entry for the adjusted Rand index
-  <https://en.wikipedia.org/wiki/Rand_index#Adjusted_Rand_index>`_
-
-|details-end|
 
 .. _mutual_info_score:
 
@@ -1591,80 +1524,77 @@ Bad (e.g. independent labelings) have non-positive scores::
 
   - NMI and MI are not adjusted against chance.
 
-.. topic:: Examples:
-
-  * :ref:`sphx_glr_auto_examples_cluster_plot_adjusted_for_chance_measures.py`: Analysis
-    of the impact of the dataset size on the value of clustering measures for
-    random assignments. This example also includes the Adjusted Rand Index.
+.. rubric:: Examples
 
+* :ref:`sphx_glr_auto_examples_cluster_plot_adjusted_for_chance_measures.py`: Analysis
+  of the impact of the dataset size on the value of clustering measures for random
+  assignments. This example also includes the Adjusted Rand Index.
 
-|details-start|
-**Mathematical formulation**
-|details-split|
+.. dropdown:: Mathematical formulation
 
-Assume two label assignments (of the same N objects), :math:`U` and :math:`V`.
-Their entropy is the amount of uncertainty for a partition set, defined by:
+  Assume two label assignments (of the same N objects), :math:`U` and :math:`V`.
+  Their entropy is the amount of uncertainty for a partition set, defined by:
 
-.. math:: H(U) = - \sum_{i=1}^{|U|}P(i)\log(P(i))
+  .. math:: H(U) = - \sum_{i=1}^{|U|}P(i)\log(P(i))
 
-where :math:`P(i) = |U_i| / N` is the probability that an object picked at
-random from :math:`U` falls into class :math:`U_i`. Likewise for :math:`V`:
+  where :math:`P(i) = |U_i| / N` is the probability that an object picked at
+  random from :math:`U` falls into class :math:`U_i`. Likewise for :math:`V`:
 
-.. math:: H(V) = - \sum_{j=1}^{|V|}P'(j)\log(P'(j))
+  .. math:: H(V) = - \sum_{j=1}^{|V|}P'(j)\log(P'(j))
 
-With :math:`P'(j) = |V_j| / N`. The mutual information (MI) between :math:`U`
-and :math:`V` is calculated by:
+  With :math:`P'(j) = |V_j| / N`. The mutual information (MI) between :math:`U`
+  and :math:`V` is calculated by:
 
-.. math:: \text{MI}(U, V) = \sum_{i=1}^{|U|}\sum_{j=1}^{|V|}P(i, j)\log\left(\frac{P(i,j)}{P(i)P'(j)}\right)
+  .. math:: \text{MI}(U, V) = \sum_{i=1}^{|U|}\sum_{j=1}^{|V|}P(i, j)\log\left(\frac{P(i,j)}{P(i)P'(j)}\right)
 
-where :math:`P(i, j) = |U_i \cap V_j| / N` is the probability that an object
-picked at random falls into both classes :math:`U_i` and :math:`V_j`.
+  where :math:`P(i, j) = |U_i \cap V_j| / N` is the probability that an object
+  picked at random falls into both classes :math:`U_i` and :math:`V_j`.
 
-It also can be expressed in set cardinality formulation:
+  It also can be expressed in set cardinality formulation:
 
-.. math:: \text{MI}(U, V) = \sum_{i=1}^{|U|} \sum_{j=1}^{|V|} \frac{|U_i \cap V_j|}{N}\log\left(\frac{N|U_i \cap V_j|}{|U_i||V_j|}\right)
+  .. math:: \text{MI}(U, V) = \sum_{i=1}^{|U|} \sum_{j=1}^{|V|} \frac{|U_i \cap V_j|}{N}\log\left(\frac{N|U_i \cap V_j|}{|U_i||V_j|}\right)
 
-The normalized mutual information is defined as
+  The normalized mutual information is defined as
 
-.. math:: \text{NMI}(U, V) = \frac{\text{MI}(U, V)}{\text{mean}(H(U), H(V))}
+  .. math:: \text{NMI}(U, V) = \frac{\text{MI}(U, V)}{\text{mean}(H(U), H(V))}
 
-This value of the mutual information and also the normalized variant is not
-adjusted for chance and will tend to increase as the number of different labels
-(clusters) increases, regardless of the actual amount of "mutual information"
-between the label assignments.
+  This value of the mutual information and also the normalized variant is not
+  adjusted for chance and will tend to increase as the number of different labels
+  (clusters) increases, regardless of the actual amount of "mutual information"
+  between the label assignments.
 
-The expected value for the mutual information can be calculated using the
-following equation [VEB2009]_. In this equation, :math:`a_i = |U_i|` (the number
-of elements in :math:`U_i`) and :math:`b_j = |V_j|` (the number of elements in
-:math:`V_j`).
+  The expected value for the mutual information can be calculated using the
+  following equation [VEB2009]_. In this equation, :math:`a_i = |U_i|` (the number
+  of elements in :math:`U_i`) and :math:`b_j = |V_j|` (the number of elements in
+  :math:`V_j`).
 
-.. math:: E[\text{MI}(U,V)]=\sum_{i=1}^{|U|} \sum_{j=1}^{|V|} \sum_{n_{ij}=(a_i+b_j-N)^+
-  }^{\min(a_i, b_j)} \frac{n_{ij}}{N}\log \left( \frac{ N.n_{ij}}{a_i b_j}\right)
-  \frac{a_i!b_j!(N-a_i)!(N-b_j)!}{N!n_{ij}!(a_i-n_{ij})!(b_j-n_{ij})!
-  (N-a_i-b_j+n_{ij})!}
+  .. math:: E[\text{MI}(U,V)]=\sum_{i=1}^{|U|} \sum_{j=1}^{|V|} \sum_{n_{ij}=(a_i+b_j-N)^+
+    }^{\min(a_i, b_j)} \frac{n_{ij}}{N}\log \left( \frac{ N.n_{ij}}{a_i b_j}\right)
+    \frac{a_i!b_j!(N-a_i)!(N-b_j)!}{N!n_{ij}!(a_i-n_{ij})!(b_j-n_{ij})!
+    (N-a_i-b_j+n_{ij})!}
 
-Using the expected value, the adjusted mutual information can then be calculated
-using a similar form to that of the adjusted Rand index:
+  Using the expected value, the adjusted mutual information can then be calculated
+  using a similar form to that of the adjusted Rand index:
 
-.. math:: \text{AMI} = \frac{\text{MI} - E[\text{MI}]}{\text{mean}(H(U), H(V)) - E[\text{MI}]}
+  .. math:: \text{AMI} = \frac{\text{MI} - E[\text{MI}]}{\text{mean}(H(U), H(V)) - E[\text{MI}]}
 
-For normalized mutual information and adjusted mutual information, the
-normalizing value is typically some *generalized* mean of the entropies of each
-clustering. Various generalized means exist, and no firm rules exist for
-preferring one over the others.  The decision is largely a field-by-field basis;
-for instance, in community detection, the arithmetic mean is most common. Each
-normalizing method provides "qualitatively similar behaviours" [YAT2016]_. In
-our implementation, this is controlled by the ``average_method`` parameter.
+  For normalized mutual information and adjusted mutual information, the
+  normalizing value is typically some *generalized* mean of the entropies of each
+  clustering. Various generalized means exist, and no firm rules exist for
+  preferring one over the others.  The decision is largely a field-by-field basis;
+  for instance, in community detection, the arithmetic mean is most common. Each
+  normalizing method provides "qualitatively similar behaviours" [YAT2016]_. In
+  our implementation, this is controlled by the ``average_method`` parameter.
 
-Vinh et al. (2010) named variants of NMI and AMI by their averaging method
-[VEB2010]_. Their 'sqrt' and 'sum' averages are the geometric and arithmetic
-means; we use these more broadly common names.
+  Vinh et al. (2010) named variants of NMI and AMI by their averaging method
+  [VEB2010]_. Their 'sqrt' and 'sum' averages are the geometric and arithmetic
+  means; we use these more broadly common names.
 
-.. topic:: References:
+  .. rubric:: References
 
-  * Strehl, Alexander, and Joydeep Ghosh (2002). "Cluster ensembles – a
+  * Strehl, Alexander, and Joydeep Ghosh (2002). "Cluster ensembles - a
     knowledge reuse framework for combining multiple partitions". Journal of
-    Machine Learning Research 3: 583–617. `doi:10.1162/153244303321897735
+    Machine Learning Research 3: 583-617. `doi:10.1162/153244303321897735
     <http://strehl.com/download/strehl-jmlr02.pdf>`_.
 
   * `Wikipedia entry for the (normalized) Mutual Information
@@ -1689,7 +1619,6 @@ means; we use these more broadly common names.
     Reports 6: 30750. `doi:10.1038/srep30750
     <https://www.nature.com/articles/srep30750>`_.
 
-|details-end|
 
 .. _homogeneity_completeness:
 
@@ -1807,57 +1736,53 @@ homogeneous but not complete::
     almost never available in practice or requires manual assignment by human
     annotators (as in the supervised learning setting).
 
-.. topic:: Examples:
+.. rubric:: Examples
 
-  * :ref:`sphx_glr_auto_examples_cluster_plot_adjusted_for_chance_measures.py`: Analysis
-    of the impact of the dataset size on the value of clustering measures for
-    random assignments.
+* :ref:`sphx_glr_auto_examples_cluster_plot_adjusted_for_chance_measures.py`: Analysis
+  of the impact of the dataset size on the value of clustering measures for
+  random assignments.
 
+.. dropdown:: Mathematical formulation
 
-|details-start|
-**Mathematical formulation**
-|details-split|
+  Homogeneity and completeness scores are formally given by:
 
-Homogeneity and completeness scores are formally given by:
+  .. math:: h = 1 - \frac{H(C|K)}{H(C)}
 
-.. math:: h = 1 - \frac{H(C|K)}{H(C)}
+  .. math:: c = 1 - \frac{H(K|C)}{H(K)}
 
-.. math:: c = 1 - \frac{H(K|C)}{H(K)}
+  where :math:`H(C|K)` is the **conditional entropy of the classes given the
+  cluster assignments** and is given by:
 
-where :math:`H(C|K)` is the **conditional entropy of the classes given the
-cluster assignments** and is given by:
+  .. math:: H(C|K) = - \sum_{c=1}^{|C|} \sum_{k=1}^{|K|} \frac{n_{c,k}}{n}
+            \cdot \log\left(\frac{n_{c,k}}{n_k}\right)
 
-.. math:: H(C|K) = - \sum_{c=1}^{|C|} \sum_{k=1}^{|K|} \frac{n_{c,k}}{n}
-          \cdot \log\left(\frac{n_{c,k}}{n_k}\right)
+  and :math:`H(C)` is the **entropy of the classes** and is given by:
 
-and :math:`H(C)` is the **entropy of the classes** and is given by:
+  .. math:: H(C) = - \sum_{c=1}^{|C|} \frac{n_c}{n} \cdot \log\left(\frac{n_c}{n}\right)
 
-.. math:: H(C) = - \sum_{c=1}^{|C|} \frac{n_c}{n} \cdot \log\left(\frac{n_c}{n}\right)
+  with :math:`n` the total number of samples, :math:`n_c` and :math:`n_k` the
+  number of samples respectively belonging to class :math:`c` and cluster
+  :math:`k`, and finally :math:`n_{c,k}` the number of samples from class
+  :math:`c` assigned to cluster :math:`k`.
 
-with :math:`n` the total number of samples, :math:`n_c` and :math:`n_k` the
-number of samples respectively belonging to class :math:`c` and cluster
-:math:`k`, and finally :math:`n_{c,k}` the number of samples from class
-:math:`c` assigned to cluster :math:`k`.
+  The **conditional entropy of clusters given class** :math:`H(K|C)` and the
+  **entropy of clusters** :math:`H(K)` are defined in a symmetric manner.
 
-The **conditional entropy of clusters given class** :math:`H(K|C)` and the
-**entropy of clusters** :math:`H(K)` are defined in a symmetric manner.
+  Rosenberg and Hirschberg further define **V-measure** as the **harmonic mean of
+  homogeneity and completeness**:
 
-Rosenberg and Hirschberg further define **V-measure** as the **harmonic mean of
-homogeneity and completeness**:
+  .. math:: v = 2 \cdot \frac{h \cdot c}{h + c}
 
-.. math:: v = 2 \cdot \frac{h \cdot c}{h + c}
+.. rubric:: References
 
-|details-end|
+* `V-Measure: A conditional entropy-based external cluster evaluation measure
+  <https://aclweb.org/anthology/D/D07/D07-1043.pdf>`_ Andrew Rosenberg and Julia
+  Hirschberg, 2007
 
-.. topic:: References:
+.. [B2011] `Identification and Characterization of Events in Social Media
+  <http://www.cs.columbia.edu/~hila/hila-thesis-distributed.pdf>`_, Hila
+  Becker, PhD Thesis.
 
- * `V-Measure: A conditional entropy-based external cluster evaluation measure
-   <https://aclweb.org/anthology/D/D07/D07-1043.pdf>`_ Andrew Rosenberg and Julia
-   Hirschberg, 2007
-
- .. [B2011] `Identification and Characterization of Events in Social Media
-   <http://www.cs.columbia.edu/~hila/hila-thesis-distributed.pdf>`_, Hila
-   Becker, PhD Thesis.
 
 .. _fowlkes_mallows_scores:
 
@@ -1934,19 +1859,15 @@ Bad (e.g. independent labelings) have zero scores::
     manual assignment by human annotators (as in the supervised learning
     setting).
 
-|details-start|
-**References**
-|details-split|
+.. dropdown:: References
 
-* E. B. Fowkles and C. L. Mallows, 1983. "A method for comparing two
-  hierarchical clusterings". Journal of the American Statistical
-  Association.
-  https://www.tandfonline.com/doi/abs/10.1080/01621459.1983.10478008
+  * E. B. Fowkles and C. L. Mallows, 1983. "A method for comparing two
+    hierarchical clusterings". Journal of the American Statistical Association.
+    https://www.tandfonline.com/doi/abs/10.1080/01621459.1983.10478008
 
-* `Wikipedia entry for the Fowlkes-Mallows Index
-  <https://en.wikipedia.org/wiki/Fowlkes-Mallows_index>`_
+  * `Wikipedia entry for the Fowlkes-Mallows Index
+    <https://en.wikipedia.org/wiki/Fowlkes-Mallows_index>`_
 
-|details-end|
 
 .. _silhouette_coefficient:
 
@@ -1990,7 +1911,6 @@ cluster analysis.
   >>> metrics.silhouette_score(X, labels, metric='euclidean')
   0.55...
 
-
 .. topic:: Advantages:
 
   - The score is bounded between -1 for incorrect clustering and +1 for highly
@@ -2005,23 +1925,18 @@ cluster analysis.
     other concepts of clusters, such as density based clusters like those
     obtained through DBSCAN.
 
-.. topic:: Examples:
+.. rubric:: Examples
 
-  * :ref:`sphx_glr_auto_examples_cluster_plot_kmeans_silhouette_analysis.py` : In
-    this example the silhouette analysis is used to choose an optimal value for
-    n_clusters.
+* :ref:`sphx_glr_auto_examples_cluster_plot_kmeans_silhouette_analysis.py` : In
+  this example the silhouette analysis is used to choose an optimal value for
+  n_clusters.
 
+.. dropdown:: References
 
-|details-start|
-**References**
-|details-split|
+  * Peter J. Rousseeuw (1987). :doi:`"Silhouettes: a Graphical Aid to the
+    Interpretation and Validation of Cluster Analysis"<10.1016/0377-0427(87)90125-7>`.
+    Computational and Applied Mathematics 20: 53-65.
 
-* Peter J. Rousseeuw (1987). :doi:`"Silhouettes: a Graphical Aid to the
-  Interpretation and Validation of Cluster
-  Analysis"<10.1016/0377-0427(87)90125-7>` . Computational and Applied
-  Mathematics 20: 53–65.
-
-|details-end|
 
 .. _calinski_harabasz_index:
 
@@ -2067,42 +1982,35 @@ cluster analysis:
     other concepts of clusters, such as density based clusters like those
     obtained through DBSCAN.
 
-|details-start|
-**Mathematical formulation**
-|details-split|
-
-For a set of data :math:`E` of size :math:`n_E` which has been clustered into
-:math:`k` clusters, the Calinski-Harabasz score :math:`s` is defined as the
-ratio of the between-clusters dispersion mean and the within-cluster
-dispersion:
+.. dropdown:: Mathematical formulation
 
-.. math::
-  s = \frac{\mathrm{tr}(B_k)}{\mathrm{tr}(W_k)} \times \frac{n_E - k}{k - 1}
+  For a set of data :math:`E` of size :math:`n_E` which has been clustered into
+  :math:`k` clusters, the Calinski-Harabasz score :math:`s` is defined as the
+  ratio of the between-clusters dispersion mean and the within-cluster
+  dispersion:
 
-where :math:`\mathrm{tr}(B_k)` is trace of the between group dispersion matrix
-and :math:`\mathrm{tr}(W_k)` is the trace of the within-cluster dispersion
-matrix defined by:
+  .. math::
+    s = \frac{\mathrm{tr}(B_k)}{\mathrm{tr}(W_k)} \times \frac{n_E - k}{k - 1}
 
-.. math:: W_k = \sum_{q=1}^k \sum_{x \in C_q} (x - c_q) (x - c_q)^T
+  where :math:`\mathrm{tr}(B_k)` is trace of the between group dispersion matrix
+  and :math:`\mathrm{tr}(W_k)` is the trace of the within-cluster dispersion
+  matrix defined by:
 
-.. math:: B_k = \sum_{q=1}^k n_q (c_q - c_E) (c_q - c_E)^T
+  .. math:: W_k = \sum_{q=1}^k \sum_{x \in C_q} (x - c_q) (x - c_q)^T
 
-with :math:`C_q` the set of points in cluster :math:`q`, :math:`c_q` the
-center of cluster :math:`q`, :math:`c_E` the center of :math:`E`, and
-:math:`n_q` the number of points in cluster :math:`q`.
+  .. math:: B_k = \sum_{q=1}^k n_q (c_q - c_E) (c_q - c_E)^T
 
-|details-end|
+  with :math:`C_q` the set of points in cluster :math:`q`, :math:`c_q` the
+  center of cluster :math:`q`, :math:`c_E` the center of :math:`E`, and
+  :math:`n_q` the number of points in cluster :math:`q`.
 
-|details-start|
-**References**
-|details-split|
+.. dropdown:: References
 
-* Caliński, T., & Harabasz, J. (1974). `"A Dendrite Method for Cluster Analysis"
-  <https://www.researchgate.net/publication/233096619_A_Dendrite_Method_for_Cluster_Analysis>`_.
-  :doi:`Communications in Statistics-theory and Methods 3: 1-27
-  <10.1080/03610927408827101>`.
+  * Caliński, T., & Harabasz, J. (1974). `"A Dendrite Method for Cluster Analysis"
+    <https://www.researchgate.net/publication/233096619_A_Dendrite_Method_for_Cluster_Analysis>`_.
+    :doi:`Communications in Statistics-theory and Methods 3: 1-27
+    <10.1080/03610927408827101>`.
 
-|details-end|
 
 .. _davies-bouldin_index:
 
@@ -2149,49 +2057,41 @@ cluster analysis as follows:
   - The usage of centroid distance limits the distance metric to Euclidean
     space.
 
+.. dropdown:: Mathematical formulation
 
-|details-start|
-**Mathematical formulation**
-|details-split|
+  The index is defined as the average similarity between each cluster :math:`C_i`
+  for :math:`i=1, ..., k` and its most similar one :math:`C_j`. In the context of
+  this index, similarity is defined as a measure :math:`R_{ij}` that trades off:
 
-The index is defined as the average similarity between each cluster :math:`C_i`
-for :math:`i=1, ..., k` and its most similar one :math:`C_j`. In the context of
-this index, similarity is defined as a measure :math:`R_{ij}` that trades off:
+  - :math:`s_i`, the average distance between each point of cluster :math:`i` and
+    the centroid of that cluster -- also know as cluster diameter.
+  - :math:`d_{ij}`, the distance between cluster centroids :math:`i` and
+    :math:`j`.
 
-- :math:`s_i`, the average distance between each point of cluster :math:`i` and
-  the centroid of that cluster -- also know as cluster diameter.
-- :math:`d_{ij}`, the distance between cluster centroids :math:`i` and
-  :math:`j`.
+  A simple choice to construct :math:`R_{ij}` so that it is nonnegative and
+  symmetric is:
 
-A simple choice to construct :math:`R_{ij}` so that it is nonnegative and
-symmetric is:
+  .. math::
+    R_{ij} = \frac{s_i + s_j}{d_{ij}}
 
-.. math::
-  R_{ij} = \frac{s_i + s_j}{d_{ij}}
+  Then the Davies-Bouldin index is defined as:
 
-Then the Davies-Bouldin index is defined as:
+  .. math::
+    DB = \frac{1}{k} \sum_{i=1}^k \max_{i \neq j} R_{ij}
 
-.. math::
-  DB = \frac{1}{k} \sum_{i=1}^k \max_{i \neq j} R_{ij}
+.. dropdown:: References
 
-|details-end|
+  * Davies, David L.; Bouldin, Donald W. (1979). :doi:`"A Cluster Separation
+    Measure" <10.1109/TPAMI.1979.4766909>` IEEE Transactions on Pattern Analysis
+    and Machine Intelligence. PAMI-1 (2): 224-227.
 
-|details-start|
-**References**
-|details-split|
+  * Halkidi, Maria; Batistakis, Yannis; Vazirgiannis, Michalis (2001). :doi:`"On
+    Clustering Validation Techniques" <10.1023/A:1012801612483>` Journal of
+    Intelligent Information Systems, 17(2-3), 107-145.
 
-* Davies, David L.; Bouldin, Donald W. (1979). :doi:`"A Cluster Separation
-  Measure" <10.1109/TPAMI.1979.4766909>` IEEE Transactions on Pattern Analysis
-  and Machine Intelligence. PAMI-1 (2): 224-227.
+  * `Wikipedia entry for Davies-Bouldin index
+    <https://en.wikipedia.org/wiki/Davies-Bouldin_index>`_.
 
-* Halkidi, Maria; Batistakis, Yannis; Vazirgiannis, Michalis (2001). :doi:`"On
-  Clustering Validation Techniques" <10.1023/A:1012801612483>` Journal of
-  Intelligent Information Systems, 17(2-3), 107-145.
-
-* `Wikipedia entry for Davies-Bouldin index
-  <https://en.wikipedia.org/wiki/Davies–Bouldin_index>`_.
-
-|details-end|
 
 .. _contingency_matrix:
 
@@ -2241,15 +2141,11 @@ of classes.
   - It doesn't give a single metric to use as an objective for clustering
     optimisation.
 
+.. dropdown:: References
 
-|details-start|
-**References**
-|details-split|
+  * `Wikipedia entry for contingency matrix
+    <https://en.wikipedia.org/wiki/Contingency_table>`_
 
-* `Wikipedia entry for contingency matrix
-  <https://en.wikipedia.org/wiki/Contingency_table>`_
-
-|details-end|
 
 .. _pair_confusion_matrix:
 
@@ -2327,11 +2223,7 @@ diagonal entries::
    array([[ 0,  0],
           [12,  0]])
 
-|details-start|
-**References**
-|details-split|
-
- * :doi:`"Comparing Partitions" <10.1007/BF01908075>` L. Hubert and P. Arabie,
-   Journal of Classification 1985
+.. dropdown:: References
 
-|details-end|
+  * :doi:`"Comparing Partitions" <10.1007/BF01908075>` L. Hubert and P. Arabie,
+    Journal of Classification 1985
diff --git a/doc/modules/compose.rst b/doc/modules/compose.rst
index 28931cf52f283..655ea551e0375 100644
--- a/doc/modules/compose.rst
+++ b/doc/modules/compose.rst
@@ -79,20 +79,16 @@ is an estimator object::
     >>> pipe
     Pipeline(steps=[('reduce_dim', PCA()), ('clf', SVC())])
 
-|details-start|
-**Shorthand version using :func:`make_pipeline`**
-|details-split|
+.. dropdown:: Shorthand version using :func:`make_pipeline`
 
-The utility function :func:`make_pipeline` is a shorthand
-for constructing pipelines;
-it takes a variable number of estimators and returns a pipeline,
-filling in the names automatically::
+  The utility function :func:`make_pipeline` is a shorthand
+  for constructing pipelines;
+  it takes a variable number of estimators and returns a pipeline,
+  filling in the names automatically::
 
-    >>> from sklearn.pipeline import make_pipeline
-    >>> make_pipeline(PCA(), SVC())
-    Pipeline(steps=[('pca', PCA()), ('svc', SVC())])
-
-|details-end|
+      >>> from sklearn.pipeline import make_pipeline
+      >>> make_pipeline(PCA(), SVC())
+      Pipeline(steps=[('pca', PCA()), ('svc', SVC())])
 
 Access pipeline steps
 .....................
@@ -108,27 +104,23 @@ permitted). This is convenient for performing only some of the transformations
     >>> pipe[-1:]
     Pipeline(steps=[('clf', SVC())])
 
-|details-start|
-**Accessing a step by name or position**
-|details-split|
-
-A specific step can also be accessed by index or name by indexing (with ``[idx]``) the
-pipeline::
+.. dropdown:: Accessing a step by name or position
 
-    >>> pipe.steps[0]
-    ('reduce_dim', PCA())
-    >>> pipe[0]
-    PCA()
-    >>> pipe['reduce_dim']
-    PCA()
+  A specific step can also be accessed by index or name by indexing (with ``[idx]``) the
+  pipeline::
 
-`Pipeline`'s `named_steps` attribute allows accessing steps by name with tab
-completion in interactive environments::
+      >>> pipe.steps[0]
+      ('reduce_dim', PCA())
+      >>> pipe[0]
+      PCA()
+      >>> pipe['reduce_dim']
+      PCA()
 
-    >>> pipe.named_steps.reduce_dim is pipe['reduce_dim']
-    True
+  `Pipeline`'s `named_steps` attribute allows accessing steps by name with tab
+  completion in interactive environments::
 
-|details-end|
+      >>> pipe.named_steps.reduce_dim is pipe['reduce_dim']
+      True
 
 Tracking feature names in a pipeline
 ....................................
@@ -149,17 +141,13 @@ pipeline slicing to get the feature names going into each step::
     >>> pipe[:-1].get_feature_names_out()
     array(['x2', 'x3'], ...)
 
-|details-start|
-**Customize feature names**
-|details-split|
-
-You can also provide custom feature names for the input data using
-``get_feature_names_out``::
+.. dropdown:: Customize feature names
 
-    >>> pipe[:-1].get_feature_names_out(iris.feature_names)
-    array(['petal length (cm)', 'petal width (cm)'], ...)
+  You can also provide custom feature names for the input data using
+  ``get_feature_names_out``::
 
-|details-end|
+      >>> pipe[:-1].get_feature_names_out(iris.feature_names)
+      array(['petal length (cm)', 'petal width (cm)'], ...)
 
 .. _pipeline_nested_parameters:
 
@@ -175,40 +163,37 @@ syntax::
     >>> pipe.set_params(clf__C=10)
     Pipeline(steps=[('reduce_dim', PCA()), ('clf', SVC(C=10))])
 
-|details-start|
-**When does it matter?**
-|details-split|
+.. dropdown:: When does it matter?
 
-This is particularly important for doing grid searches::
+  This is particularly important for doing grid searches::
 
-    >>> from sklearn.model_selection import GridSearchCV
-    >>> param_grid = dict(reduce_dim__n_components=[2, 5, 10],
-    ...                   clf__C=[0.1, 10, 100])
-    >>> grid_search = GridSearchCV(pipe, param_grid=param_grid)
+      >>> from sklearn.model_selection import GridSearchCV
+      >>> param_grid = dict(reduce_dim__n_components=[2, 5, 10],
+      ...                   clf__C=[0.1, 10, 100])
+      >>> grid_search = GridSearchCV(pipe, param_grid=param_grid)
 
-Individual steps may also be replaced as parameters, and non-final steps may be
-ignored by setting them to ``'passthrough'``::
+  Individual steps may also be replaced as parameters, and non-final steps may be
+  ignored by setting them to ``'passthrough'``::
 
-    >>> param_grid = dict(reduce_dim=['passthrough', PCA(5), PCA(10)],
-    ...                   clf=[SVC(), LogisticRegression()],
-    ...                   clf__C=[0.1, 10, 100])
-    >>> grid_search = GridSearchCV(pipe, param_grid=param_grid)
+      >>> param_grid = dict(reduce_dim=['passthrough', PCA(5), PCA(10)],
+      ...                   clf=[SVC(), LogisticRegression()],
+      ...                   clf__C=[0.1, 10, 100])
+      >>> grid_search = GridSearchCV(pipe, param_grid=param_grid)
 
-.. topic:: See Also:
+  .. seealso::
 
- * :ref:`composite_grid_search`
+    * :ref:`composite_grid_search`
 
-|details-end|
 
-.. topic:: Examples:
+.. rubric:: Examples
 
- * :ref:`sphx_glr_auto_examples_feature_selection_plot_feature_selection_pipeline.py`
- * :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_text_feature_extraction.py`
- * :ref:`sphx_glr_auto_examples_compose_plot_digits_pipe.py`
- * :ref:`sphx_glr_auto_examples_miscellaneous_plot_kernel_approximation.py`
- * :ref:`sphx_glr_auto_examples_svm_plot_svm_anova.py`
- * :ref:`sphx_glr_auto_examples_compose_plot_compare_reduction.py`
- * :ref:`sphx_glr_auto_examples_miscellaneous_plot_pipeline_display.py`
+* :ref:`sphx_glr_auto_examples_feature_selection_plot_feature_selection_pipeline.py`
+* :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_text_feature_extraction.py`
+* :ref:`sphx_glr_auto_examples_compose_plot_digits_pipe.py`
+* :ref:`sphx_glr_auto_examples_miscellaneous_plot_kernel_approximation.py`
+* :ref:`sphx_glr_auto_examples_svm_plot_svm_anova.py`
+* :ref:`sphx_glr_auto_examples_compose_plot_compare_reduction.py`
+* :ref:`sphx_glr_auto_examples_miscellaneous_plot_pipeline_display.py`
 
 
 .. _pipeline_cache:
@@ -245,53 +230,49 @@ object::
     >>> # Clear the cache directory when you don't need it anymore
     >>> rmtree(cachedir)
 
-|details-start|
-**Warning: Side effect of caching transformers**
-|details-split|
-
-Using a :class:`Pipeline` without cache enabled, it is possible to
-inspect the original instance such as::
-
-    >>> from sklearn.datasets import load_digits
-    >>> X_digits, y_digits = load_digits(return_X_y=True)
-    >>> pca1 = PCA(n_components=10)
-    >>> svm1 = SVC()
-    >>> pipe = Pipeline([('reduce_dim', pca1), ('clf', svm1)])
-    >>> pipe.fit(X_digits, y_digits)
-    Pipeline(steps=[('reduce_dim', PCA(n_components=10)), ('clf', SVC())])
-    >>> # The pca instance can be inspected directly
-    >>> pca1.components_.shape
-    (10, 64)
-
-
-Enabling caching triggers a clone of the transformers before fitting.
-Therefore, the transformer instance given to the pipeline cannot be
-inspected directly.
-In following example, accessing the :class:`~sklearn.decomposition.PCA`
-instance ``pca2`` will raise an ``AttributeError`` since ``pca2`` will be an
-unfitted transformer.
-Instead, use the attribute ``named_steps`` to inspect estimators within
-the pipeline::
-
-    >>> cachedir = mkdtemp()
-    >>> pca2 = PCA(n_components=10)
-    >>> svm2 = SVC()
-    >>> cached_pipe = Pipeline([('reduce_dim', pca2), ('clf', svm2)],
-    ...                        memory=cachedir)
-    >>> cached_pipe.fit(X_digits, y_digits)
-    Pipeline(memory=...,
-             steps=[('reduce_dim', PCA(n_components=10)), ('clf', SVC())])
-    >>> cached_pipe.named_steps['reduce_dim'].components_.shape
-    (10, 64)
-    >>> # Remove the cache directory
-    >>> rmtree(cachedir)
-
-
-|details-end|
-
-.. topic:: Examples:
-
- * :ref:`sphx_glr_auto_examples_compose_plot_compare_reduction.py`
+.. dropdown:: Side effect of caching transformers
+  :color: warning
+
+  Using a :class:`Pipeline` without cache enabled, it is possible to
+  inspect the original instance such as::
+
+      >>> from sklearn.datasets import load_digits
+      >>> X_digits, y_digits = load_digits(return_X_y=True)
+      >>> pca1 = PCA(n_components=10)
+      >>> svm1 = SVC()
+      >>> pipe = Pipeline([('reduce_dim', pca1), ('clf', svm1)])
+      >>> pipe.fit(X_digits, y_digits)
+      Pipeline(steps=[('reduce_dim', PCA(n_components=10)), ('clf', SVC())])
+      >>> # The pca instance can be inspected directly
+      >>> pca1.components_.shape
+      (10, 64)
+
+  Enabling caching triggers a clone of the transformers before fitting.
+  Therefore, the transformer instance given to the pipeline cannot be
+  inspected directly.
+  In following example, accessing the :class:`~sklearn.decomposition.PCA`
+  instance ``pca2`` will raise an ``AttributeError`` since ``pca2`` will be an
+  unfitted transformer.
+  Instead, use the attribute ``named_steps`` to inspect estimators within
+  the pipeline::
+
+      >>> cachedir = mkdtemp()
+      >>> pca2 = PCA(n_components=10)
+      >>> svm2 = SVC()
+      >>> cached_pipe = Pipeline([('reduce_dim', pca2), ('clf', svm2)],
+      ...                        memory=cachedir)
+      >>> cached_pipe.fit(X_digits, y_digits)
+      Pipeline(memory=...,
+               steps=[('reduce_dim', PCA(n_components=10)), ('clf', SVC())])
+      >>> cached_pipe.named_steps['reduce_dim'].components_.shape
+      (10, 64)
+      >>> # Remove the cache directory
+      >>> rmtree(cachedir)
+
+
+.. rubric:: Examples
+
+* :ref:`sphx_glr_auto_examples_compose_plot_compare_reduction.py`
 
 .. _transformed_target_regressor:
 
@@ -364,9 +345,9 @@ each other. However, it is possible to bypass this checking by setting
    pair of functions ``func`` and ``inverse_func``. However, setting both
    options will raise an error.
 
-.. topic:: Examples:
+.. rubric:: Examples
 
- * :ref:`sphx_glr_auto_examples_compose_plot_transformed_target.py`
+* :ref:`sphx_glr_auto_examples_compose_plot_transformed_target.py`
 
 
 .. _feature_union:
@@ -428,9 +409,9 @@ and ignored by setting to ``'drop'``::
     FeatureUnion(transformer_list=[('linear_pca', PCA()),
                                    ('kernel_pca', 'drop')])
 
-.. topic:: Examples:
+.. rubric:: Examples
 
- * :ref:`sphx_glr_auto_examples_compose_plot_feature_union.py`
+* :ref:`sphx_glr_auto_examples_compose_plot_feature_union.py`
 
 
 .. _column_transformer:
@@ -623,7 +604,7 @@ As an alternative, the HTML can be written to a file using
    >>> with open('my_estimator.html', 'w') as f:  # doctest: +SKIP
    ...     f.write(estimator_html_repr(clf))
 
-.. topic:: Examples:
+.. rubric:: Examples
 
- * :ref:`sphx_glr_auto_examples_compose_plot_column_transformer.py`
- * :ref:`sphx_glr_auto_examples_compose_plot_column_transformer_mixed_types.py`
+* :ref:`sphx_glr_auto_examples_compose_plot_column_transformer.py`
+* :ref:`sphx_glr_auto_examples_compose_plot_column_transformer_mixed_types.py`
diff --git a/doc/modules/covariance.rst b/doc/modules/covariance.rst
index 50927f9a677f6..847e489c87333 100644
--- a/doc/modules/covariance.rst
+++ b/doc/modules/covariance.rst
@@ -40,11 +40,10 @@ on whether the data are centered, so one may want to use the
 same mean vector as the training set. If not, both should be centered
 by the user, and ``assume_centered=True`` should be used.
 
-.. topic:: Examples:
+.. rubric:: Examples
 
-   * See :ref:`sphx_glr_auto_examples_covariance_plot_covariance_estimation.py` for
-     an example on how to fit an :class:`EmpiricalCovariance` object
-     to data.
+* See :ref:`sphx_glr_auto_examples_covariance_plot_covariance_estimation.py` for
+  an example on how to fit an :class:`EmpiricalCovariance` object to data.
 
 
 .. _shrunk_covariance:
@@ -84,11 +83,10 @@ Tr}\hat{\Sigma}}{p}\rm Id`.
 Choosing the amount of shrinkage, :math:`\alpha` amounts to setting a
 bias/variance trade-off, and is discussed below.
 
-.. topic:: Examples:
+.. rubric:: Examples
 
-   * See :ref:`sphx_glr_auto_examples_covariance_plot_covariance_estimation.py` for
-     an example on how to fit a :class:`ShrunkCovariance` object
-     to data.
+* See :ref:`sphx_glr_auto_examples_covariance_plot_covariance_estimation.py` for
+  an example on how to fit a :class:`ShrunkCovariance` object to data.
 
 
 Ledoit-Wolf shrinkage
@@ -121,18 +119,18 @@ fitting a :class:`LedoitWolf` object to the same sample.
     Since the population covariance is already a multiple of the identity
     matrix, the Ledoit-Wolf solution is indeed a reasonable estimate.
 
-.. topic:: Examples:
+.. rubric:: Examples
 
-   * See :ref:`sphx_glr_auto_examples_covariance_plot_covariance_estimation.py` for
-     an example on how to fit a :class:`LedoitWolf` object to data and
-     for visualizing the performances of the Ledoit-Wolf estimator in
-     terms of likelihood.
+* See :ref:`sphx_glr_auto_examples_covariance_plot_covariance_estimation.py` for
+  an example on how to fit a :class:`LedoitWolf` object to data and
+  for visualizing the performances of the Ledoit-Wolf estimator in
+  terms of likelihood.
 
-.. topic:: References:
+.. rubric:: References
 
-    .. [1] O. Ledoit and M. Wolf, "A Well-Conditioned Estimator for Large-Dimensional
-           Covariance Matrices", Journal of Multivariate Analysis, Volume 88, Issue 2,
-           February 2004, pages 365-411.
+.. [1] O. Ledoit and M. Wolf, "A Well-Conditioned Estimator for Large-Dimensional
+       Covariance Matrices", Journal of Multivariate Analysis, Volume 88, Issue 2,
+       February 2004, pages 365-411.
 
 .. _oracle_approximating_shrinkage:
 
@@ -158,22 +156,21 @@ object to the same sample.
    Bias-variance trade-off when setting the shrinkage: comparing the
    choices of Ledoit-Wolf and OAS estimators
 
-.. topic:: References:
+.. rubric:: References
 
-    .. [2] :arxiv:`"Shrinkage algorithms for MMSE covariance estimation.",
-           Chen, Y., Wiesel, A., Eldar, Y. C., & Hero, A. O.
-           IEEE Transactions on Signal Processing, 58(10), 5016-5029, 2010.
-           <0907.4698>`
+.. [2] :arxiv:`"Shrinkage algorithms for MMSE covariance estimation.",
+       Chen, Y., Wiesel, A., Eldar, Y. C., & Hero, A. O.
+       IEEE Transactions on Signal Processing, 58(10), 5016-5029, 2010.
+       <0907.4698>`
 
-.. topic:: Examples:
+.. rubric:: Examples
 
-   * See :ref:`sphx_glr_auto_examples_covariance_plot_covariance_estimation.py` for
-     an example on how to fit an :class:`OAS` object
-     to data.
+* See :ref:`sphx_glr_auto_examples_covariance_plot_covariance_estimation.py` for
+  an example on how to fit an :class:`OAS` object to data.
 
-   * See :ref:`sphx_glr_auto_examples_covariance_plot_lw_vs_oas.py` to visualize the
-     Mean Squared Error difference between a :class:`LedoitWolf` and
-     an :class:`OAS` estimator of the covariance.
+* See :ref:`sphx_glr_auto_examples_covariance_plot_lw_vs_oas.py` to visualize the
+  Mean Squared Error difference between a :class:`LedoitWolf` and
+  an :class:`OAS` estimator of the covariance.
 
 
 .. figure:: ../auto_examples/covariance/images/sphx_glr_plot_lw_vs_oas_001.png
@@ -254,20 +251,20 @@ problem is the GLasso algorithm, from the Friedman 2008 Biostatistics
 paper. It is the same algorithm as in the R ``glasso`` package.
 
 
-.. topic:: Examples:
+.. rubric:: Examples
 
-   * :ref:`sphx_glr_auto_examples_covariance_plot_sparse_cov.py`: example on synthetic
-     data showing some recovery of a structure, and comparing to other
-     covariance estimators.
+* :ref:`sphx_glr_auto_examples_covariance_plot_sparse_cov.py`: example on synthetic
+  data showing some recovery of a structure, and comparing to other
+  covariance estimators.
 
-   * :ref:`sphx_glr_auto_examples_applications_plot_stock_market.py`: example on real
-     stock market data, finding which symbols are most linked.
+* :ref:`sphx_glr_auto_examples_applications_plot_stock_market.py`: example on real
+  stock market data, finding which symbols are most linked.
 
-.. topic:: References:
+.. rubric:: References
 
-   * Friedman et al, `"Sparse inverse covariance estimation with the
-     graphical lasso" <https://biostatistics.oxfordjournals.org/content/9/3/432.short>`_,
-     Biostatistics 9, pp 432, 2008
+* Friedman et al, `"Sparse inverse covariance estimation with the
+  graphical lasso" <https://biostatistics.oxfordjournals.org/content/9/3/432.short>`_,
+  Biostatistics 9, pp 432, 2008
 
 .. _robust_covariance:
 
@@ -313,24 +310,24 @@ the same time.
 Raw estimates can be accessed as ``raw_location_`` and ``raw_covariance_``
 attributes of a :class:`MinCovDet` robust covariance estimator object.
 
-.. topic:: References:
+.. rubric:: References
 
-    .. [3] P. J. Rousseeuw. Least median of squares regression.
-           J. Am Stat Ass, 79:871, 1984.
-    .. [4] A Fast Algorithm for the Minimum Covariance Determinant Estimator,
-           1999, American Statistical Association and the American Society
-           for Quality, TECHNOMETRICS.
+.. [3] P. J. Rousseeuw. Least median of squares regression.
+       J. Am Stat Ass, 79:871, 1984.
+.. [4] A Fast Algorithm for the Minimum Covariance Determinant Estimator,
+       1999, American Statistical Association and the American Society
+       for Quality, TECHNOMETRICS.
 
-.. topic:: Examples:
+.. rubric:: Examples
 
-   * See :ref:`sphx_glr_auto_examples_covariance_plot_robust_vs_empirical_covariance.py` for
-     an example on how to fit a :class:`MinCovDet` object to data and see how
-     the estimate remains accurate despite the presence of outliers.
+* See :ref:`sphx_glr_auto_examples_covariance_plot_robust_vs_empirical_covariance.py` for
+  an example on how to fit a :class:`MinCovDet` object to data and see how
+  the estimate remains accurate despite the presence of outliers.
 
-   * See :ref:`sphx_glr_auto_examples_covariance_plot_mahalanobis_distances.py` to
-     visualize the difference between :class:`EmpiricalCovariance` and
-     :class:`MinCovDet` covariance estimators in terms of Mahalanobis distance
-     (so we get a better estimate of the precision matrix too).
+* See :ref:`sphx_glr_auto_examples_covariance_plot_mahalanobis_distances.py` to
+  visualize the difference between :class:`EmpiricalCovariance` and
+  :class:`MinCovDet` covariance estimators in terms of Mahalanobis distance
+  (so we get a better estimate of the precision matrix too).
 
 .. |robust_vs_emp| image:: ../auto_examples/covariance/images/sphx_glr_plot_robust_vs_empirical_covariance_001.png
    :target: ../auto_examples/covariance/plot_robust_vs_empirical_covariance.html
diff --git a/doc/modules/cross_decomposition.rst b/doc/modules/cross_decomposition.rst
index 8f8d217f87144..2d630de699c7a 100644
--- a/doc/modules/cross_decomposition.rst
+++ b/doc/modules/cross_decomposition.rst
@@ -92,42 +92,35 @@ Step *a)* may be performed in two ways: either by computing the whole SVD of
 values, or by directly computing the singular vectors using the power method (cf section 11.3 in [1]_),
 which corresponds to the `'nipals'` option of the `algorithm` parameter.
 
-|details-start|
-**Transforming data**
-|details-split|
+.. dropdown:: Transforming data
 
-To transform :math:`X` into :math:`\bar{X}`, we need to find a projection
-matrix :math:`P` such that :math:`\bar{X} = XP`. We know that for the
-training data, :math:`\Xi = XP`, and :math:`X = \Xi \Gamma^T`. Setting
-:math:`P = U(\Gamma^T U)^{-1}` where :math:`U` is the matrix with the
-:math:`u_k` in the columns, we have :math:`XP = X U(\Gamma^T U)^{-1} = \Xi
-(\Gamma^T U) (\Gamma^T U)^{-1} = \Xi` as desired. The rotation matrix
-:math:`P` can be accessed from the `x_rotations_` attribute.
+  To transform :math:`X` into :math:`\bar{X}`, we need to find a projection
+  matrix :math:`P` such that :math:`\bar{X} = XP`. We know that for the
+  training data, :math:`\Xi = XP`, and :math:`X = \Xi \Gamma^T`. Setting
+  :math:`P = U(\Gamma^T U)^{-1}` where :math:`U` is the matrix with the
+  :math:`u_k` in the columns, we have :math:`XP = X U(\Gamma^T U)^{-1} = \Xi
+  (\Gamma^T U) (\Gamma^T U)^{-1} = \Xi` as desired. The rotation matrix
+  :math:`P` can be accessed from the `x_rotations_` attribute.
 
-Similarly, :math:`Y` can be transformed using the rotation matrix
-:math:`V(\Delta^T V)^{-1}`, accessed via the `y_rotations_` attribute.
-|details-end|
+  Similarly, :math:`Y` can be transformed using the rotation matrix
+  :math:`V(\Delta^T V)^{-1}`, accessed via the `y_rotations_` attribute.
 
-|details-start|
-**Predicting the targets Y**
-|details-split|
+.. dropdown:: Predicting the targets `Y`
 
-To predict the targets of some data :math:`X`, we are looking for a
-coefficient matrix :math:`\beta \in R^{d \times t}` such that :math:`Y =
-X\beta`.
+  To predict the targets of some data :math:`X`, we are looking for a
+  coefficient matrix :math:`\beta \in R^{d \times t}` such that :math:`Y =
+  X\beta`.
 
-The idea is to try to predict the transformed targets :math:`\Omega` as a
-function of the transformed samples :math:`\Xi`, by computing :math:`\alpha
-\in \mathbb{R}` such that :math:`\Omega = \alpha \Xi`.
+  The idea is to try to predict the transformed targets :math:`\Omega` as a
+  function of the transformed samples :math:`\Xi`, by computing :math:`\alpha
+  \in \mathbb{R}` such that :math:`\Omega = \alpha \Xi`.
 
-Then, we have :math:`Y = \Omega \Delta^T = \alpha \Xi \Delta^T`, and since
-:math:`\Xi` is the transformed training data we have that :math:`Y = X \alpha
-P \Delta^T`, and as a result the coefficient matrix :math:`\beta = \alpha P
-\Delta^T`.
+  Then, we have :math:`Y = \Omega \Delta^T = \alpha \Xi \Delta^T`, and since
+  :math:`\Xi` is the transformed training data we have that :math:`Y = X \alpha
+  P \Delta^T`, and as a result the coefficient matrix :math:`\beta = \alpha P
+  \Delta^T`.
 
-:math:`\beta` can be accessed through the `coef_` attribute.
-
-|details-end|
+  :math:`\beta` can be accessed through the `coef_` attribute.
 
 PLSSVD
 ------
@@ -184,18 +177,13 @@ Since :class:`CCA` involves the inversion of :math:`X_k^TX_k` and
 :math:`Y_k^TY_k`, this estimator can be unstable if the number of features or
 targets is greater than the number of samples.
 
-|details-start|
-**Reference**
-|details-split|
-
-   .. [1] `A survey of Partial Least Squares (PLS) methods, with emphasis on
-      the two-block case
-      <https://stat.uw.edu/sites/default/files/files/reports/2000/tr371.pdf>`_
-      JA Wegelin
+.. rubric:: References
 
-|details-end|
+.. [1] `A survey of Partial Least Squares (PLS) methods, with emphasis on the two-block
+  case <https://stat.uw.edu/sites/default/files/files/reports/2000/tr371.pdf>`_,
+  JA Wegelin
 
-.. topic:: Examples:
+.. rubric:: Examples
 
-    * :ref:`sphx_glr_auto_examples_cross_decomposition_plot_compare_cross_decomposition.py`
-    * :ref:`sphx_glr_auto_examples_cross_decomposition_plot_pcr_vs_pls.py`
+* :ref:`sphx_glr_auto_examples_cross_decomposition_plot_compare_cross_decomposition.py`
+* :ref:`sphx_glr_auto_examples_cross_decomposition_plot_pcr_vs_pls.py`
diff --git a/doc/modules/cross_validation.rst b/doc/modules/cross_validation.rst
index 34f14fe6846a2..defcd91a6008a 100644
--- a/doc/modules/cross_validation.rst
+++ b/doc/modules/cross_validation.rst
@@ -170,36 +170,33 @@ indices, for example::
   >>> cross_val_score(clf, X, y, cv=custom_cv)
   array([1.        , 0.973...])
 
-|details-start|
-**Data transformation with held out data**
-|details-split|
+.. dropdown:: Data transformation with held-out data
 
-    Just as it is important to test a predictor on data held-out from
-    training, preprocessing (such as standardization, feature selection, etc.)
-    and similar :ref:`data transformations <data-transforms>` similarly should
-    be learnt from a training set and applied to held-out data for prediction::
+  Just as it is important to test a predictor on data held-out from
+  training, preprocessing (such as standardization, feature selection, etc.)
+  and similar :ref:`data transformations <data-transforms>` similarly should
+  be learnt from a training set and applied to held-out data for prediction::
 
-      >>> from sklearn import preprocessing
-      >>> X_train, X_test, y_train, y_test = train_test_split(
-      ...     X, y, test_size=0.4, random_state=0)
-      >>> scaler = preprocessing.StandardScaler().fit(X_train)
-      >>> X_train_transformed = scaler.transform(X_train)
-      >>> clf = svm.SVC(C=1).fit(X_train_transformed, y_train)
-      >>> X_test_transformed = scaler.transform(X_test)
-      >>> clf.score(X_test_transformed, y_test)
-      0.9333...
+    >>> from sklearn import preprocessing
+    >>> X_train, X_test, y_train, y_test = train_test_split(
+    ...     X, y, test_size=0.4, random_state=0)
+    >>> scaler = preprocessing.StandardScaler().fit(X_train)
+    >>> X_train_transformed = scaler.transform(X_train)
+    >>> clf = svm.SVC(C=1).fit(X_train_transformed, y_train)
+    >>> X_test_transformed = scaler.transform(X_test)
+    >>> clf.score(X_test_transformed, y_test)
+    0.9333...
 
-    A :class:`Pipeline <sklearn.pipeline.Pipeline>` makes it easier to compose
-    estimators, providing this behavior under cross-validation::
+  A :class:`Pipeline <sklearn.pipeline.Pipeline>` makes it easier to compose
+  estimators, providing this behavior under cross-validation::
 
-      >>> from sklearn.pipeline import make_pipeline
-      >>> clf = make_pipeline(preprocessing.StandardScaler(), svm.SVC(C=1))
-      >>> cross_val_score(clf, X, y, cv=cv)
-      array([0.977..., 0.933..., 0.955..., 0.933..., 0.977...])
+    >>> from sklearn.pipeline import make_pipeline
+    >>> clf = make_pipeline(preprocessing.StandardScaler(), svm.SVC(C=1))
+    >>> cross_val_score(clf, X, y, cv=cv)
+    array([0.977..., 0.933..., 0.955..., 0.933..., 0.977...])
 
-    See :ref:`combining_estimators`.
+  See :ref:`combining_estimators`.
 
-|details-end|
 
 .. _multimetric_cross_validation:
 
@@ -294,14 +291,14 @@ The function :func:`cross_val_predict` is appropriate for:
 The available cross validation iterators are introduced in the following
 section.
 
-.. topic:: Examples
+.. rubric:: Examples
 
-    * :ref:`sphx_glr_auto_examples_model_selection_plot_roc_crossval.py`,
-    * :ref:`sphx_glr_auto_examples_feature_selection_plot_rfe_with_cross_validation.py`,
-    * :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_digits.py`,
-    * :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_text_feature_extraction.py`,
-    * :ref:`sphx_glr_auto_examples_model_selection_plot_cv_predict.py`,
-    * :ref:`sphx_glr_auto_examples_model_selection_plot_nested_cross_validation_iris.py`.
+* :ref:`sphx_glr_auto_examples_model_selection_plot_roc_crossval.py`,
+* :ref:`sphx_glr_auto_examples_feature_selection_plot_rfe_with_cross_validation.py`,
+* :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_digits.py`,
+* :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_text_feature_extraction.py`,
+* :ref:`sphx_glr_auto_examples_model_selection_plot_cv_predict.py`,
+* :ref:`sphx_glr_auto_examples_model_selection_plot_nested_cross_validation_iris.py`.
 
 Cross validation iterators
 ==========================
@@ -442,23 +439,19 @@ then 5- or 10- fold cross validation can overestimate the generalization error.
 As a general rule, most authors, and empirical evidence, suggest that 5- or 10-
 fold cross validation should be preferred to LOO.
 
-|details-start|
-**References**
-|details-split|
+.. dropdown:: References
 
- * `<http://www.faqs.org/faqs/ai-faq/neural-nets/part3/section-12.html>`_;
- * T. Hastie, R. Tibshirani, J. Friedman,  `The Elements of Statistical Learning
-   <https://web.stanford.edu/~hastie/ElemStatLearn/>`_, Springer 2009
- * L. Breiman, P. Spector `Submodel selection and evaluation in regression: The X-random case
-   <https://digitalassets.lib.berkeley.edu/sdtr/ucb/text/197.pdf>`_, International Statistical Review 1992;
- * R. Kohavi, `A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection
-   <https://www.ijcai.org/Proceedings/95-2/Papers/016.pdf>`_, Intl. Jnt. Conf. AI
- * R. Bharat Rao, G. Fung, R. Rosales, `On the Dangers of Cross-Validation. An Experimental Evaluation
-   <https://people.csail.mit.edu/romer/papers/CrossVal_SDM08.pdf>`_, SIAM 2008;
- * G. James, D. Witten, T. Hastie, R Tibshirani, `An Introduction to
-   Statistical Learning <https://www.statlearning.com>`_, Springer 2013.
-
-|details-end|
+  * `<http://www.faqs.org/faqs/ai-faq/neural-nets/part3/section-12.html>`_;
+  * T. Hastie, R. Tibshirani, J. Friedman,  `The Elements of Statistical Learning
+    <https://web.stanford.edu/~hastie/ElemStatLearn/>`_, Springer 2009
+  * L. Breiman, P. Spector `Submodel selection and evaluation in regression: The X-random case
+    <https://digitalassets.lib.berkeley.edu/sdtr/ucb/text/197.pdf>`_, International Statistical Review 1992;
+  * R. Kohavi, `A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection
+    <https://www.ijcai.org/Proceedings/95-2/Papers/016.pdf>`_, Intl. Jnt. Conf. AI
+  * R. Bharat Rao, G. Fung, R. Rosales, `On the Dangers of Cross-Validation. An Experimental Evaluation
+    <https://people.csail.mit.edu/romer/papers/CrossVal_SDM08.pdf>`_, SIAM 2008;
+  * G. James, D. Witten, T. Hastie, R Tibshirani, `An Introduction to
+    Statistical Learning <https://www.statlearning.com>`_, Springer 2013.
 
 .. _leave_p_out:
 
@@ -700,30 +693,27 @@ Example::
   [ 0  1  4  5  6  7  8  9 11 12 13 14] [ 2  3 10 15 16 17]
   [ 1  2  3  8  9 10 12 13 14 15 16 17] [ 0  4  5  6  7 11]
 
-|details-start|
-**Implementation notes**
-|details-split|
+.. dropdown:: Implementation notes
 
-- With the current implementation full shuffle is not possible in most
-  scenarios. When shuffle=True, the following happens:
+  - With the current implementation full shuffle is not possible in most
+    scenarios. When shuffle=True, the following happens:
 
-  1. All groups are shuffled.
-  2. Groups are sorted by standard deviation of classes using stable sort.
-  3. Sorted groups are iterated over and assigned to folds.
+    1. All groups are shuffled.
+    2. Groups are sorted by standard deviation of classes using stable sort.
+    3. Sorted groups are iterated over and assigned to folds.
 
-  That means that only groups with the same standard deviation of class
-  distribution will be shuffled, which might be useful when each group has only
-  a single class.
-- The algorithm greedily assigns each group to one of n_splits test sets,
-  choosing the test set that minimises the variance in class distribution
-  across test sets. Group assignment proceeds from groups with highest to
-  lowest variance in class frequency, i.e. large groups peaked on one or few
-  classes are assigned first.
-- This split is suboptimal in a sense that it might produce imbalanced splits
-  even if perfect stratification is possible. If you have relatively close
-  distribution of classes in each group, using :class:`GroupKFold` is better.
+    That means that only groups with the same standard deviation of class
+    distribution will be shuffled, which might be useful when each group has only
+    a single class.
+  - The algorithm greedily assigns each group to one of n_splits test sets,
+    choosing the test set that minimises the variance in class distribution
+    across test sets. Group assignment proceeds from groups with highest to
+    lowest variance in class frequency, i.e. large groups peaked on one or few
+    classes are assigned first.
+  - This split is suboptimal in a sense that it might produce imbalanced splits
+    even if perfect stratification is possible. If you have relatively close
+    distribution of classes in each group, using :class:`GroupKFold` is better.
 
-|details-end|
 
 Here is a visualization of cross-validation behavior for uneven groups:
 
@@ -999,16 +989,12 @@ using brute force and internally fits ``(n_permutations + 1) * n_cv`` models.
 It is therefore only tractable with small datasets for which fitting an
 individual model is very fast.
 
-.. topic:: Examples
-
-    * :ref:`sphx_glr_auto_examples_model_selection_plot_permutation_tests_for_classification.py`
+.. rubric:: Examples
 
-|details-start|
-**References**
-|details-split|
+* :ref:`sphx_glr_auto_examples_model_selection_plot_permutation_tests_for_classification.py`
 
- * Ojala and Garriga. `Permutation Tests for Studying Classifier Performance
-   <http://www.jmlr.org/papers/volume11/ojala10a/ojala10a.pdf>`_.
-   J. Mach. Learn. Res. 2010.
+.. dropdown:: References
 
-|details-end|
+  * Ojala and Garriga. `Permutation Tests for Studying Classifier Performance
+    <http://www.jmlr.org/papers/volume11/ojala10a/ojala10a.pdf>`_.
+    J. Mach. Learn. Res. 2010.
diff --git a/doc/modules/decomposition.rst b/doc/modules/decomposition.rst
index e8241a92cfc3b..926a4482f1428 100644
--- a/doc/modules/decomposition.rst
+++ b/doc/modules/decomposition.rst
@@ -51,11 +51,11 @@ data based on the amount of variance it explains. As such it implements a
     :scale: 75%
 
 
-.. topic:: Examples:
+.. rubric:: Examples
 
-    * :ref:`sphx_glr_auto_examples_decomposition_plot_pca_iris.py`
-    * :ref:`sphx_glr_auto_examples_decomposition_plot_pca_vs_lda.py`
-    * :ref:`sphx_glr_auto_examples_decomposition_plot_pca_vs_fa_model_selection.py`
+* :ref:`sphx_glr_auto_examples_decomposition_plot_pca_iris.py`
+* :ref:`sphx_glr_auto_examples_decomposition_plot_pca_vs_lda.py`
+* :ref:`sphx_glr_auto_examples_decomposition_plot_pca_vs_fa_model_selection.py`
 
 
 .. _IncrementalPCA:
@@ -97,9 +97,9 @@ input data for each feature before applying the SVD.
     :scale: 75%
 
 
-.. topic:: Examples:
+.. rubric:: Examples
 
-    * :ref:`sphx_glr_auto_examples_decomposition_plot_incremental_pca.py`
+* :ref:`sphx_glr_auto_examples_decomposition_plot_incremental_pca.py`
 
 
 .. _RandomizedPCA:
@@ -160,20 +160,20 @@ Note: the implementation of ``inverse_transform`` in :class:`PCA` with
 ``transform`` even when ``whiten=False`` (default).
 
 
-.. topic:: Examples:
+.. rubric:: Examples
 
-    * :ref:`sphx_glr_auto_examples_applications_plot_face_recognition.py`
-    * :ref:`sphx_glr_auto_examples_decomposition_plot_faces_decomposition.py`
+* :ref:`sphx_glr_auto_examples_applications_plot_face_recognition.py`
+* :ref:`sphx_glr_auto_examples_decomposition_plot_faces_decomposition.py`
 
-.. topic:: References:
+.. rubric:: References
 
-    * Algorithm 4.3 in
-      :arxiv:`"Finding structure with randomness: Stochastic algorithms for
-      constructing approximate matrix decompositions" <0909.4061>`
-      Halko, et al., 2009
+* Algorithm 4.3 in
+  :arxiv:`"Finding structure with randomness: Stochastic algorithms for
+  constructing approximate matrix decompositions" <0909.4061>`
+  Halko, et al., 2009
 
-    * :arxiv:`"An implementation of a randomized algorithm for principal component
-      analysis" <1412.3510>` A. Szlam et al. 2014
+* :arxiv:`"An implementation of a randomized algorithm for principal component
+  analysis" <1412.3510>` A. Szlam et al. 2014
 
 .. _SparsePCA:
 
@@ -248,18 +248,18 @@ factorization, while larger values shrink many coefficients to zero.
   the algorithm is online along the features direction, not the samples
   direction.
 
-.. topic:: Examples:
+.. rubric:: Examples
 
-   * :ref:`sphx_glr_auto_examples_decomposition_plot_faces_decomposition.py`
+* :ref:`sphx_glr_auto_examples_decomposition_plot_faces_decomposition.py`
 
-.. topic:: References:
+.. rubric:: References
 
-  .. [Mrl09] `"Online Dictionary Learning for Sparse Coding"
-     <https://www.di.ens.fr/sierra/pdfs/icml09.pdf>`_
-     J. Mairal, F. Bach, J. Ponce, G. Sapiro, 2009
-  .. [Jen09] `"Structured Sparse Principal Component Analysis"
-     <https://www.di.ens.fr/~fbach/sspca_AISTATS2010.pdf>`_
-     R. Jenatton, G. Obozinski, F. Bach, 2009
+.. [Mrl09] `"Online Dictionary Learning for Sparse Coding"
+   <https://www.di.ens.fr/sierra/pdfs/icml09.pdf>`_
+   J. Mairal, F. Bach, J. Ponce, G. Sapiro, 2009
+.. [Jen09] `"Structured Sparse Principal Component Analysis"
+   <https://www.di.ens.fr/~fbach/sspca_AISTATS2010.pdf>`_
+   R. Jenatton, G. Obozinski, F. Bach, 2009
 
 
 .. _kernel_PCA:
@@ -288,22 +288,23 @@ prediction (kernel dependency estimation). :class:`KernelPCA` supports both
     :meth:`KernelPCA.inverse_transform` is an approximation. See the example
     linked below for more details.
 
-.. topic:: Examples:
+.. rubric:: Examples
 
-    * :ref:`sphx_glr_auto_examples_decomposition_plot_kernel_pca.py`
+* :ref:`sphx_glr_auto_examples_decomposition_plot_kernel_pca.py`
+* :ref:`sphx_glr_auto_examples_applications_plot_digits_denoising.py`
 
-.. topic:: References:
+.. rubric:: References
 
-    .. [Scholkopf1997] Schölkopf, Bernhard, Alexander Smola, and Klaus-Robert Müller.
-       `"Kernel principal component analysis."
-       <https://people.eecs.berkeley.edu/~wainwrig/stat241b/scholkopf_kernel.pdf>`_
-       International conference on artificial neural networks.
-       Springer, Berlin, Heidelberg, 1997.
+.. [Scholkopf1997] Schölkopf, Bernhard, Alexander Smola, and Klaus-Robert Müller.
+   `"Kernel principal component analysis."
+   <https://people.eecs.berkeley.edu/~wainwrig/stat241b/scholkopf_kernel.pdf>`_
+   International conference on artificial neural networks.
+   Springer, Berlin, Heidelberg, 1997.
 
-    .. [Bakir2003] Bakır, Gökhan H., Jason Weston, and Bernhard Schölkopf.
-       `"Learning to find pre-images."
-       <https://papers.nips.cc/paper/2003/file/ac1ad983e08ad3304a97e147f522747e-Paper.pdf>`_
-       Advances in neural information processing systems 16 (2003): 449-456.
+.. [Bakir2003] Bakır, Gökhan H., Jason Weston, and Bernhard Schölkopf.
+   `"Learning to find pre-images."
+   <https://papers.nips.cc/paper/2003/file/ac1ad983e08ad3304a97e147f522747e-Paper.pdf>`_
+   Advances in neural information processing systems 16 (2003): 449-456.
 
 .. _kPCA_Solvers:
 
@@ -321,36 +322,33 @@ is much smaller than its size. This is a situation where approximate
 eigensolvers can provide speedup with very low precision loss.
 
 
-|details-start|
-**Eigensolvers**
-|details-split|
+.. dropdown:: Eigensolvers
 
-The optional parameter ``eigen_solver='randomized'`` can be used to
-*significantly* reduce the computation time when the number of requested
-``n_components`` is small compared with the number of samples. It relies on
-randomized decomposition methods to find an approximate solution in a shorter
-time.
+    The optional parameter ``eigen_solver='randomized'`` can be used to
+    *significantly* reduce the computation time when the number of requested
+    ``n_components`` is small compared with the number of samples. It relies on
+    randomized decomposition methods to find an approximate solution in a shorter
+    time.
 
-The time complexity of the randomized :class:`KernelPCA` is
-:math:`O(n_{\mathrm{samples}}^2 \cdot n_{\mathrm{components}})`
-instead of :math:`O(n_{\mathrm{samples}}^3)` for the exact method
-implemented with ``eigen_solver='dense'``.
+    The time complexity of the randomized :class:`KernelPCA` is
+    :math:`O(n_{\mathrm{samples}}^2 \cdot n_{\mathrm{components}})`
+    instead of :math:`O(n_{\mathrm{samples}}^3)` for the exact method
+    implemented with ``eigen_solver='dense'``.
 
-The memory footprint of randomized :class:`KernelPCA` is also proportional to
-:math:`2 \cdot n_{\mathrm{samples}} \cdot n_{\mathrm{components}}` instead of
-:math:`n_{\mathrm{samples}}^2` for the exact method.
+    The memory footprint of randomized :class:`KernelPCA` is also proportional to
+    :math:`2 \cdot n_{\mathrm{samples}} \cdot n_{\mathrm{components}}` instead of
+    :math:`n_{\mathrm{samples}}^2` for the exact method.
 
-Note: this technique is the same as in :ref:`RandomizedPCA`.
+    Note: this technique is the same as in :ref:`RandomizedPCA`.
 
-In addition to the above two solvers, ``eigen_solver='arpack'`` can be used as
-an alternate way to get an approximate decomposition. In practice, this method
-only provides reasonable execution times when the number of components to find
-is extremely small. It is enabled by default when the desired number of
-components is less than 10 (strict) and the number of samples is more than 200
-(strict). See :class:`KernelPCA` for details.
+    In addition to the above two solvers, ``eigen_solver='arpack'`` can be used as
+    an alternate way to get an approximate decomposition. In practice, this method
+    only provides reasonable execution times when the number of components to find
+    is extremely small. It is enabled by default when the desired number of
+    components is less than 10 (strict) and the number of samples is more than 200
+    (strict). See :class:`KernelPCA` for details.
 
-
-.. topic:: References:
+    .. rubric:: References
 
     * *dense* solver:
       `scipy.linalg.eigh documentation
@@ -372,8 +370,6 @@ components is less than 10 (strict) and the number of samples is more than 200
       <https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.linalg.eigsh.html>`_
       R. B. Lehoucq, D. C. Sorensen, and C. Yang, (1998)
 
-|details-end|
-
 
 .. _LSA:
 
@@ -390,72 +386,67 @@ When the columnwise (per-feature) means of :math:`X`
 are subtracted from the feature values,
 truncated SVD on the resulting matrix is equivalent to PCA.
 
-|details-start|
-**About truncated SVD and latent semantic analysis (LSA)**
-|details-split|
-
-When truncated SVD is applied to term-document matrices
-(as returned by :class:`~sklearn.feature_extraction.text.CountVectorizer` or
-:class:`~sklearn.feature_extraction.text.TfidfVectorizer`),
-this transformation is known as
-`latent semantic analysis <https://nlp.stanford.edu/IR-book/pdf/18lsi.pdf>`_
-(LSA), because it transforms such matrices
-to a "semantic" space of low dimensionality.
-In particular, LSA is known to combat the effects of synonymy and polysemy
-(both of which roughly mean there are multiple meanings per word),
-which cause term-document matrices to be overly sparse
-and exhibit poor similarity under measures such as cosine similarity.
+.. dropdown:: About truncated SVD and latent semantic analysis (LSA)
 
-.. note::
-    LSA is also known as latent semantic indexing, LSI,
-    though strictly that refers to its use in persistent indexes
-    for information retrieval purposes.
+    When truncated SVD is applied to term-document matrices
+    (as returned by :class:`~sklearn.feature_extraction.text.CountVectorizer` or
+    :class:`~sklearn.feature_extraction.text.TfidfVectorizer`),
+    this transformation is known as
+    `latent semantic analysis <https://nlp.stanford.edu/IR-book/pdf/18lsi.pdf>`_
+    (LSA), because it transforms such matrices
+    to a "semantic" space of low dimensionality.
+    In particular, LSA is known to combat the effects of synonymy and polysemy
+    (both of which roughly mean there are multiple meanings per word),
+    which cause term-document matrices to be overly sparse
+    and exhibit poor similarity under measures such as cosine similarity.
 
-Mathematically, truncated SVD applied to training samples :math:`X`
-produces a low-rank approximation :math:`X`:
-
-.. math::
-    X \approx X_k = U_k \Sigma_k V_k^\top
+    .. note::
+        LSA is also known as latent semantic indexing, LSI,
+        though strictly that refers to its use in persistent indexes
+        for information retrieval purposes.
 
-After this operation, :math:`U_k \Sigma_k`
-is the transformed training set with :math:`k` features
-(called ``n_components`` in the API).
+    Mathematically, truncated SVD applied to training samples :math:`X`
+    produces a low-rank approximation :math:`X`:
 
-To also transform a test set :math:`X`, we multiply it with :math:`V_k`:
+    .. math::
+        X \approx X_k = U_k \Sigma_k V_k^\top
 
-.. math::
-    X' = X V_k
+    After this operation, :math:`U_k \Sigma_k`
+    is the transformed training set with :math:`k` features
+    (called ``n_components`` in the API).
 
-.. note::
-    Most treatments of LSA in the natural language processing (NLP)
-    and information retrieval (IR) literature
-    swap the axes of the matrix :math:`X` so that it has shape
-    ``n_features`` × ``n_samples``.
-    We present LSA in a different way that matches the scikit-learn API better,
-    but the singular values found are the same.
+    To also transform a test set :math:`X`, we multiply it with :math:`V_k`:
 
+    .. math::
+        X' = X V_k
 
-While the :class:`TruncatedSVD` transformer
-works with any feature matrix,
-using it on tf–idf matrices is recommended over raw frequency counts
-in an LSA/document processing setting.
-In particular, sublinear scaling and inverse document frequency
-should be turned on (``sublinear_tf=True, use_idf=True``)
-to bring the feature values closer to a Gaussian distribution,
-compensating for LSA's erroneous assumptions about textual data.
+    .. note::
+        Most treatments of LSA in the natural language processing (NLP)
+        and information retrieval (IR) literature
+        swap the axes of the matrix :math:`X` so that it has shape
+        ``(n_features, n_samples)``.
+        We present LSA in a different way that matches the scikit-learn API better,
+        but the singular values found are the same.
 
-|details-end|
+    While the :class:`TruncatedSVD` transformer
+    works with any feature matrix,
+    using it on tf-idf matrices is recommended over raw frequency counts
+    in an LSA/document processing setting.
+    In particular, sublinear scaling and inverse document frequency
+    should be turned on (``sublinear_tf=True, use_idf=True``)
+    to bring the feature values closer to a Gaussian distribution,
+    compensating for LSA's erroneous assumptions about textual data.
 
-.. topic:: Examples:
+.. rubric:: Examples
 
-   * :ref:`sphx_glr_auto_examples_text_plot_document_clustering.py`
+* :ref:`sphx_glr_auto_examples_text_plot_document_clustering.py`
 
-.. topic:: References:
+.. rubric:: References
 
-  * Christopher D. Manning, Prabhakar Raghavan and Hinrich Schütze (2008),
-    *Introduction to Information Retrieval*, Cambridge University Press,
-    chapter 18: `Matrix decompositions & latent semantic indexing
-    <https://nlp.stanford.edu/IR-book/pdf/18lsi.pdf>`_
+* Christopher D. Manning, Prabhakar Raghavan and Hinrich Schütze (2008),
+  *Introduction to Information Retrieval*, Cambridge University Press,
+  chapter 18: `Matrix decompositions & latent semantic indexing
+  <https://nlp.stanford.edu/IR-book/pdf/18lsi.pdf>`_
 
 
 
@@ -509,9 +500,9 @@ the split code is filled with the negative part of the code vector, only with
 a positive sign. Therefore, the split_code is non-negative.
 
 
-.. topic:: Examples:
+.. rubric:: Examples
 
-    * :ref:`sphx_glr_auto_examples_decomposition_plot_sparse_coding.py`
+* :ref:`sphx_glr_auto_examples_decomposition_plot_sparse_coding.py`
 
 
 Generic dictionary learning
@@ -591,16 +582,16 @@ extracted from part of the image of a raccoon face looks like.
     :scale: 50%
 
 
-.. topic:: Examples:
+.. rubric:: Examples
 
-  * :ref:`sphx_glr_auto_examples_decomposition_plot_image_denoising.py`
+* :ref:`sphx_glr_auto_examples_decomposition_plot_image_denoising.py`
 
 
-.. topic:: References:
+.. rubric:: References
 
-  * `"Online dictionary learning for sparse coding"
-    <https://www.di.ens.fr/sierra/pdfs/icml09.pdf>`_
-    J. Mairal, F. Bach, J. Ponce, G. Sapiro, 2009
+* `"Online dictionary learning for sparse coding"
+  <https://www.di.ens.fr/sierra/pdfs/icml09.pdf>`_
+  J. Mairal, F. Bach, J. Ponce, G. Sapiro, 2009
 
 .. _MiniBatchDictionaryLearning:
 
@@ -731,10 +722,10 @@ Varimax rotation maximizes the sum of the variances of the squared loadings,
 i.e., it tends to produce sparser factors, which are influenced by only a few
 features each (the "simple structure"). See e.g., the first example below.
 
-.. topic:: Examples:
+.. rubric:: Examples
 
-    * :ref:`sphx_glr_auto_examples_decomposition_plot_varimax_fa.py`
-    * :ref:`sphx_glr_auto_examples_decomposition_plot_pca_vs_fa_model_selection.py`
+* :ref:`sphx_glr_auto_examples_decomposition_plot_varimax_fa.py`
+* :ref:`sphx_glr_auto_examples_decomposition_plot_pca_vs_fa_model_selection.py`
 
 
 .. _ICA:
@@ -773,11 +764,11 @@ components with some sparsity:
 
 .. centered:: |pca_img4| |ica_img4|
 
-.. topic:: Examples:
+.. rubric:: Examples
 
-    * :ref:`sphx_glr_auto_examples_decomposition_plot_ica_blind_source_separation.py`
-    * :ref:`sphx_glr_auto_examples_decomposition_plot_ica_vs_pca.py`
-    * :ref:`sphx_glr_auto_examples_decomposition_plot_faces_decomposition.py`
+* :ref:`sphx_glr_auto_examples_decomposition_plot_ica_blind_source_separation.py`
+* :ref:`sphx_glr_auto_examples_decomposition_plot_ica_vs_pca.py`
+* :ref:`sphx_glr_auto_examples_decomposition_plot_faces_decomposition.py`
 
 
 .. _NMF:
@@ -900,24 +891,20 @@ Note that this definition is not valid if :math:`\beta \in (0; 1)`, yet it can
 be continuously extended to the definitions of :math:`d_{KL}` and :math:`d_{IS}`
 respectively.
 
-|details-start|
-**NMF implemented solvers**
-|details-split|
-
-:class:`NMF` implements two solvers, using Coordinate Descent ('cd') [5]_, and
-Multiplicative Update ('mu') [6]_. The 'mu' solver can optimize every
-beta-divergence, including of course the Frobenius norm (:math:`\beta=2`), the
-(generalized) Kullback-Leibler divergence (:math:`\beta=1`) and the
-Itakura-Saito divergence (:math:`\beta=0`). Note that for
-:math:`\beta \in (1; 2)`, the 'mu' solver is significantly faster than for other
-values of :math:`\beta`. Note also that with a negative (or 0, i.e.
-'itakura-saito') :math:`\beta`, the input matrix cannot contain zero values.
+.. dropdown:: NMF implemented solvers
 
-The 'cd' solver can only optimize the Frobenius norm. Due to the
-underlying non-convexity of NMF, the different solvers may converge to
-different minima, even when optimizing the same distance function.
+    :class:`NMF` implements two solvers, using Coordinate Descent ('cd') [5]_, and
+    Multiplicative Update ('mu') [6]_. The 'mu' solver can optimize every
+    beta-divergence, including of course the Frobenius norm (:math:`\beta=2`), the
+    (generalized) Kullback-Leibler divergence (:math:`\beta=1`) and the
+    Itakura-Saito divergence (:math:`\beta=0`). Note that for
+    :math:`\beta \in (1; 2)`, the 'mu' solver is significantly faster than for other
+    values of :math:`\beta`. Note also that with a negative (or 0, i.e.
+    'itakura-saito') :math:`\beta`, the input matrix cannot contain zero values.
 
-|details-end|
+    The 'cd' solver can only optimize the Frobenius norm. Due to the
+    underlying non-convexity of NMF, the different solvers may converge to
+    different minima, even when optimizing the same distance function.
 
 NMF is best used with the ``fit_transform`` method, which returns the matrix W.
 The matrix H is stored into the fitted model in the ``components_`` attribute;
@@ -935,10 +922,10 @@ stored components::
 
 
 
-.. topic:: Examples:
+.. rubric:: Examples
 
-    * :ref:`sphx_glr_auto_examples_decomposition_plot_faces_decomposition.py`
-    * :ref:`sphx_glr_auto_examples_applications_plot_topics_extraction_with_nmf_lda.py`
+* :ref:`sphx_glr_auto_examples_decomposition_plot_faces_decomposition.py`
+* :ref:`sphx_glr_auto_examples_applications_plot_topics_extraction_with_nmf_lda.py`
 
 .. _MiniBatchNMF:
 
@@ -963,33 +950,33 @@ The estimator also implements ``partial_fit``, which updates ``H`` by iterating
 only once over a mini-batch. This can be used for online learning when the data
 is not readily available from the start, or when the data does not fit into memory.
 
-.. topic:: References:
+.. rubric:: References
 
-    .. [1] `"Learning the parts of objects by non-negative matrix factorization"
-      <http://www.cs.columbia.edu/~blei/fogm/2020F/readings/LeeSeung1999.pdf>`_
-      D. Lee, S. Seung, 1999
+.. [1] `"Learning the parts of objects by non-negative matrix factorization"
+  <http://www.cs.columbia.edu/~blei/fogm/2020F/readings/LeeSeung1999.pdf>`_
+  D. Lee, S. Seung, 1999
 
-    .. [2] `"Non-negative Matrix Factorization with Sparseness Constraints"
-      <https://www.jmlr.org/papers/volume5/hoyer04a/hoyer04a.pdf>`_
-      P. Hoyer, 2004
+.. [2] `"Non-negative Matrix Factorization with Sparseness Constraints"
+  <https://www.jmlr.org/papers/volume5/hoyer04a/hoyer04a.pdf>`_
+  P. Hoyer, 2004
 
-    .. [4] `"SVD based initialization: A head start for nonnegative
-      matrix factorization"
-      <https://www.boutsidis.org/Boutsidis_PRE_08.pdf>`_
-      C. Boutsidis, E. Gallopoulos, 2008
+.. [4] `"SVD based initialization: A head start for nonnegative
+  matrix factorization"
+  <https://www.boutsidis.org/Boutsidis_PRE_08.pdf>`_
+  C. Boutsidis, E. Gallopoulos, 2008
 
-    .. [5] `"Fast local algorithms for large scale nonnegative matrix and tensor
-      factorizations."
-      <https://www.researchgate.net/profile/Anh-Huy-Phan/publication/220241471_Fast_Local_Algorithms_for_Large_Scale_Nonnegative_Matrix_and_Tensor_Factorizations>`_
-      A. Cichocki, A. Phan, 2009
+.. [5] `"Fast local algorithms for large scale nonnegative matrix and tensor
+  factorizations."
+  <https://www.researchgate.net/profile/Anh-Huy-Phan/publication/220241471_Fast_Local_Algorithms_for_Large_Scale_Nonnegative_Matrix_and_Tensor_Factorizations>`_
+  A. Cichocki, A. Phan, 2009
 
-    .. [6] :arxiv:`"Algorithms for nonnegative matrix factorization with
-           the beta-divergence" <1010.1763>`
-           C. Fevotte, J. Idier, 2011
+.. [6] :arxiv:`"Algorithms for nonnegative matrix factorization with
+  the beta-divergence" <1010.1763>`
+  C. Fevotte, J. Idier, 2011
 
-    .. [7] :arxiv:`"Online algorithms for nonnegative matrix factorization with the
-       Itakura-Saito divergence" <1106.4198>`
-       A. Lefevre, F. Bach, C. Fevotte, 2011
+.. [7] :arxiv:`"Online algorithms for nonnegative matrix factorization with the
+  Itakura-Saito divergence" <1106.4198>`
+  A. Lefevre, F. Bach, C. Fevotte, 2011
 
 .. _LatentDirichletAllocation:
 
@@ -1021,51 +1008,48 @@ of topics in the corpus and the distribution of words in the documents.
 The goal of LDA is to use the observed words to infer the hidden topic
 structure.
 
-|details-start|
-**Details on modeling text corpora**
-|details-split|
+.. dropdown:: Details on modeling text corpora
 
-When modeling text corpora, the model assumes the following generative process
-for a corpus with :math:`D` documents and :math:`K` topics, with :math:`K`
-corresponding to `n_components` in the API:
+    When modeling text corpora, the model assumes the following generative process
+    for a corpus with :math:`D` documents and :math:`K` topics, with :math:`K`
+    corresponding to `n_components` in the API:
 
-1. For each topic :math:`k \in K`, draw :math:`\beta_k \sim
-   \mathrm{Dirichlet}(\eta)`. This provides a distribution over the words,
-   i.e. the probability of a word appearing in topic :math:`k`.
-   :math:`\eta` corresponds to `topic_word_prior`.
+    1. For each topic :math:`k \in K`, draw :math:`\beta_k \sim
+       \mathrm{Dirichlet}(\eta)`. This provides a distribution over the words,
+       i.e. the probability of a word appearing in topic :math:`k`.
+       :math:`\eta` corresponds to `topic_word_prior`.
 
-2. For each document :math:`d \in D`, draw the topic proportions
-   :math:`\theta_d \sim \mathrm{Dirichlet}(\alpha)`. :math:`\alpha`
-   corresponds to `doc_topic_prior`.
+    2. For each document :math:`d \in D`, draw the topic proportions
+       :math:`\theta_d \sim \mathrm{Dirichlet}(\alpha)`. :math:`\alpha`
+       corresponds to `doc_topic_prior`.
 
-3. For each word :math:`i` in document :math:`d`:
+    3. For each word :math:`i` in document :math:`d`:
 
-   a. Draw the topic assignment :math:`z_{di} \sim \mathrm{Multinomial}
-      (\theta_d)`
-   b. Draw the observed word :math:`w_{ij} \sim \mathrm{Multinomial}
-      (\beta_{z_{di}})`
+       a. Draw the topic assignment :math:`z_{di} \sim \mathrm{Multinomial}
+          (\theta_d)`
+       b. Draw the observed word :math:`w_{ij} \sim \mathrm{Multinomial}
+          (\beta_{z_{di}})`
 
-For parameter estimation, the posterior distribution is:
+    For parameter estimation, the posterior distribution is:
 
-.. math::
-  p(z, \theta, \beta |w, \alpha, \eta) =
-    \frac{p(z, \theta, \beta|\alpha, \eta)}{p(w|\alpha, \eta)}
+    .. math::
+        p(z, \theta, \beta |w, \alpha, \eta) =
+        \frac{p(z, \theta, \beta|\alpha, \eta)}{p(w|\alpha, \eta)}
 
-Since the posterior is intractable, variational Bayesian method
-uses a simpler distribution :math:`q(z,\theta,\beta | \lambda, \phi, \gamma)`
-to approximate it, and those variational parameters :math:`\lambda`,
-:math:`\phi`, :math:`\gamma` are optimized to maximize the Evidence
-Lower Bound (ELBO):
+    Since the posterior is intractable, variational Bayesian method
+    uses a simpler distribution :math:`q(z,\theta,\beta | \lambda, \phi, \gamma)`
+    to approximate it, and those variational parameters :math:`\lambda`,
+    :math:`\phi`, :math:`\gamma` are optimized to maximize the Evidence
+    Lower Bound (ELBO):
 
-.. math::
-  \log\: P(w | \alpha, \eta) \geq L(w,\phi,\gamma,\lambda) \overset{\triangle}{=}
-    E_{q}[\log\:p(w,z,\theta,\beta|\alpha,\eta)] - E_{q}[\log\:q(z, \theta, \beta)]
+    .. math::
+        \log\: P(w | \alpha, \eta) \geq L(w,\phi,\gamma,\lambda) \overset{\triangle}{=}
+        E_{q}[\log\:p(w,z,\theta,\beta|\alpha,\eta)] - E_{q}[\log\:q(z, \theta, \beta)]
 
-Maximizing ELBO is equivalent to minimizing the Kullback-Leibler(KL) divergence
-between :math:`q(z,\theta,\beta)` and the true posterior
-:math:`p(z, \theta, \beta |w, \alpha, \eta)`.
+    Maximizing ELBO is equivalent to minimizing the Kullback-Leibler(KL) divergence
+    between :math:`q(z,\theta,\beta)` and the true posterior
+    :math:`p(z, \theta, \beta |w, \alpha, \eta)`.
 
-|details-end|
 
 :class:`LatentDirichletAllocation` implements the online variational Bayes
 algorithm and supports both online and batch update methods.
@@ -1087,27 +1071,27 @@ can be calculated from ``transform`` method.
 :class:`LatentDirichletAllocation` also implements ``partial_fit`` method. This is used
 when data can be fetched sequentially.
 
-.. topic:: Examples:
+.. rubric:: Examples
 
-    * :ref:`sphx_glr_auto_examples_applications_plot_topics_extraction_with_nmf_lda.py`
+* :ref:`sphx_glr_auto_examples_applications_plot_topics_extraction_with_nmf_lda.py`
 
-.. topic:: References:
+.. rubric:: References
 
-    * `"Latent Dirichlet Allocation"
-      <https://www.jmlr.org/papers/volume3/blei03a/blei03a.pdf>`_
-      D. Blei, A. Ng, M. Jordan, 2003
+* `"Latent Dirichlet Allocation"
+  <https://www.jmlr.org/papers/volume3/blei03a/blei03a.pdf>`_
+  D. Blei, A. Ng, M. Jordan, 2003
 
-    * `"Online Learning for Latent Dirichlet Allocation”
-      <https://papers.nips.cc/paper/3902-online-learning-for-latent-dirichlet-allocation.pdf>`_
-      M. Hoffman, D. Blei, F. Bach, 2010
+* `"Online Learning for Latent Dirichlet Allocation”
+  <https://papers.nips.cc/paper/3902-online-learning-for-latent-dirichlet-allocation.pdf>`_
+  M. Hoffman, D. Blei, F. Bach, 2010
 
-    * `"Stochastic Variational Inference"
-      <https://www.cs.columbia.edu/~blei/papers/HoffmanBleiWangPaisley2013.pdf>`_
-      M. Hoffman, D. Blei, C. Wang, J. Paisley, 2013
+* `"Stochastic Variational Inference"
+  <https://www.cs.columbia.edu/~blei/papers/HoffmanBleiWangPaisley2013.pdf>`_
+  M. Hoffman, D. Blei, C. Wang, J. Paisley, 2013
 
-    * `"The varimax criterion for analytic rotation in factor analysis"
-      <https://link.springer.com/article/10.1007%2FBF02289233>`_
-      H. F. Kaiser, 1958
+* `"The varimax criterion for analytic rotation in factor analysis"
+  <https://link.springer.com/article/10.1007%2FBF02289233>`_
+  H. F. Kaiser, 1958
 
 See also :ref:`nca_dim_reduction` for dimensionality reduction with
 Neighborhood Components Analysis.
diff --git a/doc/modules/density.rst b/doc/modules/density.rst
index 5a9b456010aa3..39264f226185d 100644
--- a/doc/modules/density.rst
+++ b/doc/modules/density.rst
@@ -113,37 +113,34 @@ forms, which are shown in the following figure:
 
 .. centered:: |kde_kernels|
 
-|details-start|
-**kernels' mathematical expressions**
-|details-split|
+.. dropdown:: Kernels' mathematical expressions
 
-The form of these kernels is as follows:
+  The form of these kernels is as follows:
 
-* Gaussian kernel (``kernel = 'gaussian'``)
+  * Gaussian kernel (``kernel = 'gaussian'``)
 
-  :math:`K(x; h) \propto \exp(- \frac{x^2}{2h^2} )`
+    :math:`K(x; h) \propto \exp(- \frac{x^2}{2h^2} )`
 
-* Tophat kernel (``kernel = 'tophat'``)
+  * Tophat kernel (``kernel = 'tophat'``)
 
-  :math:`K(x; h) \propto 1` if :math:`x < h`
+    :math:`K(x; h) \propto 1` if :math:`x < h`
 
-* Epanechnikov kernel (``kernel = 'epanechnikov'``)
+  * Epanechnikov kernel (``kernel = 'epanechnikov'``)
 
-  :math:`K(x; h) \propto 1 - \frac{x^2}{h^2}`
+    :math:`K(x; h) \propto 1 - \frac{x^2}{h^2}`
 
-* Exponential kernel (``kernel = 'exponential'``)
+  * Exponential kernel (``kernel = 'exponential'``)
 
-  :math:`K(x; h) \propto \exp(-x/h)`
+    :math:`K(x; h) \propto \exp(-x/h)`
 
-* Linear kernel (``kernel = 'linear'``)
+  * Linear kernel (``kernel = 'linear'``)
 
-  :math:`K(x; h) \propto 1 - x/h` if :math:`x < h`
+    :math:`K(x; h) \propto 1 - x/h` if :math:`x < h`
 
-* Cosine kernel (``kernel = 'cosine'``)
+  * Cosine kernel (``kernel = 'cosine'``)
 
-  :math:`K(x; h) \propto \cos(\frac{\pi x}{2h})` if :math:`x < h`
+    :math:`K(x; h) \propto \cos(\frac{\pi x}{2h})` if :math:`x < h`
 
-|details-end|
 
 The kernel density estimator can be used with any of the valid distance
 metrics (see :class:`~sklearn.metrics.DistanceMetric` for a list of
@@ -177,14 +174,14 @@ on a PCA projection of the data:
 The "new" data consists of linear combinations of the input data, with weights
 probabilistically drawn given the KDE model.
 
-.. topic:: Examples:
+.. rubric:: Examples
 
-  * :ref:`sphx_glr_auto_examples_neighbors_plot_kde_1d.py`: computation of simple kernel
-    density estimates in one dimension.
+* :ref:`sphx_glr_auto_examples_neighbors_plot_kde_1d.py`: computation of simple kernel
+  density estimates in one dimension.
 
-  * :ref:`sphx_glr_auto_examples_neighbors_plot_digits_kde_sampling.py`: an example of using
-    Kernel Density estimation to learn a generative model of the hand-written
-    digits data, and drawing new samples from this model.
+* :ref:`sphx_glr_auto_examples_neighbors_plot_digits_kde_sampling.py`: an example of using
+  Kernel Density estimation to learn a generative model of the hand-written
+  digits data, and drawing new samples from this model.
 
-  * :ref:`sphx_glr_auto_examples_neighbors_plot_species_kde.py`: an example of Kernel Density
-    estimation using the Haversine distance metric to visualize geospatial data
+* :ref:`sphx_glr_auto_examples_neighbors_plot_species_kde.py`: an example of Kernel Density
+  estimation using the Haversine distance metric to visualize geospatial data
diff --git a/doc/modules/ensemble.rst b/doc/modules/ensemble.rst
index 4237d023973f7..08c831431d197 100644
--- a/doc/modules/ensemble.rst
+++ b/doc/modules/ensemble.rst
@@ -18,10 +18,6 @@ trees, in averaging methods such as :ref:`Bagging methods <bagging>`,
 :ref:`model stacking <stacking>`, or :ref:`Voting <voting_classifier>`, or in
 boosting, as :ref:`AdaBoost <adaboost>`.
 
-.. contents::
-    :local:
-    :depth: 1
-
 .. _gradient_boosting:
 
 Gradient-boosted trees
@@ -78,10 +74,10 @@ estimators is slightly different, and some of the features from
 :class:`GradientBoostingClassifier` and :class:`GradientBoostingRegressor`
 are not yet supported, for instance some loss functions.
 
-.. topic:: Examples:
+.. rubric:: Examples
 
-  * :ref:`sphx_glr_auto_examples_inspection_plot_partial_dependence.py`
-  * :ref:`sphx_glr_auto_examples_ensemble_plot_forest_hist_grad_boosting_comparison.py`
+* :ref:`sphx_glr_auto_examples_inspection_plot_partial_dependence.py`
+* :ref:`sphx_glr_auto_examples_ensemble_plot_forest_hist_grad_boosting_comparison.py`
 
 Usage
 ^^^^^
@@ -126,43 +122,40 @@ in [XGBoost]_):
 
     \mathcal{L}(\phi) =  \sum_i l(\hat{y}_i, y_i) + \frac12 \sum_k \lambda ||w_k||^2
 
-|details-start|
-**Details on l2 regularization**:
-|details-split|
-
-It is important to notice that the loss term :math:`l(\hat{y}_i, y_i)` describes
-only half of the actual loss function except for the pinball loss and absolute
-error.
-
-The index :math:`k` refers to the k-th tree in the ensemble of trees. In the
-case of regression and binary classification, gradient boosting models grow one
-tree per iteration, then :math:`k` runs up to `max_iter`. In the case of
-multiclass classification problems, the maximal value of the index :math:`k` is
-`n_classes` :math:`\times` `max_iter`.
-
-If :math:`T_k` denotes the number of leaves in the k-th tree, then :math:`w_k`
-is a vector of length :math:`T_k`, which contains the leaf values of the form `w
-= -sum_gradient / (sum_hessian + l2_regularization)` (see equation (5) in
-[XGBoost]_).
-
-The leaf values :math:`w_k` are derived by dividing the sum of the gradients of
-the loss function by the combined sum of hessians. Adding the regularization to
-the denominator penalizes the leaves with small hessians (flat regions),
-resulting in smaller updates. Those :math:`w_k` values contribute then to the
-model's prediction for a given input that ends up in the corresponding leaf. The
-final prediction is the sum of the base prediction and the contributions from
-each tree. The result of that sum is then transformed by the inverse link
-function depending on the choice of the loss function (see
-:ref:`gradient_boosting_formulation`).
-
-Notice that the original paper [XGBoost]_ introduces a term :math:`\gamma\sum_k
-T_k` that penalizes the number of leaves (making it a smooth version of
-`max_leaf_nodes`) not presented here as it is not implemented in scikit-learn;
-whereas :math:`\lambda` penalizes the magnitude of the individual tree
-predictions before being rescaled by the learning rate, see
-:ref:`gradient_boosting_shrinkage`.
-
-|details-end|
+.. dropdown:: Details on l2 regularization
+
+  It is important to notice that the loss term :math:`l(\hat{y}_i, y_i)` describes
+  only half of the actual loss function except for the pinball loss and absolute
+  error.
+
+  The index :math:`k` refers to the k-th tree in the ensemble of trees. In the
+  case of regression and binary classification, gradient boosting models grow one
+  tree per iteration, then :math:`k` runs up to `max_iter`. In the case of
+  multiclass classification problems, the maximal value of the index :math:`k` is
+  `n_classes` :math:`\times` `max_iter`.
+
+  If :math:`T_k` denotes the number of leaves in the k-th tree, then :math:`w_k`
+  is a vector of length :math:`T_k`, which contains the leaf values of the form `w
+  = -sum_gradient / (sum_hessian + l2_regularization)` (see equation (5) in
+  [XGBoost]_).
+
+  The leaf values :math:`w_k` are derived by dividing the sum of the gradients of
+  the loss function by the combined sum of hessians. Adding the regularization to
+  the denominator penalizes the leaves with small hessians (flat regions),
+  resulting in smaller updates. Those :math:`w_k` values contribute then to the
+  model's prediction for a given input that ends up in the corresponding leaf. The
+  final prediction is the sum of the base prediction and the contributions from
+  each tree. The result of that sum is then transformed by the inverse link
+  function depending on the choice of the loss function (see
+  :ref:`gradient_boosting_formulation`).
+
+  Notice that the original paper [XGBoost]_ introduces a term :math:`\gamma\sum_k
+  T_k` that penalizes the number of leaves (making it a smooth version of
+  `max_leaf_nodes`) not presented here as it is not implemented in scikit-learn;
+  whereas :math:`\lambda` penalizes the magnitude of the individual tree
+  predictions before being rescaled by the learning rate, see
+  :ref:`gradient_boosting_shrinkage`.
+
 
 Note that **early-stopping is enabled by default if the number of samples is
 larger than 10,000**. The early-stopping behaviour is controlled via the
@@ -213,9 +206,9 @@ If no missing values were encountered for a given feature during training,
 then samples with missing values are mapped to whichever child has the most
 samples.
 
-.. topic:: Examples:
+.. rubric:: Examples
 
-  * :ref:`sphx_glr_auto_examples_ensemble_plot_hgbt_regression.py`
+* :ref:`sphx_glr_auto_examples_ensemble_plot_hgbt_regression.py`
 
 .. _sw_hgbdt:
 
@@ -302,30 +295,25 @@ the most samples (just like for continuous features). When predicting,
 categories that were not seen during fit time will be treated as missing
 values.
 
-|details-start|
-**Split finding with categorical features**:
-|details-split|
+.. dropdown:: Split finding with categorical features
 
-The canonical way of considering
-categorical splits in a tree is to consider
-all of the :math:`2^{K - 1} - 1` partitions, where :math:`K` is the number of
-categories. This can quickly become prohibitive when :math:`K` is large.
-Fortunately, since gradient boosting trees are always regression trees (even
-for classification problems), there exist a faster strategy that can yield
-equivalent splits. First, the categories of a feature are sorted according to
-the variance of the target, for each category `k`. Once the categories are
-sorted, one can consider *continuous partitions*, i.e. treat the categories
-as if they were ordered continuous values (see Fisher [Fisher1958]_ for a
-formal proof). As a result, only :math:`K - 1` splits need to be considered
-instead of :math:`2^{K - 1} - 1`. The initial sorting is a
-:math:`\mathcal{O}(K \log(K))` operation, leading to a total complexity of
-:math:`\mathcal{O}(K \log(K) + K)`, instead of :math:`\mathcal{O}(2^K)`.
+  The canonical way of considering categorical splits in a tree is to consider
+  all of the :math:`2^{K - 1} - 1` partitions, where :math:`K` is the number of
+  categories. This can quickly become prohibitive when :math:`K` is large.
+  Fortunately, since gradient boosting trees are always regression trees (even
+  for classification problems), there exist a faster strategy that can yield
+  equivalent splits. First, the categories of a feature are sorted according to
+  the variance of the target, for each category `k`. Once the categories are
+  sorted, one can consider *continuous partitions*, i.e. treat the categories
+  as if they were ordered continuous values (see Fisher [Fisher1958]_ for a
+  formal proof). As a result, only :math:`K - 1` splits need to be considered
+  instead of :math:`2^{K - 1} - 1`. The initial sorting is a
+  :math:`\mathcal{O}(K \log(K))` operation, leading to a total complexity of
+  :math:`\mathcal{O}(K \log(K) + K)`, instead of :math:`\mathcal{O}(2^K)`.
 
-|details-end|
+.. rubric:: Examples
 
-.. topic:: Examples:
-
-  * :ref:`sphx_glr_auto_examples_ensemble_plot_gradient_boosting_categorical.py`
+* :ref:`sphx_glr_auto_examples_ensemble_plot_gradient_boosting_categorical.py`
 
 .. _monotonic_cst_gbdt:
 
@@ -378,10 +366,10 @@ Also, monotonic constraints are not supported for multiclass classification.
     Since categories are unordered quantities, it is not possible to enforce
     monotonic constraints on categorical features.
 
-.. topic:: Examples:
+.. rubric:: Examples
 
-  * :ref:`sphx_glr_auto_examples_ensemble_plot_monotonic_constraints.py`
-  * :ref:`sphx_glr_auto_examples_ensemble_plot_hgbt_regression.py`
+* :ref:`sphx_glr_auto_examples_ensemble_plot_monotonic_constraints.py`
+* :ref:`sphx_glr_auto_examples_ensemble_plot_hgbt_regression.py`
 
 .. _interaction_cst_hgbt:
 
@@ -414,16 +402,16 @@ Note that features not listed in ``interaction_cst`` are automatically
 assigned an interaction group for themselves. With again 3 features, this
 means that ``[{0}]`` is equivalent to ``[{0}, {1, 2}]``.
 
-.. topic:: Examples:
+.. rubric:: Examples
 
- * :ref:`sphx_glr_auto_examples_inspection_plot_partial_dependence.py`
+* :ref:`sphx_glr_auto_examples_inspection_plot_partial_dependence.py`
 
-.. topic:: References
+.. rubric:: References
 
-  .. [Mayer2022] M. Mayer, S.C. Bourassa, M. Hoesli, and D.F. Scognamiglio.
-     2022. :doi:`Machine Learning Applications to Land and Structure Valuation
-     <10.3390/jrfm15050193>`.
-     Journal of Risk and Financial Management 15, no. 5: 193
+.. [Mayer2022] M. Mayer, S.C. Bourassa, M. Hoesli, and D.F. Scognamiglio.
+    2022. :doi:`Machine Learning Applications to Land and Structure Valuation
+    <10.3390/jrfm15050193>`.
+    Journal of Risk and Financial Management 15, no. 5: 193
 
 Low-level parallelism
 ^^^^^^^^^^^^^^^^^^^^^
@@ -479,18 +467,18 @@ Finally, many parts of the implementation of
 :class:`HistGradientBoostingClassifier` and
 :class:`HistGradientBoostingRegressor` are parallelized.
 
-.. topic:: References
+.. rubric:: References
 
-  .. [XGBoost] Tianqi Chen, Carlos Guestrin, :arxiv:`"XGBoost: A Scalable Tree
-     Boosting System" <1603.02754>`
+.. [XGBoost] Tianqi Chen, Carlos Guestrin, :arxiv:`"XGBoost: A Scalable Tree
+   Boosting System" <1603.02754>`
 
-  .. [LightGBM] Ke et. al. `"LightGBM: A Highly Efficient Gradient
-     BoostingDecision Tree" <https://papers.nips.cc/paper/
-     6907-lightgbm-a-highly-efficient-gradient-boosting-decision-tree>`_
+.. [LightGBM] Ke et. al. `"LightGBM: A Highly Efficient Gradient
+   BoostingDecision Tree" <https://papers.nips.cc/paper/
+   6907-lightgbm-a-highly-efficient-gradient-boosting-decision-tree>`_
 
-  .. [Fisher1958] Fisher, W.D. (1958). `"On Grouping for Maximum Homogeneity"
-     <http://csiss.ncgia.ucsb.edu/SPACE/workshops/2004/SAC/files/fisher.pdf>`_
-     Journal of the American Statistical Association, 53, 789-798.
+.. [Fisher1958] Fisher, W.D. (1958). `"On Grouping for Maximum Homogeneity"
+   <http://csiss.ncgia.ucsb.edu/SPACE/workshops/2004/SAC/files/fisher.pdf>`_
+   Journal of the American Statistical Association, 53, 789-798.
 
 
 
@@ -501,96 +489,88 @@ The usage and the parameters of :class:`GradientBoostingClassifier` and
 :class:`GradientBoostingRegressor` are described below. The 2 most important
 parameters of these estimators are `n_estimators` and `learning_rate`.
 
-|details-start|
-**Classification**
-|details-split|
-
-:class:`GradientBoostingClassifier` supports both binary and multi-class
-classification.
-The following example shows how to fit a gradient boosting classifier
-with 100 decision stumps as weak learners::
-
-    >>> from sklearn.datasets import make_hastie_10_2
-    >>> from sklearn.ensemble import GradientBoostingClassifier
-
-    >>> X, y = make_hastie_10_2(random_state=0)
-    >>> X_train, X_test = X[:2000], X[2000:]
-    >>> y_train, y_test = y[:2000], y[2000:]
-
-    >>> clf = GradientBoostingClassifier(n_estimators=100, learning_rate=1.0,
-    ...     max_depth=1, random_state=0).fit(X_train, y_train)
-    >>> clf.score(X_test, y_test)
-    0.913...
-
-The number of weak learners (i.e. regression trees) is controlled by the
-parameter ``n_estimators``; :ref:`The size of each tree
-<gradient_boosting_tree_size>` can be controlled either by setting the tree
-depth via ``max_depth`` or by setting the number of leaf nodes via
-``max_leaf_nodes``. The ``learning_rate`` is a hyper-parameter in the range
-(0.0, 1.0] that controls overfitting via :ref:`shrinkage
-<gradient_boosting_shrinkage>` .
-
-.. note::
-
-   Classification with more than 2 classes requires the induction
-   of ``n_classes`` regression trees at each iteration,
-   thus, the total number of induced trees equals
-   ``n_classes * n_estimators``. For datasets with a large number
-   of classes we strongly recommend to use
-   :class:`HistGradientBoostingClassifier` as an alternative to
-   :class:`GradientBoostingClassifier` .
-
-|details-end|
-
-|details-start|
-**Regression**
-|details-split|
-
-:class:`GradientBoostingRegressor` supports a number of
-:ref:`different loss functions <gradient_boosting_loss>`
-for regression which can be specified via the argument
-``loss``; the default loss function for regression is squared error
-(``'squared_error'``).
-
-::
-
-    >>> import numpy as np
-    >>> from sklearn.metrics import mean_squared_error
-    >>> from sklearn.datasets import make_friedman1
-    >>> from sklearn.ensemble import GradientBoostingRegressor
-
-    >>> X, y = make_friedman1(n_samples=1200, random_state=0, noise=1.0)
-    >>> X_train, X_test = X[:200], X[200:]
-    >>> y_train, y_test = y[:200], y[200:]
-    >>> est = GradientBoostingRegressor(
-    ...     n_estimators=100, learning_rate=0.1, max_depth=1, random_state=0,
-    ...     loss='squared_error'
-    ... ).fit(X_train, y_train)
-    >>> mean_squared_error(y_test, est.predict(X_test))
-    5.00...
-
-The figure below shows the results of applying :class:`GradientBoostingRegressor`
-with least squares loss and 500 base learners to the diabetes dataset
-(:func:`sklearn.datasets.load_diabetes`).
-The plot shows the train and test error at each iteration.
-The train error at each iteration is stored in the
-`train_score_` attribute of the gradient boosting model.
-The test error at each iterations can be obtained
-via the :meth:`~GradientBoostingRegressor.staged_predict` method which returns a
-generator that yields the predictions at each stage. Plots like these can be used
-to determine the optimal number of trees (i.e. ``n_estimators``) by early stopping.
-
-.. figure:: ../auto_examples/ensemble/images/sphx_glr_plot_gradient_boosting_regression_001.png
-   :target: ../auto_examples/ensemble/plot_gradient_boosting_regression.html
-   :align: center
-   :scale: 75
-
-|details-end|
+.. dropdown:: Classification
+
+  :class:`GradientBoostingClassifier` supports both binary and multi-class
+  classification.
+  The following example shows how to fit a gradient boosting classifier
+  with 100 decision stumps as weak learners::
+
+      >>> from sklearn.datasets import make_hastie_10_2
+      >>> from sklearn.ensemble import GradientBoostingClassifier
+
+      >>> X, y = make_hastie_10_2(random_state=0)
+      >>> X_train, X_test = X[:2000], X[2000:]
+      >>> y_train, y_test = y[:2000], y[2000:]
+
+      >>> clf = GradientBoostingClassifier(n_estimators=100, learning_rate=1.0,
+      ...     max_depth=1, random_state=0).fit(X_train, y_train)
+      >>> clf.score(X_test, y_test)
+      0.913...
+
+  The number of weak learners (i.e. regression trees) is controlled by the
+  parameter ``n_estimators``; :ref:`The size of each tree
+  <gradient_boosting_tree_size>` can be controlled either by setting the tree
+  depth via ``max_depth`` or by setting the number of leaf nodes via
+  ``max_leaf_nodes``. The ``learning_rate`` is a hyper-parameter in the range
+  (0.0, 1.0] that controls overfitting via :ref:`shrinkage
+  <gradient_boosting_shrinkage>` .
+
+  .. note::
+
+    Classification with more than 2 classes requires the induction
+    of ``n_classes`` regression trees at each iteration,
+    thus, the total number of induced trees equals
+    ``n_classes * n_estimators``. For datasets with a large number
+    of classes we strongly recommend to use
+    :class:`HistGradientBoostingClassifier` as an alternative to
+    :class:`GradientBoostingClassifier` .
+
+.. dropdown:: Regression
+
+  :class:`GradientBoostingRegressor` supports a number of
+  :ref:`different loss functions <gradient_boosting_loss>`
+  for regression which can be specified via the argument
+  ``loss``; the default loss function for regression is squared error
+  (``'squared_error'``).
+
+  ::
+
+      >>> import numpy as np
+      >>> from sklearn.metrics import mean_squared_error
+      >>> from sklearn.datasets import make_friedman1
+      >>> from sklearn.ensemble import GradientBoostingRegressor
+
+      >>> X, y = make_friedman1(n_samples=1200, random_state=0, noise=1.0)
+      >>> X_train, X_test = X[:200], X[200:]
+      >>> y_train, y_test = y[:200], y[200:]
+      >>> est = GradientBoostingRegressor(
+      ...     n_estimators=100, learning_rate=0.1, max_depth=1, random_state=0,
+      ...     loss='squared_error'
+      ... ).fit(X_train, y_train)
+      >>> mean_squared_error(y_test, est.predict(X_test))
+      5.00...
+
+  The figure below shows the results of applying :class:`GradientBoostingRegressor`
+  with least squares loss and 500 base learners to the diabetes dataset
+  (:func:`sklearn.datasets.load_diabetes`).
+  The plot shows the train and test error at each iteration.
+  The train error at each iteration is stored in the
+  `train_score_` attribute of the gradient boosting model.
+  The test error at each iterations can be obtained
+  via the :meth:`~GradientBoostingRegressor.staged_predict` method which returns a
+  generator that yields the predictions at each stage. Plots like these can be used
+  to determine the optimal number of trees (i.e. ``n_estimators``) by early stopping.
+
+  .. figure:: ../auto_examples/ensemble/images/sphx_glr_plot_gradient_boosting_regression_001.png
+    :target: ../auto_examples/ensemble/plot_gradient_boosting_regression.html
+    :align: center
+    :scale: 75
 
-.. topic:: Examples:
+.. rubric:: Examples
 
- * :ref:`sphx_glr_auto_examples_ensemble_plot_gradient_boosting_regression.py`
- * :ref:`sphx_glr_auto_examples_ensemble_plot_gradient_boosting_oob.py`
+* :ref:`sphx_glr_auto_examples_ensemble_plot_gradient_boosting_regression.py`
+* :ref:`sphx_glr_auto_examples_ensemble_plot_gradient_boosting_oob.py`
 
 .. _gradient_boosting_warm_start:
 
@@ -603,7 +583,22 @@ fitted model.
 
 ::
 
-  >>> _ = est.set_params(n_estimators=200, warm_start=True)  # set warm_start and new nr of trees
+  >>> import numpy as np
+  >>> from sklearn.metrics import mean_squared_error
+  >>> from sklearn.datasets import make_friedman1
+  >>> from sklearn.ensemble import GradientBoostingRegressor
+
+  >>> X, y = make_friedman1(n_samples=1200, random_state=0, noise=1.0)
+  >>> X_train, X_test = X[:200], X[200:]
+  >>> y_train, y_test = y[:200], y[200:]
+  >>> est = GradientBoostingRegressor(
+  ...     n_estimators=100, learning_rate=0.1, max_depth=1, random_state=0,
+  ...     loss='squared_error'
+  ... )
+  >>> est = est.fit(X_train, y_train)  # fit with 100 trees
+  >>> mean_squared_error(y_test, est.predict(X_test))
+  5.00...
+  >>> _ = est.set_params(n_estimators=200, warm_start=True)  # set warm_start and increase num of trees
   >>> _ = est.fit(X_train, y_train) # fit additional 100 trees to est
   >>> mean_squared_error(y_test, est.predict(X_test))
   3.84...
@@ -645,116 +640,108 @@ Mathematical formulation
 We first present GBRT for regression, and then detail the classification
 case.
 
-|details-start|
-**Regression**
-|details-split|
+.. dropdown:: Regression
 
-GBRT regressors are additive models whose prediction :math:`\hat{y}_i` for a
-given input :math:`x_i` is of the following form:
+  GBRT regressors are additive models whose prediction :math:`\hat{y}_i` for a
+  given input :math:`x_i` is of the following form:
 
-.. math::
-
-  \hat{y}_i = F_M(x_i) = \sum_{m=1}^{M} h_m(x_i)
+  .. math::
 
-where the :math:`h_m` are estimators called *weak learners* in the context
-of boosting. Gradient Tree Boosting uses :ref:`decision tree regressors
-<tree>` of fixed size as weak learners. The constant M corresponds to the
-`n_estimators` parameter.
+    \hat{y}_i = F_M(x_i) = \sum_{m=1}^{M} h_m(x_i)
 
-Similar to other boosting algorithms, a GBRT is built in a greedy fashion:
+  where the :math:`h_m` are estimators called *weak learners* in the context
+  of boosting. Gradient Tree Boosting uses :ref:`decision tree regressors
+  <tree>` of fixed size as weak learners. The constant M corresponds to the
+  `n_estimators` parameter.
 
-.. math::
+  Similar to other boosting algorithms, a GBRT is built in a greedy fashion:
 
-  F_m(x) = F_{m-1}(x) + h_m(x),
-
-where the newly added tree :math:`h_m` is fitted in order to minimize a sum
-of losses :math:`L_m`, given the previous ensemble :math:`F_{m-1}`:
+  .. math::
 
-.. math::
+    F_m(x) = F_{m-1}(x) + h_m(x),
 
-  h_m =  \arg\min_{h} L_m = \arg\min_{h} \sum_{i=1}^{n}
-  l(y_i, F_{m-1}(x_i) + h(x_i)),
+  where the newly added tree :math:`h_m` is fitted in order to minimize a sum
+  of losses :math:`L_m`, given the previous ensemble :math:`F_{m-1}`:
 
-where :math:`l(y_i, F(x_i))` is defined by the `loss` parameter, detailed
-in the next section.
+  .. math::
 
-By default, the initial model :math:`F_{0}` is chosen as the constant that
-minimizes the loss: for a least-squares loss, this is the empirical mean of
-the target values. The initial model can also be specified via the ``init``
-argument.
+    h_m =  \arg\min_{h} L_m = \arg\min_{h} \sum_{i=1}^{n}
+    l(y_i, F_{m-1}(x_i) + h(x_i)),
 
-Using a first-order Taylor approximation, the value of :math:`l` can be
-approximated as follows:
+  where :math:`l(y_i, F(x_i))` is defined by the `loss` parameter, detailed
+  in the next section.
 
-.. math::
+  By default, the initial model :math:`F_{0}` is chosen as the constant that
+  minimizes the loss: for a least-squares loss, this is the empirical mean of
+  the target values. The initial model can also be specified via the ``init``
+  argument.
 
-  l(y_i, F_{m-1}(x_i) + h_m(x_i)) \approx
-  l(y_i, F_{m-1}(x_i))
-  + h_m(x_i)
-  \left[ \frac{\partial l(y_i, F(x_i))}{\partial F(x_i)} \right]_{F=F_{m - 1}}.
+  Using a first-order Taylor approximation, the value of :math:`l` can be
+  approximated as follows:
 
-.. note::
+  .. math::
 
-  Briefly, a first-order Taylor approximation says that
-  :math:`l(z) \approx l(a) + (z - a) \frac{\partial l}{\partial z}(a)`.
-  Here, :math:`z` corresponds to :math:`F_{m - 1}(x_i) + h_m(x_i)`, and
-  :math:`a` corresponds to :math:`F_{m-1}(x_i)`
+    l(y_i, F_{m-1}(x_i) + h_m(x_i)) \approx
+    l(y_i, F_{m-1}(x_i))
+    + h_m(x_i)
+    \left[ \frac{\partial l(y_i, F(x_i))}{\partial F(x_i)} \right]_{F=F_{m - 1}}.
 
-The quantity :math:`\left[ \frac{\partial l(y_i, F(x_i))}{\partial F(x_i)}
-\right]_{F=F_{m - 1}}` is the derivative of the loss with respect to its
-second parameter, evaluated at :math:`F_{m-1}(x)`. It is easy to compute for
-any given :math:`F_{m - 1}(x_i)` in a closed form since the loss is
-differentiable. We will denote it by :math:`g_i`.
+  .. note::
 
-Removing the constant terms, we have:
+    Briefly, a first-order Taylor approximation says that
+    :math:`l(z) \approx l(a) + (z - a) \frac{\partial l}{\partial z}(a)`.
+    Here, :math:`z` corresponds to :math:`F_{m - 1}(x_i) + h_m(x_i)`, and
+    :math:`a` corresponds to :math:`F_{m-1}(x_i)`
 
-.. math::
+  The quantity :math:`\left[ \frac{\partial l(y_i, F(x_i))}{\partial F(x_i)}
+  \right]_{F=F_{m - 1}}` is the derivative of the loss with respect to its
+  second parameter, evaluated at :math:`F_{m-1}(x)`. It is easy to compute for
+  any given :math:`F_{m - 1}(x_i)` in a closed form since the loss is
+  differentiable. We will denote it by :math:`g_i`.
 
-  h_m \approx \arg\min_{h} \sum_{i=1}^{n} h(x_i) g_i
+  Removing the constant terms, we have:
 
-This is minimized if :math:`h(x_i)` is fitted to predict a value that is
-proportional to the negative gradient :math:`-g_i`. Therefore, at each
-iteration, **the estimator** :math:`h_m` **is fitted to predict the negative
-gradients of the samples**. The gradients are updated at each iteration.
-This can be considered as some kind of gradient descent in a functional
-space.
+  .. math::
 
-.. note::
+    h_m \approx \arg\min_{h} \sum_{i=1}^{n} h(x_i) g_i
 
-  For some losses, e.g. ``'absolute_error'`` where the gradients
-  are :math:`\pm 1`, the values predicted by a fitted :math:`h_m` are not
-  accurate enough: the tree can only output integer values. As a result, the
-  leaves values of the tree :math:`h_m` are modified once the tree is
-  fitted, such that the leaves values minimize the loss :math:`L_m`. The
-  update is loss-dependent: for the absolute error loss, the value of
-  a leaf is updated to the median of the samples in that leaf.
+  This is minimized if :math:`h(x_i)` is fitted to predict a value that is
+  proportional to the negative gradient :math:`-g_i`. Therefore, at each
+  iteration, **the estimator** :math:`h_m` **is fitted to predict the negative
+  gradients of the samples**. The gradients are updated at each iteration.
+  This can be considered as some kind of gradient descent in a functional
+  space.
 
-|details-end|
+  .. note::
 
-|details-start|
-**Classification**
-|details-split|
+    For some losses, e.g. ``'absolute_error'`` where the gradients
+    are :math:`\pm 1`, the values predicted by a fitted :math:`h_m` are not
+    accurate enough: the tree can only output integer values. As a result, the
+    leaves values of the tree :math:`h_m` are modified once the tree is
+    fitted, such that the leaves values minimize the loss :math:`L_m`. The
+    update is loss-dependent: for the absolute error loss, the value of
+    a leaf is updated to the median of the samples in that leaf.
 
-Gradient boosting for classification is very similar to the regression case.
-However, the sum of the trees :math:`F_M(x_i) = \sum_m h_m(x_i)` is not
-homogeneous to a prediction: it cannot be a class, since the trees predict
-continuous values.
+.. dropdown:: Classification
 
-The mapping from the value :math:`F_M(x_i)` to a class or a probability is
-loss-dependent. For the log-loss, the probability that
-:math:`x_i` belongs to the positive class is modeled as :math:`p(y_i = 1 |
-x_i) = \sigma(F_M(x_i))` where :math:`\sigma` is the sigmoid or expit function.
+  Gradient boosting for classification is very similar to the regression case.
+  However, the sum of the trees :math:`F_M(x_i) = \sum_m h_m(x_i)` is not
+  homogeneous to a prediction: it cannot be a class, since the trees predict
+  continuous values.
 
-For multiclass classification, K trees (for K classes) are built at each of
-the :math:`M` iterations. The probability that :math:`x_i` belongs to class
-k is modeled as a softmax of the :math:`F_{M,k}(x_i)` values.
+  The mapping from the value :math:`F_M(x_i)` to a class or a probability is
+  loss-dependent. For the log-loss, the probability that
+  :math:`x_i` belongs to the positive class is modeled as :math:`p(y_i = 1 |
+  x_i) = \sigma(F_M(x_i))` where :math:`\sigma` is the sigmoid or expit function.
 
-Note that even for a classification task, the :math:`h_m` sub-estimator is
-still a regressor, not a classifier. This is because the sub-estimators are
-trained to predict (negative) *gradients*, which are always continuous
-quantities.
+  For multiclass classification, K trees (for K classes) are built at each of
+  the :math:`M` iterations. The probability that :math:`x_i` belongs to class
+  k is modeled as a softmax of the :math:`F_{M,k}(x_i)` values.
 
-|details-end|
+  Note that even for a classification task, the :math:`h_m` sub-estimator is
+  still a regressor, not a classifier. This is because the sub-estimators are
+  trained to predict (negative) *gradients*, which are always continuous
+  quantities.
 
 .. _gradient_boosting_loss:
 
@@ -764,9 +751,7 @@ Loss Functions
 The following loss functions are supported and can be specified using
 the parameter ``loss``:
 
-|details-start|
-**Regression**
-|details-split|
+.. dropdown:: Regression
 
   * Squared error (``'squared_error'``): The natural choice for regression
     due to its superior computational properties. The initial model is
@@ -783,12 +768,7 @@ the parameter ``loss``:
     can be used to create prediction intervals
     (see :ref:`sphx_glr_auto_examples_ensemble_plot_gradient_boosting_quantile.py`).
 
-|details-end|
-
-
-|details-start|
-**Classification**
-|details-split|
+.. dropdown:: Classification
 
   * Binary log-loss (``'log-loss'``): The binomial
     negative log-likelihood loss function for binary classification. It provides
@@ -806,8 +786,6 @@ the parameter ``loss``:
     examples than ``'log-loss'``; can only be used for binary
     classification.
 
-|details-end|
-
 .. _gradient_boosting_shrinkage:
 
 Shrinkage via learning rate
@@ -874,11 +852,11 @@ the optimal number of iterations. OOB estimates are usually very pessimistic thu
 we recommend to use cross-validation instead and only use OOB if cross-validation
 is too time consuming.
 
-.. topic:: Examples:
+.. rubric:: Examples
 
- * :ref:`sphx_glr_auto_examples_ensemble_plot_gradient_boosting_regularization.py`
- * :ref:`sphx_glr_auto_examples_ensemble_plot_gradient_boosting_oob.py`
- * :ref:`sphx_glr_auto_examples_ensemble_plot_ensemble_oob.py`
+* :ref:`sphx_glr_auto_examples_ensemble_plot_gradient_boosting_regularization.py`
+* :ref:`sphx_glr_auto_examples_ensemble_plot_gradient_boosting_oob.py`
+* :ref:`sphx_glr_auto_examples_ensemble_plot_ensemble_oob.py`
 
 Interpretation with feature importance
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
@@ -921,22 +899,22 @@ Note that this computation of feature importance is based on entropy, and it
 is distinct from :func:`sklearn.inspection.permutation_importance` which is
 based on permutation of the features.
 
-.. topic:: Examples:
+.. rubric:: Examples
 
- * :ref:`sphx_glr_auto_examples_ensemble_plot_gradient_boosting_regression.py`
+* :ref:`sphx_glr_auto_examples_ensemble_plot_gradient_boosting_regression.py`
 
-.. topic:: References
+.. rubric:: References
 
-  .. [Friedman2001] Friedman, J.H. (2001). :doi:`Greedy function approximation: A gradient
-      boosting machine <10.1214/aos/1013203451>`.
-      Annals of Statistics, 29, 1189-1232.
+.. [Friedman2001] Friedman, J.H. (2001). :doi:`Greedy function approximation: A gradient
+   boosting machine <10.1214/aos/1013203451>`.
+   Annals of Statistics, 29, 1189-1232.
 
-  .. [Friedman2002] Friedman, J.H. (2002). `Stochastic gradient boosting.
-     <https://statweb.stanford.edu/~jhf/ftp/stobst.pdf>`_.
-     Computational Statistics & Data Analysis, 38, 367-378.
+.. [Friedman2002] Friedman, J.H. (2002). `Stochastic gradient boosting.
+   <https://statweb.stanford.edu/~jhf/ftp/stobst.pdf>`_.
+   Computational Statistics & Data Analysis, 38, 367-378.
 
-  .. [R2007] G. Ridgeway (2006). `Generalized Boosted Models: A guide to the gbm
-     package <https://cran.r-project.org/web/packages/gbm/vignettes/gbm.pdf>`_
+.. [R2007] G. Ridgeway (2006). `Generalized Boosted Models: A guide to the gbm
+   package <https://cran.r-project.org/web/packages/gbm/vignettes/gbm.pdf>`_
 
 .. _forest:
 
@@ -1020,9 +998,9 @@ characteristics of the dataset and the modeling task. It's a good idea
 to try both models and compare their performance and computational efficiency
 on your specific problem to determine which model is the best fit.
 
-.. topic:: Examples:
+.. rubric:: Examples
 
- * :ref:`sphx_glr_auto_examples_ensemble_plot_forest_hist_grad_boosting_comparison.py`
+* :ref:`sphx_glr_auto_examples_ensemble_plot_forest_hist_grad_boosting_comparison.py`
 
 Extremely Randomized Trees
 --------------------------
@@ -1119,20 +1097,20 @@ fast). Significant speedup can still be achieved though when building
 a large number of trees, or when building a single tree requires a fair
 amount of time (e.g., on large datasets).
 
-.. topic:: Examples:
+.. rubric:: Examples
 
- * :ref:`sphx_glr_auto_examples_ensemble_plot_forest_iris.py`
- * :ref:`sphx_glr_auto_examples_ensemble_plot_forest_importances_faces.py`
- * :ref:`sphx_glr_auto_examples_miscellaneous_plot_multioutput_face_completion.py`
+* :ref:`sphx_glr_auto_examples_ensemble_plot_forest_iris.py`
+* :ref:`sphx_glr_auto_examples_ensemble_plot_forest_importances_faces.py`
+* :ref:`sphx_glr_auto_examples_miscellaneous_plot_multioutput_face_completion.py`
 
-.. topic:: References
+.. rubric:: References
 
- .. [B2001] L. Breiman, "Random Forests", Machine Learning, 45(1), 5-32, 2001.
+.. [B2001] L. Breiman, "Random Forests", Machine Learning, 45(1), 5-32, 2001.
 
- .. [B1998] L. Breiman, "Arcing Classifiers", Annals of Statistics 1998.
+.. [B1998] L. Breiman, "Arcing Classifiers", Annals of Statistics 1998.
 
- * P. Geurts, D. Ernst., and L. Wehenkel, "Extremely randomized
-   trees", Machine Learning, 63(1), 3-42, 2006.
+* P. Geurts, D. Ernst., and L. Wehenkel, "Extremely randomized
+  trees", Machine Learning, 63(1), 3-42, 2006.
 
 .. _random_forest_feature_importance:
 
@@ -1184,16 +1162,16 @@ In practice those estimates are stored as an attribute named
 the value, the more important is the contribution of the matching feature
 to the prediction function.
 
-.. topic:: Examples:
+.. rubric:: Examples
 
- * :ref:`sphx_glr_auto_examples_ensemble_plot_forest_importances_faces.py`
- * :ref:`sphx_glr_auto_examples_ensemble_plot_forest_importances.py`
+* :ref:`sphx_glr_auto_examples_ensemble_plot_forest_importances_faces.py`
+* :ref:`sphx_glr_auto_examples_ensemble_plot_forest_importances.py`
 
-.. topic:: References
+.. rubric:: References
 
- .. [L2014] G. Louppe, :arxiv:`"Understanding Random Forests: From Theory to
-    Practice" <1407.7502>`,
-    PhD Thesis, U. of Liege, 2014.
+.. [L2014] G. Louppe, :arxiv:`"Understanding Random Forests: From Theory to
+   Practice" <1407.7502>`,
+   PhD Thesis, U. of Liege, 2014.
 
 .. _random_trees_embedding:
 
@@ -1216,15 +1194,15 @@ As neighboring data points are more likely to lie within the same leaf of a
 tree, the transformation performs an implicit, non-parametric density
 estimation.
 
-.. topic:: Examples:
+.. rubric:: Examples
 
- * :ref:`sphx_glr_auto_examples_ensemble_plot_random_forest_embedding.py`
+* :ref:`sphx_glr_auto_examples_ensemble_plot_random_forest_embedding.py`
 
- * :ref:`sphx_glr_auto_examples_manifold_plot_lle_digits.py` compares non-linear
-   dimensionality reduction techniques on handwritten digits.
+* :ref:`sphx_glr_auto_examples_manifold_plot_lle_digits.py` compares non-linear
+  dimensionality reduction techniques on handwritten digits.
 
- * :ref:`sphx_glr_auto_examples_ensemble_plot_feature_transformation.py` compares
-   supervised and unsupervised tree based feature transformations.
+* :ref:`sphx_glr_auto_examples_ensemble_plot_feature_transformation.py` compares
+  supervised and unsupervised tree based feature transformations.
 
 .. seealso::
 
@@ -1232,6 +1210,43 @@ estimation.
    representations of feature space, also these approaches focus also on
    dimensionality reduction.
 
+.. _tree_ensemble_warm_start:
+
+Fitting additional trees
+------------------------
+
+RandomForest, Extra-Trees and :class:`RandomTreesEmbedding` estimators all support
+``warm_start=True`` which allows you to add more trees to an already fitted model.
+
+::
+
+  >>> from sklearn.datasets import make_classification
+  >>> from sklearn.ensemble import RandomForestClassifier
+
+  >>> X, y = make_classification(n_samples=100, random_state=1)
+  >>> clf = RandomForestClassifier(n_estimators=10)
+  >>> clf = clf.fit(X, y)  # fit with 10 trees
+  >>> len(clf.estimators_)
+  10
+  >>> # set warm_start and increase num of estimators
+  >>> _ = clf.set_params(n_estimators=20, warm_start=True)
+  >>> _ = clf.fit(X, y) # fit additional 10 trees
+  >>> len(clf.estimators_)
+  20
+
+When ``random_state`` is also set, the internal random state is also preserved
+between ``fit`` calls. This means that training a model once with ``n`` estimators is
+the same as building the model iteratively via multiple ``fit`` calls, where the
+final number of estimators is equal to ``n``.
+
+::
+
+  >>> clf = RandomForestClassifier(n_estimators=20)  # set `n_estimators` to 10 + 10
+  >>> _ = clf.fit(X, y)  # fit `estimators_` will be the same as `clf` above
+
+Note that this differs from the usual behavior of :term:`random_state` in that it does
+*not* result in the same result across different calls.
+
 .. _bagging:
 
 Bagging meta-estimator
@@ -1283,24 +1298,23 @@ subsets of 50% of the samples and 50% of the features.
     >>> bagging = BaggingClassifier(KNeighborsClassifier(),
     ...                             max_samples=0.5, max_features=0.5)
 
-.. topic:: Examples:
+.. rubric:: Examples
 
- * :ref:`sphx_glr_auto_examples_ensemble_plot_bias_variance.py`
+* :ref:`sphx_glr_auto_examples_ensemble_plot_bias_variance.py`
 
-.. topic:: References
+.. rubric:: References
 
-  .. [B1999] L. Breiman, "Pasting small votes for classification in large
-         databases and on-line", Machine Learning, 36(1), 85-103, 1999.
+.. [B1999] L. Breiman, "Pasting small votes for classification in large
+   databases and on-line", Machine Learning, 36(1), 85-103, 1999.
 
-  .. [B1996] L. Breiman, "Bagging predictors", Machine Learning, 24(2),
-         123-140, 1996.
+.. [B1996] L. Breiman, "Bagging predictors", Machine Learning, 24(2),
+   123-140, 1996.
 
-  .. [H1998] T. Ho, "The random subspace method for constructing decision
-         forests", Pattern Analysis and Machine Intelligence, 20(8), 832-844,
-         1998.
+.. [H1998] T. Ho, "The random subspace method for constructing decision
+   forests", Pattern Analysis and Machine Intelligence, 20(8), 832-844, 1998.
 
-  .. [LG2012] G. Louppe and P. Geurts, "Ensembles on Random Patches",
-         Machine Learning and Knowledge Discovery in Databases, 346-361, 2012.
+.. [LG2012] G. Louppe and P. Geurts, "Ensembles on Random Patches",
+   Machine Learning and Knowledge Discovery in Databases, 346-361, 2012.
 
 
 
@@ -1455,29 +1469,25 @@ Optionally, weights can be provided for the individual classifiers::
    ...     voting='soft', weights=[2,5,1]
    ... )
 
-|details-start|
-**Using the `VotingClassifier` with `GridSearchCV`**
-|details-split|
+.. dropdown:: Using the :class:`VotingClassifier` with :class:`~sklearn.model_selection.GridSearchCV`
 
-The :class:`VotingClassifier` can also be used together with
-:class:`~sklearn.model_selection.GridSearchCV` in order to tune the
-hyperparameters of the individual estimators::
+  The :class:`VotingClassifier` can also be used together with
+  :class:`~sklearn.model_selection.GridSearchCV` in order to tune the
+  hyperparameters of the individual estimators::
 
-   >>> from sklearn.model_selection import GridSearchCV
-   >>> clf1 = LogisticRegression(random_state=1)
-   >>> clf2 = RandomForestClassifier(random_state=1)
-   >>> clf3 = GaussianNB()
-   >>> eclf = VotingClassifier(
-   ...     estimators=[('lr', clf1), ('rf', clf2), ('gnb', clf3)],
-   ...     voting='soft'
-   ... )
-
-   >>> params = {'lr__C': [1.0, 100.0], 'rf__n_estimators': [20, 200]}
+    >>> from sklearn.model_selection import GridSearchCV
+    >>> clf1 = LogisticRegression(random_state=1)
+    >>> clf2 = RandomForestClassifier(random_state=1)
+    >>> clf3 = GaussianNB()
+    >>> eclf = VotingClassifier(
+    ...     estimators=[('lr', clf1), ('rf', clf2), ('gnb', clf3)],
+    ...     voting='soft'
+    ... )
 
-   >>> grid = GridSearchCV(estimator=eclf, param_grid=params, cv=5)
-   >>> grid = grid.fit(iris.data, iris.target)
+    >>> params = {'lr__C': [1.0, 100.0], 'rf__n_estimators': [20, 200]}
 
-|details-end|
+    >>> grid = GridSearchCV(estimator=eclf, param_grid=params, cv=5)
+    >>> grid = grid.fit(iris.data, iris.target)
 
 .. _voting_regressor:
 
@@ -1515,9 +1525,9 @@ The following example shows how to fit the VotingRegressor::
     :align: center
     :scale: 75%
 
-.. topic:: Examples:
+.. rubric:: Examples
 
-  * :ref:`sphx_glr_auto_examples_ensemble_plot_voting_regressor.py`
+* :ref:`sphx_glr_auto_examples_ensemble_plot_voting_regressor.py`
 
 .. _stacking:
 
@@ -1581,8 +1591,8 @@ availability, tested in the order of preference: `predict_proba`,
 `decision_function` and `predict`.
 
 A :class:`StackingRegressor` and :class:`StackingClassifier` can be used as
-any other regressor or classifier, exposing a `predict`, `predict_proba`, and
-`decision_function` methods, e.g.::
+any other regressor or classifier, exposing a `predict`, `predict_proba`, or
+`decision_function` method, e.g.::
 
    >>> y_pred = reg.predict(X_test)
    >>> from sklearn.metrics import r2_score
@@ -1636,10 +1646,10 @@ computationally expensive.
     ...       .format(multi_layer_regressor.score(X_test, y_test)))
     R2 score: 0.53
 
-.. topic:: References
+.. rubric:: References
 
-   .. [W1992] Wolpert, David H. "Stacked generalization." Neural networks 5.2
-      (1992): 241-259.
+.. [W1992] Wolpert, David H. "Stacked generalization." Neural networks 5.2
+   (1992): 241-259.
 
 
 
@@ -1705,27 +1715,26 @@ The main parameters to tune to obtain good results are ``n_estimators`` and
 the complexity of the base estimators (e.g., its depth ``max_depth`` or
 minimum required number of samples to consider a split ``min_samples_split``).
 
-.. topic:: Examples:
+.. rubric:: Examples
 
- * :ref:`sphx_glr_auto_examples_ensemble_plot_adaboost_multiclass.py` shows the performance
-   of AdaBoost on a multi-class problem.
+* :ref:`sphx_glr_auto_examples_ensemble_plot_adaboost_multiclass.py` shows the performance
+  of AdaBoost on a multi-class problem.
 
- * :ref:`sphx_glr_auto_examples_ensemble_plot_adaboost_twoclass.py` shows the decision boundary
-   and decision function values for a non-linearly separable two-class problem
-   using AdaBoost-SAMME.
+* :ref:`sphx_glr_auto_examples_ensemble_plot_adaboost_twoclass.py` shows the decision boundary
+  and decision function values for a non-linearly separable two-class problem
+  using AdaBoost-SAMME.
 
- * :ref:`sphx_glr_auto_examples_ensemble_plot_adaboost_regression.py` demonstrates regression
-   with the AdaBoost.R2 algorithm.
+* :ref:`sphx_glr_auto_examples_ensemble_plot_adaboost_regression.py` demonstrates regression
+  with the AdaBoost.R2 algorithm.
 
-.. topic:: References
+.. rubric:: References
 
- .. [FS1995] Y. Freund, and R. Schapire, "A Decision-Theoretic Generalization of
-             On-Line Learning and an Application to Boosting", 1997.
+.. [FS1995] Y. Freund, and R. Schapire, "A Decision-Theoretic Generalization of
+   On-Line Learning and an Application to Boosting", 1997.
 
- .. [ZZRH2009] J. Zhu, H. Zou, S. Rosset, T. Hastie. "Multi-class AdaBoost",
-               2009.
+.. [ZZRH2009] J. Zhu, H. Zou, S. Rosset, T. Hastie. "Multi-class AdaBoost", 2009.
 
- .. [D1997] H. Drucker. "Improving Regressors using Boosting Techniques", 1997.
+.. [D1997] H. Drucker. "Improving Regressors using Boosting Techniques", 1997.
 
- .. [HTF] T. Hastie, R. Tibshirani and J. Friedman, "Elements of
-              Statistical Learning Ed. 2", Springer, 2009.
+.. [HTF] T. Hastie, R. Tibshirani and J. Friedman, "Elements of Statistical Learning
+   Ed. 2", Springer, 2009.
diff --git a/doc/modules/feature_extraction.rst b/doc/modules/feature_extraction.rst
index 7ac538a89849b..2181014644e15 100644
--- a/doc/modules/feature_extraction.rst
+++ b/doc/modules/feature_extraction.rst
@@ -206,35 +206,32 @@ Note the use of a generator comprehension,
 which introduces laziness into the feature extraction:
 tokens are only processed on demand from the hasher.
 
-|details-start|
-**Implementation details**
-|details-split|
+.. dropdown:: Implementation details
 
-:class:`FeatureHasher` uses the signed 32-bit variant of MurmurHash3.
-As a result (and because of limitations in ``scipy.sparse``),
-the maximum number of features supported is currently :math:`2^{31} - 1`.
+  :class:`FeatureHasher` uses the signed 32-bit variant of MurmurHash3.
+  As a result (and because of limitations in ``scipy.sparse``),
+  the maximum number of features supported is currently :math:`2^{31} - 1`.
 
-The original formulation of the hashing trick by Weinberger et al.
-used two separate hash functions :math:`h` and :math:`\xi`
-to determine the column index and sign of a feature, respectively.
-The present implementation works under the assumption
-that the sign bit of MurmurHash3 is independent of its other bits.
+  The original formulation of the hashing trick by Weinberger et al.
+  used two separate hash functions :math:`h` and :math:`\xi`
+  to determine the column index and sign of a feature, respectively.
+  The present implementation works under the assumption
+  that the sign bit of MurmurHash3 is independent of its other bits.
 
-Since a simple modulo is used to transform the hash function to a column index,
-it is advisable to use a power of two as the ``n_features`` parameter;
-otherwise the features will not be mapped evenly to the columns.
+  Since a simple modulo is used to transform the hash function to a column index,
+  it is advisable to use a power of two as the ``n_features`` parameter;
+  otherwise the features will not be mapped evenly to the columns.
 
-.. topic:: References:
+  .. rubric:: References
 
   * `MurmurHash3 <https://github.com/aappleby/smhasher>`_.
 
-|details-end|
 
-.. topic:: References:
+.. rubric:: References
 
- * Kilian Weinberger, Anirban Dasgupta, John Langford, Alex Smola and
-   Josh Attenberg (2009). `Feature hashing for large scale multitask learning
-   <https://alex.smola.org/papers/2009/Weinbergeretal09.pdf>`_. Proc. ICML.
+* Kilian Weinberger, Anirban Dasgupta, John Langford, Alex Smola and
+  Josh Attenberg (2009). `Feature hashing for large scale multitask learning
+  <https://alex.smola.org/papers/2009/Weinbergeretal09.pdf>`_. Proc. ICML.
 
 .. _text_feature_extraction:
 
@@ -310,7 +307,7 @@ counting in a single class::
 
 This model has many parameters, however the default values are quite
 reasonable (please see  the :ref:`reference documentation
-<text_feature_extraction_ref>` for the details)::
+<feature_extraction_ref-from-text>` for the details)::
 
   >>> vectorizer = CountVectorizer()
   >>> vectorizer
@@ -422,12 +419,12 @@ tokenizer, so if *we've* is in ``stop_words``, but *ve* is not, *ve* will
 be retained from *we've* in transformed text.  Our vectorizers will try to
 identify and warn about some kinds of inconsistencies.
 
-.. topic:: References
+.. rubric:: References
 
-    .. [NQY18] J. Nothman, H. Qin and R. Yurchak (2018).
-               `"Stop Word Lists in Free Open-source Software Packages"
-               <https://aclweb.org/anthology/W18-2502>`__.
-               In *Proc. Workshop for NLP Open Source Software*.
+.. [NQY18] J. Nothman, H. Qin and R. Yurchak (2018).
+   `"Stop Word Lists in Free Open-source Software Packages"
+   <https://aclweb.org/anthology/W18-2502>`__.
+   In *Proc. Workshop for NLP Open Source Software*.
 
 
 .. _tfidf:
@@ -492,132 +489,126 @@ class::
   TfidfTransformer(smooth_idf=False)
 
 Again please see the :ref:`reference documentation
-<text_feature_extraction_ref>` for the details on all the parameters.
-
-|details-start|
-**Numeric example of a tf-idf matrix**
-|details-split|
-
-Let's take an example with the following counts. The first term is present
-100% of the time hence not very interesting. The two other features only
-in less than 50% of the time hence probably more representative of the
-content of the documents::
-
-  >>> counts = [[3, 0, 1],
-  ...           [2, 0, 0],
-  ...           [3, 0, 0],
-  ...           [4, 0, 0],
-  ...           [3, 2, 0],
-  ...           [3, 0, 2]]
-  ...
-  >>> tfidf = transformer.fit_transform(counts)
-  >>> tfidf
-  <6x3 sparse matrix of type '<... 'numpy.float64'>'
-      with 9 stored elements in Compressed Sparse ... format>
+<feature_extraction_ref-from-text>` for the details on all the parameters.
 
-  >>> tfidf.toarray()
-  array([[0.81940995, 0.        , 0.57320793],
-         [1.        , 0.        , 0.        ],
-         [1.        , 0.        , 0.        ],
-         [1.        , 0.        , 0.        ],
-         [0.47330339, 0.88089948, 0.        ],
-         [0.58149261, 0.        , 0.81355169]])
+.. dropdown:: Numeric example of a tf-idf matrix
 
-Each row is normalized to have unit Euclidean norm:
+  Let's take an example with the following counts. The first term is present
+  100% of the time hence not very interesting. The two other features only
+  in less than 50% of the time hence probably more representative of the
+  content of the documents::
 
-:math:`v_{norm} = \frac{v}{||v||_2} = \frac{v}{\sqrt{v{_1}^2 +
-v{_2}^2 + \dots + v{_n}^2}}`
+    >>> counts = [[3, 0, 1],
+    ...           [2, 0, 0],
+    ...           [3, 0, 0],
+    ...           [4, 0, 0],
+    ...           [3, 2, 0],
+    ...           [3, 0, 2]]
+    ...
+    >>> tfidf = transformer.fit_transform(counts)
+    >>> tfidf
+    <6x3 sparse matrix of type '<... 'numpy.float64'>'
+        with 9 stored elements in Compressed Sparse ... format>
 
-For example, we can compute the tf-idf of the first term in the first
-document in the `counts` array as follows:
+    >>> tfidf.toarray()
+    array([[0.81940995, 0.        , 0.57320793],
+          [1.        , 0.        , 0.        ],
+          [1.        , 0.        , 0.        ],
+          [1.        , 0.        , 0.        ],
+          [0.47330339, 0.88089948, 0.        ],
+          [0.58149261, 0.        , 0.81355169]])
 
-:math:`n = 6`
+  Each row is normalized to have unit Euclidean norm:
 
-:math:`\text{df}(t)_{\text{term1}} = 6`
+  :math:`v_{norm} = \frac{v}{||v||_2} = \frac{v}{\sqrt{v{_1}^2 +
+  v{_2}^2 + \dots + v{_n}^2}}`
 
-:math:`\text{idf}(t)_{\text{term1}} =
-\log \frac{n}{\text{df}(t)} + 1 = \log(1)+1 = 1`
+  For example, we can compute the tf-idf of the first term in the first
+  document in the `counts` array as follows:
 
-:math:`\text{tf-idf}_{\text{term1}} = \text{tf} \times \text{idf} = 3 \times 1 = 3`
+  :math:`n = 6`
 
-Now, if we repeat this computation for the remaining 2 terms in the document,
-we get
+  :math:`\text{df}(t)_{\text{term1}} = 6`
 
-:math:`\text{tf-idf}_{\text{term2}} = 0 \times (\log(6/1)+1) = 0`
+  :math:`\text{idf}(t)_{\text{term1}} =
+  \log \frac{n}{\text{df}(t)} + 1 = \log(1)+1 = 1`
 
-:math:`\text{tf-idf}_{\text{term3}} = 1 \times (\log(6/2)+1) \approx 2.0986`
+  :math:`\text{tf-idf}_{\text{term1}} = \text{tf} \times \text{idf} = 3 \times 1 = 3`
 
-and the vector of raw tf-idfs:
+  Now, if we repeat this computation for the remaining 2 terms in the document,
+  we get
 
-:math:`\text{tf-idf}_{\text{raw}} = [3, 0, 2.0986].`
+  :math:`\text{tf-idf}_{\text{term2}} = 0 \times (\log(6/1)+1) = 0`
 
+  :math:`\text{tf-idf}_{\text{term3}} = 1 \times (\log(6/2)+1) \approx 2.0986`
 
-Then, applying the Euclidean (L2) norm, we obtain the following tf-idfs
-for document 1:
+  and the vector of raw tf-idfs:
 
-:math:`\frac{[3, 0, 2.0986]}{\sqrt{\big(3^2 + 0^2 + 2.0986^2\big)}}
-= [ 0.819,  0,  0.573].`
+  :math:`\text{tf-idf}_{\text{raw}} = [3, 0, 2.0986].`
 
-Furthermore, the default parameter ``smooth_idf=True`` adds "1" to the numerator
-and  denominator as if an extra document was seen containing every term in the
-collection exactly once, which prevents zero divisions:
 
-:math:`\text{idf}(t) = \log{\frac{1 + n}{1+\text{df}(t)}} + 1`
+  Then, applying the Euclidean (L2) norm, we obtain the following tf-idfs
+  for document 1:
 
-Using this modification, the tf-idf of the third term in document 1 changes to
-1.8473:
+  :math:`\frac{[3, 0, 2.0986]}{\sqrt{\big(3^2 + 0^2 + 2.0986^2\big)}}
+  = [ 0.819,  0,  0.573].`
 
-:math:`\text{tf-idf}_{\text{term3}} = 1 \times \log(7/3)+1 \approx 1.8473`
+  Furthermore, the default parameter ``smooth_idf=True`` adds "1" to the numerator
+  and  denominator as if an extra document was seen containing every term in the
+  collection exactly once, which prevents zero divisions:
 
-And the L2-normalized tf-idf changes to
+  :math:`\text{idf}(t) = \log{\frac{1 + n}{1+\text{df}(t)}} + 1`
 
-:math:`\frac{[3, 0, 1.8473]}{\sqrt{\big(3^2 + 0^2 + 1.8473^2\big)}}
-= [0.8515, 0, 0.5243]`::
+  Using this modification, the tf-idf of the third term in document 1 changes to
+  1.8473:
 
-  >>> transformer = TfidfTransformer()
-  >>> transformer.fit_transform(counts).toarray()
-  array([[0.85151335, 0.        , 0.52433293],
-         [1.        , 0.        , 0.        ],
-         [1.        , 0.        , 0.        ],
-         [1.        , 0.        , 0.        ],
-         [0.55422893, 0.83236428, 0.        ],
-         [0.63035731, 0.        , 0.77630514]])
+  :math:`\text{tf-idf}_{\text{term3}} = 1 \times \log(7/3)+1 \approx 1.8473`
 
-The weights of each
-feature computed by the ``fit`` method call are stored in a model
-attribute::
+  And the L2-normalized tf-idf changes to
 
-  >>> transformer.idf_
-  array([1. ..., 2.25..., 1.84...])
+  :math:`\frac{[3, 0, 1.8473]}{\sqrt{\big(3^2 + 0^2 + 1.8473^2\big)}}
+  = [0.8515, 0, 0.5243]`::
 
+    >>> transformer = TfidfTransformer()
+    >>> transformer.fit_transform(counts).toarray()
+    array([[0.85151335, 0.        , 0.52433293],
+          [1.        , 0.        , 0.        ],
+          [1.        , 0.        , 0.        ],
+          [1.        , 0.        , 0.        ],
+          [0.55422893, 0.83236428, 0.        ],
+          [0.63035731, 0.        , 0.77630514]])
 
+  The weights of each
+  feature computed by the ``fit`` method call are stored in a model
+  attribute::
 
+    >>> transformer.idf_
+    array([1. ..., 2.25..., 1.84...])
 
-As tf–idf is very often used for text features, there is also another
-class called :class:`TfidfVectorizer` that combines all the options of
-:class:`CountVectorizer` and :class:`TfidfTransformer` in a single model::
+  As tf-idf is very often used for text features, there is also another
+  class called :class:`TfidfVectorizer` that combines all the options of
+  :class:`CountVectorizer` and :class:`TfidfTransformer` in a single model::
 
-  >>> from sklearn.feature_extraction.text import TfidfVectorizer
-  >>> vectorizer = TfidfVectorizer()
-  >>> vectorizer.fit_transform(corpus)
-  <4x9 sparse matrix of type '<... 'numpy.float64'>'
-      with 19 stored elements in Compressed Sparse ... format>
+    >>> from sklearn.feature_extraction.text import TfidfVectorizer
+    >>> vectorizer = TfidfVectorizer()
+    >>> vectorizer.fit_transform(corpus)
+    <4x9 sparse matrix of type '<... 'numpy.float64'>'
+        with 19 stored elements in Compressed Sparse ... format>
 
-While the tf–idf normalization is often very useful, there might
-be cases where the binary occurrence markers might offer better
-features. This can be achieved by using the ``binary`` parameter
-of :class:`CountVectorizer`. In particular, some estimators such as
-:ref:`bernoulli_naive_bayes` explicitly model discrete boolean random
-variables. Also, very short texts are likely to have noisy tf–idf values
-while the binary occurrence info is more stable.
+  While the tf-idf normalization is often very useful, there might
+  be cases where the binary occurrence markers might offer better
+  features. This can be achieved by using the ``binary`` parameter
+  of :class:`CountVectorizer`. In particular, some estimators such as
+  :ref:`bernoulli_naive_bayes` explicitly model discrete boolean random
+  variables. Also, very short texts are likely to have noisy tf-idf values
+  while the binary occurrence info is more stable.
 
-As usual the best way to adjust the feature extraction parameters
-is to use a cross-validated grid search, for instance by pipelining the
-feature extractor with a classifier:
+  As usual the best way to adjust the feature extraction parameters
+  is to use a cross-validated grid search, for instance by pipelining the
+  feature extractor with a classifier:
 
-* :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_text_feature_extraction.py`
+  * :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_text_feature_extraction.py`
 
-|details-end|
 
 Decoding text files
 -------------------
@@ -646,64 +637,60 @@ or ``"replace"``. See the documentation for the Python function
 ``bytes.decode`` for more details
 (type ``help(bytes.decode)`` at the Python prompt).
 
-|details-start|
-**Troubleshooting decoding text**
-|details-split|
-
-If you are having trouble decoding text, here are some things to try:
-
-- Find out what the actual encoding of the text is. The file might come
-  with a header or README that tells you the encoding, or there might be some
-  standard encoding you can assume based on where the text comes from.
-
-- You may be able to find out what kind of encoding it is in general
-  using the UNIX command ``file``. The Python ``chardet`` module comes with
-  a script called ``chardetect.py`` that will guess the specific encoding,
-  though you cannot rely on its guess being correct.
-
-- You could try UTF-8 and disregard the errors. You can decode byte
-  strings with ``bytes.decode(errors='replace')`` to replace all
-  decoding errors with a meaningless character, or set
-  ``decode_error='replace'`` in the vectorizer. This may damage the
-  usefulness of your features.
-
-- Real text may come from a variety of sources that may have used different
-  encodings, or even be sloppily decoded in a different encoding than the
-  one it was encoded with. This is common in text retrieved from the Web.
-  The Python package `ftfy`_ can automatically sort out some classes of
-  decoding errors, so you could try decoding the unknown text as ``latin-1``
-  and then using ``ftfy`` to fix errors.
-
-- If the text is in a mish-mash of encodings that is simply too hard to sort
-  out (which is the case for the 20 Newsgroups dataset), you can fall back on
-  a simple single-byte encoding such as ``latin-1``. Some text may display
-  incorrectly, but at least the same sequence of bytes will always represent
-  the same feature.
-
-For example, the following snippet uses ``chardet``
-(not shipped with scikit-learn, must be installed separately)
-to figure out the encoding of three texts.
-It then vectorizes the texts and prints the learned vocabulary.
-The output is not shown here.
-
-  >>> import chardet    # doctest: +SKIP
-  >>> text1 = b"Sei mir gegr\xc3\xbc\xc3\x9ft mein Sauerkraut"
-  >>> text2 = b"holdselig sind deine Ger\xfcche"
-  >>> text3 = b"\xff\xfeA\x00u\x00f\x00 \x00F\x00l\x00\xfc\x00g\x00e\x00l\x00n\x00 \x00d\x00e\x00s\x00 \x00G\x00e\x00s\x00a\x00n\x00g\x00e\x00s\x00,\x00 \x00H\x00e\x00r\x00z\x00l\x00i\x00e\x00b\x00c\x00h\x00e\x00n\x00,\x00 \x00t\x00r\x00a\x00g\x00 \x00i\x00c\x00h\x00 \x00d\x00i\x00c\x00h\x00 \x00f\x00o\x00r\x00t\x00"
-  >>> decoded = [x.decode(chardet.detect(x)['encoding'])
-  ...            for x in (text1, text2, text3)]        # doctest: +SKIP
-  >>> v = CountVectorizer().fit(decoded).vocabulary_    # doctest: +SKIP
-  >>> for term in v: print(v)                           # doctest: +SKIP
-
-(Depending on the version of ``chardet``, it might get the first one wrong.)
-
-For an introduction to Unicode and character encodings in general,
-see Joel Spolsky's `Absolute Minimum Every Software Developer Must Know
-About Unicode <https://www.joelonsoftware.com/articles/Unicode.html>`_.
-
-.. _`ftfy`: https://github.com/LuminosoInsight/python-ftfy
-
-|details-end|
+.. dropdown:: Troubleshooting decoding text
+
+  If you are having trouble decoding text, here are some things to try:
+
+  - Find out what the actual encoding of the text is. The file might come
+    with a header or README that tells you the encoding, or there might be some
+    standard encoding you can assume based on where the text comes from.
+
+  - You may be able to find out what kind of encoding it is in general
+    using the UNIX command ``file``. The Python ``chardet`` module comes with
+    a script called ``chardetect.py`` that will guess the specific encoding,
+    though you cannot rely on its guess being correct.
+
+  - You could try UTF-8 and disregard the errors. You can decode byte
+    strings with ``bytes.decode(errors='replace')`` to replace all
+    decoding errors with a meaningless character, or set
+    ``decode_error='replace'`` in the vectorizer. This may damage the
+    usefulness of your features.
+
+  - Real text may come from a variety of sources that may have used different
+    encodings, or even be sloppily decoded in a different encoding than the
+    one it was encoded with. This is common in text retrieved from the Web.
+    The Python package `ftfy <https://github.com/LuminosoInsight/python-ftfy>`__
+    can automatically sort out some classes of
+    decoding errors, so you could try decoding the unknown text as ``latin-1``
+    and then using ``ftfy`` to fix errors.
+
+  - If the text is in a mish-mash of encodings that is simply too hard to sort
+    out (which is the case for the 20 Newsgroups dataset), you can fall back on
+    a simple single-byte encoding such as ``latin-1``. Some text may display
+    incorrectly, but at least the same sequence of bytes will always represent
+    the same feature.
+
+  For example, the following snippet uses ``chardet``
+  (not shipped with scikit-learn, must be installed separately)
+  to figure out the encoding of three texts.
+  It then vectorizes the texts and prints the learned vocabulary.
+  The output is not shown here.
+
+    >>> import chardet    # doctest: +SKIP
+    >>> text1 = b"Sei mir gegr\xc3\xbc\xc3\x9ft mein Sauerkraut"
+    >>> text2 = b"holdselig sind deine Ger\xfcche"
+    >>> text3 = b"\xff\xfeA\x00u\x00f\x00 \x00F\x00l\x00\xfc\x00g\x00e\x00l\x00n\x00 \x00d\x00e\x00s\x00 \x00G\x00e\x00s\x00a\x00n\x00g\x00e\x00s\x00,\x00 \x00H\x00e\x00r\x00z\x00l\x00i\x00e\x00b\x00c\x00h\x00e\x00n\x00,\x00 \x00t\x00r\x00a\x00g\x00 \x00i\x00c\x00h\x00 \x00d\x00i\x00c\x00h\x00 \x00f\x00o\x00r\x00t\x00"
+    >>> decoded = [x.decode(chardet.detect(x)['encoding'])
+    ...            for x in (text1, text2, text3)]        # doctest: +SKIP
+    >>> v = CountVectorizer().fit(decoded).vocabulary_    # doctest: +SKIP
+    >>> for term in v: print(v)                           # doctest: +SKIP
+
+  (Depending on the version of ``chardet``, it might get the first one wrong.)
+
+  For an introduction to Unicode and character encodings in general,
+  see Joel Spolsky's `Absolute Minimum Every Software Developer Must Know
+  About Unicode <https://www.joelonsoftware.com/articles/Unicode.html>`_.
+
 
 Applications and examples
 -------------------------
@@ -884,28 +871,25 @@ The :class:`HashingVectorizer` also comes with the following limitations:
   model. A :class:`TfidfTransformer` can be appended to it in a pipeline if
   required.
 
-|details-start|
-**Performing out-of-core scaling with HashingVectorizer**
-|details-split|
+.. dropdown:: Performing out-of-core scaling with HashingVectorizer
 
-An interesting development of using a :class:`HashingVectorizer` is the ability
-to perform `out-of-core`_ scaling. This means that we can learn from data that
-does not fit into the computer's main memory.
+  An interesting development of using a :class:`HashingVectorizer` is the ability
+  to perform `out-of-core`_ scaling. This means that we can learn from data that
+  does not fit into the computer's main memory.
 
-.. _out-of-core: https://en.wikipedia.org/wiki/Out-of-core_algorithm
+  .. _out-of-core: https://en.wikipedia.org/wiki/Out-of-core_algorithm
 
-A strategy to implement out-of-core scaling is to stream data to the estimator
-in mini-batches. Each mini-batch is vectorized using :class:`HashingVectorizer`
-so as to guarantee that the input space of the estimator has always the same
-dimensionality. The amount of memory used at any time is thus bounded by the
-size of a mini-batch. Although there is no limit to the amount of data that can
-be ingested using such an approach, from a practical point of view the learning
-time is often limited by the CPU time one wants to spend on the task.
+  A strategy to implement out-of-core scaling is to stream data to the estimator
+  in mini-batches. Each mini-batch is vectorized using :class:`HashingVectorizer`
+  so as to guarantee that the input space of the estimator has always the same
+  dimensionality. The amount of memory used at any time is thus bounded by the
+  size of a mini-batch. Although there is no limit to the amount of data that can
+  be ingested using such an approach, from a practical point of view the learning
+  time is often limited by the CPU time one wants to spend on the task.
 
-For a full-fledged example of out-of-core scaling in a text classification
-task see :ref:`sphx_glr_auto_examples_applications_plot_out_of_core_classification.py`.
+  For a full-fledged example of out-of-core scaling in a text classification
+  task see :ref:`sphx_glr_auto_examples_applications_plot_out_of_core_classification.py`.
 
-|details-end|
 
 Customizing the vectorizer classes
 ----------------------------------
@@ -945,65 +929,58 @@ parameters it is possible to derive from the class and override the
 ``build_preprocessor``, ``build_tokenizer`` and ``build_analyzer``
 factory methods instead of passing custom functions.
 
-|details-start|
-**Tips and tricks**
-|details-split|
-
-Some tips and tricks:
-
-* If documents are pre-tokenized by an external package, then store them in
-  files (or strings) with the tokens separated by whitespace and pass
-  ``analyzer=str.split``
-* Fancy token-level analysis such as stemming, lemmatizing, compound
-  splitting, filtering based on part-of-speech, etc. are not included in the
-  scikit-learn codebase, but can be added by customizing either the
-  tokenizer or the analyzer.
-  Here's a ``CountVectorizer`` with a tokenizer and lemmatizer using
-  `NLTK <https://www.nltk.org/>`_::
-
-      >>> from nltk import word_tokenize          # doctest: +SKIP
-      >>> from nltk.stem import WordNetLemmatizer # doctest: +SKIP
-      >>> class LemmaTokenizer:
-      ...     def __init__(self):
-      ...         self.wnl = WordNetLemmatizer()
-      ...     def __call__(self, doc):
-      ...         return [self.wnl.lemmatize(t) for t in word_tokenize(doc)]
-      ...
-      >>> vect = CountVectorizer(tokenizer=LemmaTokenizer())  # doctest: +SKIP
-
-  (Note that this will not filter out punctuation.)
-
-
-  The following example will, for instance, transform some British spelling
-  to American spelling::
-
-      >>> import re
-      >>> def to_british(tokens):
-      ...     for t in tokens:
-      ...         t = re.sub(r"(...)our$", r"\1or", t)
-      ...         t = re.sub(r"([bt])re$", r"\1er", t)
-      ...         t = re.sub(r"([iy])s(e$|ing|ation)", r"\1z\2", t)
-      ...         t = re.sub(r"ogue$", "og", t)
-      ...         yield t
-      ...
-      >>> class CustomVectorizer(CountVectorizer):
-      ...     def build_tokenizer(self):
-      ...         tokenize = super().build_tokenizer()
-      ...         return lambda doc: list(to_british(tokenize(doc)))
-      ...
-      >>> print(CustomVectorizer().build_analyzer()(u"color colour"))
-      [...'color', ...'color']
-
-  for other styles of preprocessing; examples include stemming, lemmatization,
-  or normalizing numerical tokens, with the latter illustrated in:
-
-  * :ref:`sphx_glr_auto_examples_bicluster_plot_bicluster_newsgroups.py`
-
-
-Customizing the vectorizer can also be useful when handling Asian languages
-that do not use an explicit word separator such as whitespace.
-
-|details-end|
+.. dropdown:: Tips and tricks
+  :color: success
+
+  * If documents are pre-tokenized by an external package, then store them in
+    files (or strings) with the tokens separated by whitespace and pass
+    ``analyzer=str.split``
+  * Fancy token-level analysis such as stemming, lemmatizing, compound
+    splitting, filtering based on part-of-speech, etc. are not included in the
+    scikit-learn codebase, but can be added by customizing either the
+    tokenizer or the analyzer.
+    Here's a ``CountVectorizer`` with a tokenizer and lemmatizer using
+    `NLTK <https://www.nltk.org/>`_::
+
+        >>> from nltk import word_tokenize          # doctest: +SKIP
+        >>> from nltk.stem import WordNetLemmatizer # doctest: +SKIP
+        >>> class LemmaTokenizer:
+        ...     def __init__(self):
+        ...         self.wnl = WordNetLemmatizer()
+        ...     def __call__(self, doc):
+        ...         return [self.wnl.lemmatize(t) for t in word_tokenize(doc)]
+        ...
+        >>> vect = CountVectorizer(tokenizer=LemmaTokenizer())  # doctest: +SKIP
+
+    (Note that this will not filter out punctuation.)
+
+    The following example will, for instance, transform some British spelling
+    to American spelling::
+
+        >>> import re
+        >>> def to_british(tokens):
+        ...     for t in tokens:
+        ...         t = re.sub(r"(...)our$", r"\1or", t)
+        ...         t = re.sub(r"([bt])re$", r"\1er", t)
+        ...         t = re.sub(r"([iy])s(e$|ing|ation)", r"\1z\2", t)
+        ...         t = re.sub(r"ogue$", "og", t)
+        ...         yield t
+        ...
+        >>> class CustomVectorizer(CountVectorizer):
+        ...     def build_tokenizer(self):
+        ...         tokenize = super().build_tokenizer()
+        ...         return lambda doc: list(to_british(tokenize(doc)))
+        ...
+        >>> print(CustomVectorizer().build_analyzer()(u"color colour"))
+        [...'color', ...'color']
+
+    for other styles of preprocessing; examples include stemming, lemmatization,
+    or normalizing numerical tokens, with the latter illustrated in:
+
+    * :ref:`sphx_glr_auto_examples_bicluster_plot_bicluster_newsgroups.py`
+
+  Customizing the vectorizer can also be useful when handling Asian languages
+  that do not use an explicit word separator such as whitespace.
 
 .. _image_feature_extraction:
 
diff --git a/doc/modules/feature_selection.rst b/doc/modules/feature_selection.rst
index 1b5ce57b0074f..6746f2f65da00 100644
--- a/doc/modules/feature_selection.rst
+++ b/doc/modules/feature_selection.rst
@@ -114,11 +114,11 @@ applied to non-negative features, such as frequencies.
     feature selection as well. One needs to provide a `score_func` where `y=None`.
     The `score_func` should use internally `X` to compute the scores.
 
-.. topic:: Examples:
+.. rubric:: Examples
 
-    * :ref:`sphx_glr_auto_examples_feature_selection_plot_feature_selection.py`
+* :ref:`sphx_glr_auto_examples_feature_selection_plot_feature_selection.py`
 
-    * :ref:`sphx_glr_auto_examples_feature_selection_plot_f_test_vs_mi.py`
+* :ref:`sphx_glr_auto_examples_feature_selection_plot_f_test_vs_mi.py`
 
 .. _rfe:
 
@@ -144,14 +144,14 @@ of selected features and aggregated together. Finally, the scores are averaged
 across folds and the number of features selected is set to the number of
 features that maximize the cross-validation score.
 
-.. topic:: Examples:
+.. rubric:: Examples
 
-    * :ref:`sphx_glr_auto_examples_feature_selection_plot_rfe_digits.py`: A recursive feature elimination example
-      showing the relevance of pixels in a digit classification task.
+* :ref:`sphx_glr_auto_examples_feature_selection_plot_rfe_digits.py`: A recursive feature elimination example
+  showing the relevance of pixels in a digit classification task.
 
-    * :ref:`sphx_glr_auto_examples_feature_selection_plot_rfe_with_cross_validation.py`: A recursive feature
-      elimination example with automatic tuning of the number of features
-      selected with cross-validation.
+* :ref:`sphx_glr_auto_examples_feature_selection_plot_rfe_with_cross_validation.py`: A recursive feature
+  elimination example with automatic tuning of the number of features
+  selected with cross-validation.
 
 .. _select_from_model:
 
@@ -171,9 +171,9 @@ Available heuristics are "mean", "median" and float multiples of these like
 
 For examples on how it is to be used refer to the sections below.
 
-.. topic:: Examples
+.. rubric:: Examples
 
-    * :ref:`sphx_glr_auto_examples_feature_selection_plot_select_from_model_diabetes.py`
+* :ref:`sphx_glr_auto_examples_feature_selection_plot_select_from_model_diabetes.py`
 
 .. _l1_feature_selection:
 
@@ -207,42 +207,39 @@ With SVMs and logistic-regression, the parameter C controls the sparsity:
 the smaller C the fewer features selected. With Lasso, the higher the
 alpha parameter, the fewer features selected.
 
-.. topic:: Examples:
+.. rubric:: Examples
 
-    * :ref:`sphx_glr_auto_examples_linear_model_plot_lasso_dense_vs_sparse_data.py`.
+* :ref:`sphx_glr_auto_examples_linear_model_plot_lasso_dense_vs_sparse_data.py`.
 
 .. _compressive_sensing:
 
-|details-start|
-**L1-recovery and compressive sensing**
-|details-split|
-
-For a good choice of alpha, the :ref:`lasso` can fully recover the
-exact set of non-zero variables using only few observations, provided
-certain specific conditions are met. In particular, the number of
-samples should be "sufficiently large", or L1 models will perform at
-random, where "sufficiently large" depends on the number of non-zero
-coefficients, the logarithm of the number of features, the amount of
-noise, the smallest absolute value of non-zero coefficients, and the
-structure of the design matrix X. In addition, the design matrix must
-display certain specific properties, such as not being too correlated.
-
-There is no general rule to select an alpha parameter for recovery of
-non-zero coefficients. It can by set by cross-validation
-(:class:`~sklearn.linear_model.LassoCV` or
-:class:`~sklearn.linear_model.LassoLarsCV`), though this may lead to
-under-penalized models: including a small number of non-relevant variables
-is not detrimental to prediction score. BIC
-(:class:`~sklearn.linear_model.LassoLarsIC`) tends, on the opposite, to set
-high values of alpha.
-
-.. topic:: Reference
-
-   Richard G. Baraniuk "Compressive Sensing", IEEE Signal
-   Processing Magazine [120] July 2007
-   http://users.isr.ist.utl.pt/~aguiar/CS_notes.pdf
-
-|details-end|
+.. dropdown:: L1-recovery and compressive sensing
+
+  For a good choice of alpha, the :ref:`lasso` can fully recover the
+  exact set of non-zero variables using only few observations, provided
+  certain specific conditions are met. In particular, the number of
+  samples should be "sufficiently large", or L1 models will perform at
+  random, where "sufficiently large" depends on the number of non-zero
+  coefficients, the logarithm of the number of features, the amount of
+  noise, the smallest absolute value of non-zero coefficients, and the
+  structure of the design matrix X. In addition, the design matrix must
+  display certain specific properties, such as not being too correlated.
+
+  There is no general rule to select an alpha parameter for recovery of
+  non-zero coefficients. It can by set by cross-validation
+  (:class:`~sklearn.linear_model.LassoCV` or
+  :class:`~sklearn.linear_model.LassoLarsCV`), though this may lead to
+  under-penalized models: including a small number of non-relevant variables
+  is not detrimental to prediction score. BIC
+  (:class:`~sklearn.linear_model.LassoLarsIC`) tends, on the opposite, to set
+  high values of alpha.
+
+  .. rubric:: References
+
+  Richard G. Baraniuk "Compressive Sensing", IEEE Signal
+  Processing Magazine [120] July 2007
+  http://users.isr.ist.utl.pt/~aguiar/CS_notes.pdf
+
 
 Tree-based feature selection
 ----------------------------
@@ -268,14 +265,13 @@ meta-transformer)::
   >>> X_new.shape               # doctest: +SKIP
   (150, 2)
 
-.. topic:: Examples:
+.. rubric:: Examples
 
-    * :ref:`sphx_glr_auto_examples_ensemble_plot_forest_importances.py`: example on
-      synthetic data showing the recovery of the actually meaningful
-      features.
+* :ref:`sphx_glr_auto_examples_ensemble_plot_forest_importances.py`: example on
+  synthetic data showing the recovery of the actually meaningful features.
 
-    * :ref:`sphx_glr_auto_examples_ensemble_plot_forest_importances_faces.py`: example
-      on face recognition data.
+* :ref:`sphx_glr_auto_examples_ensemble_plot_forest_importances_faces.py`: example
+  on face recognition data.
 
 .. _sequential_feature_selection:
 
@@ -299,38 +295,35 @@ instead of starting with no features and greedily adding features, we start
 with *all* the features and greedily *remove* features from the set. The
 `direction` parameter controls whether forward or backward SFS is used.
 
-|details-start|
-**Detail on Sequential Feature Selection**
-|details-split|
-
-In general, forward and backward selection do not yield equivalent results.
-Also, one may be much faster than the other depending on the requested number
-of selected features: if we have 10 features and ask for 7 selected features,
-forward selection would need to perform 7 iterations while backward selection
-would only need to perform 3.
-
-SFS differs from :class:`~sklearn.feature_selection.RFE` and
-:class:`~sklearn.feature_selection.SelectFromModel` in that it does not
-require the underlying model to expose a `coef_` or `feature_importances_`
-attribute. It may however be slower considering that more models need to be
-evaluated, compared to the other approaches. For example in backward
-selection, the iteration going from `m` features to `m - 1` features using k-fold
-cross-validation requires fitting `m * k` models, while
-:class:`~sklearn.feature_selection.RFE` would require only a single fit, and
-:class:`~sklearn.feature_selection.SelectFromModel` always just does a single
-fit and requires no iterations.
-
-.. topic:: Reference
-
-   .. [sfs] Ferri et al, `Comparative study of techniques for
+.. dropdown:: Details on Sequential Feature Selection
+
+  In general, forward and backward selection do not yield equivalent results.
+  Also, one may be much faster than the other depending on the requested number
+  of selected features: if we have 10 features and ask for 7 selected features,
+  forward selection would need to perform 7 iterations while backward selection
+  would only need to perform 3.
+
+  SFS differs from :class:`~sklearn.feature_selection.RFE` and
+  :class:`~sklearn.feature_selection.SelectFromModel` in that it does not
+  require the underlying model to expose a `coef_` or `feature_importances_`
+  attribute. It may however be slower considering that more models need to be
+  evaluated, compared to the other approaches. For example in backward
+  selection, the iteration going from `m` features to `m - 1` features using k-fold
+  cross-validation requires fitting `m * k` models, while
+  :class:`~sklearn.feature_selection.RFE` would require only a single fit, and
+  :class:`~sklearn.feature_selection.SelectFromModel` always just does a single
+  fit and requires no iterations.
+
+  .. rubric:: References
+
+  .. [sfs] Ferri et al, `Comparative study of techniques for
       large-scale feature selection
       <https://citeseerx.ist.psu.edu/doc_view/pid/5fedabbb3957bbb442802e012d829ee0629a01b6>`_.
 
-|details-end|
 
-.. topic:: Examples
+.. rubric:: Examples
 
-    * :ref:`sphx_glr_auto_examples_feature_selection_plot_select_from_model_diabetes.py`
+* :ref:`sphx_glr_auto_examples_feature_selection_plot_select_from_model_diabetes.py`
 
 Feature selection as part of a pipeline
 =======================================
diff --git a/doc/modules/gaussian_process.rst b/doc/modules/gaussian_process.rst
index 58e56a557ed73..fb87120205f96 100644
--- a/doc/modules/gaussian_process.rst
+++ b/doc/modules/gaussian_process.rst
@@ -88,12 +88,12 @@ the API of standard scikit-learn estimators, :class:`GaussianProcessRegressor`:
   externally for other ways of selecting hyperparameters, e.g., via
   Markov chain Monte Carlo.
 
-.. topic:: Examples
+.. rubric:: Examples
 
-   * :ref:`sphx_glr_auto_examples_gaussian_process_plot_gpr_noisy_targets.py`
-   * :ref:`sphx_glr_auto_examples_gaussian_process_plot_gpr_noisy.py`
-   * :ref:`sphx_glr_auto_examples_gaussian_process_plot_compare_gpr_krr.py`
-   * :ref:`sphx_glr_auto_examples_gaussian_process_plot_gpr_co2.py`
+* :ref:`sphx_glr_auto_examples_gaussian_process_plot_gpr_noisy_targets.py`
+* :ref:`sphx_glr_auto_examples_gaussian_process_plot_gpr_noisy.py`
+* :ref:`sphx_glr_auto_examples_gaussian_process_plot_compare_gpr_krr.py`
+* :ref:`sphx_glr_auto_examples_gaussian_process_plot_gpr_co2.py`
 
 .. _gpc:
 
@@ -239,93 +239,88 @@ also invariant to rotations in the input space. For more details, we refer to
 Chapter 4 of [RW2006]_. For guidance on how to best combine different kernels,
 we refer to [Duv2014]_.
 
-|details-start|
-**Gaussian Process Kernel API**
-|details-split|
-
-The main usage of a :class:`Kernel` is to compute the GP's covariance between
-datapoints. For this, the method ``__call__`` of the kernel can be called. This
-method can either be used to compute the "auto-covariance" of all pairs of
-datapoints in a 2d array X, or the "cross-covariance" of all combinations
-of datapoints of a 2d array X with datapoints in a 2d array Y. The following
-identity holds true for all kernels k (except for the :class:`WhiteKernel`):
-``k(X) == K(X, Y=X)``
-
-If only the diagonal of the auto-covariance is being used, the method ``diag()``
-of a kernel can be called, which is more computationally efficient than the
-equivalent call to ``__call__``: ``np.diag(k(X, X)) == k.diag(X)``
-
-Kernels are parameterized by a vector :math:`\theta` of hyperparameters. These
-hyperparameters can for instance control length-scales or periodicity of a
-kernel (see below). All kernels support computing analytic gradients
-of the kernel's auto-covariance with respect to :math:`log(\theta)` via setting
-``eval_gradient=True`` in the ``__call__`` method.
-That is, a ``(len(X), len(X), len(theta))`` array is returned where the entry
-``[i, j, l]`` contains :math:`\frac{\partial k_\theta(x_i, x_j)}{\partial log(\theta_l)}`.
-This gradient is used by the Gaussian process (both regressor and classifier)
-in computing the gradient of the log-marginal-likelihood, which in turn is used
-to determine the value of :math:`\theta`, which maximizes the log-marginal-likelihood,
-via gradient ascent. For each hyperparameter, the initial value and the
-bounds need to be specified when creating an instance of the kernel. The
-current value of :math:`\theta` can be get and set via the property
-``theta`` of the kernel object. Moreover, the bounds of the hyperparameters can be
-accessed by the property ``bounds`` of the kernel. Note that both properties
-(theta and bounds) return log-transformed values of the internally used values
-since those are typically more amenable to gradient-based optimization.
-The specification of each hyperparameter is stored in the form of an instance of
-:class:`Hyperparameter` in the respective kernel. Note that a kernel using a
-hyperparameter with name "x" must have the attributes self.x and self.x_bounds.
-
-The abstract base class for all kernels is :class:`Kernel`. Kernel implements a
-similar interface as :class:`~sklearn.base.BaseEstimator`, providing the
-methods ``get_params()``, ``set_params()``, and ``clone()``. This allows
-setting kernel values also via meta-estimators such as
-:class:`~sklearn.pipeline.Pipeline` or
-:class:`~sklearn.model_selection.GridSearchCV`. Note that due to the nested
-structure of kernels (by applying kernel operators, see below), the names of
-kernel parameters might become relatively complicated. In general, for a binary
-kernel operator, parameters of the left operand are prefixed with ``k1__`` and
-parameters of the right operand with ``k2__``. An additional convenience method
-is ``clone_with_theta(theta)``, which returns a cloned version of the kernel
-but with the hyperparameters set to ``theta``. An illustrative example:
-
-    >>> from sklearn.gaussian_process.kernels import ConstantKernel, RBF
-    >>> kernel = ConstantKernel(constant_value=1.0, constant_value_bounds=(0.0, 10.0)) * RBF(length_scale=0.5, length_scale_bounds=(0.0, 10.0)) + RBF(length_scale=2.0, length_scale_bounds=(0.0, 10.0))
-    >>> for hyperparameter in kernel.hyperparameters: print(hyperparameter)
-    Hyperparameter(name='k1__k1__constant_value', value_type='numeric', bounds=array([[ 0., 10.]]), n_elements=1, fixed=False)
-    Hyperparameter(name='k1__k2__length_scale', value_type='numeric', bounds=array([[ 0., 10.]]), n_elements=1, fixed=False)
-    Hyperparameter(name='k2__length_scale', value_type='numeric', bounds=array([[ 0., 10.]]), n_elements=1, fixed=False)
-    >>> params = kernel.get_params()
-    >>> for key in sorted(params): print("%s : %s" % (key, params[key]))
-    k1 : 1**2 * RBF(length_scale=0.5)
-    k1__k1 : 1**2
-    k1__k1__constant_value : 1.0
-    k1__k1__constant_value_bounds : (0.0, 10.0)
-    k1__k2 : RBF(length_scale=0.5)
-    k1__k2__length_scale : 0.5
-    k1__k2__length_scale_bounds : (0.0, 10.0)
-    k2 : RBF(length_scale=2)
-    k2__length_scale : 2.0
-    k2__length_scale_bounds : (0.0, 10.0)
-    >>> print(kernel.theta)  # Note: log-transformed
-    [ 0.         -0.69314718  0.69314718]
-    >>> print(kernel.bounds)  # Note: log-transformed
-    [[      -inf 2.30258509]
-     [      -inf 2.30258509]
-     [      -inf 2.30258509]]
-
-
-All Gaussian process kernels are interoperable with :mod:`sklearn.metrics.pairwise`
-and vice versa: instances of subclasses of :class:`Kernel` can be passed as
-``metric`` to ``pairwise_kernels`` from :mod:`sklearn.metrics.pairwise`. Moreover,
-kernel functions from pairwise can be used as GP kernels by using the wrapper
-class :class:`PairwiseKernel`. The only caveat is that the gradient of
-the hyperparameters is not analytic but numeric and all those kernels support
-only isotropic distances. The parameter ``gamma`` is considered to be a
-hyperparameter and may be optimized. The other kernel parameters are set
-directly at initialization and are kept fixed.
-
-|details-end|
+.. dropdown:: Gaussian Process Kernel API
+
+   The main usage of a :class:`Kernel` is to compute the GP's covariance between
+   datapoints. For this, the method ``__call__`` of the kernel can be called. This
+   method can either be used to compute the "auto-covariance" of all pairs of
+   datapoints in a 2d array X, or the "cross-covariance" of all combinations
+   of datapoints of a 2d array X with datapoints in a 2d array Y. The following
+   identity holds true for all kernels k (except for the :class:`WhiteKernel`):
+   ``k(X) == K(X, Y=X)``
+
+   If only the diagonal of the auto-covariance is being used, the method ``diag()``
+   of a kernel can be called, which is more computationally efficient than the
+   equivalent call to ``__call__``: ``np.diag(k(X, X)) == k.diag(X)``
+
+   Kernels are parameterized by a vector :math:`\theta` of hyperparameters. These
+   hyperparameters can for instance control length-scales or periodicity of a
+   kernel (see below). All kernels support computing analytic gradients
+   of the kernel's auto-covariance with respect to :math:`log(\theta)` via setting
+   ``eval_gradient=True`` in the ``__call__`` method.
+   That is, a ``(len(X), len(X), len(theta))`` array is returned where the entry
+   ``[i, j, l]`` contains :math:`\frac{\partial k_\theta(x_i, x_j)}{\partial log(\theta_l)}`.
+   This gradient is used by the Gaussian process (both regressor and classifier)
+   in computing the gradient of the log-marginal-likelihood, which in turn is used
+   to determine the value of :math:`\theta`, which maximizes the log-marginal-likelihood,
+   via gradient ascent. For each hyperparameter, the initial value and the
+   bounds need to be specified when creating an instance of the kernel. The
+   current value of :math:`\theta` can be get and set via the property
+   ``theta`` of the kernel object. Moreover, the bounds of the hyperparameters can be
+   accessed by the property ``bounds`` of the kernel. Note that both properties
+   (theta and bounds) return log-transformed values of the internally used values
+   since those are typically more amenable to gradient-based optimization.
+   The specification of each hyperparameter is stored in the form of an instance of
+   :class:`Hyperparameter` in the respective kernel. Note that a kernel using a
+   hyperparameter with name "x" must have the attributes self.x and self.x_bounds.
+
+   The abstract base class for all kernels is :class:`Kernel`. Kernel implements a
+   similar interface as :class:`~sklearn.base.BaseEstimator`, providing the
+   methods ``get_params()``, ``set_params()``, and ``clone()``. This allows
+   setting kernel values also via meta-estimators such as
+   :class:`~sklearn.pipeline.Pipeline` or
+   :class:`~sklearn.model_selection.GridSearchCV`. Note that due to the nested
+   structure of kernels (by applying kernel operators, see below), the names of
+   kernel parameters might become relatively complicated. In general, for a binary
+   kernel operator, parameters of the left operand are prefixed with ``k1__`` and
+   parameters of the right operand with ``k2__``. An additional convenience method
+   is ``clone_with_theta(theta)``, which returns a cloned version of the kernel
+   but with the hyperparameters set to ``theta``. An illustrative example:
+
+      >>> from sklearn.gaussian_process.kernels import ConstantKernel, RBF
+      >>> kernel = ConstantKernel(constant_value=1.0, constant_value_bounds=(0.0, 10.0)) * RBF(length_scale=0.5, length_scale_bounds=(0.0, 10.0)) + RBF(length_scale=2.0, length_scale_bounds=(0.0, 10.0))
+      >>> for hyperparameter in kernel.hyperparameters: print(hyperparameter)
+      Hyperparameter(name='k1__k1__constant_value', value_type='numeric', bounds=array([[ 0., 10.]]), n_elements=1, fixed=False)
+      Hyperparameter(name='k1__k2__length_scale', value_type='numeric', bounds=array([[ 0., 10.]]), n_elements=1, fixed=False)
+      Hyperparameter(name='k2__length_scale', value_type='numeric', bounds=array([[ 0., 10.]]), n_elements=1, fixed=False)
+      >>> params = kernel.get_params()
+      >>> for key in sorted(params): print("%s : %s" % (key, params[key]))
+      k1 : 1**2 * RBF(length_scale=0.5)
+      k1__k1 : 1**2
+      k1__k1__constant_value : 1.0
+      k1__k1__constant_value_bounds : (0.0, 10.0)
+      k1__k2 : RBF(length_scale=0.5)
+      k1__k2__length_scale : 0.5
+      k1__k2__length_scale_bounds : (0.0, 10.0)
+      k2 : RBF(length_scale=2)
+      k2__length_scale : 2.0
+      k2__length_scale_bounds : (0.0, 10.0)
+      >>> print(kernel.theta)  # Note: log-transformed
+      [ 0.         -0.69314718  0.69314718]
+      >>> print(kernel.bounds)  # Note: log-transformed
+      [[      -inf 2.30258509]
+      [      -inf 2.30258509]
+      [      -inf 2.30258509]]
+
+   All Gaussian process kernels are interoperable with :mod:`sklearn.metrics.pairwise`
+   and vice versa: instances of subclasses of :class:`Kernel` can be passed as
+   ``metric`` to ``pairwise_kernels`` from :mod:`sklearn.metrics.pairwise`. Moreover,
+   kernel functions from pairwise can be used as GP kernels by using the wrapper
+   class :class:`PairwiseKernel`. The only caveat is that the gradient of
+   the hyperparameters is not analytic but numeric and all those kernels support
+   only isotropic distances. The parameter ``gamma`` is considered to be a
+   hyperparameter and may be optimized. The other kernel parameters are set
+   directly at initialization and are kept fixed.
 
 Basic kernels
 -------------
@@ -388,42 +383,38 @@ The :class:`Matern` kernel is a stationary kernel and a generalization of the
 :class:`RBF` kernel. It has an additional parameter :math:`\nu` which controls
 the smoothness of the resulting function. It is parameterized by a length-scale parameter :math:`l>0`, which can either be a scalar (isotropic variant of the kernel) or a vector with the same number of dimensions as the inputs :math:`x` (anisotropic variant of the kernel).
 
-|details-start|
-**Mathematical implementation of Matérn kernel**
-|details-split|
+.. dropdown:: Mathematical implementation of Matérn kernel
 
-The kernel is given by:
-
-.. math::
+   The kernel is given by:
 
-    k(x_i, x_j) = \frac{1}{\Gamma(\nu)2^{\nu-1}}\Bigg(\frac{\sqrt{2\nu}}{l} d(x_i , x_j )\Bigg)^\nu K_\nu\Bigg(\frac{\sqrt{2\nu}}{l} d(x_i , x_j )\Bigg),
+   .. math::
 
-where :math:`d(\cdot,\cdot)` is the Euclidean distance, :math:`K_\nu(\cdot)` is a modified Bessel function and :math:`\Gamma(\cdot)` is the gamma function.
-As :math:`\nu\rightarrow\infty`, the Matérn kernel converges to the RBF kernel.
-When :math:`\nu = 1/2`, the Matérn kernel becomes identical to the absolute
-exponential kernel, i.e.,
+      k(x_i, x_j) = \frac{1}{\Gamma(\nu)2^{\nu-1}}\Bigg(\frac{\sqrt{2\nu}}{l} d(x_i , x_j )\Bigg)^\nu K_\nu\Bigg(\frac{\sqrt{2\nu}}{l} d(x_i , x_j )\Bigg),
 
-.. math::
-    k(x_i, x_j) = \exp \Bigg(- \frac{1}{l} d(x_i , x_j ) \Bigg) \quad \quad \nu= \tfrac{1}{2}
+   where :math:`d(\cdot,\cdot)` is the Euclidean distance, :math:`K_\nu(\cdot)` is a modified Bessel function and :math:`\Gamma(\cdot)` is the gamma function.
+   As :math:`\nu\rightarrow\infty`, the Matérn kernel converges to the RBF kernel.
+   When :math:`\nu = 1/2`, the Matérn kernel becomes identical to the absolute
+   exponential kernel, i.e.,
 
-In particular, :math:`\nu = 3/2`:
+   .. math::
+      k(x_i, x_j) = \exp \Bigg(- \frac{1}{l} d(x_i , x_j ) \Bigg) \quad \quad \nu= \tfrac{1}{2}
 
-.. math::
-    k(x_i, x_j) =  \Bigg(1 + \frac{\sqrt{3}}{l} d(x_i , x_j )\Bigg) \exp \Bigg(-\frac{\sqrt{3}}{l} d(x_i , x_j ) \Bigg) \quad \quad \nu= \tfrac{3}{2}
+   In particular, :math:`\nu = 3/2`:
 
-and :math:`\nu = 5/2`:
+   .. math::
+      k(x_i, x_j) =  \Bigg(1 + \frac{\sqrt{3}}{l} d(x_i , x_j )\Bigg) \exp \Bigg(-\frac{\sqrt{3}}{l} d(x_i , x_j ) \Bigg) \quad \quad \nu= \tfrac{3}{2}
 
-.. math::
-    k(x_i, x_j) = \Bigg(1 + \frac{\sqrt{5}}{l} d(x_i , x_j ) +\frac{5}{3l} d(x_i , x_j )^2 \Bigg) \exp \Bigg(-\frac{\sqrt{5}}{l} d(x_i , x_j ) \Bigg) \quad \quad \nu= \tfrac{5}{2}
+   and :math:`\nu = 5/2`:
 
-are popular choices for learning functions that are not infinitely
-differentiable (as assumed by the RBF kernel) but at least once (:math:`\nu =
-3/2`) or twice differentiable (:math:`\nu = 5/2`).
+   .. math::
+      k(x_i, x_j) = \Bigg(1 + \frac{\sqrt{5}}{l} d(x_i , x_j ) +\frac{5}{3l} d(x_i , x_j )^2 \Bigg) \exp \Bigg(-\frac{\sqrt{5}}{l} d(x_i , x_j ) \Bigg) \quad \quad \nu= \tfrac{5}{2}
 
-The flexibility of controlling the smoothness of the learned function via :math:`\nu`
-allows adapting to the properties of the true underlying functional relation.
+   are popular choices for learning functions that are not infinitely
+   differentiable (as assumed by the RBF kernel) but at least once (:math:`\nu =
+   3/2`) or twice differentiable (:math:`\nu = 5/2`).
 
-|details-end|
+   The flexibility of controlling the smoothness of the learned function via :math:`\nu`
+   allows adapting to the properties of the true underlying functional relation.
 
 The prior and posterior of a GP resulting from a Matérn kernel are shown in
 the following figure:
diff --git a/doc/modules/grid_search.rst b/doc/modules/grid_search.rst
index 01c5a5c72ee52..12ee76d8e4d39 100644
--- a/doc/modules/grid_search.rst
+++ b/doc/modules/grid_search.rst
@@ -72,35 +72,35 @@ evaluated and the best combination is retained.
 
 .. currentmodule:: sklearn.model_selection
 
-.. topic:: Examples:
+.. rubric:: Examples
 
-    - See :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_digits.py` for an example of
-      Grid Search computation on the digits dataset.
+- See :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_digits.py` for an example of
+  Grid Search computation on the digits dataset.
 
-    - See :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_text_feature_extraction.py` for an example
-      of Grid Search coupling parameters from a text documents feature
-      extractor (n-gram count vectorizer and TF-IDF transformer) with a
-      classifier (here a linear SVM trained with SGD with either elastic
-      net or L2 penalty) using a :class:`~sklearn.pipeline.Pipeline` instance.
+- See :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_text_feature_extraction.py` for an example
+  of Grid Search coupling parameters from a text documents feature
+  extractor (n-gram count vectorizer and TF-IDF transformer) with a
+  classifier (here a linear SVM trained with SGD with either elastic
+  net or L2 penalty) using a :class:`~sklearn.pipeline.Pipeline` instance.
 
-    - See :ref:`sphx_glr_auto_examples_model_selection_plot_nested_cross_validation_iris.py`
-      for an example of Grid Search within a cross validation loop on the iris
-      dataset. This is the best practice for evaluating the performance of a
-      model with grid search.
+- See :ref:`sphx_glr_auto_examples_model_selection_plot_nested_cross_validation_iris.py`
+  for an example of Grid Search within a cross validation loop on the iris
+  dataset. This is the best practice for evaluating the performance of a
+  model with grid search.
 
-    - See :ref:`sphx_glr_auto_examples_model_selection_plot_multi_metric_evaluation.py`
-      for an example of :class:`GridSearchCV` being used to evaluate multiple
-      metrics simultaneously.
+- See :ref:`sphx_glr_auto_examples_model_selection_plot_multi_metric_evaluation.py`
+  for an example of :class:`GridSearchCV` being used to evaluate multiple
+  metrics simultaneously.
 
-    - See :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_refit_callable.py`
-      for an example of using ``refit=callable`` interface in
-      :class:`GridSearchCV`. The example shows how this interface adds certain
-      amount of flexibility in identifying the "best" estimator. This interface
-      can also be used in multiple metrics evaluation.
+- See :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_refit_callable.py`
+  for an example of using ``refit=callable`` interface in
+  :class:`GridSearchCV`. The example shows how this interface adds certain
+  amount of flexibility in identifying the "best" estimator. This interface
+  can also be used in multiple metrics evaluation.
 
-    - See :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_stats.py`
-      for an example of how to do a statistical comparison on the outputs of
-      :class:`GridSearchCV`.
+- See :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_stats.py`
+  for an example of how to do a statistical comparison on the outputs of
+  :class:`GridSearchCV`.
 
 .. _randomized_parameter_search:
 
@@ -161,16 +161,16 @@ variable that is log-uniformly distributed between ``1e0`` and ``1e3``::
    'kernel': ['rbf'],
    'class_weight':['balanced', None]}
 
-.. topic:: Examples:
+.. rubric:: Examples
 
-    * :ref:`sphx_glr_auto_examples_model_selection_plot_randomized_search.py` compares the usage and efficiency
-      of randomized search and grid search.
+* :ref:`sphx_glr_auto_examples_model_selection_plot_randomized_search.py` compares the usage and efficiency
+  of randomized search and grid search.
 
-.. topic:: References:
+.. rubric:: References
 
-    * Bergstra, J. and Bengio, Y.,
-      Random search for hyper-parameter optimization,
-      The Journal of Machine Learning Research (2012)
+* Bergstra, J. and Bengio, Y.,
+  Random search for hyper-parameter optimization,
+  The Journal of Machine Learning Research (2012)
 
 .. _successive_halving_user_guide:
 
@@ -222,10 +222,10 @@ need to explicitly import ``enable_halving_search_cv``::
   >>> from sklearn.model_selection import HalvingGridSearchCV
   >>> from sklearn.model_selection import HalvingRandomSearchCV
 
-.. topic:: Examples:
+.. rubric:: Examples
 
-    * :ref:`sphx_glr_auto_examples_model_selection_plot_successive_halving_heatmap.py`
-    * :ref:`sphx_glr_auto_examples_model_selection_plot_successive_halving_iterations.py`
+* :ref:`sphx_glr_auto_examples_model_selection_plot_successive_halving_heatmap.py`
+* :ref:`sphx_glr_auto_examples_model_selection_plot_successive_halving_iterations.py`
 
 Choosing ``min_resources`` and the number of candidates
 -------------------------------------------------------
@@ -528,15 +528,16 @@ In the example above, the best parameter combination is ``{'criterion':
 since it has reached the last iteration (3) with the highest score:
 0.96.
 
-.. topic:: References:
+.. rubric:: References
 
-    .. [1] K. Jamieson, A. Talwalkar,
-       `Non-stochastic Best Arm Identification and Hyperparameter
-       Optimization <http://proceedings.mlr.press/v51/jamieson16.html>`_, in
-       proc. of Machine Learning Research, 2016.
-    .. [2] L. Li, K. Jamieson, G. DeSalvo, A. Rostamizadeh, A. Talwalkar,
-       :arxiv:`Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization
-       <1603.06560>`, in Machine Learning Research 18, 2018.
+.. [1] K. Jamieson, A. Talwalkar,
+   `Non-stochastic Best Arm Identification and Hyperparameter
+   Optimization <http://proceedings.mlr.press/v51/jamieson16.html>`_, in
+   proc. of Machine Learning Research, 2016.
+
+.. [2] L. Li, K. Jamieson, G. DeSalvo, A. Rostamizadeh, A. Talwalkar,
+   :arxiv:`Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization
+   <1603.06560>`, in Machine Learning Research 18, 2018.
 
 .. _grid_search_tips:
 
diff --git a/doc/modules/impute.rst b/doc/modules/impute.rst
index f5879cbffc0a5..1431f26132338 100644
--- a/doc/modules/impute.rst
+++ b/doc/modules/impute.rst
@@ -224,13 +224,13 @@ neighbors of samples with missing values::
 
 For another example on usage, see :ref:`sphx_glr_auto_examples_impute_plot_missing_values.py`.
 
-.. topic:: References
+.. rubric:: References
 
-  .. [OL2001] `Olga Troyanskaya, Michael Cantor, Gavin Sherlock, Pat Brown,
-      Trevor Hastie, Robert Tibshirani, David Botstein and Russ B. Altman,
-      Missing value estimation methods for DNA microarrays, BIOINFORMATICS
-      Vol. 17 no. 6, 2001 Pages 520-525.
-      <https://academic.oup.com/bioinformatics/article/17/6/520/272365>`_
+.. [OL2001] `Olga Troyanskaya, Michael Cantor, Gavin Sherlock, Pat Brown,
+    Trevor Hastie, Robert Tibshirani, David Botstein and Russ B. Altman,
+    Missing value estimation methods for DNA microarrays, BIOINFORMATICS
+    Vol. 17 no. 6, 2001 Pages 520-525.
+    <https://academic.oup.com/bioinformatics/article/17/6/520/272365>`_
 
 Keeping the number of features constant
 =======================================
diff --git a/doc/modules/isotonic.rst b/doc/modules/isotonic.rst
index 6cfdc1669de5d..50fbdb24e72c7 100644
--- a/doc/modules/isotonic.rst
+++ b/doc/modules/isotonic.rst
@@ -32,6 +32,6 @@ thus form a function that is piecewise linear:
    :target: ../auto_examples/miscellaneous/plot_isotonic_regression.html
    :align: center
 
-.. topic:: Examples:
+.. rubric:: Examples
 
-  * :ref:`sphx_glr_auto_examples_miscellaneous_plot_isotonic_regression.py`
+* :ref:`sphx_glr_auto_examples_miscellaneous_plot_isotonic_regression.py`
diff --git a/doc/modules/kernel_approximation.rst b/doc/modules/kernel_approximation.rst
index 0c67c36178e3b..305c3cc6601fb 100644
--- a/doc/modules/kernel_approximation.rst
+++ b/doc/modules/kernel_approximation.rst
@@ -88,12 +88,12 @@ function or a precomputed kernel matrix. The number of samples used - which is
 also the dimensionality of the features computed - is given by the parameter
 ``n_components``.
 
-.. topic:: Examples:
+.. rubric:: Examples
 
-    * See the example entitled
-      :ref:`sphx_glr_auto_examples_applications_plot_cyclical_feature_engineering.py`,
-      that shows an efficient machine learning pipeline that uses a
-      :class:`Nystroem` kernel.
+* See the example entitled
+  :ref:`sphx_glr_auto_examples_applications_plot_cyclical_feature_engineering.py`,
+  that shows an efficient machine learning pipeline that uses a
+  :class:`Nystroem` kernel.
 
 .. _rbf_kernel_approx:
 
@@ -143,9 +143,9 @@ use of larger feature spaces more efficient.
 
     Comparing an exact RBF kernel (left) with the approximation (right)
 
-.. topic:: Examples:
+.. rubric:: Examples
 
-    * :ref:`sphx_glr_auto_examples_miscellaneous_plot_kernel_approximation.py`
+* :ref:`sphx_glr_auto_examples_miscellaneous_plot_kernel_approximation.py`
 
 .. _additive_chi_kernel_approx:
 
@@ -241,9 +241,9 @@ In addition, this method can transform samples in
 time, where :math:`n_{\text{components}}` is the desired output dimension,
 determined by ``n_components``.
 
-.. topic:: Examples:
+.. rubric:: Examples
 
-    * :ref:`sphx_glr_auto_examples_kernel_approximation_plot_scalable_poly_kernels.py`
+* :ref:`sphx_glr_auto_examples_kernel_approximation_plot_scalable_poly_kernels.py`
 
 .. _tensor_sketch_kernel_approx:
 
@@ -283,29 +283,29 @@ The classes in this submodule allow to approximate the embedding
 or store training examples.
 
 
-.. topic:: References:
-
-    .. [WS2001] `"Using the Nyström method to speed up kernel machines"
-      <https://papers.nips.cc/paper_files/paper/2000/hash/19de10adbaa1b2ee13f77f679fa1483a-Abstract.html>`_
-      Williams, C.K.I.; Seeger, M. - 2001.
-    .. [RR2007] `"Random features for large-scale kernel machines"
-      <https://papers.nips.cc/paper/2007/hash/013a006f03dbc5392effeb8f18fda755-Abstract.html>`_
-      Rahimi, A. and Recht, B. - Advances in neural information processing 2007,
-    .. [LS2010] `"Random Fourier approximations for skewed multiplicative histogram kernels"
-      <https://www.researchgate.net/publication/221114584_Random_Fourier_Approximations_for_Skewed_Multiplicative_Histogram_Kernels>`_
-      Li, F., Ionescu, C., and Sminchisescu, C.
-      - Pattern Recognition,  DAGM 2010, Lecture Notes in Computer Science.
-    .. [VZ2010] `"Efficient additive kernels via explicit feature maps"
-      <https://www.robots.ox.ac.uk/~vgg/publications/2011/Vedaldi11/vedaldi11.pdf>`_
-      Vedaldi, A. and Zisserman, A. - Computer Vision and Pattern Recognition 2010
-    .. [VVZ2010] `"Generalized RBF feature maps for Efficient Detection"
-      <https://www.robots.ox.ac.uk/~vgg/publications/2010/Sreekanth10/sreekanth10.pdf>`_
-      Vempati, S. and Vedaldi, A. and Zisserman, A. and Jawahar, CV - 2010
-    .. [PP2013] :doi:`"Fast and scalable polynomial kernels via explicit feature maps"
-      <10.1145/2487575.2487591>`
-      Pham, N., & Pagh, R. - 2013
-    .. [CCF2002] `"Finding frequent items in data streams"
-      <https://www.cs.princeton.edu/courses/archive/spring04/cos598B/bib/CharikarCF.pdf>`_
-      Charikar, M., Chen, K., & Farach-Colton - 2002
-    .. [WIKICS] `"Wikipedia: Count sketch"
-      <https://en.wikipedia.org/wiki/Count_sketch>`_
+.. rubric:: References
+
+.. [WS2001] `"Using the Nyström method to speed up kernel machines"
+  <https://papers.nips.cc/paper_files/paper/2000/hash/19de10adbaa1b2ee13f77f679fa1483a-Abstract.html>`_
+  Williams, C.K.I.; Seeger, M. - 2001.
+.. [RR2007] `"Random features for large-scale kernel machines"
+  <https://papers.nips.cc/paper/2007/hash/013a006f03dbc5392effeb8f18fda755-Abstract.html>`_
+  Rahimi, A. and Recht, B. - Advances in neural information processing 2007,
+.. [LS2010] `"Random Fourier approximations for skewed multiplicative histogram kernels"
+  <https://www.researchgate.net/publication/221114584_Random_Fourier_Approximations_for_Skewed_Multiplicative_Histogram_Kernels>`_
+  Li, F., Ionescu, C., and Sminchisescu, C.
+  - Pattern Recognition,  DAGM 2010, Lecture Notes in Computer Science.
+.. [VZ2010] `"Efficient additive kernels via explicit feature maps"
+  <https://www.robots.ox.ac.uk/~vgg/publications/2011/Vedaldi11/vedaldi11.pdf>`_
+  Vedaldi, A. and Zisserman, A. - Computer Vision and Pattern Recognition 2010
+.. [VVZ2010] `"Generalized RBF feature maps for Efficient Detection"
+  <https://www.robots.ox.ac.uk/~vgg/publications/2010/Sreekanth10/sreekanth10.pdf>`_
+  Vempati, S. and Vedaldi, A. and Zisserman, A. and Jawahar, CV - 2010
+.. [PP2013] :doi:`"Fast and scalable polynomial kernels via explicit feature maps"
+  <10.1145/2487575.2487591>`
+  Pham, N., & Pagh, R. - 2013
+.. [CCF2002] `"Finding frequent items in data streams"
+  <https://www.cs.princeton.edu/courses/archive/spring04/cos598B/bib/CharikarCF.pdf>`_
+  Charikar, M., Chen, K., & Farach-Colton - 2002
+.. [WIKICS] `"Wikipedia: Count sketch"
+  <https://en.wikipedia.org/wiki/Count_sketch>`_
diff --git a/doc/modules/kernel_ridge.rst b/doc/modules/kernel_ridge.rst
index 5d25ce71f5ea1..fcc19a49628c4 100644
--- a/doc/modules/kernel_ridge.rst
+++ b/doc/modules/kernel_ridge.rst
@@ -55,11 +55,11 @@ dense model.
    :target: ../auto_examples/miscellaneous/plot_kernel_ridge_regression.html
    :align: center
 
-.. topic:: Examples
+.. rubric:: Examples
 
-    * :ref:`sphx_glr_auto_examples_miscellaneous_plot_kernel_ridge_regression.py`
+* :ref:`sphx_glr_auto_examples_miscellaneous_plot_kernel_ridge_regression.py`
 
-.. topic:: References:
+.. rubric:: References
 
-    .. [M2012] "Machine Learning: A Probabilistic Perspective"
-      Murphy, K. P. - chapter 14.4.3, pp. 492-493, The MIT Press, 2012
+.. [M2012] "Machine Learning: A Probabilistic Perspective"
+   Murphy, K. P. - chapter 14.4.3, pp. 492-493, The MIT Press, 2012
diff --git a/doc/modules/lda_qda.rst b/doc/modules/lda_qda.rst
index 850a848fe3f73..0d264ec662a9f 100644
--- a/doc/modules/lda_qda.rst
+++ b/doc/modules/lda_qda.rst
@@ -29,10 +29,10 @@ Discriminant Analysis can only learn linear boundaries, while Quadratic
 Discriminant Analysis can learn quadratic boundaries and is therefore more
 flexible.
 
-.. topic:: Examples:
+.. rubric:: Examples
 
-    :ref:`sphx_glr_auto_examples_classification_plot_lda_qda.py`: Comparison of LDA and QDA
-    on synthetic data.
+* :ref:`sphx_glr_auto_examples_classification_plot_lda_qda.py`: Comparison of LDA and
+  QDA on synthetic data.
 
 Dimensionality reduction using Linear Discriminant Analysis
 ===========================================================
@@ -49,10 +49,10 @@ This is implemented in the `transform` method. The desired dimensionality can
 be set using the ``n_components`` parameter. This parameter has no influence
 on the `fit` and `predict` methods.
 
-.. topic:: Examples:
+.. rubric:: Examples
 
-    :ref:`sphx_glr_auto_examples_decomposition_plot_pca_vs_lda.py`: Comparison of LDA and PCA
-    for dimensionality reduction of the Iris dataset
+* :ref:`sphx_glr_auto_examples_decomposition_plot_pca_vs_lda.py`: Comparison of LDA and
+  PCA for dimensionality reduction of the Iris dataset
 
 .. _lda_qda_math:
 
@@ -194,7 +194,7 @@ Oracle Approximating Shrinkage estimator :class:`sklearn.covariance.OAS`
 yields a smaller Mean Squared Error than the one given by Ledoit and Wolf's
 formula used with shrinkage="auto". In LDA, the data are assumed to be gaussian
 conditionally to the class. If these assumptions hold, using LDA with
-the OAS estimator of covariance will yield a better classification 
+the OAS estimator of covariance will yield a better classification
 accuracy than if Ledoit and Wolf or the empirical covariance estimator is used.
 
 The covariance estimator can be chosen using with the ``covariance_estimator``
@@ -210,10 +210,10 @@ class. A covariance estimator should have a :term:`fit` method and a
 
 .. centered:: |shrinkage|
 
-.. topic:: Examples:
+.. rubric:: Examples
 
-    :ref:`sphx_glr_auto_examples_classification_plot_lda.py`: Comparison of LDA classifiers
-    with Empirical, Ledoit Wolf and OAS covariance estimator.
+* :ref:`sphx_glr_auto_examples_classification_plot_lda.py`: Comparison of LDA classifiers
+  with Empirical, Ledoit Wolf and OAS covariance estimator.
 
 Estimation algorithms
 =====================
@@ -253,13 +253,13 @@ transform, and it supports shrinkage. However, the 'eigen' solver needs to
 compute the covariance matrix, so it might not be suitable for situations with
 a high number of features.
 
-.. topic:: References:
+.. rubric:: References
 
-   .. [1] "The Elements of Statistical Learning", Hastie T., Tibshirani R.,
-      Friedman J., Section 4.3, p.106-119, 2008.
+.. [1] "The Elements of Statistical Learning", Hastie T., Tibshirani R.,
+    Friedman J., Section 4.3, p.106-119, 2008.
 
-   .. [2] Ledoit O, Wolf M. Honey, I Shrunk the Sample Covariance Matrix.
-      The Journal of Portfolio Management 30(4), 110-119, 2004.
+.. [2] Ledoit O, Wolf M. Honey, I Shrunk the Sample Covariance Matrix.
+    The Journal of Portfolio Management 30(4), 110-119, 2004.
 
-   .. [3] R. O. Duda, P. E. Hart, D. G. Stork. Pattern Classification
-      (Second Edition), section 2.6.2.
+.. [3] R. O. Duda, P. E. Hart, D. G. Stork. Pattern Classification
+    (Second Edition), section 2.6.2.
diff --git a/doc/modules/learning_curve.rst b/doc/modules/learning_curve.rst
index 3d458a1a67416..f5af5a748500a 100644
--- a/doc/modules/learning_curve.rst
+++ b/doc/modules/learning_curve.rst
@@ -39,11 +39,11 @@ easy to see whether the estimator suffers from bias or variance. However, in
 high-dimensional spaces, models can become very difficult to visualize. For
 this reason, it is often helpful to use the tools described below.
 
-.. topic:: Examples:
+.. rubric:: Examples
 
-   * :ref:`sphx_glr_auto_examples_model_selection_plot_underfitting_overfitting.py`
-   * :ref:`sphx_glr_auto_examples_model_selection_plot_validation_curve.py`
-   * :ref:`sphx_glr_auto_examples_model_selection_plot_learning_curve.py`
+* :ref:`sphx_glr_auto_examples_model_selection_plot_underfitting_overfitting.py`
+* :ref:`sphx_glr_auto_examples_model_selection_plot_validation_curve.py`
+* :ref:`sphx_glr_auto_examples_model_selection_plot_learning_curve.py`
 
 
 .. _validation_curve:
diff --git a/doc/modules/linear_model.rst b/doc/modules/linear_model.rst
index b92a8c2a01019..d06101adabdb5 100644
--- a/doc/modules/linear_model.rst
+++ b/doc/modules/linear_model.rst
@@ -57,9 +57,9 @@ to random errors in the observed target, producing a large
 variance. This situation of *multicollinearity* can arise, for
 example, when data are collected without an experimental design.
 
-.. topic:: Examples:
+.. rubric:: Examples
 
-   * :ref:`sphx_glr_auto_examples_linear_model_plot_ols.py`
+* :ref:`sphx_glr_auto_examples_linear_model_plot_ols.py`
 
 Non-Negative Least Squares
 --------------------------
@@ -71,9 +71,9 @@ quantities (e.g., frequency counts or prices of goods).
 parameter: when set to `True` `Non-Negative Least Squares
 <https://en.wikipedia.org/wiki/Non-negative_least_squares>`_ are then applied.
 
-.. topic:: Examples:
+.. rubric:: Examples
 
-   * :ref:`sphx_glr_auto_examples_linear_model_plot_nnls.py`
+* :ref:`sphx_glr_auto_examples_linear_model_plot_nnls.py`
 
 Ordinary Least Squares Complexity
 ---------------------------------
@@ -172,11 +172,11 @@ Machines
 <https://en.wikipedia.org/wiki/Least-squares_support-vector_machine>`_ with
 a linear kernel.
 
-.. topic:: Examples:
+.. rubric:: Examples
 
-  * :ref:`sphx_glr_auto_examples_linear_model_plot_ridge_path.py`
-  * :ref:`sphx_glr_auto_examples_text_plot_document_classification_20newsgroups.py`
-  * :ref:`sphx_glr_auto_examples_inspection_plot_linear_model_coefficient_interpretation.py`
+* :ref:`sphx_glr_auto_examples_linear_model_plot_ridge_path.py`
+* :ref:`sphx_glr_auto_examples_text_plot_document_classification_20newsgroups.py`
+* :ref:`sphx_glr_auto_examples_inspection_plot_linear_model_coefficient_interpretation.py`
 
 Ridge Complexity
 ----------------
@@ -216,13 +216,11 @@ cross-validation with :class:`~sklearn.model_selection.GridSearchCV`, for
 example `cv=10` for 10-fold cross-validation, rather than Leave-One-Out
 Cross-Validation.
 
-.. topic:: References:
-
+.. dropdown:: References
 
   .. [RL2007] "Notes on Regularized Least Squares", Rifkin & Lippert (`technical report
     <http://cbcl.mit.edu/publications/ps/MIT-CSAIL-TR-2007-025.pdf>`_,
-    `course slides
-    <https://www.mit.edu/~9.520/spring07/Classes/rlsslides.pdf>`_).
+    `course slides <https://www.mit.edu/~9.520/spring07/Classes/rlsslides.pdf>`_).
 
 .. _lasso:
 
@@ -262,11 +260,11 @@ for another implementation::
 The function :func:`lasso_path` is useful for lower-level tasks, as it
 computes the coefficients along the full path of possible values.
 
-.. topic:: Examples:
+.. rubric:: Examples
 
-  * :ref:`sphx_glr_auto_examples_linear_model_plot_lasso_and_elasticnet.py`
-  * :ref:`sphx_glr_auto_examples_applications_plot_tomography_l1_reconstruction.py`
-  * :ref:`sphx_glr_auto_examples_inspection_plot_linear_model_coefficient_interpretation.py`
+* :ref:`sphx_glr_auto_examples_linear_model_plot_lasso_and_elasticnet.py`
+* :ref:`sphx_glr_auto_examples_applications_plot_tomography_l1_reconstruction.py`
+* :ref:`sphx_glr_auto_examples_inspection_plot_linear_model_coefficient_interpretation.py`
 
 
 .. note:: **Feature selection with Lasso**
@@ -275,23 +273,19 @@ computes the coefficients along the full path of possible values.
       thus be used to perform feature selection, as detailed in
       :ref:`l1_feature_selection`.
 
-|details-start|
-**References**
-|details-split|
-
-The following two references explain the iterations
-used in the coordinate descent solver of scikit-learn, as well as
-the duality gap computation used for convergence control.
+.. dropdown:: References
 
-* "Regularization Path For Generalized linear Models by Coordinate Descent",
-  Friedman, Hastie & Tibshirani, J Stat Softw, 2010 (`Paper
-  <https://www.jstatsoft.org/article/view/v033i01/v33i01.pdf>`__).
-* "An Interior-Point Method for Large-Scale L1-Regularized Least Squares,"
-  S. J. Kim, K. Koh, M. Lustig, S. Boyd and D. Gorinevsky,
-  in IEEE Journal of Selected Topics in Signal Processing, 2007
-  (`Paper <https://web.stanford.edu/~boyd/papers/pdf/l1_ls.pdf>`__)
+  The following two references explain the iterations
+  used in the coordinate descent solver of scikit-learn, as well as
+  the duality gap computation used for convergence control.
 
-|details-end|
+  * "Regularization Path For Generalized linear Models by Coordinate Descent",
+    Friedman, Hastie & Tibshirani, J Stat Softw, 2010 (`Paper
+    <https://www.jstatsoft.org/article/view/v033i01/v33i01.pdf>`__).
+  * "An Interior-Point Method for Large-Scale L1-Regularized Least Squares,"
+    S. J. Kim, K. Koh, M. Lustig, S. Boyd and D. Gorinevsky,
+    in IEEE Journal of Selected Topics in Signal Processing, 2007
+    (`Paper <https://web.stanford.edu/~boyd/papers/pdf/l1_ls.pdf>`__)
 
 Setting regularization parameter
 --------------------------------
@@ -348,10 +342,10 @@ the problem is badly conditioned (e.g. more features than samples).
     :align: center
     :scale: 50%
 
-.. topic:: Examples:
+.. rubric:: Examples
 
-  * :ref:`sphx_glr_auto_examples_linear_model_plot_lasso_model_selection.py`
-  * :ref:`sphx_glr_auto_examples_linear_model_plot_lasso_lars_ic.py`
+* :ref:`sphx_glr_auto_examples_linear_model_plot_lasso_model_selection.py`
+* :ref:`sphx_glr_auto_examples_linear_model_plot_lasso_lars_ic.py`
 
 .. _aic_bic:
 
@@ -362,59 +356,57 @@ The definition of AIC (and thus BIC) might differ in the literature. In this
 section, we give more information regarding the criterion computed in
 scikit-learn.
 
-|details-start|
-**Mathematical details**
-|details-split|
+.. dropdown:: Mathematical details
 
-The AIC criterion is defined as:
+  The AIC criterion is defined as:
 
-.. math::
-    AIC = -2 \log(\hat{L}) + 2 d
+  .. math::
+      AIC = -2 \log(\hat{L}) + 2 d
 
-where :math:`\hat{L}` is the maximum likelihood of the model and
-:math:`d` is the number of parameters (as well referred to as degrees of
-freedom in the previous section).
+  where :math:`\hat{L}` is the maximum likelihood of the model and
+  :math:`d` is the number of parameters (as well referred to as degrees of
+  freedom in the previous section).
 
-The definition of BIC replace the constant :math:`2` by :math:`\log(N)`:
+  The definition of BIC replace the constant :math:`2` by :math:`\log(N)`:
 
-.. math::
-    BIC = -2 \log(\hat{L}) + \log(N) d
+  .. math::
+      BIC = -2 \log(\hat{L}) + \log(N) d
 
-where :math:`N` is the number of samples.
+  where :math:`N` is the number of samples.
 
-For a linear Gaussian model, the maximum log-likelihood is defined as:
+  For a linear Gaussian model, the maximum log-likelihood is defined as:
 
-.. math::
-    \log(\hat{L}) = - \frac{n}{2} \log(2 \pi) - \frac{n}{2} \ln(\sigma^2) - \frac{\sum_{i=1}^{n} (y_i - \hat{y}_i)^2}{2\sigma^2}
+  .. math::
+      \log(\hat{L}) = - \frac{n}{2} \log(2 \pi) - \frac{n}{2} \ln(\sigma^2) - \frac{\sum_{i=1}^{n} (y_i - \hat{y}_i)^2}{2\sigma^2}
 
-where :math:`\sigma^2` is an estimate of the noise variance,
-:math:`y_i` and :math:`\hat{y}_i` are respectively the true and predicted
-targets, and :math:`n` is the number of samples.
+  where :math:`\sigma^2` is an estimate of the noise variance,
+  :math:`y_i` and :math:`\hat{y}_i` are respectively the true and predicted
+  targets, and :math:`n` is the number of samples.
 
-Plugging the maximum log-likelihood in the AIC formula yields:
+  Plugging the maximum log-likelihood in the AIC formula yields:
 
-.. math::
-    AIC = n \log(2 \pi \sigma^2) + \frac{\sum_{i=1}^{n} (y_i - \hat{y}_i)^2}{\sigma^2} + 2 d
+  .. math::
+      AIC = n \log(2 \pi \sigma^2) + \frac{\sum_{i=1}^{n} (y_i - \hat{y}_i)^2}{\sigma^2} + 2 d
 
-The first term of the above expression is sometimes discarded since it is a
-constant when :math:`\sigma^2` is provided. In addition,
-it is sometimes stated that the AIC is equivalent to the :math:`C_p` statistic
-[12]_. In a strict sense, however, it is equivalent only up to some constant
-and a multiplicative factor.
+  The first term of the above expression is sometimes discarded since it is a
+  constant when :math:`\sigma^2` is provided. In addition,
+  it is sometimes stated that the AIC is equivalent to the :math:`C_p` statistic
+  [12]_. In a strict sense, however, it is equivalent only up to some constant
+  and a multiplicative factor.
 
-At last, we mentioned above that :math:`\sigma^2` is an estimate of the
-noise variance. In :class:`LassoLarsIC` when the parameter `noise_variance` is
-not provided (default), the noise variance is estimated via the unbiased
-estimator [13]_ defined as:
+  At last, we mentioned above that :math:`\sigma^2` is an estimate of the
+  noise variance. In :class:`LassoLarsIC` when the parameter `noise_variance` is
+  not provided (default), the noise variance is estimated via the unbiased
+  estimator [13]_ defined as:
 
-.. math::
-    \sigma^2 = \frac{\sum_{i=1}^{n} (y_i - \hat{y}_i)^2}{n - p}
+  .. math::
+      \sigma^2 = \frac{\sum_{i=1}^{n} (y_i - \hat{y}_i)^2}{n - p}
 
-where :math:`p` is the number of features and :math:`\hat{y}_i` is the
-predicted target using an ordinary least squares regression. Note, that this
-formula is valid only when `n_samples > n_features`.
+  where :math:`p` is the number of features and :math:`\hat{y}_i` is the
+  predicted target using an ordinary least squares regression. Note, that this
+  formula is valid only when `n_samples > n_features`.
 
-.. topic:: References:
+  .. rubric:: References
 
   .. [12] :arxiv:`Zou, Hui, Trevor Hastie, and Robert Tibshirani.
           "On the degrees of freedom of the lasso."
@@ -426,8 +418,6 @@ formula is valid only when `n_samples > n_features`.
           Neural computation 15.7 (2003): 1691-1714.
           <10.1162/089976603321891864>`
 
-|details-end|
-
 Comparison with the regularization parameter of SVM
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
@@ -463,33 +453,29 @@ the MultiTaskLasso are full columns.
 
 .. centered:: Fitting a time-series model, imposing that any active feature be active at all times.
 
-.. topic:: Examples:
-
-  * :ref:`sphx_glr_auto_examples_linear_model_plot_multi_task_lasso_support.py`
+.. rubric:: Examples
 
+* :ref:`sphx_glr_auto_examples_linear_model_plot_multi_task_lasso_support.py`
 
-|details-start|
-**Mathematical details**
-|details-split|
 
-Mathematically, it consists of a linear model trained with a mixed
-:math:`\ell_1` :math:`\ell_2`-norm for regularization.
-The objective function to minimize is:
+.. dropdown:: Mathematical details
 
-.. math::  \min_{W} { \frac{1}{2n_{\text{samples}}} ||X W - Y||_{\text{Fro}} ^ 2 + \alpha ||W||_{21}}
+  Mathematically, it consists of a linear model trained with a mixed
+  :math:`\ell_1` :math:`\ell_2`-norm for regularization.
+  The objective function to minimize is:
 
-where :math:`\text{Fro}` indicates the Frobenius norm
+  .. math::  \min_{W} { \frac{1}{2n_{\text{samples}}} ||X W - Y||_{\text{Fro}} ^ 2 + \alpha ||W||_{21}}
 
-.. math:: ||A||_{\text{Fro}} = \sqrt{\sum_{ij} a_{ij}^2}
+  where :math:`\text{Fro}` indicates the Frobenius norm
 
-and :math:`\ell_1` :math:`\ell_2` reads
+  .. math:: ||A||_{\text{Fro}} = \sqrt{\sum_{ij} a_{ij}^2}
 
-.. math:: ||A||_{2 1} = \sum_i \sqrt{\sum_j a_{ij}^2}.
+  and :math:`\ell_1` :math:`\ell_2` reads
 
-The implementation in the class :class:`MultiTaskLasso` uses
-coordinate descent as the algorithm to fit the coefficients.
+  .. math:: ||A||_{2 1} = \sum_i \sqrt{\sum_j a_{ij}^2}.
 
-|details-end|
+  The implementation in the class :class:`MultiTaskLasso` uses
+  coordinate descent as the algorithm to fit the coefficients.
 
 .. _elastic_net:
 
@@ -526,28 +512,25 @@ The objective function to minimize is in this case
 The class :class:`ElasticNetCV` can be used to set the parameters
 ``alpha`` (:math:`\alpha`) and ``l1_ratio`` (:math:`\rho`) by cross-validation.
 
-.. topic:: Examples:
-
-  * :ref:`sphx_glr_auto_examples_linear_model_plot_lasso_and_elasticnet.py`
-  * :ref:`sphx_glr_auto_examples_linear_model_plot_lasso_coordinate_descent_path.py`
+.. rubric:: Examples
 
-|details-start|
-**References**
-|details-split|
+* :ref:`sphx_glr_auto_examples_linear_model_plot_lasso_and_elasticnet.py`
+* :ref:`sphx_glr_auto_examples_linear_model_plot_lasso_coordinate_descent_path.py`
+* :ref:`sphx_glr_auto_examples_linear_model_plot_elastic_net_precomputed_gram_matrix_with_weighted_samples.py`
 
-The following two references explain the iterations
-used in the coordinate descent solver of scikit-learn, as well as
-the duality gap computation used for convergence control.
+.. dropdown:: References
 
-* "Regularization Path For Generalized linear Models by Coordinate Descent",
-  Friedman, Hastie & Tibshirani, J Stat Softw, 2010 (`Paper
-  <https://www.jstatsoft.org/article/view/v033i01/v33i01.pdf>`__).
-* "An Interior-Point Method for Large-Scale L1-Regularized Least Squares,"
-  S. J. Kim, K. Koh, M. Lustig, S. Boyd and D. Gorinevsky,
-  in IEEE Journal of Selected Topics in Signal Processing, 2007
-  (`Paper <https://web.stanford.edu/~boyd/papers/pdf/l1_ls.pdf>`__)
+  The following two references explain the iterations
+  used in the coordinate descent solver of scikit-learn, as well as
+  the duality gap computation used for convergence control.
 
-|details-end|
+  * "Regularization Path For Generalized linear Models by Coordinate Descent",
+    Friedman, Hastie & Tibshirani, J Stat Softw, 2010 (`Paper
+    <https://www.jstatsoft.org/article/view/v033i01/v33i01.pdf>`__).
+  * "An Interior-Point Method for Large-Scale L1-Regularized Least Squares,"
+    S. J. Kim, K. Koh, M. Lustig, S. Boyd and D. Gorinevsky,
+    in IEEE Journal of Selected Topics in Signal Processing, 2007
+    (`Paper <https://web.stanford.edu/~boyd/papers/pdf/l1_ls.pdf>`__)
 
 .. _multi_task_elastic_net:
 
@@ -640,37 +623,33 @@ function of the norm of its coefficients.
    >>> reg.coef_
    array([0.6..., 0.        ])
 
-.. topic:: Examples:
+.. rubric:: Examples
 
- * :ref:`sphx_glr_auto_examples_linear_model_plot_lasso_lars.py`
+* :ref:`sphx_glr_auto_examples_linear_model_plot_lasso_lars.py`
 
 The Lars algorithm provides the full path of the coefficients along
 the regularization parameter almost for free, thus a common operation
 is to retrieve the path with one of the functions :func:`lars_path`
 or :func:`lars_path_gram`.
 
-|details-start|
-**Mathematical formulation**
-|details-split|
+.. dropdown:: Mathematical formulation
 
-The algorithm is similar to forward stepwise regression, but instead
-of including features at each step, the estimated coefficients are
-increased in a direction equiangular to each one's correlations with
-the residual.
+  The algorithm is similar to forward stepwise regression, but instead
+  of including features at each step, the estimated coefficients are
+  increased in a direction equiangular to each one's correlations with
+  the residual.
 
-Instead of giving a vector result, the LARS solution consists of a
-curve denoting the solution for each value of the :math:`\ell_1` norm of the
-parameter vector. The full coefficients path is stored in the array
-``coef_path_`` of shape `(n_features, max_features + 1)`. The first
-column is always zero.
+  Instead of giving a vector result, the LARS solution consists of a
+  curve denoting the solution for each value of the :math:`\ell_1` norm of the
+  parameter vector. The full coefficients path is stored in the array
+  ``coef_path_`` of shape `(n_features, max_features + 1)`. The first
+  column is always zero.
 
-.. topic:: References:
+  .. rubric:: References
 
- * Original Algorithm is detailed in the paper `Least Angle Regression
-   <https://www-stat.stanford.edu/~hastie/Papers/LARS/LeastAngle_2002.pdf>`_
-   by Hastie et al.
-
-|details-end|
+  * Original Algorithm is detailed in the paper `Least Angle Regression
+    <https://www-stat.stanford.edu/~hastie/Papers/LARS/LeastAngle_2002.pdf>`_
+    by Hastie et al.
 
 .. _omp:
 
@@ -701,21 +680,17 @@ residual is recomputed using an orthogonal projection on the space of the
 previously chosen dictionary elements.
 
 
-.. topic:: Examples:
-
- * :ref:`sphx_glr_auto_examples_linear_model_plot_omp.py`
+.. rubric:: Examples
 
-|details-start|
-**References**
-|details-split|
+* :ref:`sphx_glr_auto_examples_linear_model_plot_omp.py`
 
-* https://www.cs.technion.ac.il/~ronrubin/Publications/KSVD-OMP-v2.pdf
+.. dropdown:: References
 
-* `Matching pursuits with time-frequency dictionaries
-  <https://www.di.ens.fr/~mallat/papiers/MallatPursuit93.pdf>`_,
-  S. G. Mallat, Z. Zhang,
+  * https://www.cs.technion.ac.il/~ronrubin/Publications/KSVD-OMP-v2.pdf
 
-|details-end|
+  * `Matching pursuits with time-frequency dictionaries
+    <https://www.di.ens.fr/~mallat/papiers/MallatPursuit93.pdf>`_,
+    S. G. Mallat, Z. Zhang,
 
 .. _bayesian_regression:
 
@@ -754,17 +729,13 @@ The disadvantages of Bayesian regression include:
 
 - Inference of the model can be time consuming.
 
-|details-start|
-**References**
-|details-split|
-
-* A good introduction to Bayesian methods is given in C. Bishop: Pattern
-  Recognition and Machine learning
+.. dropdown:: References
 
-* Original Algorithm is detailed in the  book `Bayesian learning for neural
-  networks` by Radford M. Neal
+  * A good introduction to Bayesian methods is given in C. Bishop: Pattern
+    Recognition and Machine learning
 
-|details-end|
+  * Original Algorithm is detailed in the  book `Bayesian learning for neural
+    networks` by Radford M. Neal
 
 .. _bayesian_ridge_regression:
 
@@ -821,21 +792,17 @@ Due to the Bayesian framework, the weights found are slightly different to the
 ones found by :ref:`ordinary_least_squares`. However, Bayesian Ridge Regression
 is more robust to ill-posed problems.
 
-.. topic:: Examples:
+.. rubric:: Examples
 
- * :ref:`sphx_glr_auto_examples_linear_model_plot_bayesian_ridge_curvefit.py`
+* :ref:`sphx_glr_auto_examples_linear_model_plot_bayesian_ridge_curvefit.py`
 
-|details-start|
-**References**
-|details-split|
+.. dropdown:: References
 
-* Section 3.3 in Christopher M. Bishop: Pattern Recognition and Machine Learning, 2006
+  * Section 3.3 in Christopher M. Bishop: Pattern Recognition and Machine Learning, 2006
 
-* David J. C. MacKay, `Bayesian Interpolation <https://citeseerx.ist.psu.edu/doc_view/pid/b14c7cc3686e82ba40653c6dff178356a33e5e2c>`_, 1992.
+  * David J. C. MacKay, `Bayesian Interpolation <https://citeseerx.ist.psu.edu/doc_view/pid/b14c7cc3686e82ba40653c6dff178356a33e5e2c>`_, 1992.
 
-* Michael E. Tipping, `Sparse Bayesian Learning and the Relevance Vector Machine <https://www.jmlr.org/papers/volume1/tipping01a/tipping01a.pdf>`_, 2001.
-
-|details-end|
+  * Michael E. Tipping, `Sparse Bayesian Learning and the Relevance Vector Machine <https://www.jmlr.org/papers/volume1/tipping01a/tipping01a.pdf>`_, 2001.
 
 .. _automatic_relevance_determination:
 
@@ -867,20 +834,20 @@ ARD is also known in the literature as *Sparse Bayesian Learning* and *Relevance
 Vector Machine* [3]_ [4]_. For a worked-out comparison between ARD and `Bayesian
 Ridge Regression`_, see the example below.
 
-.. topic:: Examples:
+.. rubric:: Examples
 
-  * :ref:`sphx_glr_auto_examples_linear_model_plot_ard.py`
+* :ref:`sphx_glr_auto_examples_linear_model_plot_ard.py`
 
 
-.. topic:: References:
+.. rubric:: References
 
-  .. [1] Christopher M. Bishop: Pattern Recognition and Machine Learning, Chapter 7.2.1
+.. [1] Christopher M. Bishop: Pattern Recognition and Machine Learning, Chapter 7.2.1
 
-  .. [2] David Wipf and Srikantan Nagarajan: `A New View of Automatic Relevance Determination <https://papers.nips.cc/paper/3372-a-new-view-of-automatic-relevance-determination.pdf>`_
+.. [2] David Wipf and Srikantan Nagarajan: `A New View of Automatic Relevance Determination <https://papers.nips.cc/paper/3372-a-new-view-of-automatic-relevance-determination.pdf>`_
 
-  .. [3] Michael E. Tipping: `Sparse Bayesian Learning and the Relevance Vector Machine <https://www.jmlr.org/papers/volume1/tipping01a/tipping01a.pdf>`_
+.. [3] Michael E. Tipping: `Sparse Bayesian Learning and the Relevance Vector Machine <https://www.jmlr.org/papers/volume1/tipping01a/tipping01a.pdf>`_
 
-  .. [4] Tristan Fletcher: `Relevance Vector Machines Explained <https://citeseerx.ist.psu.edu/doc_view/pid/3dc9d625404fdfef6eaccc3babddefe4c176abd4>`_
+.. [4] Tristan Fletcher: `Relevance Vector Machines Explained <https://citeseerx.ist.psu.edu/doc_view/pid/3dc9d625404fdfef6eaccc3babddefe4c176abd4>`_
 
 .. _Logistic_regression:
 
@@ -917,17 +884,13 @@ regularization.
     implemented in scikit-learn, so it expects a categorical target, making
     the Logistic Regression a classifier.
 
-.. topic:: Examples
-
-  * :ref:`sphx_glr_auto_examples_linear_model_plot_logistic_l1_l2_sparsity.py`
-
-  * :ref:`sphx_glr_auto_examples_linear_model_plot_logistic_path.py`
+.. rubric:: Examples
 
-  * :ref:`sphx_glr_auto_examples_linear_model_plot_logistic_multinomial.py`
-
-  * :ref:`sphx_glr_auto_examples_linear_model_plot_sparse_logistic_regression_20newsgroups.py`
-
-  * :ref:`sphx_glr_auto_examples_linear_model_plot_sparse_logistic_regression_mnist.py`
+* :ref:`sphx_glr_auto_examples_linear_model_plot_logistic_l1_l2_sparsity.py`
+* :ref:`sphx_glr_auto_examples_linear_model_plot_logistic_path.py`
+* :ref:`sphx_glr_auto_examples_linear_model_plot_logistic_multinomial.py`
+* :ref:`sphx_glr_auto_examples_linear_model_plot_sparse_logistic_regression_20newsgroups.py`
+* :ref:`sphx_glr_auto_examples_linear_model_plot_sparse_logistic_regression_mnist.py`
 
 Binary Case
 -----------
@@ -999,47 +962,43 @@ logistic regression, see also `log-linear model
    especially important when using regularization. The choice of overparameterization can be
    detrimental for unpenalized models since then the solution may not be unique, as shown in [16]_.
 
-|details-start|
-**Mathematical details**
-|details-split|
+.. dropdown:: Mathematical details
 
-Let :math:`y_i \in {1, \ldots, K}` be the label (ordinal) encoded target variable for observation :math:`i`.
-Instead of a single coefficient vector, we now have
-a matrix of coefficients :math:`W` where each row vector :math:`W_k` corresponds to class
-:math:`k`. We aim at predicting the class probabilities :math:`P(y_i=k|X_i)` via
-:meth:`~sklearn.linear_model.LogisticRegression.predict_proba` as:
+  Let :math:`y_i \in {1, \ldots, K}` be the label (ordinal) encoded target variable for observation :math:`i`.
+  Instead of a single coefficient vector, we now have
+  a matrix of coefficients :math:`W` where each row vector :math:`W_k` corresponds to class
+  :math:`k`. We aim at predicting the class probabilities :math:`P(y_i=k|X_i)` via
+  :meth:`~sklearn.linear_model.LogisticRegression.predict_proba` as:
 
-.. math:: \hat{p}_k(X_i) = \frac{\exp(X_i W_k + W_{0, k})}{\sum_{l=0}^{K-1} \exp(X_i W_l + W_{0, l})}.
+  .. math:: \hat{p}_k(X_i) = \frac{\exp(X_i W_k + W_{0, k})}{\sum_{l=0}^{K-1} \exp(X_i W_l + W_{0, l})}.
 
-The objective for the optimization becomes
+  The objective for the optimization becomes
 
-.. math::
-  \min_W -\frac{1}{S}\sum_{i=1}^n \sum_{k=0}^{K-1} s_{ik} [y_i = k] \log(\hat{p}_k(X_i))
-  + \frac{r(W)}{S C}\,.
+  .. math::
+    \min_W -\frac{1}{S}\sum_{i=1}^n \sum_{k=0}^{K-1} s_{ik} [y_i = k] \log(\hat{p}_k(X_i))
+    + \frac{r(W)}{S C}\,,
 
-Where :math:`[P]` represents the Iverson bracket which evaluates to :math:`0`
-if :math:`P` is false, otherwise it evaluates to :math:`1`.
+  where :math:`[P]` represents the Iverson bracket which evaluates to :math:`0`
+  if :math:`P` is false, otherwise it evaluates to :math:`1`.
 
-Again, :math:`s_{ik}` are the weights assigned by the user (multiplication of sample
-weights and class weights) with their sum :math:`S = \sum_{i=1}^n \sum_{k=0}^{K-1} s_{ik}`.
+  Again, :math:`s_{ik}` are the weights assigned by the user (multiplication of sample
+  weights and class weights) with their sum :math:`S = \sum_{i=1}^n \sum_{k=0}^{K-1} s_{ik}`.
 
-We currently provide four choices
-for the regularization term :math:`r(W)` via the `penalty` argument, where :math:`m`
-is the number of features:
+  We currently provide four choices
+  for the regularization term :math:`r(W)` via the `penalty` argument, where :math:`m`
+  is the number of features:
 
-+----------------+----------------------------------------------------------------------------------+
-| penalty        | :math:`r(W)`                                                                     |
-+================+==================================================================================+
-| `None`         | :math:`0`                                                                        |
-+----------------+----------------------------------------------------------------------------------+
-| :math:`\ell_1` | :math:`\|W\|_{1,1} = \sum_{i=1}^m\sum_{j=1}^{K}|W_{i,j}|`                        |
-+----------------+----------------------------------------------------------------------------------+
-| :math:`\ell_2` | :math:`\frac{1}{2}\|W\|_F^2 = \frac{1}{2}\sum_{i=1}^m\sum_{j=1}^{K} W_{i,j}^2`   |
-+----------------+----------------------------------------------------------------------------------+
-| `ElasticNet`   | :math:`\frac{1 - \rho}{2}\|W\|_F^2 + \rho \|W\|_{1,1}`                           |
-+----------------+----------------------------------------------------------------------------------+
-
-|details-end|
+  +----------------+----------------------------------------------------------------------------------+
+  | penalty        | :math:`r(W)`                                                                     |
+  +================+==================================================================================+
+  | `None`         | :math:`0`                                                                        |
+  +----------------+----------------------------------------------------------------------------------+
+  | :math:`\ell_1` | :math:`\|W\|_{1,1} = \sum_{i=1}^m\sum_{j=1}^{K}|W_{i,j}|`                        |
+  +----------------+----------------------------------------------------------------------------------+
+  | :math:`\ell_2` | :math:`\frac{1}{2}\|W\|_F^2 = \frac{1}{2}\sum_{i=1}^m\sum_{j=1}^{K} W_{i,j}^2`   |
+  +----------------+----------------------------------------------------------------------------------+
+  | `ElasticNet`   | :math:`\frac{1 - \rho}{2}\|W\|_F^2 + \rho \|W\|_{1,1}`                           |
+  +----------------+----------------------------------------------------------------------------------+
 
 Solvers
 -------
@@ -1047,24 +1006,24 @@ Solvers
 The solvers implemented in the class :class:`LogisticRegression`
 are "lbfgs", "liblinear", "newton-cg", "newton-cholesky", "sag" and "saga":
 
-The following table summarizes the penalties supported by each solver:
+The following table summarizes the penalties and multinomial multiclass supported by each solver:
 
 +------------------------------+-----------------+-------------+-----------------+-----------------------+-----------+------------+
 |                              |                       **Solvers**                                                                |
 +------------------------------+-------------+-----------------+-----------------+-----------------------+-----------+------------+
 | **Penalties**                | **'lbfgs'** | **'liblinear'** | **'newton-cg'** | **'newton-cholesky'** | **'sag'** | **'saga'** |
 +------------------------------+-------------+-----------------+-----------------+-----------------------+-----------+------------+
-| Multinomial + L2 penalty     |     yes     |       no        |       yes       |     no                |    yes    |    yes     |
+| L2 penalty                   |     yes     |       no        |       yes       |     no                |    yes    |    yes     |
 +------------------------------+-------------+-----------------+-----------------+-----------------------+-----------+------------+
-| OVR + L2 penalty             |     yes     |       yes       |       yes       |     yes               |    yes    |    yes     |
+| L1 penalty                   |     no      |       yes       |       no        |     no                |    no     |    yes     |
 +------------------------------+-------------+-----------------+-----------------+-----------------------+-----------+------------+
-| Multinomial + L1 penalty     |     no      |       no        |       no        |     no                |    no     |    yes     |
+| Elastic-Net (L1 + L2)        |     no      |       no        |       no        |     no                |    no     |    yes     |
 +------------------------------+-------------+-----------------+-----------------+-----------------------+-----------+------------+
-| OVR + L1 penalty             |     no      |       yes       |       no        |     no                |    no     |    yes     |
+| No penalty ('none')          |     yes     |       no        |       yes       |     yes               |    yes    |    yes     |
 +------------------------------+-------------+-----------------+-----------------+-----------------------+-----------+------------+
-| Elastic-Net                  |     no      |       no        |       no        |     no                |    no     |    yes     |
+| **Multiclass support**       |                                                                                                  |
 +------------------------------+-------------+-----------------+-----------------+-----------------------+-----------+------------+
-| No penalty ('none')          |     yes     |       no        |       yes       |     yes               |    yes    |    yes     |
+| multinomial multiclass       |     yes     |       no        |       yes       |     no                |    yes    |    yes     |
 +------------------------------+-------------+-----------------+-----------------+-----------------------+-----------+------------+
 | **Behaviors**                |                                                                                                  |
 +------------------------------+-------------+-----------------+-----------------+-----------------------+-----------+------------+
@@ -1096,56 +1055,54 @@ with ``fit_intercept=False`` and having many samples with ``decision_function``
 zero, is likely to be a underfit, bad model and you are advised to set
 ``fit_intercept=True`` and increase the ``intercept_scaling``.
 
-|details-start|
-**Solvers' details**
-|details-split|
-
-* The solver "liblinear" uses a coordinate descent (CD) algorithm, and relies
-  on the excellent C++ `LIBLINEAR library
-  <https://www.csie.ntu.edu.tw/~cjlin/liblinear/>`_, which is shipped with
-  scikit-learn. However, the CD algorithm implemented in liblinear cannot learn
-  a true multinomial (multiclass) model; instead, the optimization problem is
-  decomposed in a "one-vs-rest" fashion so separate binary classifiers are
-  trained for all classes. This happens under the hood, so
-  :class:`LogisticRegression` instances using this solver behave as multiclass
-  classifiers. For :math:`\ell_1` regularization :func:`sklearn.svm.l1_min_c` allows to
-  calculate the lower bound for C in order to get a non "null" (all feature
-  weights to zero) model.
-
-* The "lbfgs", "newton-cg" and "sag" solvers only support :math:`\ell_2`
-  regularization or no regularization, and are found to converge faster for some
-  high-dimensional data. Setting `multi_class` to "multinomial" with these solvers
-  learns a true multinomial logistic regression model [5]_, which means that its
-  probability estimates should be better calibrated than the default "one-vs-rest"
-  setting.
-
-* The "sag" solver uses Stochastic Average Gradient descent [6]_. It is faster
-  than other solvers for large datasets, when both the number of samples and the
-  number of features are large.
-
-* The "saga" solver [7]_ is a variant of "sag" that also supports the
-  non-smooth `penalty="l1"`. This is therefore the solver of choice for sparse
-  multinomial logistic regression. It is also the only solver that supports
-  `penalty="elasticnet"`.
-
-* The "lbfgs" is an optimization algorithm that approximates the
-  Broyden–Fletcher–Goldfarb–Shanno algorithm [8]_, which belongs to
-  quasi-Newton methods. As such, it can deal with a wide range of different training
-  data and is therefore the default solver. Its performance, however, suffers on poorly
-  scaled datasets and on datasets with one-hot encoded categorical features with rare
-  categories.
-
-* The "newton-cholesky" solver is an exact Newton solver that calculates the hessian
-  matrix and solves the resulting linear system. It is a very good choice for
-  `n_samples` >> `n_features`, but has a few shortcomings: Only :math:`\ell_2`
-  regularization is supported. Furthermore, because the hessian matrix is explicitly
-  computed, the memory usage has a quadratic dependency on `n_features` as well as on
-  `n_classes`. As a consequence, only the one-vs-rest scheme is implemented for the
-  multiclass case.
-
-For a comparison of some of these solvers, see [9]_.
-
-.. topic:: References:
+.. dropdown:: Solvers' details
+
+  * The solver "liblinear" uses a coordinate descent (CD) algorithm, and relies
+    on the excellent C++ `LIBLINEAR library
+    <https://www.csie.ntu.edu.tw/~cjlin/liblinear/>`_, which is shipped with
+    scikit-learn. However, the CD algorithm implemented in liblinear cannot learn
+    a true multinomial (multiclass) model; instead, the optimization problem is
+    decomposed in a "one-vs-rest" fashion so separate binary classifiers are
+    trained for all classes. This happens under the hood, so
+    :class:`LogisticRegression` instances using this solver behave as multiclass
+    classifiers. For :math:`\ell_1` regularization :func:`sklearn.svm.l1_min_c` allows to
+    calculate the lower bound for C in order to get a non "null" (all feature
+    weights to zero) model.
+
+  * The "lbfgs", "newton-cg" and "sag" solvers only support :math:`\ell_2`
+    regularization or no regularization, and are found to converge faster for some
+    high-dimensional data. Setting `multi_class` to "multinomial" with these solvers
+    learns a true multinomial logistic regression model [5]_, which means that its
+    probability estimates should be better calibrated than the default "one-vs-rest"
+    setting.
+
+  * The "sag" solver uses Stochastic Average Gradient descent [6]_. It is faster
+    than other solvers for large datasets, when both the number of samples and the
+    number of features are large.
+
+  * The "saga" solver [7]_ is a variant of "sag" that also supports the
+    non-smooth `penalty="l1"`. This is therefore the solver of choice for sparse
+    multinomial logistic regression. It is also the only solver that supports
+    `penalty="elasticnet"`.
+
+  * The "lbfgs" is an optimization algorithm that approximates the
+    Broyden–Fletcher–Goldfarb–Shanno algorithm [8]_, which belongs to
+    quasi-Newton methods. As such, it can deal with a wide range of different training
+    data and is therefore the default solver. Its performance, however, suffers on poorly
+    scaled datasets and on datasets with one-hot encoded categorical features with rare
+    categories.
+
+  * The "newton-cholesky" solver is an exact Newton solver that calculates the hessian
+    matrix and solves the resulting linear system. It is a very good choice for
+    `n_samples` >> `n_features`, but has a few shortcomings: Only :math:`\ell_2`
+    regularization is supported. Furthermore, because the hessian matrix is explicitly
+    computed, the memory usage has a quadratic dependency on `n_features` as well as on
+    `n_classes`. As a consequence, only the one-vs-rest scheme is implemented for the
+    multiclass case.
+
+  For a comparison of some of these solvers, see [9]_.
+
+  .. rubric:: References
 
   .. [5] Christopher M. Bishop: Pattern Recognition and Machine Learning, Chapter 4.3.4
 
@@ -1164,8 +1121,6 @@ For a comparison of some of these solvers, see [9]_.
       "A Blockwise Descent Algorithm for Group-penalized Multiresponse and
       Multinomial Regression." <1311.6529>`
 
-|details-end|
-
 
 .. note:: **Feature selection with sparse logistic regression**
 
@@ -1262,38 +1217,34 @@ The choice of the distribution depends on the problem at hand:
   used for multiclass classification.
 
 
-|details-start|
-**Examples of use cases**
-|details-split|
-
-* Agriculture / weather modeling:  number of rain events per year (Poisson),
-  amount of rainfall per event (Gamma), total rainfall per year (Tweedie /
-  Compound Poisson Gamma).
-* Risk modeling / insurance policy pricing:  number of claim events /
-  policyholder per year (Poisson), cost per event (Gamma), total cost per
-  policyholder per year (Tweedie / Compound Poisson Gamma).
-* Credit Default: probability that a loan can't be paid back (Bernoulli).
-* Fraud Detection: probability that a financial transaction like a cash transfer
-  is a fraudulent transaction (Bernoulli).
-* Predictive maintenance: number of production interruption events per year
-  (Poisson), duration of interruption (Gamma), total interruption time per year
-  (Tweedie / Compound Poisson Gamma).
-* Medical Drug Testing: probability of curing a patient in a set of trials or
-  probability that a patient will experience side effects (Bernoulli).
-* News Classification: classification of news articles into three categories
-  namely Business News, Politics and Entertainment news (Categorical).
+.. dropdown:: Examples of use cases
 
-|details-end|
+  * Agriculture / weather modeling:  number of rain events per year (Poisson),
+    amount of rainfall per event (Gamma), total rainfall per year (Tweedie /
+    Compound Poisson Gamma).
+  * Risk modeling / insurance policy pricing:  number of claim events /
+    policyholder per year (Poisson), cost per event (Gamma), total cost per
+    policyholder per year (Tweedie / Compound Poisson Gamma).
+  * Credit Default: probability that a loan can't be paid back (Bernoulli).
+  * Fraud Detection: probability that a financial transaction like a cash transfer
+    is a fraudulent transaction (Bernoulli).
+  * Predictive maintenance: number of production interruption events per year
+    (Poisson), duration of interruption (Gamma), total interruption time per year
+    (Tweedie / Compound Poisson Gamma).
+  * Medical Drug Testing: probability of curing a patient in a set of trials or
+    probability that a patient will experience side effects (Bernoulli).
+  * News Classification: classification of news articles into three categories
+    namely Business News, Politics and Entertainment news (Categorical).
 
-.. topic:: References:
+.. rubric:: References
 
-  .. [10] McCullagh, Peter; Nelder, John (1989). Generalized Linear Models,
-      Second Edition. Boca Raton: Chapman and Hall/CRC. ISBN 0-412-31760-5.
+.. [10] McCullagh, Peter; Nelder, John (1989). Generalized Linear Models,
+    Second Edition. Boca Raton: Chapman and Hall/CRC. ISBN 0-412-31760-5.
 
-  .. [11] Jørgensen, B. (1992). The theory of exponential dispersion models
-      and analysis of deviance. Monografias de matemática, no. 51.  See also
-      `Exponential dispersion model.
-      <https://en.wikipedia.org/wiki/Exponential_dispersion_model>`_
+.. [11] Jørgensen, B. (1992). The theory of exponential dispersion models
+    and analysis of deviance. Monografias de matemática, no. 51.  See also
+    `Exponential dispersion model.
+    <https://en.wikipedia.org/wiki/Exponential_dispersion_model>`_
 
 Usage
 -----
@@ -1327,37 +1278,33 @@ Usage example::
     -0.7638...
 
 
-.. topic:: Examples
+.. rubric:: Examples
 
-  * :ref:`sphx_glr_auto_examples_linear_model_plot_poisson_regression_non_normal_loss.py`
-  * :ref:`sphx_glr_auto_examples_linear_model_plot_tweedie_regression_insurance_claims.py`
+* :ref:`sphx_glr_auto_examples_linear_model_plot_poisson_regression_non_normal_loss.py`
+* :ref:`sphx_glr_auto_examples_linear_model_plot_tweedie_regression_insurance_claims.py`
 
-|details-start|
-**Practical considerations**
-|details-split|
+.. dropdown:: Practical considerations
 
-The feature matrix `X` should be standardized before fitting. This ensures
-that the penalty treats features equally.
+  The feature matrix `X` should be standardized before fitting. This ensures
+  that the penalty treats features equally.
 
-Since the linear predictor :math:`Xw` can be negative and Poisson,
-Gamma and Inverse Gaussian distributions don't support negative values, it
-is necessary to apply an inverse link function that guarantees the
-non-negativeness. For example with `link='log'`, the inverse link function
-becomes :math:`h(Xw)=\exp(Xw)`.
+  Since the linear predictor :math:`Xw` can be negative and Poisson,
+  Gamma and Inverse Gaussian distributions don't support negative values, it
+  is necessary to apply an inverse link function that guarantees the
+  non-negativeness. For example with `link='log'`, the inverse link function
+  becomes :math:`h(Xw)=\exp(Xw)`.
 
-If you want to model a relative frequency, i.e. counts per exposure (time,
-volume, ...) you can do so by using a Poisson distribution and passing
-:math:`y=\frac{\mathrm{counts}}{\mathrm{exposure}}` as target values
-together with :math:`\mathrm{exposure}` as sample weights. For a concrete
-example see e.g.
-:ref:`sphx_glr_auto_examples_linear_model_plot_tweedie_regression_insurance_claims.py`.
+  If you want to model a relative frequency, i.e. counts per exposure (time,
+  volume, ...) you can do so by using a Poisson distribution and passing
+  :math:`y=\frac{\mathrm{counts}}{\mathrm{exposure}}` as target values
+  together with :math:`\mathrm{exposure}` as sample weights. For a concrete
+  example see e.g.
+  :ref:`sphx_glr_auto_examples_linear_model_plot_tweedie_regression_insurance_claims.py`.
 
-When performing cross-validation for the `power` parameter of
-`TweedieRegressor`, it is advisable to specify an explicit `scoring` function,
-because the default scorer :meth:`TweedieRegressor.score` is a function of
-`power` itself.
-
-|details-end|
+  When performing cross-validation for the `power` parameter of
+  `TweedieRegressor`, it is advisable to specify an explicit `scoring` function,
+  because the default scorer :meth:`TweedieRegressor.score` is a function of
+  `power` itself.
 
 Stochastic Gradient Descent - SGD
 =================================
@@ -1415,15 +1362,11 @@ For classification, :class:`PassiveAggressiveClassifier` can be used with
 ``loss='epsilon_insensitive'`` (PA-I) or
 ``loss='squared_epsilon_insensitive'`` (PA-II).
 
-|details-start|
-**References**
-|details-split|
-
-* `"Online Passive-Aggressive Algorithms"
-  <http://jmlr.csail.mit.edu/papers/volume7/crammer06a/crammer06a.pdf>`_
-  K. Crammer, O. Dekel, J. Keshat, S. Shalev-Shwartz, Y. Singer - JMLR 7 (2006)
+.. dropdown:: References
 
-|details-end|
+  * `"Online Passive-Aggressive Algorithms"
+    <http://jmlr.csail.mit.edu/papers/volume7/crammer06a/crammer06a.pdf>`_
+    K. Crammer, O. Dekel, J. Keshat, S. Shalev-Shwartz, Y. Singer - JMLR 7 (2006)
 
 Robustness regression: outliers and modeling errors
 =====================================================
@@ -1533,56 +1476,48 @@ estimated only from the determined inliers.
    :align: center
    :scale: 50%
 
-.. topic:: Examples
-
-  * :ref:`sphx_glr_auto_examples_linear_model_plot_ransac.py`
-  * :ref:`sphx_glr_auto_examples_linear_model_plot_robust_fit.py`
-
-|details-start|
-**Details of the algorithm**
-|details-split|
-
-Each iteration performs the following steps:
-
-1. Select ``min_samples`` random samples from the original data and check
-   whether the set of data is valid (see ``is_data_valid``).
-2. Fit a model to the random subset (``estimator.fit``) and check
-   whether the estimated model is valid (see ``is_model_valid``).
-3. Classify all data as inliers or outliers by calculating the residuals
-   to the estimated model (``estimator.predict(X) - y``) - all data
-   samples with absolute residuals smaller than or equal to the
-   ``residual_threshold`` are considered as inliers.
-4. Save fitted model as best model if number of inlier samples is
-   maximal. In case the current estimated model has the same number of
-   inliers, it is only considered as the best model if it has better score.
-
-These steps are performed either a maximum number of times (``max_trials``) or
-until one of the special stop criteria are met (see ``stop_n_inliers`` and
-``stop_score``). The final model is estimated using all inlier samples (consensus
-set) of the previously determined best model.
-
-The ``is_data_valid`` and ``is_model_valid`` functions allow to identify and reject
-degenerate combinations of random sub-samples. If the estimated model is not
-needed for identifying degenerate cases, ``is_data_valid`` should be used as it
-is called prior to fitting the model and thus leading to better computational
-performance.
-
-|details-end|
-
-|details-start|
-**References**
-|details-split|
-
-* https://en.wikipedia.org/wiki/RANSAC
-* `"Random Sample Consensus: A Paradigm for Model Fitting with Applications to
-  Image Analysis and Automated Cartography"
-  <https://www.cs.ait.ac.th/~mdailey/cvreadings/Fischler-RANSAC.pdf>`_
-  Martin A. Fischler and Robert C. Bolles - SRI International (1981)
-* `"Performance Evaluation of RANSAC Family"
-  <http://www.bmva.org/bmvc/2009/Papers/Paper355/Paper355.pdf>`_
-  Sunglok Choi, Taemin Kim and Wonpil Yu - BMVC (2009)
-
-|details-end|
+.. rubric:: Examples
+
+* :ref:`sphx_glr_auto_examples_linear_model_plot_ransac.py`
+* :ref:`sphx_glr_auto_examples_linear_model_plot_robust_fit.py`
+
+.. dropdown:: Details of the algorithm
+
+  Each iteration performs the following steps:
+
+  1. Select ``min_samples`` random samples from the original data and check
+     whether the set of data is valid (see ``is_data_valid``).
+  2. Fit a model to the random subset (``estimator.fit``) and check
+     whether the estimated model is valid (see ``is_model_valid``).
+  3. Classify all data as inliers or outliers by calculating the residuals
+     to the estimated model (``estimator.predict(X) - y``) - all data
+     samples with absolute residuals smaller than or equal to the
+     ``residual_threshold`` are considered as inliers.
+  4. Save fitted model as best model if number of inlier samples is
+     maximal. In case the current estimated model has the same number of
+     inliers, it is only considered as the best model if it has better score.
+
+  These steps are performed either a maximum number of times (``max_trials``) or
+  until one of the special stop criteria are met (see ``stop_n_inliers`` and
+  ``stop_score``). The final model is estimated using all inlier samples (consensus
+  set) of the previously determined best model.
+
+  The ``is_data_valid`` and ``is_model_valid`` functions allow to identify and reject
+  degenerate combinations of random sub-samples. If the estimated model is not
+  needed for identifying degenerate cases, ``is_data_valid`` should be used as it
+  is called prior to fitting the model and thus leading to better computational
+  performance.
+
+.. dropdown:: References
+
+  * https://en.wikipedia.org/wiki/RANSAC
+  * `"Random Sample Consensus: A Paradigm for Model Fitting with Applications to
+    Image Analysis and Automated Cartography"
+    <https://www.cs.ait.ac.th/~mdailey/cvreadings/Fischler-RANSAC.pdf>`_
+    Martin A. Fischler and Robert C. Bolles - SRI International (1981)
+  * `"Performance Evaluation of RANSAC Family"
+    <http://www.bmva.org/bmvc/2009/Papers/Paper355/Paper355.pdf>`_
+    Sunglok Choi, Taemin Kim and Wonpil Yu - BMVC (2009)
 
 .. _theil_sen_regression:
 
@@ -1595,47 +1530,45 @@ that the robustness of the estimator decreases quickly with the dimensionality
 of the problem. It loses its robustness properties and becomes no
 better than an ordinary least squares in high dimension.
 
-.. topic:: Examples:
+.. rubric:: Examples
 
-  * :ref:`sphx_glr_auto_examples_linear_model_plot_theilsen.py`
-  * :ref:`sphx_glr_auto_examples_linear_model_plot_robust_fit.py`
+* :ref:`sphx_glr_auto_examples_linear_model_plot_theilsen.py`
+* :ref:`sphx_glr_auto_examples_linear_model_plot_robust_fit.py`
 
 
-|details-start|
-**Theoretical considerations**
-|details-split|
+.. dropdown:: Theoretical considerations
 
-:class:`TheilSenRegressor` is comparable to the :ref:`Ordinary Least Squares
-(OLS) <ordinary_least_squares>` in terms of asymptotic efficiency and as an
-unbiased estimator. In contrast to OLS, Theil-Sen is a non-parametric
-method which means it makes no assumption about the underlying
-distribution of the data. Since Theil-Sen is a median-based estimator, it
-is more robust against corrupted data aka outliers. In univariate
-setting, Theil-Sen has a breakdown point of about 29.3% in case of a
-simple linear regression which means that it can tolerate arbitrary
-corrupted data of up to 29.3%.
+  :class:`TheilSenRegressor` is comparable to the :ref:`Ordinary Least Squares
+  (OLS) <ordinary_least_squares>` in terms of asymptotic efficiency and as an
+  unbiased estimator. In contrast to OLS, Theil-Sen is a non-parametric
+  method which means it makes no assumption about the underlying
+  distribution of the data. Since Theil-Sen is a median-based estimator, it
+  is more robust against corrupted data aka outliers. In univariate
+  setting, Theil-Sen has a breakdown point of about 29.3% in case of a
+  simple linear regression which means that it can tolerate arbitrary
+  corrupted data of up to 29.3%.
 
-.. figure:: ../auto_examples/linear_model/images/sphx_glr_plot_theilsen_001.png
-   :target: ../auto_examples/linear_model/plot_theilsen.html
-   :align: center
-   :scale: 50%
+  .. figure:: ../auto_examples/linear_model/images/sphx_glr_plot_theilsen_001.png
+    :target: ../auto_examples/linear_model/plot_theilsen.html
+    :align: center
+    :scale: 50%
 
-The implementation of :class:`TheilSenRegressor` in scikit-learn follows a
-generalization to a multivariate linear regression model [#f1]_ using the
-spatial median which is a generalization of the median to multiple
-dimensions [#f2]_.
+  The implementation of :class:`TheilSenRegressor` in scikit-learn follows a
+  generalization to a multivariate linear regression model [#f1]_ using the
+  spatial median which is a generalization of the median to multiple
+  dimensions [#f2]_.
 
-In terms of time and space complexity, Theil-Sen scales according to
+  In terms of time and space complexity, Theil-Sen scales according to
 
-.. math::
-    \binom{n_{\text{samples}}}{n_{\text{subsamples}}}
+  .. math::
+      \binom{n_{\text{samples}}}{n_{\text{subsamples}}}
 
-which makes it infeasible to be applied exhaustively to problems with a
-large number of samples and features. Therefore, the magnitude of a
-subpopulation can be chosen to limit the time and space complexity by
-considering only a random subset of all possible combinations.
+  which makes it infeasible to be applied exhaustively to problems with a
+  large number of samples and features. Therefore, the magnitude of a
+  subpopulation can be chosen to limit the time and space complexity by
+  considering only a random subset of all possible combinations.
 
-.. topic:: References:
+  .. rubric:: References
 
   .. [#f1] Xin Dang, Hanxiang Peng, Xueqin Wang and Heping Zhang: `Theil-Sen Estimators in a Multiple Linear Regression Model. <http://home.olemiss.edu/~xdang/papers/MTSE.pdf>`_
 
@@ -1643,8 +1576,6 @@ considering only a random subset of all possible combinations.
 
   Also see the `Wikipedia page <https://en.wikipedia.org/wiki/Theil%E2%80%93Sen_estimator>`_
 
-|details-end|
-
 
 .. _huber_regression:
 
@@ -1663,39 +1594,35 @@ but gives a lesser weight to them.
    :align: center
    :scale: 50%
 
-.. topic:: Examples:
+.. rubric:: Examples
 
-  * :ref:`sphx_glr_auto_examples_linear_model_plot_huber_vs_ridge.py`
+* :ref:`sphx_glr_auto_examples_linear_model_plot_huber_vs_ridge.py`
 
-|details-start|
-**Mathematical details**
-|details-split|
+.. dropdown:: Mathematical details
 
-The loss function that :class:`HuberRegressor` minimizes is given by
+  The loss function that :class:`HuberRegressor` minimizes is given by
 
-.. math::
+  .. math::
 
-  \min_{w, \sigma} {\sum_{i=1}^n\left(\sigma + H_{\epsilon}\left(\frac{X_{i}w - y_{i}}{\sigma}\right)\sigma\right) + \alpha {||w||_2}^2}
+    \min_{w, \sigma} {\sum_{i=1}^n\left(\sigma + H_{\epsilon}\left(\frac{X_{i}w - y_{i}}{\sigma}\right)\sigma\right) + \alpha {||w||_2}^2}
 
-where
+  where
 
-.. math::
+  .. math::
 
-  H_{\epsilon}(z) = \begin{cases}
-        z^2, & \text {if } |z| < \epsilon, \\
-        2\epsilon|z| - \epsilon^2, & \text{otherwise}
-  \end{cases}
+    H_{\epsilon}(z) = \begin{cases}
+          z^2, & \text {if } |z| < \epsilon, \\
+          2\epsilon|z| - \epsilon^2, & \text{otherwise}
+    \end{cases}
 
-It is advised to set the parameter ``epsilon`` to 1.35 to achieve 95%
-statistical efficiency.
+  It is advised to set the parameter ``epsilon`` to 1.35 to achieve 95%
+  statistical efficiency.
 
-.. topic:: References:
+  .. rubric:: References
 
   * Peter J. Huber, Elvezio M. Ronchetti: Robust Statistics, Concomitant scale
     estimates, pg 172
 
-|details-end|
-
 The :class:`HuberRegressor` differs from using :class:`SGDRegressor` with loss set to `huber`
 in the following ways.
 
@@ -1745,59 +1672,51 @@ Most implementations of quantile regression are based on linear programming
 problem. The current implementation is based on
 :func:`scipy.optimize.linprog`.
 
-.. topic:: Examples:
+.. rubric:: Examples
 
-  * :ref:`sphx_glr_auto_examples_linear_model_plot_quantile_regression.py`
+* :ref:`sphx_glr_auto_examples_linear_model_plot_quantile_regression.py`
 
-|details-start|
-**Mathematical details**
-|details-split|
+.. dropdown:: Mathematical details
 
-As a linear model, the :class:`QuantileRegressor` gives linear predictions
-:math:`\hat{y}(w, X) = Xw` for the :math:`q`-th quantile, :math:`q \in (0, 1)`.
-The weights or coefficients :math:`w` are then found by the following
-minimization problem:
+  As a linear model, the :class:`QuantileRegressor` gives linear predictions
+  :math:`\hat{y}(w, X) = Xw` for the :math:`q`-th quantile, :math:`q \in (0, 1)`.
+  The weights or coefficients :math:`w` are then found by the following
+  minimization problem:
 
-.. math::
-    \min_{w} {\frac{1}{n_{\text{samples}}}
-    \sum_i PB_q(y_i - X_i w) + \alpha ||w||_1}.
+  .. math::
+      \min_{w} {\frac{1}{n_{\text{samples}}}
+      \sum_i PB_q(y_i - X_i w) + \alpha ||w||_1}.
 
-This consists of the pinball loss (also known as linear loss),
-see also :class:`~sklearn.metrics.mean_pinball_loss`,
+  This consists of the pinball loss (also known as linear loss),
+  see also :class:`~sklearn.metrics.mean_pinball_loss`,
 
-.. math::
-    PB_q(t) = q \max(t, 0) + (1 - q) \max(-t, 0) =
-    \begin{cases}
-        q t, & t > 0, \\
-        0,    & t = 0, \\
-        (q-1) t, & t < 0
-    \end{cases}
-
-and the L1 penalty controlled by parameter ``alpha``, similar to
-:class:`Lasso`.
+  .. math::
+      PB_q(t) = q \max(t, 0) + (1 - q) \max(-t, 0) =
+      \begin{cases}
+          q t, & t > 0, \\
+          0,    & t = 0, \\
+          (q-1) t, & t < 0
+      \end{cases}
 
-As the pinball loss is only linear in the residuals, quantile regression is
-much more robust to outliers than squared error based estimation of the mean.
-Somewhat in between is the :class:`HuberRegressor`.
+  and the L1 penalty controlled by parameter ``alpha``, similar to
+  :class:`Lasso`.
 
-|details-end|
+  As the pinball loss is only linear in the residuals, quantile regression is
+  much more robust to outliers than squared error based estimation of the mean.
+  Somewhat in between is the :class:`HuberRegressor`.
 
-|details-start|
-**References**
-|details-split|
+.. dropdown:: References
 
-* Koenker, R., & Bassett Jr, G. (1978). `Regression quantiles.
-  <https://gib.people.uic.edu/RQ.pdf>`_
-  Econometrica: journal of the Econometric Society, 33-50.
+  * Koenker, R., & Bassett Jr, G. (1978). `Regression quantiles.
+    <https://gib.people.uic.edu/RQ.pdf>`_
+    Econometrica: journal of the Econometric Society, 33-50.
 
-* Portnoy, S., & Koenker, R. (1997). :doi:`The Gaussian hare and the Laplacian
-  tortoise: computability of squared-error versus absolute-error estimators.
-  Statistical Science, 12, 279-300 <10.1214/ss/1030037960>`.
+  * Portnoy, S., & Koenker, R. (1997). :doi:`The Gaussian hare and the Laplacian
+    tortoise: computability of squared-error versus absolute-error estimators.
+    Statistical Science, 12, 279-300 <10.1214/ss/1030037960>`.
 
-* Koenker, R. (2005). :doi:`Quantile Regression <10.1017/CBO9780511754098>`.
-  Cambridge University Press.
-
-|details-end|
+  * Koenker, R. (2005). :doi:`Quantile Regression <10.1017/CBO9780511754098>`.
+    Cambridge University Press.
 
 
 .. _polynomial_regression:
@@ -1812,38 +1731,34 @@ on nonlinear functions of the data.  This approach maintains the generally
 fast performance of linear methods, while allowing them to fit a much wider
 range of data.
 
-|details-start|
-**Mathematical details**
-|details-split|
-
-For example, a simple linear regression can be extended by constructing
-**polynomial features** from the coefficients.  In the standard linear
-regression case, you might have a model that looks like this for
-two-dimensional data:
+.. dropdown:: Mathematical details
 
-.. math::    \hat{y}(w, x) = w_0 + w_1 x_1 + w_2 x_2
+  For example, a simple linear regression can be extended by constructing
+  **polynomial features** from the coefficients.  In the standard linear
+  regression case, you might have a model that looks like this for
+  two-dimensional data:
 
-If we want to fit a paraboloid to the data instead of a plane, we can combine
-the features in second-order polynomials, so that the model looks like this:
+  .. math::    \hat{y}(w, x) = w_0 + w_1 x_1 + w_2 x_2
 
-.. math::    \hat{y}(w, x) = w_0 + w_1 x_1 + w_2 x_2 + w_3 x_1 x_2 + w_4 x_1^2 + w_5 x_2^2
+  If we want to fit a paraboloid to the data instead of a plane, we can combine
+  the features in second-order polynomials, so that the model looks like this:
 
-The (sometimes surprising) observation is that this is *still a linear model*:
-to see this, imagine creating a new set of features
+  .. math::    \hat{y}(w, x) = w_0 + w_1 x_1 + w_2 x_2 + w_3 x_1 x_2 + w_4 x_1^2 + w_5 x_2^2
 
-.. math::  z = [x_1, x_2, x_1 x_2, x_1^2, x_2^2]
+  The (sometimes surprising) observation is that this is *still a linear model*:
+  to see this, imagine creating a new set of features
 
-With this re-labeling of the data, our problem can be written
+  .. math::  z = [x_1, x_2, x_1 x_2, x_1^2, x_2^2]
 
-.. math::    \hat{y}(w, z) = w_0 + w_1 z_1 + w_2 z_2 + w_3 z_3 + w_4 z_4 + w_5 z_5
+  With this re-labeling of the data, our problem can be written
 
-We see that the resulting *polynomial regression* is in the same class of
-linear models we considered above (i.e. the model is linear in :math:`w`)
-and can be solved by the same techniques.  By considering linear fits within
-a higher-dimensional space built with these basis functions, the model has the
-flexibility to fit a much broader range of data.
+  .. math::    \hat{y}(w, z) = w_0 + w_1 z_1 + w_2 z_2 + w_3 z_3 + w_4 z_4 + w_5 z_5
 
-|details-end|
+  We see that the resulting *polynomial regression* is in the same class of
+  linear models we considered above (i.e. the model is linear in :math:`w`)
+  and can be solved by the same techniques.  By considering linear fits within
+  a higher-dimensional space built with these basis functions, the model has the
+  flexibility to fit a much broader range of data.
 
 Here is an example of applying this idea to one-dimensional data, using
 polynomial features of varying degrees:
diff --git a/doc/modules/manifold.rst b/doc/modules/manifold.rst
index 7cc6776e37daa..785fba3097edf 100644
--- a/doc/modules/manifold.rst
+++ b/doc/modules/manifold.rst
@@ -102,13 +102,13 @@ unsupervised: it learns the high-dimensional structure of the data
 from the data itself, without the use of predetermined classifications.
 
 
-.. topic:: Examples:
+.. rubric:: Examples
 
-    * See :ref:`sphx_glr_auto_examples_manifold_plot_lle_digits.py` for an example of
-      dimensionality reduction on handwritten digits.
+* See :ref:`sphx_glr_auto_examples_manifold_plot_lle_digits.py` for an example of
+  dimensionality reduction on handwritten digits.
 
-    * See :ref:`sphx_glr_auto_examples_manifold_plot_compare_methods.py` for an example of
-      dimensionality reduction on a toy "S-curve" dataset.
+* See :ref:`sphx_glr_auto_examples_manifold_plot_compare_methods.py` for an example of
+  dimensionality reduction on a toy "S-curve" dataset.
 
 The manifold learning implementations available in scikit-learn are
 summarized below
@@ -130,47 +130,43 @@ distances between all points.  Isomap can be performed with the object
    :align: center
    :scale: 50
 
-|details-start|
-**Complexity**
-|details-split|
+.. dropdown:: Complexity
 
-The Isomap algorithm comprises three stages:
+  The Isomap algorithm comprises three stages:
 
-1. **Nearest neighbor search.**  Isomap uses
-   :class:`~sklearn.neighbors.BallTree` for efficient neighbor search.
-   The cost is approximately :math:`O[D \log(k) N \log(N)]`, for :math:`k`
-   nearest neighbors of :math:`N` points in :math:`D` dimensions.
+  1. **Nearest neighbor search.**  Isomap uses
+     :class:`~sklearn.neighbors.BallTree` for efficient neighbor search.
+     The cost is approximately :math:`O[D \log(k) N \log(N)]`, for :math:`k`
+     nearest neighbors of :math:`N` points in :math:`D` dimensions.
 
-2. **Shortest-path graph search.**  The most efficient known algorithms
-   for this are *Dijkstra's Algorithm*, which is approximately
-   :math:`O[N^2(k + \log(N))]`, or the *Floyd-Warshall algorithm*, which
-   is :math:`O[N^3]`.  The algorithm can be selected by the user with
-   the ``path_method`` keyword of ``Isomap``.  If unspecified, the code
-   attempts to choose the best algorithm for the input data.
+  2. **Shortest-path graph search.**  The most efficient known algorithms
+     for this are *Dijkstra's Algorithm*, which is approximately
+     :math:`O[N^2(k + \log(N))]`, or the *Floyd-Warshall algorithm*, which
+     is :math:`O[N^3]`.  The algorithm can be selected by the user with
+     the ``path_method`` keyword of ``Isomap``.  If unspecified, the code
+     attempts to choose the best algorithm for the input data.
 
-3. **Partial eigenvalue decomposition.**  The embedding is encoded in the
-   eigenvectors corresponding to the :math:`d` largest eigenvalues of the
-   :math:`N \times N` isomap kernel.  For a dense solver, the cost is
-   approximately :math:`O[d N^2]`.  This cost can often be improved using
-   the ``ARPACK`` solver.  The eigensolver can be specified by the user
-   with the ``eigen_solver`` keyword of ``Isomap``.  If unspecified, the
-   code attempts to choose the best algorithm for the input data.
+  3. **Partial eigenvalue decomposition.**  The embedding is encoded in the
+     eigenvectors corresponding to the :math:`d` largest eigenvalues of the
+     :math:`N \times N` isomap kernel.  For a dense solver, the cost is
+     approximately :math:`O[d N^2]`.  This cost can often be improved using
+     the ``ARPACK`` solver.  The eigensolver can be specified by the user
+     with the ``eigen_solver`` keyword of ``Isomap``.  If unspecified, the
+     code attempts to choose the best algorithm for the input data.
 
-The overall complexity of Isomap is
-:math:`O[D \log(k) N \log(N)] + O[N^2(k + \log(N))] + O[d N^2]`.
+  The overall complexity of Isomap is
+  :math:`O[D \log(k) N \log(N)] + O[N^2(k + \log(N))] + O[d N^2]`.
 
-* :math:`N` : number of training data points
-* :math:`D` : input dimension
-* :math:`k` : number of nearest neighbors
-* :math:`d` : output dimension
+  * :math:`N` : number of training data points
+  * :math:`D` : input dimension
+  * :math:`k` : number of nearest neighbors
+  * :math:`d` : output dimension
 
-|details-end|
+.. rubric:: References
 
-.. topic:: References:
-
-   * `"A global geometric framework for nonlinear dimensionality reduction"
-     <http://science.sciencemag.org/content/290/5500/2319.full>`_
-     Tenenbaum, J.B.; De Silva, V.; & Langford, J.C.  Science 290 (5500)
+* `"A global geometric framework for nonlinear dimensionality reduction"
+  <http://science.sciencemag.org/content/290/5500/2319.full>`_
+  Tenenbaum, J.B.; De Silva, V.; & Langford, J.C.  Science 290 (5500)
 
 .. _locally_linear_embedding:
 
@@ -191,36 +187,32 @@ Locally linear embedding can be performed with function
    :align: center
    :scale: 50
 
-|details-start|
-**Complexity**
-|details-split|
-
-The standard LLE algorithm comprises three stages:
+.. dropdown:: Complexity
 
-1. **Nearest Neighbors Search**.  See discussion under Isomap above.
+  The standard LLE algorithm comprises three stages:
 
-2. **Weight Matrix Construction**. :math:`O[D N k^3]`.
-   The construction of the LLE weight matrix involves the solution of a
-   :math:`k \times k` linear equation for each of the :math:`N` local
-   neighborhoods
+  1. **Nearest Neighbors Search**.  See discussion under Isomap above.
 
-3. **Partial Eigenvalue Decomposition**. See discussion under Isomap above.
+  2. **Weight Matrix Construction**. :math:`O[D N k^3]`.
+     The construction of the LLE weight matrix involves the solution of a
+     :math:`k \times k` linear equation for each of the :math:`N` local
+     neighborhoods.
 
-The overall complexity of standard LLE is
-:math:`O[D \log(k) N \log(N)] + O[D N k^3] + O[d N^2]`.
+  3. **Partial Eigenvalue Decomposition**. See discussion under Isomap above.
 
-* :math:`N` : number of training data points
-* :math:`D` : input dimension
-* :math:`k` : number of nearest neighbors
-* :math:`d` : output dimension
+  The overall complexity of standard LLE is
+  :math:`O[D \log(k) N \log(N)] + O[D N k^3] + O[d N^2]`.
 
-|details-end|
+  * :math:`N` : number of training data points
+  * :math:`D` : input dimension
+  * :math:`k` : number of nearest neighbors
+  * :math:`d` : output dimension
 
-.. topic:: References:
+.. rubric:: References
 
-   * `"Nonlinear dimensionality reduction by locally linear embedding"
-     <http://www.sciencemag.org/content/290/5500/2323.full>`_
-     Roweis, S. & Saul, L.  Science 290:2323 (2000)
+* `"Nonlinear dimensionality reduction by locally linear embedding"
+  <http://www.sciencemag.org/content/290/5500/2323.full>`_
+  Roweis, S. & Saul, L.  Science 290:2323 (2000)
 
 
 Modified Locally Linear Embedding
@@ -248,38 +240,34 @@ It requires ``n_neighbors > n_components``.
    :align: center
    :scale: 50
 
-|details-start|
-**Complexity**
-|details-split|
-
-The MLLE algorithm comprises three stages:
+.. dropdown:: Complexity
 
-1. **Nearest Neighbors Search**.  Same as standard LLE
+  The MLLE algorithm comprises three stages:
 
-2. **Weight Matrix Construction**. Approximately
-   :math:`O[D N k^3] + O[N (k-D) k^2]`.  The first term is exactly equivalent
-   to that of standard LLE.  The second term has to do with constructing the
-   weight matrix from multiple weights.  In practice, the added cost of
-   constructing the MLLE weight matrix is relatively small compared to the
-   cost of stages 1 and 3.
+  1. **Nearest Neighbors Search**.  Same as standard LLE
 
-3. **Partial Eigenvalue Decomposition**. Same as standard LLE
+  2. **Weight Matrix Construction**. Approximately
+     :math:`O[D N k^3] + O[N (k-D) k^2]`.  The first term is exactly equivalent
+     to that of standard LLE.  The second term has to do with constructing the
+     weight matrix from multiple weights.  In practice, the added cost of
+     constructing the MLLE weight matrix is relatively small compared to the
+     cost of stages 1 and 3.
 
-The overall complexity of MLLE is
-:math:`O[D \log(k) N \log(N)] + O[D N k^3] + O[N (k-D) k^2] + O[d N^2]`.
+  3. **Partial Eigenvalue Decomposition**. Same as standard LLE
 
-* :math:`N` : number of training data points
-* :math:`D` : input dimension
-* :math:`k` : number of nearest neighbors
-* :math:`d` : output dimension
+  The overall complexity of MLLE is
+  :math:`O[D \log(k) N \log(N)] + O[D N k^3] + O[N (k-D) k^2] + O[d N^2]`.
 
-|details-end|
+  * :math:`N` : number of training data points
+  * :math:`D` : input dimension
+  * :math:`k` : number of nearest neighbors
+  * :math:`d` : output dimension
 
-.. topic:: References:
+.. rubric:: References
 
-   * `"MLLE: Modified Locally Linear Embedding Using Multiple Weights"
-     <https://citeseerx.ist.psu.edu/doc_view/pid/0b060fdbd92cbcc66b383bcaa9ba5e5e624d7ee3>`_
-     Zhang, Z. & Wang, J.
+* `"MLLE: Modified Locally Linear Embedding Using Multiple Weights"
+  <https://citeseerx.ist.psu.edu/doc_view/pid/0b060fdbd92cbcc66b383bcaa9ba5e5e624d7ee3>`_
+  Zhang, Z. & Wang, J.
 
 
 Hessian Eigenmapping
@@ -301,36 +289,32 @@ It requires ``n_neighbors > n_components * (n_components + 3) / 2``.
    :align: center
    :scale: 50
 
-|details-start|
-**Complexity**
-|details-split|
+.. dropdown:: Complexity
 
 The HLLE algorithm comprises three stages:
 
-1. **Nearest Neighbors Search**.  Same as standard LLE
+  1. **Nearest Neighbors Search**.  Same as standard LLE
 
-2. **Weight Matrix Construction**. Approximately
-   :math:`O[D N k^3] + O[N d^6]`.  The first term reflects a similar
-   cost to that of standard LLE.  The second term comes from a QR
-   decomposition of the local hessian estimator.
+  2. **Weight Matrix Construction**. Approximately
+     :math:`O[D N k^3] + O[N d^6]`.  The first term reflects a similar
+     cost to that of standard LLE.  The second term comes from a QR
+     decomposition of the local hessian estimator.
 
-3. **Partial Eigenvalue Decomposition**. Same as standard LLE
+  3. **Partial Eigenvalue Decomposition**. Same as standard LLE
 
-The overall complexity of standard HLLE is
-:math:`O[D \log(k) N \log(N)] + O[D N k^3] + O[N d^6] + O[d N^2]`.
+  The overall complexity of standard HLLE is
+  :math:`O[D \log(k) N \log(N)] + O[D N k^3] + O[N d^6] + O[d N^2]`.
 
-* :math:`N` : number of training data points
-* :math:`D` : input dimension
-* :math:`k` : number of nearest neighbors
-* :math:`d` : output dimension
+  * :math:`N` : number of training data points
+  * :math:`D` : input dimension
+  * :math:`k` : number of nearest neighbors
+  * :math:`d` : output dimension
 
-|details-end|
+.. rubric:: References
 
-.. topic:: References:
-
-   * `"Hessian Eigenmaps: Locally linear embedding techniques for
-     high-dimensional data" <http://www.pnas.org/content/100/10/5591>`_
-     Donoho, D. & Grimes, C. Proc Natl Acad Sci USA. 100:5591 (2003)
+* `"Hessian Eigenmaps: Locally linear embedding techniques for
+  high-dimensional data" <http://www.pnas.org/content/100/10/5591>`_
+  Donoho, D. & Grimes, C. Proc Natl Acad Sci USA. 100:5591 (2003)
 
 .. _spectral_embedding:
 
@@ -348,38 +332,34 @@ preserving local distances. Spectral embedding can be  performed with the
 function :func:`spectral_embedding` or its object-oriented counterpart
 :class:`SpectralEmbedding`.
 
-|details-start|
-**Complexity**
-|details-split|
-
-The Spectral Embedding (Laplacian Eigenmaps) algorithm comprises three stages:
+.. dropdown:: Complexity
 
-1. **Weighted Graph Construction**. Transform the raw input data into
-   graph representation using affinity (adjacency) matrix representation.
+  The Spectral Embedding (Laplacian Eigenmaps) algorithm comprises three stages:
 
-2. **Graph Laplacian Construction**. unnormalized Graph Laplacian
-   is constructed as :math:`L = D - A` for and normalized one as
-   :math:`L = D^{-\frac{1}{2}} (D - A) D^{-\frac{1}{2}}`.
+  1. **Weighted Graph Construction**. Transform the raw input data into
+     graph representation using affinity (adjacency) matrix representation.
 
-3. **Partial Eigenvalue Decomposition**. Eigenvalue decomposition is
-   done on graph Laplacian
+  2. **Graph Laplacian Construction**. unnormalized Graph Laplacian
+     is constructed as :math:`L = D - A` for and normalized one as
+     :math:`L = D^{-\frac{1}{2}} (D - A) D^{-\frac{1}{2}}`.
 
-The overall complexity of spectral embedding is
-:math:`O[D \log(k) N \log(N)] + O[D N k^3] + O[d N^2]`.
+  3. **Partial Eigenvalue Decomposition**. Eigenvalue decomposition is
+     done on graph Laplacian.
 
-* :math:`N` : number of training data points
-* :math:`D` : input dimension
-* :math:`k` : number of nearest neighbors
-* :math:`d` : output dimension
+  The overall complexity of spectral embedding is
+  :math:`O[D \log(k) N \log(N)] + O[D N k^3] + O[d N^2]`.
 
-|details-end|
+  * :math:`N` : number of training data points
+  * :math:`D` : input dimension
+  * :math:`k` : number of nearest neighbors
+  * :math:`d` : output dimension
 
-.. topic:: References:
+.. rubric:: References
 
-   * `"Laplacian Eigenmaps for Dimensionality Reduction
-     and Data Representation"
-     <https://web.cse.ohio-state.edu/~mbelkin/papers/LEM_NC_03.pdf>`_
-     M. Belkin, P. Niyogi, Neural Computation, June 2003; 15 (6):1373-1396
+* `"Laplacian Eigenmaps for Dimensionality Reduction
+  and Data Representation"
+  <https://web.cse.ohio-state.edu/~mbelkin/papers/LEM_NC_03.pdf>`_
+  M. Belkin, P. Niyogi, Neural Computation, June 2003; 15 (6):1373-1396
 
 
 Local Tangent Space Alignment
@@ -399,36 +379,32 @@ tangent spaces to learn the embedding.  LTSA can be performed with function
    :align: center
    :scale: 50
 
-|details-start|
-**Complexity**
-|details-split|
+.. dropdown:: Complexity
 
-The LTSA algorithm comprises three stages:
+  The LTSA algorithm comprises three stages:
 
-1. **Nearest Neighbors Search**.  Same as standard LLE
+  1. **Nearest Neighbors Search**.  Same as standard LLE
 
-2. **Weight Matrix Construction**. Approximately
-   :math:`O[D N k^3] + O[k^2 d]`.  The first term reflects a similar
-   cost to that of standard LLE.
+  2. **Weight Matrix Construction**. Approximately
+     :math:`O[D N k^3] + O[k^2 d]`.  The first term reflects a similar
+     cost to that of standard LLE.
 
-3. **Partial Eigenvalue Decomposition**. Same as standard LLE
+  3. **Partial Eigenvalue Decomposition**. Same as standard LLE
 
-The overall complexity of standard LTSA is
-:math:`O[D \log(k) N \log(N)] + O[D N k^3] + O[k^2 d] + O[d N^2]`.
+  The overall complexity of standard LTSA is
+  :math:`O[D \log(k) N \log(N)] + O[D N k^3] + O[k^2 d] + O[d N^2]`.
 
-* :math:`N` : number of training data points
-* :math:`D` : input dimension
-* :math:`k` : number of nearest neighbors
-* :math:`d` : output dimension
+  * :math:`N` : number of training data points
+  * :math:`D` : input dimension
+  * :math:`k` : number of nearest neighbors
+  * :math:`d` : output dimension
 
-|details-end|
+.. rubric:: References
 
-.. topic:: References:
-
-   * :arxiv:`"Principal manifolds and nonlinear dimensionality reduction via
-     tangent space alignment"
-     <cs/0212008>`
-     Zhang, Z. & Zha, H. Journal of Shanghai Univ. 8:406 (2004)
+* :arxiv:`"Principal manifolds and nonlinear dimensionality reduction via
+  tangent space alignment"
+  <cs/0212008>`
+  Zhang, Z. & Zha, H. Journal of Shanghai Univ. 8:406 (2004)
 
 .. _multidimensional_scaling:
 
@@ -467,67 +443,59 @@ the similarities chosen in some optimal ways. The objective, called the
 stress, is then defined by :math:`\sum_{i < j} d_{ij}(X) - \hat{d}_{ij}(X)`
 
 
-|details-start|
-**Metric MDS**
-|details-split|
-
-The simplest metric :class:`MDS` model, called *absolute MDS*, disparities are defined by
-:math:`\hat{d}_{ij} = S_{ij}`. With absolute MDS, the value :math:`S_{ij}`
-should then correspond exactly to the distance between point :math:`i` and
-:math:`j` in the embedding point.
+.. dropdown:: Metric MDS
 
-Most commonly, disparities are set to :math:`\hat{d}_{ij} = b S_{ij}`.
+  The simplest metric :class:`MDS` model, called *absolute MDS*, disparities are defined by
+  :math:`\hat{d}_{ij} = S_{ij}`. With absolute MDS, the value :math:`S_{ij}`
+  should then correspond exactly to the distance between point :math:`i` and
+  :math:`j` in the embedding point.
 
-|details-end|
+  Most commonly, disparities are set to :math:`\hat{d}_{ij} = b S_{ij}`.
 
-|details-start|
-**Nonmetric MDS**
-|details-split|
+.. dropdown:: Nonmetric MDS
 
-Non metric :class:`MDS` focuses on the ordination of the data. If
-:math:`S_{ij} > S_{jk}`, then the embedding should enforce :math:`d_{ij} <
-d_{jk}`. For this reason, we discuss it in terms of dissimilarities
-(:math:`\delta_{ij}`) instead of similarities (:math:`S_{ij}`). Note that
-dissimilarities can easily be obtained from similarities through a simple
-transform, e.g. :math:`\delta_{ij}=c_1-c_2 S_{ij}` for some real constants
-:math:`c_1, c_2`. A simple algorithm to enforce proper ordination is to use a
-monotonic regression of :math:`d_{ij}` on :math:`\delta_{ij}`, yielding
-disparities :math:`\hat{d}_{ij}` in the same order as :math:`\delta_{ij}`.
+  Non metric :class:`MDS` focuses on the ordination of the data. If
+  :math:`S_{ij} > S_{jk}`, then the embedding should enforce :math:`d_{ij} <
+  d_{jk}`. For this reason, we discuss it in terms of dissimilarities
+  (:math:`\delta_{ij}`) instead of similarities (:math:`S_{ij}`). Note that
+  dissimilarities can easily be obtained from similarities through a simple
+  transform, e.g. :math:`\delta_{ij}=c_1-c_2 S_{ij}` for some real constants
+  :math:`c_1, c_2`. A simple algorithm to enforce proper ordination is to use a
+  monotonic regression of :math:`d_{ij}` on :math:`\delta_{ij}`, yielding
+  disparities :math:`\hat{d}_{ij}` in the same order as :math:`\delta_{ij}`.
 
-A trivial solution to this problem is to set all the points on the origin. In
-order to avoid that, the disparities :math:`\hat{d}_{ij}` are normalized. Note
-that since we only care about relative ordering, our objective should be
-invariant to simple translation and scaling, however the stress used in metric
-MDS is sensitive to scaling. To address this, non-metric MDS may use a
-normalized stress, known as Stress-1 defined as
+  A trivial solution to this problem is to set all the points on the origin. In
+  order to avoid that, the disparities :math:`\hat{d}_{ij}` are normalized. Note
+  that since we only care about relative ordering, our objective should be
+  invariant to simple translation and scaling, however the stress used in metric
+  MDS is sensitive to scaling. To address this, non-metric MDS may use a
+  normalized stress, known as Stress-1 defined as
 
-.. math::
-    \sqrt{\frac{\sum_{i < j} (d_{ij} - \hat{d}_{ij})^2}{\sum_{i < j} d_{ij}^2}}.
+  .. math::
+      \sqrt{\frac{\sum_{i < j} (d_{ij} - \hat{d}_{ij})^2}{\sum_{i < j} d_{ij}^2}}.
 
-The use of normalized Stress-1 can be enabled by setting `normalized_stress=True`,
-however it is only compatible with the non-metric MDS problem and will be ignored
-in the metric case.
-
-.. figure:: ../auto_examples/manifold/images/sphx_glr_plot_mds_001.png
-   :target: ../auto_examples/manifold/plot_mds.html
-   :align: center
-   :scale: 60
+  The use of normalized Stress-1 can be enabled by setting `normalized_stress=True`,
+  however it is only compatible with the non-metric MDS problem and will be ignored
+  in the metric case.
 
-|details-end|
+  .. figure:: ../auto_examples/manifold/images/sphx_glr_plot_mds_001.png
+    :target: ../auto_examples/manifold/plot_mds.html
+    :align: center
+    :scale: 60
 
-.. topic:: References:
+.. rubric:: References
 
-  * `"Modern Multidimensional Scaling - Theory and Applications"
-    <https://www.springer.com/fr/book/9780387251509>`_
-    Borg, I.; Groenen P. Springer Series in Statistics (1997)
+* `"Modern Multidimensional Scaling - Theory and Applications"
+  <https://www.springer.com/fr/book/9780387251509>`_
+  Borg, I.; Groenen P. Springer Series in Statistics (1997)
 
-  * `"Nonmetric multidimensional scaling: a numerical method"
-    <http://cda.psych.uiuc.edu/psychometrika_highly_cited_articles/kruskal_1964b.pdf>`_
-    Kruskal, J. Psychometrika, 29 (1964)
+* `"Nonmetric multidimensional scaling: a numerical method"
+  <http://cda.psych.uiuc.edu/psychometrika_highly_cited_articles/kruskal_1964b.pdf>`_
+  Kruskal, J. Psychometrika, 29 (1964)
 
-  * `"Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis"
-    <http://cda.psych.uiuc.edu/psychometrika_highly_cited_articles/kruskal_1964a.pdf>`_
-    Kruskal, J. Psychometrika, 29, (1964)
+* `"Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis"
+  <http://cda.psych.uiuc.edu/psychometrika_highly_cited_articles/kruskal_1964a.pdf>`_
+  Kruskal, J. Psychometrika, 29, (1964)
 
 .. _t_sne:
 
@@ -575,120 +543,110 @@ The disadvantages to using t-SNE are roughly:
    :align: center
    :scale: 50
 
-|details-start|
-**Optimizing t-SNE**
-|details-split|
-
-The main purpose of t-SNE is visualization of high-dimensional data. Hence,
-it works best when the data will be embedded on two or three dimensions.
-
-Optimizing the KL divergence can be a little bit tricky sometimes. There are
-five parameters that control the optimization of t-SNE and therefore possibly
-the quality of the resulting embedding:
-
-* perplexity
-* early exaggeration factor
-* learning rate
-* maximum number of iterations
-* angle (not used in the exact method)
-
-The perplexity is defined as :math:`k=2^{(S)}` where :math:`S` is the Shannon
-entropy of the conditional probability distribution. The perplexity of a
-:math:`k`-sided die is :math:`k`, so that :math:`k` is effectively the number of
-nearest neighbors t-SNE considers when generating the conditional probabilities.
-Larger perplexities lead to more nearest neighbors and less sensitive to small
-structure. Conversely a lower perplexity considers a smaller number of
-neighbors, and thus ignores more global information in favour of the
-local neighborhood. As dataset sizes get larger more points will be
-required to get a reasonable sample of the local neighborhood, and hence
-larger perplexities may be required. Similarly noisier datasets will require
-larger perplexity values to encompass enough local neighbors to see beyond
-the background noise.
-
-The maximum number of iterations is usually high enough and does not need
-any tuning. The optimization consists of two phases: the early exaggeration
-phase and the final optimization. During early exaggeration the joint
-probabilities in the original space will be artificially increased by
-multiplication with a given factor. Larger factors result in larger gaps
-between natural clusters in the data. If the factor is too high, the KL
-divergence could increase during this phase. Usually it does not have to be
-tuned. A critical parameter is the learning rate. If it is too low gradient
-descent will get stuck in a bad local minimum. If it is too high the KL
-divergence will increase during optimization. A heuristic suggested in
-Belkina et al. (2019) is to set the learning rate to the sample size
-divided by the early exaggeration factor. We implement this heuristic
-as `learning_rate='auto'` argument. More tips can be found in
-Laurens van der Maaten's FAQ (see references). The last parameter, angle,
-is a tradeoff between performance and accuracy. Larger angles imply that we
-can approximate larger regions by a single point, leading to better speed
-but less accurate results.
-
-`"How to Use t-SNE Effectively" <https://distill.pub/2016/misread-tsne/>`_
-provides a good discussion of the effects of the various parameters, as well
-as interactive plots to explore the effects of different parameters.
-
-|details-end|
-
-|details-start|
-**Barnes-Hut t-SNE**
-|details-split|
-
-The Barnes-Hut t-SNE that has been implemented here is usually much slower than
-other manifold learning algorithms. The optimization is quite difficult
-and the computation of the gradient is :math:`O[d N log(N)]`, where :math:`d`
-is the number of output dimensions and :math:`N` is the number of samples. The
-Barnes-Hut method improves on the exact method where t-SNE complexity is
-:math:`O[d N^2]`, but has several other notable differences:
-
-* The Barnes-Hut implementation only works when the target dimensionality is 3
-  or less. The 2D case is typical when building visualizations.
-* Barnes-Hut only works with dense input data. Sparse data matrices can only be
-  embedded with the exact method or can be approximated by a dense low rank
-  projection for instance using :class:`~sklearn.decomposition.PCA`
-* Barnes-Hut is an approximation of the exact method. The approximation is
-  parameterized with the angle parameter, therefore the angle parameter is
-  unused when method="exact"
-* Barnes-Hut is significantly more scalable. Barnes-Hut can be used to embed
-  hundred of thousands of data points while the exact method can handle
-  thousands of samples before becoming computationally intractable
-
-For visualization purpose (which is the main use case of t-SNE), using the
-Barnes-Hut method is strongly recommended. The exact t-SNE method is useful
-for checking the theoretically properties of the embedding possibly in higher
-dimensional space but limit to small datasets due to computational constraints.
-
-Also note that the digits labels roughly match the natural grouping found by
-t-SNE while the linear 2D projection of the PCA model yields a representation
-where label regions largely overlap. This is a strong clue that this data can
-be well separated by non linear methods that focus on the local structure (e.g.
-an SVM with a Gaussian RBF kernel). However, failing to visualize well
-separated homogeneously labeled groups with t-SNE in 2D does not necessarily
-imply that the data cannot be correctly classified by a supervised model. It
-might be the case that 2 dimensions are not high enough to accurately represent
-the internal structure of the data.
-
-|details-end|
-
-.. topic:: References:
-
-  * `"Visualizing High-Dimensional Data Using t-SNE"
-    <https://jmlr.org/papers/v9/vandermaaten08a.html>`_
-    van der Maaten, L.J.P.; Hinton, G. Journal of Machine Learning Research
-    (2008)
-
-  * `"t-Distributed Stochastic Neighbor Embedding"
-    <https://lvdmaaten.github.io/tsne/>`_
-    van der Maaten, L.J.P.
-
-  * `"Accelerating t-SNE using Tree-Based Algorithms"
-    <https://lvdmaaten.github.io/publications/papers/JMLR_2014.pdf>`_
-    van der Maaten, L.J.P.; Journal of Machine Learning Research 15(Oct):3221-3245, 2014.
-
-  * `"Automated optimized parameters for T-distributed stochastic neighbor
-    embedding improve visualization and analysis of large datasets"
-    <https://www.nature.com/articles/s41467-019-13055-y>`_
-    Belkina, A.C., Ciccolella, C.O., Anno, R., Halpert, R., Spidlen, J.,
-    Snyder-Cappione, J.E., Nature Communications 10, 5415 (2019).
+.. dropdown:: Optimizing t-SNE
+
+  The main purpose of t-SNE is visualization of high-dimensional data. Hence,
+  it works best when the data will be embedded on two or three dimensions.
+
+  Optimizing the KL divergence can be a little bit tricky sometimes. There are
+  five parameters that control the optimization of t-SNE and therefore possibly
+  the quality of the resulting embedding:
+
+  * perplexity
+  * early exaggeration factor
+  * learning rate
+  * maximum number of iterations
+  * angle (not used in the exact method)
+
+  The perplexity is defined as :math:`k=2^{(S)}` where :math:`S` is the Shannon
+  entropy of the conditional probability distribution. The perplexity of a
+  :math:`k`-sided die is :math:`k`, so that :math:`k` is effectively the number of
+  nearest neighbors t-SNE considers when generating the conditional probabilities.
+  Larger perplexities lead to more nearest neighbors and less sensitive to small
+  structure. Conversely a lower perplexity considers a smaller number of
+  neighbors, and thus ignores more global information in favour of the
+  local neighborhood. As dataset sizes get larger more points will be
+  required to get a reasonable sample of the local neighborhood, and hence
+  larger perplexities may be required. Similarly noisier datasets will require
+  larger perplexity values to encompass enough local neighbors to see beyond
+  the background noise.
+
+  The maximum number of iterations is usually high enough and does not need
+  any tuning. The optimization consists of two phases: the early exaggeration
+  phase and the final optimization. During early exaggeration the joint
+  probabilities in the original space will be artificially increased by
+  multiplication with a given factor. Larger factors result in larger gaps
+  between natural clusters in the data. If the factor is too high, the KL
+  divergence could increase during this phase. Usually it does not have to be
+  tuned. A critical parameter is the learning rate. If it is too low gradient
+  descent will get stuck in a bad local minimum. If it is too high the KL
+  divergence will increase during optimization. A heuristic suggested in
+  Belkina et al. (2019) is to set the learning rate to the sample size
+  divided by the early exaggeration factor. We implement this heuristic
+  as `learning_rate='auto'` argument. More tips can be found in
+  Laurens van der Maaten's FAQ (see references). The last parameter, angle,
+  is a tradeoff between performance and accuracy. Larger angles imply that we
+  can approximate larger regions by a single point, leading to better speed
+  but less accurate results.
+
+  `"How to Use t-SNE Effectively" <https://distill.pub/2016/misread-tsne/>`_
+  provides a good discussion of the effects of the various parameters, as well
+  as interactive plots to explore the effects of different parameters.
+
+.. dropdown:: Barnes-Hut t-SNE
+
+  The Barnes-Hut t-SNE that has been implemented here is usually much slower than
+  other manifold learning algorithms. The optimization is quite difficult
+  and the computation of the gradient is :math:`O[d N log(N)]`, where :math:`d`
+  is the number of output dimensions and :math:`N` is the number of samples. The
+  Barnes-Hut method improves on the exact method where t-SNE complexity is
+  :math:`O[d N^2]`, but has several other notable differences:
+
+  * The Barnes-Hut implementation only works when the target dimensionality is 3
+    or less. The 2D case is typical when building visualizations.
+  * Barnes-Hut only works with dense input data. Sparse data matrices can only be
+    embedded with the exact method or can be approximated by a dense low rank
+    projection for instance using :class:`~sklearn.decomposition.PCA`
+  * Barnes-Hut is an approximation of the exact method. The approximation is
+    parameterized with the angle parameter, therefore the angle parameter is
+    unused when method="exact"
+  * Barnes-Hut is significantly more scalable. Barnes-Hut can be used to embed
+    hundred of thousands of data points while the exact method can handle
+    thousands of samples before becoming computationally intractable
+
+  For visualization purpose (which is the main use case of t-SNE), using the
+  Barnes-Hut method is strongly recommended. The exact t-SNE method is useful
+  for checking the theoretically properties of the embedding possibly in higher
+  dimensional space but limit to small datasets due to computational constraints.
+
+  Also note that the digits labels roughly match the natural grouping found by
+  t-SNE while the linear 2D projection of the PCA model yields a representation
+  where label regions largely overlap. This is a strong clue that this data can
+  be well separated by non linear methods that focus on the local structure (e.g.
+  an SVM with a Gaussian RBF kernel). However, failing to visualize well
+  separated homogeneously labeled groups with t-SNE in 2D does not necessarily
+  imply that the data cannot be correctly classified by a supervised model. It
+  might be the case that 2 dimensions are not high enough to accurately represent
+  the internal structure of the data.
+
+.. rubric:: References
+
+* `"Visualizing High-Dimensional Data Using t-SNE"
+  <https://jmlr.org/papers/v9/vandermaaten08a.html>`_
+  van der Maaten, L.J.P.; Hinton, G. Journal of Machine Learning Research (2008)
+
+* `"t-Distributed Stochastic Neighbor Embedding"
+  <https://lvdmaaten.github.io/tsne/>`_ van der Maaten, L.J.P.
+
+* `"Accelerating t-SNE using Tree-Based Algorithms"
+  <https://lvdmaaten.github.io/publications/papers/JMLR_2014.pdf>`_
+  van der Maaten, L.J.P.; Journal of Machine Learning Research 15(Oct):3221-3245, 2014.
+
+* `"Automated optimized parameters for T-distributed stochastic neighbor
+  embedding improve visualization and analysis of large datasets"
+  <https://www.nature.com/articles/s41467-019-13055-y>`_
+  Belkina, A.C., Ciccolella, C.O., Anno, R., Halpert, R., Spidlen, J.,
+  Snyder-Cappione, J.E., Nature Communications 10, 5415 (2019).
 
 Tips on practical use
 =====================
diff --git a/doc/modules/metrics.rst b/doc/modules/metrics.rst
index caea39319e869..63ea797223c22 100644
--- a/doc/modules/metrics.rst
+++ b/doc/modules/metrics.rst
@@ -87,11 +87,11 @@ represented as tf-idf vectors.
 can produce normalized vectors, in which case :func:`cosine_similarity`
 is equivalent to :func:`linear_kernel`, only slower.)
 
-.. topic:: References:
+.. rubric:: References
 
-    * C.D. Manning, P. Raghavan and H. Schütze (2008). Introduction to
-      Information Retrieval. Cambridge University Press.
-      https://nlp.stanford.edu/IR-book/html/htmledition/the-vector-space-model-for-scoring-1.html
+* C.D. Manning, P. Raghavan and H. Schütze (2008). Introduction to
+  Information Retrieval. Cambridge University Press.
+  https://nlp.stanford.edu/IR-book/html/htmledition/the-vector-space-model-for-scoring-1.html
 
 .. _linear_kernel:
 
@@ -222,10 +222,10 @@ which is a distance between discrete probability distributions.
 
 The chi squared kernel is most commonly used on histograms (bags) of visual words.
 
-.. topic:: References:
+.. rubric:: References
 
-    * Zhang, J. and Marszalek, M. and Lazebnik, S. and Schmid, C.
-      Local features and kernels for classification of texture and object
-      categories: A comprehensive study
-      International Journal of Computer Vision 2007
-      https://hal.archives-ouvertes.fr/hal-00171412/document
+* Zhang, J. and Marszalek, M. and Lazebnik, S. and Schmid, C.
+  Local features and kernels for classification of texture and object
+  categories: A comprehensive study
+  International Journal of Computer Vision 2007
+  https://hal.archives-ouvertes.fr/hal-00171412/document
diff --git a/doc/modules/mixture.rst b/doc/modules/mixture.rst
index df5d8020a1369..1fd72c3158336 100644
--- a/doc/modules/mixture.rst
+++ b/doc/modules/mixture.rst
@@ -60,128 +60,111 @@ full covariance.
    :align: center
    :scale: 75%
 
-.. topic:: Examples:
+.. rubric:: Examples
 
-    * See :ref:`sphx_glr_auto_examples_mixture_plot_gmm_covariances.py` for an example of
-      using the Gaussian mixture as clustering on the iris dataset.
+* See :ref:`sphx_glr_auto_examples_mixture_plot_gmm_covariances.py` for an example of
+  using the Gaussian mixture as clustering on the iris dataset.
 
-    * See :ref:`sphx_glr_auto_examples_mixture_plot_gmm_pdf.py` for an example on plotting the
-      density estimation.
+* See :ref:`sphx_glr_auto_examples_mixture_plot_gmm_pdf.py` for an example on plotting the
+  density estimation.
 
-|details-start|
-**Pros and cons of class GaussianMixture**
-|details-split|
+.. dropdown:: Pros and cons of class GaussianMixture
 
-.. topic:: Pros:
+  .. rubric:: Pros
 
-    :Speed: It is the fastest algorithm for learning mixture models
+  :Speed: It is the fastest algorithm for learning mixture models
 
-    :Agnostic: As this algorithm maximizes only the likelihood, it
-      will not bias the means towards zero, or bias the cluster sizes to
-      have specific structures that might or might not apply.
+  :Agnostic: As this algorithm maximizes only the likelihood, it
+    will not bias the means towards zero, or bias the cluster sizes to
+    have specific structures that might or might not apply.
 
-.. topic:: Cons:
+  .. rubric:: Cons
 
-    :Singularities: When one has insufficiently many points per
-      mixture, estimating the covariance matrices becomes difficult,
-      and the algorithm is known to diverge and find solutions with
-      infinite likelihood unless one regularizes the covariances artificially.
+  :Singularities: When one has insufficiently many points per
+    mixture, estimating the covariance matrices becomes difficult,
+    and the algorithm is known to diverge and find solutions with
+    infinite likelihood unless one regularizes the covariances artificially.
 
-    :Number of components: This algorithm will always use all the
-      components it has access to, needing held-out data
-      or information theoretical criteria to decide how many components to use
-      in the absence of external cues.
+  :Number of components: This algorithm will always use all the
+    components it has access to, needing held-out data
+    or information theoretical criteria to decide how many components to use
+    in the absence of external cues.
 
-|details-end|
+.. dropdown:: Selecting the number of components in a classical Gaussian Mixture model
 
+  The BIC criterion can be used to select the number of components in a Gaussian
+  Mixture in an efficient way. In theory, it recovers the true number of
+  components only in the asymptotic regime (i.e. if much data is available and
+  assuming that the data was actually generated i.i.d. from a mixture of Gaussian
+  distribution). Note that using a :ref:`Variational Bayesian Gaussian mixture <bgmm>`
+  avoids the specification of the number of components for a Gaussian mixture
+  model.
 
-|details-start|
-**Selecting the number of components in a classical Gaussian Mixture model**
-|details-split|
+  .. figure:: ../auto_examples/mixture/images/sphx_glr_plot_gmm_selection_002.png
+    :target: ../auto_examples/mixture/plot_gmm_selection.html
+    :align: center
+    :scale: 50%
 
-The BIC criterion can be used to select the number of components in a Gaussian
-Mixture in an efficient way. In theory, it recovers the true number of
-components only in the asymptotic regime (i.e. if much data is available and
-assuming that the data was actually generated i.i.d. from a mixture of Gaussian
-distribution). Note that using a :ref:`Variational Bayesian Gaussian mixture <bgmm>`
-avoids the specification of the number of components for a Gaussian mixture
-model.
+  .. rubric:: Examples
 
-.. figure:: ../auto_examples/mixture/images/sphx_glr_plot_gmm_selection_002.png
-   :target: ../auto_examples/mixture/plot_gmm_selection.html
-   :align: center
-   :scale: 50%
-
-.. topic:: Examples:
-
-    * See :ref:`sphx_glr_auto_examples_mixture_plot_gmm_selection.py` for an example
-      of model selection performed with classical Gaussian mixture.
-
-|details-end|
+  * See :ref:`sphx_glr_auto_examples_mixture_plot_gmm_selection.py` for an example
+    of model selection performed with classical Gaussian mixture.
 
 .. _expectation_maximization:
 
-|details-start|
-**Estimation algorithm expectation-maximization**
-|details-split|
-
-The main difficulty in learning Gaussian mixture models from unlabeled
-data is that one usually doesn't know which points came from
-which latent component (if one has access to this information it gets
-very easy to fit a separate Gaussian distribution to each set of
-points). `Expectation-maximization
-<https://en.wikipedia.org/wiki/Expectation%E2%80%93maximization_algorithm>`_
-is a well-founded statistical
-algorithm to get around this problem by an iterative process. First
-one assumes random components (randomly centered on data points,
-learned from k-means, or even just normally distributed around the
-origin) and computes for each point a probability of being generated by
-each component of the model. Then, one tweaks the
-parameters to maximize the likelihood of the data given those
-assignments. Repeating this process is guaranteed to always converge
-to a local optimum.
-
-|details-end|
-
-|details-start|
-**Choice of the Initialization method**
-|details-split|
-
-There is a choice of four initialization methods (as well as inputting user defined
-initial means) to generate the initial centers for the model components:
-
-k-means (default)
-  This applies a traditional k-means clustering algorithm.
-  This can be computationally expensive compared to other initialization methods.
-
-k-means++
-  This uses the initialization method of k-means clustering: k-means++.
-  This will pick the first center at random from the data. Subsequent centers will be
-  chosen from a weighted distribution of the data favouring points further away from
-  existing centers. k-means++ is the default initialization for k-means so will be
-  quicker than running a full k-means but can still take a significant amount of
-  time for large data sets with many components.
-
-random_from_data
-  This will pick random data points from the input data as the initial
-  centers. This is a very fast method of initialization but can produce non-convergent
-  results if the chosen points are too close to each other.
-
-random
-  Centers are chosen as a small perturbation away from the mean of all data.
-  This method is simple but can lead to the model taking longer to converge.
-
-.. figure:: ../auto_examples/mixture/images/sphx_glr_plot_gmm_init_001.png
-   :target: ../auto_examples/mixture/plot_gmm_init.html
-   :align: center
-   :scale: 50%
-
-.. topic:: Examples:
-
-    * See :ref:`sphx_glr_auto_examples_mixture_plot_gmm_init.py` for an example of
-      using different initializations in Gaussian Mixture.
-
-|details-end|
+.. dropdown:: Estimation algorithm expectation-maximization
+
+  The main difficulty in learning Gaussian mixture models from unlabeled
+  data is that one usually doesn't know which points came from
+  which latent component (if one has access to this information it gets
+  very easy to fit a separate Gaussian distribution to each set of
+  points). `Expectation-maximization
+  <https://en.wikipedia.org/wiki/Expectation%E2%80%93maximization_algorithm>`_
+  is a well-founded statistical
+  algorithm to get around this problem by an iterative process. First
+  one assumes random components (randomly centered on data points,
+  learned from k-means, or even just normally distributed around the
+  origin) and computes for each point a probability of being generated by
+  each component of the model. Then, one tweaks the
+  parameters to maximize the likelihood of the data given those
+  assignments. Repeating this process is guaranteed to always converge
+  to a local optimum.
+
+.. dropdown:: Choice of the Initialization method
+
+  There is a choice of four initialization methods (as well as inputting user defined
+  initial means) to generate the initial centers for the model components:
+
+  k-means (default)
+    This applies a traditional k-means clustering algorithm.
+    This can be computationally expensive compared to other initialization methods.
+
+  k-means++
+    This uses the initialization method of k-means clustering: k-means++.
+    This will pick the first center at random from the data. Subsequent centers will be
+    chosen from a weighted distribution of the data favouring points further away from
+    existing centers. k-means++ is the default initialization for k-means so will be
+    quicker than running a full k-means but can still take a significant amount of
+    time for large data sets with many components.
+
+  random_from_data
+    This will pick random data points from the input data as the initial
+    centers. This is a very fast method of initialization but can produce non-convergent
+    results if the chosen points are too close to each other.
+
+  random
+    Centers are chosen as a small perturbation away from the mean of all data.
+    This method is simple but can lead to the model taking longer to converge.
+
+  .. figure:: ../auto_examples/mixture/images/sphx_glr_plot_gmm_init_001.png
+    :target: ../auto_examples/mixture/plot_gmm_init.html
+    :align: center
+    :scale: 50%
+
+  .. rubric:: Examples
+
+  * See :ref:`sphx_glr_auto_examples_mixture_plot_gmm_init.py` for an example of
+    using different initializations in Gaussian Mixture.
 
 .. _bgmm:
 
@@ -276,63 +259,58 @@ from the two resulting mixtures.
 
 
 
-.. topic:: Examples:
-
-    * See :ref:`sphx_glr_auto_examples_mixture_plot_gmm.py` for an example on
-      plotting the confidence ellipsoids for both :class:`GaussianMixture`
-      and :class:`BayesianGaussianMixture`.
-
-    * :ref:`sphx_glr_auto_examples_mixture_plot_gmm_sin.py` shows using
-      :class:`GaussianMixture` and :class:`BayesianGaussianMixture` to fit a
-      sine wave.
+.. rubric:: Examples
 
-    * See :ref:`sphx_glr_auto_examples_mixture_plot_concentration_prior.py`
-      for an example plotting the confidence ellipsoids for the
-      :class:`BayesianGaussianMixture` with different
-      ``weight_concentration_prior_type`` for different values of the parameter
-      ``weight_concentration_prior``.
+* See :ref:`sphx_glr_auto_examples_mixture_plot_gmm.py` for an example on
+  plotting the confidence ellipsoids for both :class:`GaussianMixture`
+  and :class:`BayesianGaussianMixture`.
 
-|details-start|
-**Pros and cons of variational inference with BayesianGaussianMixture**
-|details-split|
+* :ref:`sphx_glr_auto_examples_mixture_plot_gmm_sin.py` shows using
+  :class:`GaussianMixture` and :class:`BayesianGaussianMixture` to fit a
+  sine wave.
 
-.. topic:: Pros:
+* See :ref:`sphx_glr_auto_examples_mixture_plot_concentration_prior.py`
+  for an example plotting the confidence ellipsoids for the
+  :class:`BayesianGaussianMixture` with different
+  ``weight_concentration_prior_type`` for different values of the parameter
+  ``weight_concentration_prior``.
 
-    :Automatic selection: when ``weight_concentration_prior`` is small enough and
-      ``n_components`` is larger than what is found necessary by the model, the
-      Variational Bayesian mixture model has a natural tendency to set some mixture
-      weights values close to zero. This makes it possible to let the model choose
-      a suitable number of effective components automatically. Only an upper bound
-      of this number needs to be provided. Note however that the "ideal" number of
-      active components is very application specific and is typically ill-defined
-      in a data exploration setting.
+.. dropdown:: Pros and cons of variational inference with BayesianGaussianMixture
 
-    :Less sensitivity to the number of parameters: unlike finite models, which will
-      almost always use all components as much as they can, and hence will produce
-      wildly different solutions for different numbers of components, the
-      variational inference with a Dirichlet process prior
-      (``weight_concentration_prior_type='dirichlet_process'``) won't change much
-      with changes to the parameters, leading to more stability and less tuning.
+  .. rubric:: Pros
 
-    :Regularization: due to the incorporation of prior information,
-      variational solutions have less pathological special cases than
-      expectation-maximization solutions.
+  :Automatic selection: when ``weight_concentration_prior`` is small enough and
+    ``n_components`` is larger than what is found necessary by the model, the
+    Variational Bayesian mixture model has a natural tendency to set some mixture
+    weights values close to zero. This makes it possible to let the model choose
+    a suitable number of effective components automatically. Only an upper bound
+    of this number needs to be provided. Note however that the "ideal" number of
+    active components is very application specific and is typically ill-defined
+    in a data exploration setting.
 
+  :Less sensitivity to the number of parameters: unlike finite models, which will
+    almost always use all components as much as they can, and hence will produce
+    wildly different solutions for different numbers of components, the
+    variational inference with a Dirichlet process prior
+    (``weight_concentration_prior_type='dirichlet_process'``) won't change much
+    with changes to the parameters, leading to more stability and less tuning.
 
-.. topic:: Cons:
+  :Regularization: due to the incorporation of prior information,
+    variational solutions have less pathological special cases than
+    expectation-maximization solutions.
 
-    :Speed: the extra parametrization necessary for variational inference makes
-      inference slower, although not by much.
+  .. rubric:: Cons
 
-    :Hyperparameters: this algorithm needs an extra hyperparameter
-      that might need experimental tuning via cross-validation.
+  :Speed: the extra parametrization necessary for variational inference makes
+    inference slower, although not by much.
 
-    :Bias: there are many implicit biases in the inference algorithms (and also in
-      the Dirichlet process if used), and whenever there is a mismatch between
-      these biases and the data it might be possible to fit better models using a
-      finite mixture.
+  :Hyperparameters: this algorithm needs an extra hyperparameter
+    that might need experimental tuning via cross-validation.
 
-|details-end|
+  :Bias: there are many implicit biases in the inference algorithms (and also in
+    the Dirichlet process if used), and whenever there is a mismatch between
+    these biases and the data it might be possible to fit better models using a
+    finite mixture.
 
 .. _dirichlet_process:
 
diff --git a/doc/modules/model_evaluation.rst b/doc/modules/model_evaluation.rst
index 59f014b732e35..81615b4419bba 100644
--- a/doc/modules/model_evaluation.rst
+++ b/doc/modules/model_evaluation.rst
@@ -77,6 +77,7 @@ Scoring                                Function
 'roc_auc_ovo'                          :func:`metrics.roc_auc_score`
 'roc_auc_ovr_weighted'                 :func:`metrics.roc_auc_score`
 'roc_auc_ovo_weighted'                 :func:`metrics.roc_auc_score`
+'d2_log_loss_score'                    :func:`metrics.d2_log_loss_score`
 
 **Clustering**
 'adjusted_mutual_info_score'           :func:`metrics.adjusted_mutual_info_score`
@@ -171,58 +172,53 @@ measuring a prediction error given ground truth and prediction:
   parameter description below).
 
 
-|details-start|
-**Custom scorer objects**
-|details-split|
-
-
-The second use case is to build a completely custom scorer object
-from a simple python function using :func:`make_scorer`, which can
-take several parameters:
-
-* the python function you want to use (``my_custom_loss_func``
-  in the example below)
-
-* whether the python function returns a score (``greater_is_better=True``,
-  the default) or a loss (``greater_is_better=False``).  If a loss, the output
-  of the python function is negated by the scorer object, conforming to
-  the cross validation convention that scorers return higher values for better models.
-
-* for classification metrics only: whether the python function you provided requires
-  continuous decision certainties. If the scoring function only accepts probability
-  estimates (e.g. :func:`metrics.log_loss`) then one needs to set the parameter
-  `response_method`, thus in this case `response_method="predict_proba"`. Some scoring
-  function do not necessarily require probability estimates but rather non-thresholded
-  decision values (e.g. :func:`metrics.roc_auc_score`). In this case, one provides a
-  list such as `response_method=["decision_function", "predict_proba"]`. In this case,
-  the scorer will use the first available method, in the order given in the list,
-  to compute the scores.
-
-* any additional parameters, such as ``beta`` or ``labels`` in :func:`f1_score`.
-
-Here is an example of building custom scorers, and of using the
-``greater_is_better`` parameter::
-
-    >>> import numpy as np
-    >>> def my_custom_loss_func(y_true, y_pred):
-    ...     diff = np.abs(y_true - y_pred).max()
-    ...     return np.log1p(diff)
-    ...
-    >>> # score will negate the return value of my_custom_loss_func,
-    >>> # which will be np.log(2), 0.693, given the values for X
-    >>> # and y defined below.
-    >>> score = make_scorer(my_custom_loss_func, greater_is_better=False)
-    >>> X = [[1], [1]]
-    >>> y = [0, 1]
-    >>> from sklearn.dummy import DummyClassifier
-    >>> clf = DummyClassifier(strategy='most_frequent', random_state=0)
-    >>> clf = clf.fit(X, y)
-    >>> my_custom_loss_func(y, clf.predict(X))
-    0.69...
-    >>> score(clf, X, y)
-    -0.69...
-
-|details-end|
+.. dropdown:: Custom scorer objects
+
+  The second use case is to build a completely custom scorer object
+  from a simple python function using :func:`make_scorer`, which can
+  take several parameters:
+
+  * the python function you want to use (``my_custom_loss_func``
+    in the example below)
+
+  * whether the python function returns a score (``greater_is_better=True``,
+    the default) or a loss (``greater_is_better=False``).  If a loss, the output
+    of the python function is negated by the scorer object, conforming to
+    the cross validation convention that scorers return higher values for better models.
+
+  * for classification metrics only: whether the python function you provided requires
+    continuous decision certainties. If the scoring function only accepts probability
+    estimates (e.g. :func:`metrics.log_loss`) then one needs to set the parameter
+    `response_method`, thus in this case `response_method="predict_proba"`. Some scoring
+    function do not necessarily require probability estimates but rather non-thresholded
+    decision values (e.g. :func:`metrics.roc_auc_score`). In this case, one provides a
+    list such as `response_method=["decision_function", "predict_proba"]`. In this case,
+    the scorer will use the first available method, in the order given in the list,
+    to compute the scores.
+
+  * any additional parameters, such as ``beta`` or ``labels`` in :func:`f1_score`.
+
+  Here is an example of building custom scorers, and of using the
+  ``greater_is_better`` parameter::
+
+      >>> import numpy as np
+      >>> def my_custom_loss_func(y_true, y_pred):
+      ...     diff = np.abs(y_true - y_pred).max()
+      ...     return np.log1p(diff)
+      ...
+      >>> # score will negate the return value of my_custom_loss_func,
+      >>> # which will be np.log(2), 0.693, given the values for X
+      >>> # and y defined below.
+      >>> score = make_scorer(my_custom_loss_func, greater_is_better=False)
+      >>> X = [[1], [1]]
+      >>> y = [0, 1]
+      >>> from sklearn.dummy import DummyClassifier
+      >>> clf = DummyClassifier(strategy='most_frequent', random_state=0)
+      >>> clf = clf.fit(X, y)
+      >>> my_custom_loss_func(y, clf.predict(X))
+      0.69...
+      >>> score(clf, X, y)
+      -0.69...
 
 .. _diy_scoring:
 
@@ -233,51 +229,47 @@ You can generate even more flexible model scorers by constructing your own
 scoring object from scratch, without using the :func:`make_scorer` factory.
 
 
-|details-start|
-**How to build a scorer from scratch**
-|details-split|
+.. dropdown:: How to build a scorer from scratch
 
-For a callable to be a scorer, it needs to meet the protocol specified by
-the following two rules:
+  For a callable to be a scorer, it needs to meet the protocol specified by
+  the following two rules:
 
-- It can be called with parameters ``(estimator, X, y)``, where ``estimator``
-  is the model that should be evaluated, ``X`` is validation data, and ``y`` is
-  the ground truth target for ``X`` (in the supervised case) or ``None`` (in the
-  unsupervised case).
+  - It can be called with parameters ``(estimator, X, y)``, where ``estimator``
+    is the model that should be evaluated, ``X`` is validation data, and ``y`` is
+    the ground truth target for ``X`` (in the supervised case) or ``None`` (in the
+    unsupervised case).
 
-- It returns a floating point number that quantifies the
-  ``estimator`` prediction quality on ``X``, with reference to ``y``.
-  Again, by convention higher numbers are better, so if your scorer
-  returns loss, that value should be negated.
+  - It returns a floating point number that quantifies the
+    ``estimator`` prediction quality on ``X``, with reference to ``y``.
+    Again, by convention higher numbers are better, so if your scorer
+    returns loss, that value should be negated.
 
-- Advanced: If it requires extra metadata to be passed to it, it should expose
-  a ``get_metadata_routing`` method returning the requested metadata. The user
-  should be able to set the requested metadata via a ``set_score_request``
-  method. Please see :ref:`User Guide <metadata_routing>` and :ref:`Developer
-  Guide <sphx_glr_auto_examples_miscellaneous_plot_metadata_routing.py>` for
-  more details.
+  - Advanced: If it requires extra metadata to be passed to it, it should expose
+    a ``get_metadata_routing`` method returning the requested metadata. The user
+    should be able to set the requested metadata via a ``set_score_request``
+    method. Please see :ref:`User Guide <metadata_routing>` and :ref:`Developer
+    Guide <sphx_glr_auto_examples_miscellaneous_plot_metadata_routing.py>` for
+    more details.
 
 
-.. note:: **Using custom scorers in functions where n_jobs > 1**
+  .. note:: **Using custom scorers in functions where n_jobs > 1**
 
-    While defining the custom scoring function alongside the calling function
-    should work out of the box with the default joblib backend (loky),
-    importing it from another module will be a more robust approach and work
-    independently of the joblib backend.
+      While defining the custom scoring function alongside the calling function
+      should work out of the box with the default joblib backend (loky),
+      importing it from another module will be a more robust approach and work
+      independently of the joblib backend.
 
-    For example, to use ``n_jobs`` greater than 1 in the example below,
-    ``custom_scoring_function`` function is saved in a user-created module
-    (``custom_scorer_module.py``) and imported::
+      For example, to use ``n_jobs`` greater than 1 in the example below,
+      ``custom_scoring_function`` function is saved in a user-created module
+      (``custom_scorer_module.py``) and imported::
 
-        >>> from custom_scorer_module import custom_scoring_function # doctest: +SKIP
-        >>> cross_val_score(model,
-        ...  X_train,
-        ...  y_train,
-        ...  scoring=make_scorer(custom_scoring_function, greater_is_better=False),
-        ...  cv=5,
-        ...  n_jobs=-1) # doctest: +SKIP
-
-|details-end|
+          >>> from custom_scorer_module import custom_scoring_function # doctest: +SKIP
+          >>> cross_val_score(model,
+          ...  X_train,
+          ...  y_train,
+          ...  scoring=make_scorer(custom_scoring_function, greater_is_better=False),
+          ...  cv=5,
+          ...  n_jobs=-1) # doctest: +SKIP
 
 .. _multimetric_scoring:
 
@@ -377,6 +369,7 @@ Some also work in the multilabel case:
    recall_score
    roc_auc_score
    zero_one_loss
+   d2_log_loss_score
 
 And some work with binary and multilabel (but not multiclass) problems:
 
@@ -472,11 +465,11 @@ In the multilabel case with binary label indicators::
   >>> accuracy_score(np.array([[0, 1], [1, 1]]), np.ones((2, 2)))
   0.5
 
-.. topic:: Example:
+.. rubric:: Examples
 
-  * See :ref:`sphx_glr_auto_examples_model_selection_plot_permutation_tests_for_classification.py`
-    for an example of accuracy score usage using permutations of
-    the dataset.
+* See :ref:`sphx_glr_auto_examples_model_selection_plot_permutation_tests_for_classification.py`
+  for an example of accuracy score usage using permutations of
+  the dataset.
 
 .. _top_k_accuracy_score:
 
@@ -587,22 +580,20 @@ or *informedness*.
     * Balanced Accuracy as described in [Urbanowicz2015]_: the average of sensitivity and specificity
       is computed for each class and then averaged over total number of classes.
 
-.. topic:: References:
-
-  .. [Guyon2015] I. Guyon, K. Bennett, G. Cawley, H.J. Escalante, S. Escalera, T.K. Ho, N. Macià,
-     B. Ray, M. Saeed, A.R. Statnikov, E. Viegas, `Design of the 2015 ChaLearn AutoML Challenge
-     <https://ieeexplore.ieee.org/document/7280767>`_,
-     IJCNN 2015.
-  .. [Mosley2013] L. Mosley, `A balanced approach to the multi-class imbalance problem
-     <https://lib.dr.iastate.edu/etd/13537/>`_,
-     IJCV 2010.
-  .. [Kelleher2015] John. D. Kelleher, Brian Mac Namee, Aoife D'Arcy, `Fundamentals of
-     Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples,
-     and Case Studies <https://mitpress.mit.edu/books/fundamentals-machine-learning-predictive-data-analytics>`_,
-     2015.
-  .. [Urbanowicz2015] Urbanowicz R.J.,  Moore, J.H. :doi:`ExSTraCS 2.0: description
-      and evaluation of a scalable learning classifier
-      system <10.1007/s12065-015-0128-8>`, Evol. Intel. (2015) 8: 89.
+.. rubric:: References
+
+.. [Guyon2015] I. Guyon, K. Bennett, G. Cawley, H.J. Escalante, S. Escalera, T.K. Ho, N. Macià,
+    B. Ray, M. Saeed, A.R. Statnikov, E. Viegas, `Design of the 2015 ChaLearn AutoML Challenge
+    <https://ieeexplore.ieee.org/document/7280767>`_, IJCNN 2015.
+.. [Mosley2013] L. Mosley, `A balanced approach to the multi-class imbalance problem
+    <https://lib.dr.iastate.edu/etd/13537/>`_, IJCV 2010.
+.. [Kelleher2015] John. D. Kelleher, Brian Mac Namee, Aoife D'Arcy, `Fundamentals of
+    Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples,
+    and Case Studies <https://mitpress.mit.edu/books/fundamentals-machine-learning-predictive-data-analytics>`_,
+    2015.
+.. [Urbanowicz2015] Urbanowicz R.J.,  Moore, J.H. :doi:`ExSTraCS 2.0: description
+    and evaluation of a scalable learning classifier
+    system <10.1007/s12065-015-0128-8>`, Evol. Intel. (2015) 8: 89.
 
 .. _cohen_kappa:
 
@@ -681,19 +672,19 @@ false negatives and true positives as follows::
   >>> tn, fp, fn, tp
   (2, 1, 2, 3)
 
-.. topic:: Example:
+.. rubric:: Examples
 
-  * See :ref:`sphx_glr_auto_examples_model_selection_plot_confusion_matrix.py`
-    for an example of using a confusion matrix to evaluate classifier output
-    quality.
+* See :ref:`sphx_glr_auto_examples_model_selection_plot_confusion_matrix.py`
+  for an example of using a confusion matrix to evaluate classifier output
+  quality.
 
-  * See :ref:`sphx_glr_auto_examples_classification_plot_digits_classification.py`
-    for an example of using a confusion matrix to classify
-    hand-written digits.
+* See :ref:`sphx_glr_auto_examples_classification_plot_digits_classification.py`
+  for an example of using a confusion matrix to classify
+  hand-written digits.
 
-  * See :ref:`sphx_glr_auto_examples_text_plot_document_classification_20newsgroups.py`
-    for an example of using a confusion matrix to classify text
-    documents.
+* See :ref:`sphx_glr_auto_examples_text_plot_document_classification_20newsgroups.py`
+  for an example of using a confusion matrix to classify text
+  documents.
 
 .. _classification_report:
 
@@ -720,15 +711,15 @@ and inferred labels::
    weighted avg       0.67      0.60      0.59         5
    <BLANKLINE>
 
-.. topic:: Example:
+.. rubric:: Examples
 
-  * See :ref:`sphx_glr_auto_examples_classification_plot_digits_classification.py`
-    for an example of classification report usage for
-    hand-written digits.
+* See :ref:`sphx_glr_auto_examples_classification_plot_digits_classification.py`
+  for an example of classification report usage for
+  hand-written digits.
 
-  * See :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_digits.py`
-    for an example of classification report usage for
-    grid search with nested cross-validation.
+* See :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_digits.py`
+  for an example of classification report usage for
+  grid search with nested cross-validation.
 
 .. _hamming_loss:
 
@@ -846,31 +837,31 @@ precision-recall curve as follows.
         :scale: 75
         :align: center
 
-.. topic:: Examples:
+.. rubric:: Examples
 
-  * See :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_digits.py`
-    for an example of :func:`precision_score` and :func:`recall_score` usage
-    to estimate parameters using grid search with nested cross-validation.
+* See :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_digits.py`
+  for an example of :func:`precision_score` and :func:`recall_score` usage
+  to estimate parameters using grid search with nested cross-validation.
 
-  * See :ref:`sphx_glr_auto_examples_model_selection_plot_precision_recall.py`
-    for an example of :func:`precision_recall_curve` usage to evaluate
-    classifier output quality.
+* See :ref:`sphx_glr_auto_examples_model_selection_plot_precision_recall.py`
+  for an example of :func:`precision_recall_curve` usage to evaluate
+  classifier output quality.
 
-.. topic:: References:
+.. rubric:: References
 
-  .. [Manning2008] C.D. Manning, P. Raghavan, H. Schütze, `Introduction to Information Retrieval
-     <https://nlp.stanford.edu/IR-book/html/htmledition/evaluation-of-ranked-retrieval-results-1.html>`_,
-     2008.
-  .. [Everingham2010] M. Everingham, L. Van Gool, C.K.I. Williams, J. Winn, A. Zisserman,
-     `The Pascal Visual Object Classes (VOC) Challenge
-     <https://citeseerx.ist.psu.edu/doc_view/pid/b6bebfd529b233f00cb854b7d8070319600cf59d>`_,
-     IJCV 2010.
-  .. [Davis2006] J. Davis, M. Goadrich, `The Relationship Between Precision-Recall and ROC Curves
-     <https://www.biostat.wisc.edu/~page/rocpr.pdf>`_,
-     ICML 2006.
-  .. [Flach2015] P.A. Flach, M. Kull, `Precision-Recall-Gain Curves: PR Analysis Done Right
-     <https://papers.nips.cc/paper/5867-precision-recall-gain-curves-pr-analysis-done-right.pdf>`_,
-     NIPS 2015.
+.. [Manning2008] C.D. Manning, P. Raghavan, H. Schütze, `Introduction to Information Retrieval
+    <https://nlp.stanford.edu/IR-book/html/htmledition/evaluation-of-ranked-retrieval-results-1.html>`_,
+    2008.
+.. [Everingham2010] M. Everingham, L. Van Gool, C.K.I. Williams, J. Winn, A. Zisserman,
+    `The Pascal Visual Object Classes (VOC) Challenge
+    <https://citeseerx.ist.psu.edu/doc_view/pid/b6bebfd529b233f00cb854b7d8070319600cf59d>`_,
+    IJCV 2010.
+.. [Davis2006] J. Davis, M. Goadrich, `The Relationship Between Precision-Recall and ROC Curves
+    <https://www.biostat.wisc.edu/~page/rocpr.pdf>`_,
+    ICML 2006.
+.. [Flach2015] P.A. Flach, M. Kull, `Precision-Recall-Gain Curves: PR Analysis Done Right
+    <https://papers.nips.cc/paper/5867-precision-recall-gain-curves-pr-analysis-done-right.pdf>`_,
+    NIPS 2015.
 
 Binary classification
 ^^^^^^^^^^^^^^^^^^^^^
@@ -1039,10 +1030,10 @@ Similarly, labels not present in the data sample may be accounted for in macro-a
   >>> metrics.precision_score(y_true, y_pred, labels=[0, 1, 2, 3], average='macro')
   0.166...
 
-.. topic:: References:
+.. rubric:: References
 
-    .. [OB2019] :arxiv:`Opitz, J., & Burst, S. (2019). "Macro f1 and macro f1."
-       <1911.03347>`
+.. [OB2019] :arxiv:`Opitz, J., & Burst, S. (2019). "Macro f1 and macro f1."
+    <1911.03347>`
 
 .. _jaccard_similarity_score:
 
@@ -1494,65 +1485,57 @@ correspond to the probability estimates that a sample belongs to a particular
 class. The OvO and OvR algorithms support weighting uniformly
 (``average='macro'``) and by prevalence (``average='weighted'``).
 
-|details-start|
-**One-vs-one Algorithm**
-|details-split|
-
-Computes the average AUC of all possible pairwise
-combinations of classes. [HT2001]_ defines a multiclass AUC metric weighted
-uniformly:
-
-.. math::
+.. dropdown:: One-vs-one Algorithm
 
-   \frac{1}{c(c-1)}\sum_{j=1}^{c}\sum_{k > j}^c (\text{AUC}(j | k) +
-   \text{AUC}(k | j))
+  Computes the average AUC of all possible pairwise
+  combinations of classes. [HT2001]_ defines a multiclass AUC metric weighted
+  uniformly:
 
-where :math:`c` is the number of classes and :math:`\text{AUC}(j | k)` is the
-AUC with class :math:`j` as the positive class and class :math:`k` as the
-negative class. In general,
-:math:`\text{AUC}(j | k) \neq \text{AUC}(k | j))` in the multiclass
-case. This algorithm is used by setting the keyword argument ``multiclass``
-to ``'ovo'`` and ``average`` to ``'macro'``.
+  .. math::
 
-The [HT2001]_ multiclass AUC metric can be extended to be weighted by the
-prevalence:
+    \frac{1}{c(c-1)}\sum_{j=1}^{c}\sum_{k > j}^c (\text{AUC}(j | k) +
+    \text{AUC}(k | j))
 
-.. math::
+  where :math:`c` is the number of classes and :math:`\text{AUC}(j | k)` is the
+  AUC with class :math:`j` as the positive class and class :math:`k` as the
+  negative class. In general,
+  :math:`\text{AUC}(j | k) \neq \text{AUC}(k | j))` in the multiclass
+  case. This algorithm is used by setting the keyword argument ``multiclass``
+  to ``'ovo'`` and ``average`` to ``'macro'``.
 
-   \frac{1}{c(c-1)}\sum_{j=1}^{c}\sum_{k > j}^c p(j \cup k)(
-   \text{AUC}(j | k) + \text{AUC}(k | j))
+  The [HT2001]_ multiclass AUC metric can be extended to be weighted by the
+  prevalence:
 
-where :math:`c` is the number of classes. This algorithm is used by setting
-the keyword argument ``multiclass`` to ``'ovo'`` and ``average`` to
-``'weighted'``. The ``'weighted'`` option returns a prevalence-weighted average
-as described in [FC2009]_.
+  .. math::
 
-|details-end|
+    \frac{1}{c(c-1)}\sum_{j=1}^{c}\sum_{k > j}^c p(j \cup k)(
+    \text{AUC}(j | k) + \text{AUC}(k | j))
 
-|details-start|
-**One-vs-rest Algorithm**
-|details-split|
+  where :math:`c` is the number of classes. This algorithm is used by setting
+  the keyword argument ``multiclass`` to ``'ovo'`` and ``average`` to
+  ``'weighted'``. The ``'weighted'`` option returns a prevalence-weighted average
+  as described in [FC2009]_.
 
-Computes the AUC of each class against the rest
-[PD2000]_. The algorithm is functionally the same as the multilabel case. To
-enable this algorithm set the keyword argument ``multiclass`` to ``'ovr'``.
-Additionally to ``'macro'`` [F2006]_ and ``'weighted'`` [F2001]_ averaging, OvR
-supports ``'micro'`` averaging.
+.. dropdown:: One-vs-rest Algorithm
 
-In applications where a high false positive rate is not tolerable the parameter
-``max_fpr`` of :func:`roc_auc_score` can be used to summarize the ROC curve up
-to the given limit.
+  Computes the AUC of each class against the rest
+  [PD2000]_. The algorithm is functionally the same as the multilabel case. To
+  enable this algorithm set the keyword argument ``multiclass`` to ``'ovr'``.
+  Additionally to ``'macro'`` [F2006]_ and ``'weighted'`` [F2001]_ averaging, OvR
+  supports ``'micro'`` averaging.
 
-The following figure shows the micro-averaged ROC curve and its corresponding
-ROC-AUC score for a classifier aimed to distinguish the different species in
-the :ref:`iris_dataset`:
+  In applications where a high false positive rate is not tolerable the parameter
+  ``max_fpr`` of :func:`roc_auc_score` can be used to summarize the ROC curve up
+  to the given limit.
 
-.. image:: ../auto_examples/model_selection/images/sphx_glr_plot_roc_002.png
-   :target: ../auto_examples/model_selection/plot_roc.html
-   :scale: 75
-   :align: center
+  The following figure shows the micro-averaged ROC curve and its corresponding
+  ROC-AUC score for a classifier aimed to distinguish the different species in
+  the :ref:`iris_dataset`:
 
-|details-end|
+  .. image:: ../auto_examples/model_selection/images/sphx_glr_plot_roc_002.png
+    :target: ../auto_examples/model_selection/plot_roc.html
+    :scale: 75
+    :align: center
 
 .. _roc_auc_multilabel:
 
@@ -1582,46 +1565,43 @@ And the decision values do not require such processing.
   >>> roc_auc_score(y, y_score, average=None)
   array([0.81..., 0.84... , 0.93..., 0.87..., 0.94...])
 
-.. topic:: Examples:
+.. rubric:: Examples
 
-  * See :ref:`sphx_glr_auto_examples_model_selection_plot_roc.py`
-    for an example of using ROC to
-    evaluate the quality of the output of a classifier.
+* See :ref:`sphx_glr_auto_examples_model_selection_plot_roc.py` for an example of
+  using ROC to evaluate the quality of the output of a classifier.
 
-  * See :ref:`sphx_glr_auto_examples_model_selection_plot_roc_crossval.py`
-    for an example of using ROC to
-    evaluate classifier output quality, using cross-validation.
+* See :ref:`sphx_glr_auto_examples_model_selection_plot_roc_crossval.py`  for an
+  example of using ROC to evaluate classifier output quality, using cross-validation.
 
-  * See :ref:`sphx_glr_auto_examples_applications_plot_species_distribution_modeling.py`
-    for an example of using ROC to
-    model species distribution.
+* See :ref:`sphx_glr_auto_examples_applications_plot_species_distribution_modeling.py`
+  for an example of using ROC to model species distribution.
 
-.. topic:: References:
+.. rubric:: References
 
-    .. [HT2001] Hand, D.J. and Till, R.J., (2001). `A simple generalisation
-       of the area under the ROC curve for multiple class classification problems.
-       <http://link.springer.com/article/10.1023/A:1010920819831>`_
-       Machine learning, 45(2), pp. 171-186.
+.. [HT2001] Hand, D.J. and Till, R.J., (2001). `A simple generalisation
+   of the area under the ROC curve for multiple class classification problems.
+   <http://link.springer.com/article/10.1023/A:1010920819831>`_
+   Machine learning, 45(2), pp. 171-186.
 
-    .. [FC2009] Ferri, Cèsar & Hernandez-Orallo, Jose & Modroiu, R. (2009).
-       `An Experimental Comparison of Performance Measures for Classification.
-       <https://www.math.ucdavis.edu/~saito/data/roc/ferri-class-perf-metrics.pdf>`_
-       Pattern Recognition Letters. 30. 27-38.
+.. [FC2009] Ferri, Cèsar & Hernandez-Orallo, Jose & Modroiu, R. (2009).
+   `An Experimental Comparison of Performance Measures for Classification.
+   <https://www.math.ucdavis.edu/~saito/data/roc/ferri-class-perf-metrics.pdf>`_
+   Pattern Recognition Letters. 30. 27-38.
 
-    .. [PD2000] Provost, F., Domingos, P. (2000). `Well-trained PETs: Improving
-       probability estimation trees
-       <https://fosterprovost.com/publication/well-trained-pets-improving-probability-estimation-trees/>`_
-       (Section 6.2), CeDER Working Paper #IS-00-04, Stern School of Business,
-       New York University.
+.. [PD2000] Provost, F., Domingos, P. (2000). `Well-trained PETs: Improving
+   probability estimation trees
+   <https://fosterprovost.com/publication/well-trained-pets-improving-probability-estimation-trees/>`_
+   (Section 6.2), CeDER Working Paper #IS-00-04, Stern School of Business,
+   New York University.
 
-    .. [F2006] Fawcett, T., 2006. `An introduction to ROC analysis.
-       <http://www.sciencedirect.com/science/article/pii/S016786550500303X>`_
-       Pattern Recognition Letters, 27(8), pp. 861-874.
+.. [F2006] Fawcett, T., 2006. `An introduction to ROC analysis.
+   <http://www.sciencedirect.com/science/article/pii/S016786550500303X>`_
+   Pattern Recognition Letters, 27(8), pp. 861-874.
 
-    .. [F2001] Fawcett, T., 2001. `Using rule sets to maximize
-       ROC performance <https://ieeexplore.ieee.org/document/989510/>`_
-       In Data Mining, 2001.
-       Proceedings IEEE International Conference, pp. 131-138.
+.. [F2001] Fawcett, T., 2001. `Using rule sets to maximize
+   ROC performance <https://ieeexplore.ieee.org/document/989510/>`_
+   In Data Mining, 2001.
+   Proceedings IEEE International Conference, pp. 131-138.
 
 .. _det_curve:
 
@@ -1657,67 +1637,57 @@ same classification task:
    :scale: 75
    :align: center
 
-.. topic:: Examples:
+.. dropdown:: Properties
 
-  * See :ref:`sphx_glr_auto_examples_model_selection_plot_det.py`
-    for an example comparison between receiver operating characteristic (ROC)
-    curves and Detection error tradeoff (DET) curves.
+  * DET curves form a linear curve in normal deviate scale if the detection
+    scores are normally (or close-to normally) distributed.
+    It was shown by [Navratil2007]_ that the reverse is not necessarily true and
+    even more general distributions are able to produce linear DET curves.
 
-|details-start|
-**Properties**
-|details-split|
+  * The normal deviate scale transformation spreads out the points such that a
+    comparatively larger space of plot is occupied.
+    Therefore curves with similar classification performance might be easier to
+    distinguish on a DET plot.
 
-* DET curves form a linear curve in normal deviate scale if the detection
-  scores are normally (or close-to normally) distributed.
-  It was shown by [Navratil2007]_ that the reverse is not necessarily true and
-  even more general distributions are able to produce linear DET curves.
+  * With False Negative Rate being "inverse" to True Positive Rate the point
+    of perfection for DET curves is the origin (in contrast to the top left
+    corner for ROC curves).
 
-* The normal deviate scale transformation spreads out the points such that a
-  comparatively larger space of plot is occupied.
-  Therefore curves with similar classification performance might be easier to
-  distinguish on a DET plot.
+.. dropdown:: Applications and limitations
 
-* With False Negative Rate being "inverse" to True Positive Rate the point
-  of perfection for DET curves is the origin (in contrast to the top left
-  corner for ROC curves).
+  DET curves are intuitive to read and hence allow quick visual assessment of a
+  classifier's performance.
+  Additionally DET curves can be consulted for threshold analysis and operating
+  point selection.
+  This is particularly helpful if a comparison of error types is required.
 
-|details-end|
+  On the other hand DET curves do not provide their metric as a single number.
+  Therefore for either automated evaluation or comparison to other
+  classification tasks metrics like the derived area under ROC curve might be
+  better suited.
 
-|details-start|
-**Applications and limitations**
-|details-split|
+.. rubric:: Examples
 
-DET curves are intuitive to read and hence allow quick visual assessment of a
-classifier's performance.
-Additionally DET curves can be consulted for threshold analysis and operating
-point selection.
-This is particularly helpful if a comparison of error types is required.
+* See :ref:`sphx_glr_auto_examples_model_selection_plot_det.py`
+  for an example comparison between receiver operating characteristic (ROC)
+  curves and Detection error tradeoff (DET) curves.
 
-On the other hand DET curves do not provide their metric as a single number.
-Therefore for either automated evaluation or comparison to other
-classification tasks metrics like the derived area under ROC curve might be
-better suited.
+.. rubric:: References
 
-|details-end|
+.. [WikipediaDET2017] Wikipedia contributors. Detection error tradeoff.
+    Wikipedia, The Free Encyclopedia. September 4, 2017, 23:33 UTC.
+    Available at: https://en.wikipedia.org/w/index.php?title=Detection_error_tradeoff&oldid=798982054.
+    Accessed February 19, 2018.
 
-.. topic:: References:
+.. [Martin1997] A. Martin, G. Doddington, T. Kamm, M. Ordowski, and M. Przybocki,
+    `The DET Curve in Assessment of Detection Task Performance
+    <https://ccc.inaoep.mx/~villasen/bib/martin97det.pdf>`_, NIST 1997.
 
-  .. [WikipediaDET2017] Wikipedia contributors. Detection error tradeoff.
-     Wikipedia, The Free Encyclopedia. September 4, 2017, 23:33 UTC.
-     Available at: https://en.wikipedia.org/w/index.php?title=Detection_error_tradeoff&oldid=798982054.
-     Accessed February 19, 2018.
-
-  .. [Martin1997] A. Martin, G. Doddington, T. Kamm, M. Ordowski, and M. Przybocki,
-     `The DET Curve in Assessment of Detection Task Performance
-     <https://ccc.inaoep.mx/~villasen/bib/martin97det.pdf>`_,
-     NIST 1997.
-
-  .. [Navratil2007] J. Navractil and D. Klusacek,
-     "`On Linear DETs,
-     <https://ieeexplore.ieee.org/document/4218079>`_"
-     2007 IEEE International Conference on Acoustics,
-     Speech and Signal Processing - ICASSP '07, Honolulu,
-     HI, 2007, pp. IV-229-IV-232.
+.. [Navratil2007] J. Navractil and D. Klusacek,
+    `"On Linear DETs" <https://ieeexplore.ieee.org/document/4218079>`_,
+    2007 IEEE International Conference on Acoustics,
+    Speech and Signal Processing - ICASSP '07, Honolulu,
+    HI, 2007, pp. IV-229-IV-232.
 
 .. _zero_one_loss:
 
@@ -1765,11 +1735,11 @@ set [0,1] has an error::
   >>> zero_one_loss(np.array([[0, 1], [1, 1]]), np.ones((2, 2)),  normalize=False)
   1.0
 
-.. topic:: Example:
+.. rubric:: Examples
 
-  * See :ref:`sphx_glr_auto_examples_feature_selection_plot_rfe_with_cross_validation.py`
-    for an example of zero one loss usage to perform recursive feature
-    elimination with cross-validation.
+* See :ref:`sphx_glr_auto_examples_feature_selection_plot_rfe_with_cross_validation.py`
+  for an example of zero one loss usage to perform recursive feature
+  elimination with cross-validation.
 
 .. _brier_score_loss:
 
@@ -1825,28 +1795,27 @@ necessarily mean a better calibrated model. "Only when refinement loss remains
 the same does a lower Brier score loss always mean better calibration"
 [Bella2012]_, [Flach2008]_.
 
-.. topic:: Example:
+.. rubric:: Examples
 
-  * See :ref:`sphx_glr_auto_examples_calibration_plot_calibration.py`
-    for an example of Brier score loss usage to perform probability
-    calibration of classifiers.
+* See :ref:`sphx_glr_auto_examples_calibration_plot_calibration.py`
+  for an example of Brier score loss usage to perform probability
+  calibration of classifiers.
 
-.. topic:: References:
+.. rubric:: References
 
-  .. [Brier1950] G. Brier, `Verification of forecasts expressed in terms of
-    probability
-    <ftp://ftp.library.noaa.gov/docs.lib/htdocs/rescue/mwr/078/mwr-078-01-0001.pdf>`_,
-    Monthly weather review 78.1 (1950)
+.. [Brier1950] G. Brier, `Verification of forecasts expressed in terms of probability
+  <ftp://ftp.library.noaa.gov/docs.lib/htdocs/rescue/mwr/078/mwr-078-01-0001.pdf>`_,
+  Monthly weather review 78.1 (1950)
 
-  .. [Bella2012] Bella, Ferri, Hernández-Orallo, and Ramírez-Quintana
-    `"Calibration of Machine Learning Models"
-    <http://dmip.webs.upv.es/papers/BFHRHandbook2010.pdf>`_
-    in Khosrow-Pour, M. "Machine learning: concepts, methodologies, tools
-    and applications." Hershey, PA: Information Science Reference (2012).
+.. [Bella2012] Bella, Ferri, Hernández-Orallo, and Ramírez-Quintana
+  `"Calibration of Machine Learning Models"
+  <http://dmip.webs.upv.es/papers/BFHRHandbook2010.pdf>`_
+  in Khosrow-Pour, M. "Machine learning: concepts, methodologies, tools
+  and applications." Hershey, PA: Information Science Reference (2012).
 
-  .. [Flach2008] Flach, Peter, and Edson Matsubara. `"On classification, ranking,
-    and probability estimation." <https://drops.dagstuhl.de/opus/volltexte/2008/1382/>`_
-    Dagstuhl Seminar Proceedings. Schloss Dagstuhl-Leibniz-Zentrum fr Informatik (2008).
+.. [Flach2008] Flach, Peter, and Edson Matsubara. `"On classification, ranking,
+  and probability estimation." <https://drops.dagstuhl.de/opus/volltexte/2008/1382/>`_
+  Dagstuhl Seminar Proceedings. Schloss Dagstuhl-Leibniz-Zentrum fr Informatik (2008).
 
 .. _class_likelihood_ratios:
 
@@ -1899,82 +1868,72 @@ counts ``tp`` (see `the wikipedia page
 <https://en.wikipedia.org/wiki/Likelihood_ratios_in_diagnostic_testing>`_ for
 the actual formulas).
 
-.. topic:: Examples:
-
-  * :ref:`sphx_glr_auto_examples_model_selection_plot_likelihood_ratios.py`
-
-|details-start|
-**Interpretation across varying prevalence**
-|details-split|
+.. rubric:: Examples
 
-Both class likelihood ratios are interpretable in terms of an odds ratio
-(pre-test and post-tests):
+* :ref:`sphx_glr_auto_examples_model_selection_plot_likelihood_ratios.py`
 
-.. math::
+.. dropdown:: Interpretation across varying prevalence
 
-   \text{post-test odds} = \text{Likelihood ratio} \times \text{pre-test odds}.
+  Both class likelihood ratios are interpretable in terms of an odds ratio
+  (pre-test and post-tests):
 
-Odds are in general related to probabilities via
+  .. math::
 
-.. math::
+    \text{post-test odds} = \text{Likelihood ratio} \times \text{pre-test odds}.
 
-   \text{odds} = \frac{\text{probability}}{1 - \text{probability}},
+  Odds are in general related to probabilities via
 
-or equivalently
+  .. math::
 
-.. math::
+    \text{odds} = \frac{\text{probability}}{1 - \text{probability}},
 
-   \text{probability} = \frac{\text{odds}}{1 + \text{odds}}.
+  or equivalently
 
-On a given population, the pre-test probability is given by the prevalence. By
-converting odds to probabilities, the likelihood ratios can be translated into a
-probability of truly belonging to either class before and after a classifier
-prediction:
+  .. math::
 
-.. math::
+    \text{probability} = \frac{\text{odds}}{1 + \text{odds}}.
 
-   \text{post-test odds} = \text{Likelihood ratio} \times
-   \frac{\text{pre-test probability}}{1 - \text{pre-test probability}},
+  On a given population, the pre-test probability is given by the prevalence. By
+  converting odds to probabilities, the likelihood ratios can be translated into a
+  probability of truly belonging to either class before and after a classifier
+  prediction:
 
-.. math::
+  .. math::
 
-   \text{post-test probability} = \frac{\text{post-test odds}}{1 + \text{post-test odds}}.
+    \text{post-test odds} = \text{Likelihood ratio} \times
+    \frac{\text{pre-test probability}}{1 - \text{pre-test probability}},
 
-|details-end|
+  .. math::
 
-|details-start|
-**Mathematical divergences**
-|details-split|
+    \text{post-test probability} = \frac{\text{post-test odds}}{1 + \text{post-test odds}}.
 
-The positive likelihood ratio is undefined when :math:`fp = 0`, which can be
-interpreted as the classifier perfectly identifying positive cases. If :math:`fp
-= 0` and additionally :math:`tp = 0`, this leads to a zero/zero division. This
-happens, for instance, when using a `DummyClassifier` that always predicts the
-negative class and therefore the interpretation as a perfect classifier is lost.
+.. dropdown:: Mathematical divergences
 
-The negative likelihood ratio is undefined when :math:`tn = 0`. Such divergence
-is invalid, as :math:`LR_- > 1` would indicate an increase in the odds of a
-sample belonging to the positive class after being classified as negative, as if
-the act of classifying caused the positive condition. This includes the case of
-a `DummyClassifier` that always predicts the positive class (i.e. when
-:math:`tn=fn=0`).
+  The positive likelihood ratio is undefined when :math:`fp = 0`, which can be
+  interpreted as the classifier perfectly identifying positive cases. If :math:`fp
+  = 0` and additionally :math:`tp = 0`, this leads to a zero/zero division. This
+  happens, for instance, when using a `DummyClassifier` that always predicts the
+  negative class and therefore the interpretation as a perfect classifier is lost.
 
-Both class likelihood ratios are undefined when :math:`tp=fn=0`, which means
-that no samples of the positive class were present in the testing set. This can
-also happen when cross-validating highly imbalanced data.
+  The negative likelihood ratio is undefined when :math:`tn = 0`. Such divergence
+  is invalid, as :math:`LR_- > 1` would indicate an increase in the odds of a
+  sample belonging to the positive class after being classified as negative, as if
+  the act of classifying caused the positive condition. This includes the case of
+  a `DummyClassifier` that always predicts the positive class (i.e. when
+  :math:`tn=fn=0`).
 
-In all the previous cases the :func:`class_likelihood_ratios` function raises by
-default an appropriate warning message and returns `nan` to avoid pollution when
-averaging over cross-validation folds.
+  Both class likelihood ratios are undefined when :math:`tp=fn=0`, which means
+  that no samples of the positive class were present in the testing set. This can
+  also happen when cross-validating highly imbalanced data.
 
-For a worked-out demonstration of the :func:`class_likelihood_ratios` function,
-see the example below.
+  In all the previous cases the :func:`class_likelihood_ratios` function raises by
+  default an appropriate warning message and returns `nan` to avoid pollution when
+  averaging over cross-validation folds.
 
-|details-end|
+  For a worked-out demonstration of the :func:`class_likelihood_ratios` function,
+  see the example below.
 
-|details-start|
-**References**
-|details-split|
+.. dropdown:: References
 
   * `Wikipedia entry for Likelihood ratios in diagnostic testing
     <https://en.wikipedia.org/wiki/Likelihood_ratios_in_diagnostic_testing>`_
@@ -1984,7 +1943,68 @@ see the example below.
     values with disease prevalence.
     Statistics in medicine, 16(9), 981-991.
 
-|details-end|
+
+.. _d2_score_classification:
+
+D² score for classification
+---------------------------
+
+The D² score computes the fraction of deviance explained.
+It is a generalization of R², where the squared error is generalized and replaced
+by a classification deviance of choice :math:`\text{dev}(y, \hat{y})`
+(e.g., Log loss). D² is a form of a *skill score*.
+It is calculated as
+
+.. math::
+
+  D^2(y, \hat{y}) = 1 - \frac{\text{dev}(y, \hat{y})}{\text{dev}(y, y_{\text{null}})} \,.
+
+Where :math:`y_{\text{null}}` is the optimal prediction of an intercept-only model
+(e.g., the per-class proportion of `y_true` in the case of the Log loss).
+
+Like R², the best possible score is 1.0 and it can be negative (because the
+model can be arbitrarily worse). A constant model that always predicts
+:math:`y_{\text{null}}`, disregarding the input features, would get a D² score
+of 0.0.
+
+.. dropdown:: D2 log loss score
+
+  The :func:`d2_log_loss_score` function implements the special case
+  of D² with the log loss, see :ref:`log_loss`, i.e.:
+
+  .. math::
+
+    \text{dev}(y, \hat{y}) = \text{log_loss}(y, \hat{y}).
+
+  Here are some usage examples of the :func:`d2_log_loss_score` function::
+
+    >>> from sklearn.metrics import d2_log_loss_score
+    >>> y_true = [1, 1, 2, 3]
+    >>> y_pred = [
+    ...    [0.5, 0.25, 0.25],
+    ...    [0.5, 0.25, 0.25],
+    ...    [0.5, 0.25, 0.25],
+    ...    [0.5, 0.25, 0.25],
+    ... ]
+    >>> d2_log_loss_score(y_true, y_pred)
+    0.0
+    >>> y_true = [1, 2, 3]
+    >>> y_pred = [
+    ...     [0.98, 0.01, 0.01],
+    ...     [0.01, 0.98, 0.01],
+    ...     [0.01, 0.01, 0.98],
+    ... ]
+    >>> d2_log_loss_score(y_true, y_pred)
+    0.981...
+    >>> y_true = [1, 2, 3]
+    >>> y_pred = [
+    ...     [0.1, 0.6, 0.3],
+    ...     [0.1, 0.6, 0.3],
+    ...     [0.4, 0.5, 0.1],
+    ... ]
+    >>> d2_log_loss_score(y_true, y_pred)
+    -0.552...
+
 
 .. _multilabel_ranking_metrics:
 
@@ -2124,14 +2144,11 @@ Here is a small example of usage of this function::
     0.0
 
 
-|details-start|
-**References**
-|details-split|
+.. dropdown:: References
 
   * Tsoumakas, G., Katakis, I., & Vlahavas, I. (2010). Mining multi-label data. In
     Data mining and knowledge discovery handbook (pp. 667-685). Springer US.
 
-|details-end|
 
 .. _ndcg:
 
@@ -2177,9 +2194,7 @@ DCG score is
 and the NDCG score is the DCG score divided by the DCG score obtained for
 :math:`y`.
 
-|details-start|
-**References**
-|details-split|
+.. dropdown:: References
 
   * `Wikipedia entry for Discounted Cumulative Gain
     <https://en.wikipedia.org/wiki/Discounted_cumulative_gain>`_
@@ -2197,7 +2212,6 @@ and the NDCG score is the DCG score divided by the DCG score obtained for
     European conference on information retrieval (pp. 414-421). Springer,
     Berlin, Heidelberg.
 
-|details-end|
 
 .. _regression_metrics:
 
@@ -2230,9 +2244,6 @@ leads to a weighting of each individual score by the variance of the
 corresponding target variable. This setting quantifies the globally captured
 unscaled variance. If the target variables are of different scale, then this
 score puts more importance on explaining the higher variance variables.
-``multioutput='variance_weighted'`` is the default value for :func:`r2_score`
-for backward compatibility. This will be changed to ``uniform_average`` in the
-future.
 
 .. _r2_score:
 
@@ -2310,11 +2321,11 @@ Here is a small example of usage of the :func:`r2_score` function::
   >>> r2_score(y_true, y_pred, force_finite=False)
   -inf
 
-.. topic:: Example:
+.. rubric:: Examples
 
-  * See :ref:`sphx_glr_auto_examples_linear_model_plot_lasso_and_elasticnet.py`
-    for an example of R² score usage to
-    evaluate Lasso and Elastic Net on sparse signals.
+* See :ref:`sphx_glr_auto_examples_linear_model_plot_lasso_and_elasticnet.py`
+  for an example of R² score usage to
+  evaluate Lasso and Elastic Net on sparse signals.
 
 .. _mean_absolute_error:
 
@@ -2381,11 +2392,10 @@ function::
   >>> mean_squared_error(y_true, y_pred)
   0.7083...
 
-.. topic:: Examples:
+.. rubric:: Examples
 
-  * See :ref:`sphx_glr_auto_examples_ensemble_plot_gradient_boosting_regression.py`
-    for an example of mean squared error usage to
-    evaluate gradient boosting regression.
+* See :ref:`sphx_glr_auto_examples_ensemble_plot_gradient_boosting_regression.py`
+  for an example of mean squared error usage to evaluate gradient boosting regression.
 
 Taking the square root of the MSE, called the root mean squared error (RMSE), is another
 common metric that provides a measure in the same units as the target variable. RSME is
@@ -2723,12 +2733,12 @@ It is also possible to build scorer objects for hyper-parameter tuning. The
 sign of the loss must be switched to ensure that greater means better as
 explained in the example linked below.
 
-.. topic:: Example:
+.. rubric:: Examples
 
-  * See :ref:`sphx_glr_auto_examples_ensemble_plot_gradient_boosting_quantile.py`
-    for an example of using the pinball loss to evaluate and tune the
-    hyper-parameters of quantile regression models on data with non-symmetric
-    noise and outliers.
+* See :ref:`sphx_glr_auto_examples_ensemble_plot_gradient_boosting_quantile.py`
+  for an example of using the pinball loss to evaluate and tune the
+  hyper-parameters of quantile regression models on data with non-symmetric
+  noise and outliers.
 
 .. _d2_score:
 
@@ -2754,77 +2764,66 @@ model can be arbitrarily worse). A constant model that always predicts
 :math:`y_{\text{null}}`, disregarding the input features, would get a D² score
 of 0.0.
 
-|details-start|
-**D² Tweedie score**
-|details-split|
-
-The :func:`d2_tweedie_score` function implements the special case of D²
-where :math:`\text{dev}(y, \hat{y})` is the Tweedie deviance, see :ref:`mean_tweedie_deviance`.
-It is also known as D² Tweedie and is related to McFadden's likelihood ratio index.
-
-The argument ``power`` defines the Tweedie power as for
-:func:`mean_tweedie_deviance`. Note that for `power=0`,
-:func:`d2_tweedie_score` equals :func:`r2_score` (for single targets).
+.. dropdown:: D² Tweedie score
 
-A scorer object with a specific choice of ``power`` can be built by::
+  The :func:`d2_tweedie_score` function implements the special case of D²
+  where :math:`\text{dev}(y, \hat{y})` is the Tweedie deviance, see :ref:`mean_tweedie_deviance`.
+  It is also known as D² Tweedie and is related to McFadden's likelihood ratio index.
 
-  >>> from sklearn.metrics import d2_tweedie_score, make_scorer
-  >>> d2_tweedie_score_15 = make_scorer(d2_tweedie_score, power=1.5)
+  The argument ``power`` defines the Tweedie power as for
+  :func:`mean_tweedie_deviance`. Note that for `power=0`,
+  :func:`d2_tweedie_score` equals :func:`r2_score` (for single targets).
 
-|details-end|
+  A scorer object with a specific choice of ``power`` can be built by::
 
-|details-start|
-**D² pinball score**
-|details-split|
+    >>> from sklearn.metrics import d2_tweedie_score, make_scorer
+    >>> d2_tweedie_score_15 = make_scorer(d2_tweedie_score, power=1.5)
 
-The :func:`d2_pinball_score` function implements the special case
-of D² with the pinball loss, see :ref:`pinball_loss`, i.e.:
+.. dropdown:: D² pinball score
 
-.. math::
+  The :func:`d2_pinball_score` function implements the special case
+  of D² with the pinball loss, see :ref:`pinball_loss`, i.e.:
 
-  \text{dev}(y, \hat{y}) = \text{pinball}(y, \hat{y}).
+  .. math::
 
-The argument ``alpha`` defines the slope of the pinball loss as for
-:func:`mean_pinball_loss` (:ref:`pinball_loss`). It determines the
-quantile level ``alpha`` for which the pinball loss and also D²
-are optimal. Note that for `alpha=0.5` (the default) :func:`d2_pinball_score`
-equals :func:`d2_absolute_error_score`.
+    \text{dev}(y, \hat{y}) = \text{pinball}(y, \hat{y}).
 
-A scorer object with a specific choice of ``alpha`` can be built by::
+  The argument ``alpha`` defines the slope of the pinball loss as for
+  :func:`mean_pinball_loss` (:ref:`pinball_loss`). It determines the
+  quantile level ``alpha`` for which the pinball loss and also D²
+  are optimal. Note that for `alpha=0.5` (the default) :func:`d2_pinball_score`
+  equals :func:`d2_absolute_error_score`.
 
-  >>> from sklearn.metrics import d2_pinball_score, make_scorer
-  >>> d2_pinball_score_08 = make_scorer(d2_pinball_score, alpha=0.8)
+  A scorer object with a specific choice of ``alpha`` can be built by::
 
-|details-end|
+    >>> from sklearn.metrics import d2_pinball_score, make_scorer
+    >>> d2_pinball_score_08 = make_scorer(d2_pinball_score, alpha=0.8)
 
-|details-start|
-**D² absolute error score**
-|details-split|
+.. dropdown:: D² absolute error score
 
-The :func:`d2_absolute_error_score` function implements the special case of
-the :ref:`mean_absolute_error`:
+  The :func:`d2_absolute_error_score` function implements the special case of
+  the :ref:`mean_absolute_error`:
 
-.. math::
+  .. math::
 
-  \text{dev}(y, \hat{y}) = \text{MAE}(y, \hat{y}).
+    \text{dev}(y, \hat{y}) = \text{MAE}(y, \hat{y}).
 
-Here are some usage examples of the :func:`d2_absolute_error_score` function::
+  Here are some usage examples of the :func:`d2_absolute_error_score` function::
 
-  >>> from sklearn.metrics import d2_absolute_error_score
-  >>> y_true = [3, -0.5, 2, 7]
-  >>> y_pred = [2.5, 0.0, 2, 8]
-  >>> d2_absolute_error_score(y_true, y_pred)
-  0.764...
-  >>> y_true = [1, 2, 3]
-  >>> y_pred = [1, 2, 3]
-  >>> d2_absolute_error_score(y_true, y_pred)
-  1.0
-  >>> y_true = [1, 2, 3]
-  >>> y_pred = [2, 2, 2]
-  >>> d2_absolute_error_score(y_true, y_pred)
-  0.0
+    >>> from sklearn.metrics import d2_absolute_error_score
+    >>> y_true = [3, -0.5, 2, 7]
+    >>> y_pred = [2.5, 0.0, 2, 8]
+    >>> d2_absolute_error_score(y_true, y_pred)
+    0.764...
+    >>> y_true = [1, 2, 3]
+    >>> y_pred = [1, 2, 3]
+    >>> d2_absolute_error_score(y_true, y_pred)
+    1.0
+    >>> y_true = [1, 2, 3]
+    >>> y_pred = [2, 2, 2]
+    >>> d2_absolute_error_score(y_true, y_pred)
+    0.0
 
-|details-end|
 
 .. _visualization_regression_evaluation:
 
@@ -2894,12 +2893,12 @@ model might be useful.
 Refer to the example below to see a model evaluation that makes use of this
 display.
 
-.. topic:: Example:
+.. rubric:: Examples
 
-  * See :ref:`sphx_glr_auto_examples_compose_plot_transformed_target.py` for
-    an example on how to use :class:`~sklearn.metrics.PredictionErrorDisplay`
-    to visualize the prediction quality improvement of a regression model
-    obtained by transforming the target before learning.
+* See :ref:`sphx_glr_auto_examples_compose_plot_transformed_target.py` for
+  an example on how to use :class:`~sklearn.metrics.PredictionErrorDisplay`
+  to visualize the prediction quality improvement of a regression model
+  obtained by transforming the target before learning.
 
 .. _clustering_metrics:
 
diff --git a/doc/modules/multiclass.rst b/doc/modules/multiclass.rst
index 88f822dad091e..b5f7611bdfd91 100644
--- a/doc/modules/multiclass.rst
+++ b/doc/modules/multiclass.rst
@@ -63,8 +63,8 @@ can provide additional strategies beyond what is built-in:
   - :class:`semi_supervised.LabelSpreading`
   - :class:`discriminant_analysis.LinearDiscriminantAnalysis`
   - :class:`svm.LinearSVC` (setting multi_class="crammer_singer")
-  - :class:`linear_model.LogisticRegression` (setting multi_class="multinomial")
-  - :class:`linear_model.LogisticRegressionCV` (setting multi_class="multinomial")
+  - :class:`linear_model.LogisticRegression` (with most solvers)
+  - :class:`linear_model.LogisticRegressionCV` (with most solvers)
   - :class:`neural_network.MLPClassifier`
   - :class:`neighbors.NearestCentroid`
   - :class:`discriminant_analysis.QuadraticDiscriminantAnalysis`
@@ -86,8 +86,8 @@ can provide additional strategies beyond what is built-in:
   - :class:`ensemble.GradientBoostingClassifier`
   - :class:`gaussian_process.GaussianProcessClassifier` (setting multi_class = "one_vs_rest")
   - :class:`svm.LinearSVC` (setting multi_class="ovr")
-  - :class:`linear_model.LogisticRegression` (setting multi_class="ovr")
-  - :class:`linear_model.LogisticRegressionCV` (setting multi_class="ovr")
+  - :class:`linear_model.LogisticRegression` (most solvers)
+  - :class:`linear_model.LogisticRegressionCV` (most solvers)
   - :class:`linear_model.SGDClassifier`
   - :class:`linear_model.Perceptron`
   - :class:`linear_model.PassiveAggressiveClassifier`
@@ -222,9 +222,9 @@ in which cell [i, j] indicates the presence of label j in sample i.
     :scale: 75%
 
 
-.. topic:: Examples:
+.. rubric:: Examples
 
-    * :ref:`sphx_glr_auto_examples_miscellaneous_plot_multilabel.py`
+* :ref:`sphx_glr_auto_examples_miscellaneous_plot_multilabel.py`
 
 .. _ovo_classification:
 
@@ -263,10 +263,10 @@ Below is an example of multiclass learning using OvO::
          2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])
 
 
-.. topic:: References:
+.. rubric:: References
 
-    * "Pattern Recognition and Machine Learning. Springer",
-      Christopher M. Bishop, page 183, (First Edition)
+* "Pattern Recognition and Machine Learning. Springer",
+  Christopher M. Bishop, page 183, (First Edition)
 
 .. _ecoc:
 
@@ -321,21 +321,16 @@ Below is an example of multiclass learning using Output-Codes::
          2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 1, 1, 2, 2, 2,
          2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])
 
-.. topic:: References:
+.. rubric:: References
 
-    * "Solving multiclass learning problems via error-correcting output codes",
-      Dietterich T., Bakiri G.,
-      Journal of Artificial Intelligence Research 2,
-      1995.
+* "Solving multiclass learning problems via error-correcting output codes",
+  Dietterich T., Bakiri G., Journal of Artificial Intelligence Research 2, 1995.
 
-    .. [3] "The error coding method and PICTs",
-        James G., Hastie T.,
-        Journal of Computational and Graphical statistics 7,
-        1998.
+.. [3] "The error coding method and PICTs", James G., Hastie T.,
+  Journal of Computational and Graphical statistics 7, 1998.
 
-    * "The Elements of Statistical Learning",
-      Hastie T., Tibshirani R., Friedman J., page 606 (second-edition)
-      2008.
+* "The Elements of Statistical Learning",
+  Hastie T., Tibshirani R., Friedman J., page 606 (second-edition), 2008.
 
 .. _multilabel_classification:
 
@@ -432,10 +427,10 @@ one does not know the optimal ordering of the models in the chain so
 typically many randomly ordered chains are fit and their predictions are
 averaged together.
 
-.. topic:: References:
+.. rubric:: References
 
-    Jesse Read, Bernhard Pfahringer, Geoff Holmes, Eibe Frank,
-        "Classifier Chains for Multi-label Classification", 2009.
+* Jesse Read, Bernhard Pfahringer, Geoff Holmes, Eibe Frank,
+  "Classifier Chains for Multi-label Classification", 2009.
 
 .. _multiclass_multioutput_classification:
 
diff --git a/doc/modules/naive_bayes.rst b/doc/modules/naive_bayes.rst
index 05ca928dfae0b..6e80ec6145919 100644
--- a/doc/modules/naive_bayes.rst
+++ b/doc/modules/naive_bayes.rst
@@ -69,15 +69,11 @@ On the flip side, although naive Bayes is known as a decent classifier,
 it is known to be a bad estimator, so the probability outputs from
 ``predict_proba`` are not to be taken too seriously.
 
-|details-start|
-**References**
-|details-split|
+.. dropdown:: References
 
-* H. Zhang (2004). `The optimality of Naive Bayes.
-  <https://www.cs.unb.ca/~hzhang/publications/FLAIRS04ZhangH.pdf>`_
-  Proc. FLAIRS.
-
-|details-end|
+   * H. Zhang (2004). `The optimality of Naive Bayes.
+     <https://www.cs.unb.ca/~hzhang/publications/FLAIRS04ZhangH.pdf>`_
+     Proc. FLAIRS.
 
 .. _gaussian_naive_bayes:
 
@@ -153,47 +149,40 @@ The inventors of CNB show empirically that the parameter estimates for CNB are
 more stable than those for MNB. Further, CNB regularly outperforms MNB (often
 by a considerable margin) on text classification tasks.
 
-|details-start|
-**Weights calculation**
-|details-split|
-
-The procedure for calculating the weights is as follows:
+.. dropdown:: Weights calculation
 
-.. math::
+   The procedure for calculating the weights is as follows:
 
-    \hat{\theta}_{ci} = \frac{\alpha_i + \sum_{j:y_j \neq c} d_{ij}}
-                             {\alpha + \sum_{j:y_j \neq c} \sum_{k} d_{kj}}
+   .. math::
 
-    w_{ci} = \log \hat{\theta}_{ci}
+      \hat{\theta}_{ci} = \frac{\alpha_i + \sum_{j:y_j \neq c} d_{ij}}
+                              {\alpha + \sum_{j:y_j \neq c} \sum_{k} d_{kj}}
 
-    w_{ci} = \frac{w_{ci}}{\sum_{j} |w_{cj}|}
+      w_{ci} = \log \hat{\theta}_{ci}
 
-where the summations are over all documents :math:`j` not in class :math:`c`,
-:math:`d_{ij}` is either the count or tf-idf value of term :math:`i` in document
-:math:`j`, :math:`\alpha_i` is a smoothing hyperparameter like that found in
-MNB, and :math:`\alpha = \sum_{i} \alpha_i`. The second normalization addresses
-the tendency for longer documents to dominate parameter estimates in MNB. The
-classification rule is:
+      w_{ci} = \frac{w_{ci}}{\sum_{j} |w_{cj}|}
 
-.. math::
+   where the summations are over all documents :math:`j` not in class :math:`c`,
+   :math:`d_{ij}` is either the count or tf-idf value of term :math:`i` in document
+   :math:`j`, :math:`\alpha_i` is a smoothing hyperparameter like that found in
+   MNB, and :math:`\alpha = \sum_{i} \alpha_i`. The second normalization addresses
+   the tendency for longer documents to dominate parameter estimates in MNB. The
+   classification rule is:
 
-    \hat{c} = \arg\min_c \sum_{i} t_i w_{ci}
+   .. math::
 
-i.e., a document is assigned to the class that is the *poorest* complement
-match.
+      \hat{c} = \arg\min_c \sum_{i} t_i w_{ci}
 
-|details-end|
+   i.e., a document is assigned to the class that is the *poorest* complement
+   match.
 
-|details-start|
-**References**
-|details-split|
+.. dropdown:: References
 
-* Rennie, J. D., Shih, L., Teevan, J., & Karger, D. R. (2003).
-  `Tackling the poor assumptions of naive bayes text classifiers.
-  <https://people.csail.mit.edu/jrennie/papers/icml03-nb.pdf>`_
-  In ICML (Vol. 3, pp. 616-623).
+   * Rennie, J. D., Shih, L., Teevan, J., & Karger, D. R. (2003).
+     `Tackling the poor assumptions of naive bayes text classifiers.
+     <https://people.csail.mit.edu/jrennie/papers/icml03-nb.pdf>`_
+     In ICML (Vol. 3, pp. 616-623).
 
-|details-end|
 
 .. _bernoulli_naive_bayes:
 
@@ -224,24 +213,21 @@ count vectors) may be used to train and use this classifier. :class:`BernoulliNB
 might perform better on some datasets, especially those with shorter documents.
 It is advisable to evaluate both models, if time permits.
 
-|details-start|
-**References**
-|details-split|
+.. dropdown:: References
 
-* C.D. Manning, P. Raghavan and H. Schütze (2008). Introduction to
-  Information Retrieval. Cambridge University Press, pp. 234-265.
+   * C.D. Manning, P. Raghavan and H. Schütze (2008). Introduction to
+     Information Retrieval. Cambridge University Press, pp. 234-265.
 
-* A. McCallum and K. Nigam (1998).
-  `A comparison of event models for Naive Bayes text classification.
-  <https://citeseerx.ist.psu.edu/doc_view/pid/04ce064505b1635583fa0d9cc07cac7e9ea993cc>`_
-  Proc. AAAI/ICML-98 Workshop on Learning for Text Categorization, pp. 41-48.
+   * A. McCallum and K. Nigam (1998).
+     `A comparison of event models for Naive Bayes text classification.
+     <https://citeseerx.ist.psu.edu/doc_view/pid/04ce064505b1635583fa0d9cc07cac7e9ea993cc>`_
+     Proc. AAAI/ICML-98 Workshop on Learning for Text Categorization, pp. 41-48.
 
-* V. Metsis, I. Androutsopoulos and G. Paliouras (2006).
-  `Spam filtering with Naive Bayes -- Which Naive Bayes?
-  <https://citeseerx.ist.psu.edu/doc_view/pid/8bd0934b366b539ec95e683ae39f8abb29ccc757>`_
-  3rd Conf. on Email and Anti-Spam (CEAS).
+   * V. Metsis, I. Androutsopoulos and G. Paliouras (2006).
+     `Spam filtering with Naive Bayes -- Which Naive Bayes?
+     <https://citeseerx.ist.psu.edu/doc_view/pid/8bd0934b366b539ec95e683ae39f8abb29ccc757>`_
+     3rd Conf. on Email and Anti-Spam (CEAS).
 
-|details-end|
 
 .. _categorical_naive_bayes:
 
@@ -258,25 +244,21 @@ For each feature :math:`i` in the training set :math:`X`,
 of X conditioned on the class y. The index set of the samples is defined as
 :math:`J = \{ 1, \dots, m \}`, with :math:`m` as the number of samples.
 
-|details-start|
-**Probability calculation**
-|details-split|
-
-The probability of category :math:`t` in feature :math:`i` given class
-:math:`c` is estimated as:
+.. dropdown:: Probability calculation
 
-.. math::
+   The probability of category :math:`t` in feature :math:`i` given class
+   :math:`c` is estimated as:
 
-    P(x_i = t \mid y = c \: ;\, \alpha) = \frac{ N_{tic} + \alpha}{N_{c} +
-                                           \alpha n_i},
+   .. math::
 
-where :math:`N_{tic} = |\{j \in J \mid x_{ij} = t, y_j = c\}|` is the number
-of times category :math:`t` appears in the samples :math:`x_{i}`, which belong
-to class :math:`c`, :math:`N_{c} = |\{ j \in J\mid y_j = c\}|` is the number
-of samples with class c, :math:`\alpha` is a smoothing parameter and
-:math:`n_i` is the number of available categories of feature :math:`i`.
+      P(x_i = t \mid y = c \: ;\, \alpha) = \frac{ N_{tic} + \alpha}{N_{c} +
+                                             \alpha n_i},
 
-|details-end|
+   where :math:`N_{tic} = |\{j \in J \mid x_{ij} = t, y_j = c\}|` is the number
+   of times category :math:`t` appears in the samples :math:`x_{i}`, which belong
+   to class :math:`c`, :math:`N_{c} = |\{ j \in J\mid y_j = c\}|` is the number
+   of samples with class c, :math:`\alpha` is a smoothing parameter and
+   :math:`n_i` is the number of available categories of feature :math:`i`.
 
 :class:`CategoricalNB` assumes that the sample matrix :math:`X` is encoded (for
 instance with the help of :class:`~sklearn.preprocessing.OrdinalEncoder`) such
diff --git a/doc/modules/neighbors.rst b/doc/modules/neighbors.rst
index b081b29572d8a..de0eff67018bc 100644
--- a/doc/modules/neighbors.rst
+++ b/doc/modules/neighbors.rst
@@ -192,10 +192,10 @@ distance can be supplied to compute the weights.
 
 .. centered:: |classification_1|
 
-.. topic:: Examples:
+.. rubric:: Examples
 
-  * :ref:`sphx_glr_auto_examples_neighbors_plot_classification.py`: an example of
-    classification using nearest neighbors.
+* :ref:`sphx_glr_auto_examples_neighbors_plot_classification.py`: an example of
+  classification using nearest neighbors.
 
 .. _regression:
 
@@ -241,13 +241,13 @@ the lower half of those faces.
    :align: center
 
 
-.. topic:: Examples:
+.. rubric:: Examples
 
-  * :ref:`sphx_glr_auto_examples_neighbors_plot_regression.py`: an example of regression
-    using nearest neighbors.
+* :ref:`sphx_glr_auto_examples_neighbors_plot_regression.py`: an example of regression
+  using nearest neighbors.
 
-  * :ref:`sphx_glr_auto_examples_miscellaneous_plot_multioutput_face_completion.py`: an example of
-    multi-output regression using nearest neighbors.
+* :ref:`sphx_glr_auto_examples_miscellaneous_plot_multioutput_face_completion.py`:
+  an example of multi-output regression using nearest neighbors.
 
 
 Nearest Neighbor Algorithms
@@ -304,15 +304,13 @@ In scikit-learn, KD tree neighbors searches are specified using the
 keyword ``algorithm = 'kd_tree'``, and are computed using the class
 :class:`KDTree`.
 
-|details-start|
-**References**
-|details-split|
 
-   * `"Multidimensional binary search trees used for associative searching"
-     <https://dl.acm.org/citation.cfm?doid=361002.361007>`_,
-     Bentley, J.L., Communications of the ACM (1975)
+.. dropdown:: References
+
+  * `"Multidimensional binary search trees used for associative searching"
+    <https://dl.acm.org/citation.cfm?doid=361002.361007>`_,
+    Bentley, J.L., Communications of the ACM (1975)
 
-|details-end|
 
 .. _ball_tree:
 
@@ -345,156 +343,142 @@ neighbors searches are specified using the keyword ``algorithm = 'ball_tree'``,
 and are computed using the class :class:`BallTree`.
 Alternatively, the user can work with the :class:`BallTree` class directly.
 
-|details-start|
-**References**
-|details-split|
-
-   * `"Five Balltree Construction Algorithms"
-     <https://citeseerx.ist.psu.edu/doc_view/pid/17ac002939f8e950ffb32ec4dc8e86bdd8cb5ff1>`_,
-     Omohundro, S.M., International Computer Science Institute
-     Technical Report (1989)
-
-|details-end|
-
-|details-start|
-**Choice of Nearest Neighbors Algorithm**
-|details-split|
-
-The optimal algorithm for a given dataset is a complicated choice, and
-depends on a number of factors:
-
-* number of samples :math:`N` (i.e. ``n_samples``) and dimensionality
-  :math:`D` (i.e. ``n_features``).
-
-  * *Brute force* query time grows as :math:`O[D N]`
-  * *Ball tree* query time grows as approximately :math:`O[D \log(N)]`
-  * *KD tree* query time changes with :math:`D` in a way that is difficult
-    to precisely characterise.  For small :math:`D` (less than 20 or so)
-    the cost is approximately :math:`O[D\log(N)]`, and the KD tree
-    query can be very efficient.
-    For larger :math:`D`, the cost increases to nearly :math:`O[DN]`, and
-    the overhead due to the tree
-    structure can lead to queries which are slower than brute force.
-
-  For small data sets (:math:`N` less than 30 or so), :math:`\log(N)` is
-  comparable to :math:`N`, and brute force algorithms can be more efficient
-  than a tree-based approach.  Both :class:`KDTree` and :class:`BallTree`
-  address this through providing a *leaf size* parameter: this controls the
-  number of samples at which a query switches to brute-force.  This allows both
-  algorithms to approach the efficiency of a brute-force computation for small
-  :math:`N`.
-
-* data structure: *intrinsic dimensionality* of the data and/or *sparsity*
-  of the data. Intrinsic dimensionality refers to the dimension
-  :math:`d \le D` of a manifold on which the data lies, which can be linearly
-  or non-linearly embedded in the parameter space. Sparsity refers to the
-  degree to which the data fills the parameter space (this is to be
-  distinguished from the concept as used in "sparse" matrices.  The data
-  matrix may have no zero entries, but the **structure** can still be
-  "sparse" in this sense).
-
-  * *Brute force* query time is unchanged by data structure.
-  * *Ball tree* and *KD tree* query times can be greatly influenced
-    by data structure.  In general, sparser data with a smaller intrinsic
-    dimensionality leads to faster query times.  Because the KD tree
-    internal representation is aligned with the parameter axes, it will not
-    generally show as much improvement as ball tree for arbitrarily
-    structured data.
-
-  Datasets used in machine learning tend to be very structured, and are
-  very well-suited for tree-based queries.
-
-* number of neighbors :math:`k` requested for a query point.
-
-  * *Brute force* query time is largely unaffected by the value of :math:`k`
-  * *Ball tree* and *KD tree* query time will become slower as :math:`k`
-    increases.  This is due to two effects: first, a larger :math:`k` leads
-    to the necessity to search a larger portion of the parameter space.
-    Second, using :math:`k > 1` requires internal queueing of results
-    as the tree is traversed.
-
-  As :math:`k` becomes large compared to :math:`N`, the ability to prune
-  branches in a tree-based query is reduced.  In this situation, Brute force
-  queries can be more efficient.
-
-* number of query points.  Both the ball tree and the KD Tree
-  require a construction phase.  The cost of this construction becomes
-  negligible when amortized over many queries.  If only a small number of
-  queries will be performed, however, the construction can make up
-  a significant fraction of the total cost.  If very few query points
-  will be required, brute force is better than a tree-based method.
-
-Currently, ``algorithm = 'auto'`` selects ``'brute'`` if any of the following
-conditions are verified:
-
-* input data is sparse
-* ``metric = 'precomputed'``
-* :math:`D > 15`
-* :math:`k >= N/2`
-* ``effective_metric_`` isn't in the ``VALID_METRICS`` list for either
-  ``'kd_tree'`` or ``'ball_tree'``
-
-Otherwise, it selects the first out of ``'kd_tree'`` and ``'ball_tree'`` that
-has ``effective_metric_`` in its ``VALID_METRICS`` list. This heuristic is
-based on the following assumptions:
-
-* the number of query points is at least the same order as the number of
-  training points
-* ``leaf_size`` is close to its default value of ``30``
-* when :math:`D > 15`, the intrinsic dimensionality of the data is generally
-  too high for tree-based methods
-
-|details-end|
-
-|details-start|
-**Effect of ``leaf_size``**
-|details-split|
-
-As noted above, for small sample sizes a brute force search can be more
-efficient than a tree-based query.  This fact is accounted for in the ball
-tree and KD tree by internally switching to brute force searches within
-leaf nodes.  The level of this switch can be specified with the parameter
-``leaf_size``.  This parameter choice has many effects:
-
-**construction time**
-  A larger ``leaf_size`` leads to a faster tree construction time, because
-  fewer nodes need to be created
-
-**query time**
-  Both a large or small ``leaf_size`` can lead to suboptimal query cost.
-  For ``leaf_size`` approaching 1, the overhead involved in traversing
-  nodes can significantly slow query times.  For ``leaf_size`` approaching
-  the size of the training set, queries become essentially brute force.
-  A good compromise between these is ``leaf_size = 30``, the default value
-  of the parameter.
-
-**memory**
-  As ``leaf_size`` increases, the memory required to store a tree structure
-  decreases.  This is especially important in the case of ball tree, which
-  stores a :math:`D`-dimensional centroid for each node.  The required
-  storage space for :class:`BallTree` is approximately ``1 / leaf_size`` times
-  the size of the training set.
-
-``leaf_size`` is not referenced for brute force queries.
-|details-end|
-
-|details-start|
-**Valid Metrics for Nearest Neighbor Algorithms**
-|details-split|
-
-For a list of available metrics, see the documentation of the
-:class:`~sklearn.metrics.DistanceMetric` class and the metrics listed in
-`sklearn.metrics.pairwise.PAIRWISE_DISTANCE_FUNCTIONS`. Note that the "cosine"
-metric uses :func:`~sklearn.metrics.pairwise.cosine_distances`.
-
-A list of valid metrics for any of the above algorithms can be obtained by using their
-``valid_metric`` attribute. For example, valid metrics for ``KDTree`` can be generated by:
-
-    >>> from sklearn.neighbors import KDTree
-    >>> print(sorted(KDTree.valid_metrics))
-    ['chebyshev', 'cityblock', 'euclidean', 'infinity', 'l1', 'l2', 'manhattan', 'minkowski', 'p']
 
-|details-end|
+.. dropdown:: References
+
+  * `"Five Balltree Construction Algorithms"
+    <https://citeseerx.ist.psu.edu/doc_view/pid/17ac002939f8e950ffb32ec4dc8e86bdd8cb5ff1>`_,
+    Omohundro, S.M., International Computer Science Institute
+    Technical Report (1989)
+
+.. dropdown:: Choice of Nearest Neighbors Algorithm
+
+  The optimal algorithm for a given dataset is a complicated choice, and
+  depends on a number of factors:
+
+  * number of samples :math:`N` (i.e. ``n_samples``) and dimensionality
+    :math:`D` (i.e. ``n_features``).
+
+    * *Brute force* query time grows as :math:`O[D N]`
+    * *Ball tree* query time grows as approximately :math:`O[D \log(N)]`
+    * *KD tree* query time changes with :math:`D` in a way that is difficult
+      to precisely characterise.  For small :math:`D` (less than 20 or so)
+      the cost is approximately :math:`O[D\log(N)]`, and the KD tree
+      query can be very efficient.
+      For larger :math:`D`, the cost increases to nearly :math:`O[DN]`, and
+      the overhead due to the tree
+      structure can lead to queries which are slower than brute force.
+
+    For small data sets (:math:`N` less than 30 or so), :math:`\log(N)` is
+    comparable to :math:`N`, and brute force algorithms can be more efficient
+    than a tree-based approach.  Both :class:`KDTree` and :class:`BallTree`
+    address this through providing a *leaf size* parameter: this controls the
+    number of samples at which a query switches to brute-force.  This allows both
+    algorithms to approach the efficiency of a brute-force computation for small
+    :math:`N`.
+
+  * data structure: *intrinsic dimensionality* of the data and/or *sparsity*
+    of the data. Intrinsic dimensionality refers to the dimension
+    :math:`d \le D` of a manifold on which the data lies, which can be linearly
+    or non-linearly embedded in the parameter space. Sparsity refers to the
+    degree to which the data fills the parameter space (this is to be
+    distinguished from the concept as used in "sparse" matrices.  The data
+    matrix may have no zero entries, but the **structure** can still be
+    "sparse" in this sense).
+
+    * *Brute force* query time is unchanged by data structure.
+    * *Ball tree* and *KD tree* query times can be greatly influenced
+      by data structure.  In general, sparser data with a smaller intrinsic
+      dimensionality leads to faster query times.  Because the KD tree
+      internal representation is aligned with the parameter axes, it will not
+      generally show as much improvement as ball tree for arbitrarily
+      structured data.
+
+    Datasets used in machine learning tend to be very structured, and are
+    very well-suited for tree-based queries.
+
+  * number of neighbors :math:`k` requested for a query point.
+
+    * *Brute force* query time is largely unaffected by the value of :math:`k`
+    * *Ball tree* and *KD tree* query time will become slower as :math:`k`
+      increases.  This is due to two effects: first, a larger :math:`k` leads
+      to the necessity to search a larger portion of the parameter space.
+      Second, using :math:`k > 1` requires internal queueing of results
+      as the tree is traversed.
+
+    As :math:`k` becomes large compared to :math:`N`, the ability to prune
+    branches in a tree-based query is reduced.  In this situation, Brute force
+    queries can be more efficient.
+
+  * number of query points.  Both the ball tree and the KD Tree
+    require a construction phase.  The cost of this construction becomes
+    negligible when amortized over many queries.  If only a small number of
+    queries will be performed, however, the construction can make up
+    a significant fraction of the total cost.  If very few query points
+    will be required, brute force is better than a tree-based method.
+
+  Currently, ``algorithm = 'auto'`` selects ``'brute'`` if any of the following
+  conditions are verified:
+
+  * input data is sparse
+  * ``metric = 'precomputed'``
+  * :math:`D > 15`
+  * :math:`k >= N/2`
+  * ``effective_metric_`` isn't in the ``VALID_METRICS`` list for either
+    ``'kd_tree'`` or ``'ball_tree'``
+
+  Otherwise, it selects the first out of ``'kd_tree'`` and ``'ball_tree'`` that
+  has ``effective_metric_`` in its ``VALID_METRICS`` list. This heuristic is
+  based on the following assumptions:
+
+  * the number of query points is at least the same order as the number of
+    training points
+  * ``leaf_size`` is close to its default value of ``30``
+  * when :math:`D > 15`, the intrinsic dimensionality of the data is generally
+    too high for tree-based methods
+
+.. dropdown:: Effect of ``leaf_size``
+
+  As noted above, for small sample sizes a brute force search can be more
+  efficient than a tree-based query.  This fact is accounted for in the ball
+  tree and KD tree by internally switching to brute force searches within
+  leaf nodes.  The level of this switch can be specified with the parameter
+  ``leaf_size``.  This parameter choice has many effects:
+
+  **construction time**
+    A larger ``leaf_size`` leads to a faster tree construction time, because
+    fewer nodes need to be created
+
+  **query time**
+    Both a large or small ``leaf_size`` can lead to suboptimal query cost.
+    For ``leaf_size`` approaching 1, the overhead involved in traversing
+    nodes can significantly slow query times.  For ``leaf_size`` approaching
+    the size of the training set, queries become essentially brute force.
+    A good compromise between these is ``leaf_size = 30``, the default value
+    of the parameter.
+
+  **memory**
+    As ``leaf_size`` increases, the memory required to store a tree structure
+    decreases.  This is especially important in the case of ball tree, which
+    stores a :math:`D`-dimensional centroid for each node.  The required
+    storage space for :class:`BallTree` is approximately ``1 / leaf_size`` times
+    the size of the training set.
+
+  ``leaf_size`` is not referenced for brute force queries.
+
+.. dropdown:: Valid Metrics for Nearest Neighbor Algorithms
+
+  For a list of available metrics, see the documentation of the
+  :class:`~sklearn.metrics.DistanceMetric` class and the metrics listed in
+  `sklearn.metrics.pairwise.PAIRWISE_DISTANCE_FUNCTIONS`. Note that the "cosine"
+  metric uses :func:`~sklearn.metrics.pairwise.cosine_distances`.
+
+  A list of valid metrics for any of the above algorithms can be obtained by using their
+  ``valid_metric`` attribute. For example, valid metrics for ``KDTree`` can be generated by:
+
+      >>> from sklearn.neighbors import KDTree
+      >>> print(sorted(KDTree.valid_metrics))
+      ['chebyshev', 'cityblock', 'euclidean', 'infinity', 'l1', 'l2', 'manhattan', 'minkowski', 'p']
 
 .. _nearest_centroid_classifier:
 
@@ -547,10 +531,10 @@ the model from 0.81 to 0.82.
 
 .. centered:: |nearest_centroid_1| |nearest_centroid_2|
 
-.. topic:: Examples:
+.. rubric:: Examples
 
-  * :ref:`sphx_glr_auto_examples_neighbors_plot_nearest_centroid.py`: an example of
-    classification using nearest centroid with different shrink thresholds.
+* :ref:`sphx_glr_auto_examples_neighbors_plot_nearest_centroid.py`: an example of
+  classification using nearest centroid with different shrink thresholds.
 
 .. _neighbors_transformer:
 
@@ -635,17 +619,17 @@ implementation with special data types. The precomputed neighbors
   include one extra neighbor in a custom nearest neighbors estimator, since
   unnecessary neighbors will be filtered by following estimators.
 
-.. topic:: Examples:
+.. rubric:: Examples
 
-  * :ref:`sphx_glr_auto_examples_neighbors_approximate_nearest_neighbors.py`:
-    an example of pipelining :class:`KNeighborsTransformer` and
-    :class:`~sklearn.manifold.TSNE`. Also proposes two custom nearest neighbors
-    estimators based on external packages.
+* :ref:`sphx_glr_auto_examples_neighbors_approximate_nearest_neighbors.py`:
+  an example of pipelining :class:`KNeighborsTransformer` and
+  :class:`~sklearn.manifold.TSNE`. Also proposes two custom nearest neighbors
+  estimators based on external packages.
 
-  * :ref:`sphx_glr_auto_examples_neighbors_plot_caching_nearest_neighbors.py`:
-    an example of pipelining :class:`KNeighborsTransformer` and
-    :class:`KNeighborsClassifier` to enable caching of the neighbors graph
-    during a hyper-parameter grid-search.
+* :ref:`sphx_glr_auto_examples_neighbors_plot_caching_nearest_neighbors.py`:
+  an example of pipelining :class:`KNeighborsTransformer` and
+  :class:`KNeighborsClassifier` to enable caching of the neighbors graph
+  during a hyper-parameter grid-search.
 
 .. _nca:
 
@@ -769,11 +753,11 @@ by each method. Each data sample belongs to one of 10 classes.
 .. centered:: |nca_dim_reduction_1| |nca_dim_reduction_2| |nca_dim_reduction_3|
 
 
-.. topic:: Examples:
+.. rubric:: Examples
 
- * :ref:`sphx_glr_auto_examples_neighbors_plot_nca_classification.py`
- * :ref:`sphx_glr_auto_examples_neighbors_plot_nca_dim_reduction.py`
- * :ref:`sphx_glr_auto_examples_manifold_plot_lle_digits.py`
+* :ref:`sphx_glr_auto_examples_neighbors_plot_nca_classification.py`
+* :ref:`sphx_glr_auto_examples_neighbors_plot_nca_dim_reduction.py`
+* :ref:`sphx_glr_auto_examples_manifold_plot_lle_digits.py`
 
 .. _nca_mathematical_formulation:
 
@@ -806,20 +790,17 @@ space:
   p_{i j} = \frac{\exp(-||L x_i - L x_j||^2)}{\sum\limits_{k \ne
             i} {\exp{-(||L x_i - L x_k||^2)}}} , \quad p_{i i} = 0
 
-|details-start|
-**Mahalanobis distance**
-|details-split|
+.. dropdown:: Mahalanobis distance
 
-NCA can be seen as learning a (squared) Mahalanobis distance metric:
+  NCA can be seen as learning a (squared) Mahalanobis distance metric:
 
-.. math::
+  .. math::
 
-    || L(x_i - x_j)||^2 = (x_i - x_j)^TM(x_i - x_j),
+      || L(x_i - x_j)||^2 = (x_i - x_j)^TM(x_i - x_j),
 
-where :math:`M = L^T L` is a symmetric positive semi-definite matrix of size
-``(n_features, n_features)``.
+  where :math:`M = L^T L` is a symmetric positive semi-definite matrix of size
+  ``(n_features, n_features)``.
 
-|details-end|
 
 Implementation
 --------------
@@ -851,14 +832,12 @@ complexity equals ``n_components * n_features * n_samples_test``. There is no
 added space complexity in the operation.
 
 
-.. topic:: References:
-
-    .. [1] `"Neighbourhood Components Analysis"
-      <http://www.cs.nyu.edu/~roweis/papers/ncanips.pdf>`_,
-      J. Goldberger, S. Roweis, G. Hinton, R. Salakhutdinov, Advances in
-      Neural Information Processing Systems, Vol. 17, May 2005, pp. 513-520.
+.. rubric:: References
 
-    `Wikipedia entry on Neighborhood Components Analysis
-    <https://en.wikipedia.org/wiki/Neighbourhood_components_analysis>`_
+.. [1] `"Neighbourhood Components Analysis"
+  <http://www.cs.nyu.edu/~roweis/papers/ncanips.pdf>`_,
+  J. Goldberger, S. Roweis, G. Hinton, R. Salakhutdinov, Advances in
+  Neural Information Processing Systems, Vol. 17, May 2005, pp. 513-520.
 
-|details-end|
+* `Wikipedia entry on Neighborhood Components Analysis
+  <https://en.wikipedia.org/wiki/Neighbourhood_components_analysis>`_
diff --git a/doc/modules/neural_networks_supervised.rst b/doc/modules/neural_networks_supervised.rst
index 95d0a1be38238..5c6baecb7e2ff 100644
--- a/doc/modules/neural_networks_supervised.rst
+++ b/doc/modules/neural_networks_supervised.rst
@@ -49,33 +49,30 @@ The module contains the public attributes ``coefs_`` and ``intercepts_``.
 :math:`i+1`. ``intercepts_`` is a list of bias vectors, where the vector
 at index :math:`i` represents the bias values added to layer :math:`i+1`.
 
-|details-start|
-**Advantages and disadvantages of Multi-layer Perceptron**
-|details-split|
+.. dropdown:: Advantages and disadvantages of Multi-layer Perceptron
 
-The advantages of Multi-layer Perceptron are:
+  The advantages of Multi-layer Perceptron are:
 
-+ Capability to learn non-linear models.
+  + Capability to learn non-linear models.
 
-+ Capability to learn models in real-time (on-line learning)
-  using ``partial_fit``.
+  + Capability to learn models in real-time (on-line learning)
+    using ``partial_fit``.
 
 
-The disadvantages of Multi-layer Perceptron (MLP) include:
+  The disadvantages of Multi-layer Perceptron (MLP) include:
 
-+ MLP with hidden layers have a non-convex loss function where there exists
-  more than one local minimum. Therefore different random weight
-  initializations can lead to different validation accuracy.
+  + MLP with hidden layers have a non-convex loss function where there exists
+    more than one local minimum. Therefore different random weight
+    initializations can lead to different validation accuracy.
 
-+ MLP requires tuning a number of hyperparameters such as the number of
-  hidden neurons, layers, and iterations.
+  + MLP requires tuning a number of hyperparameters such as the number of
+    hidden neurons, layers, and iterations.
 
-+ MLP is sensitive to feature scaling.
+  + MLP is sensitive to feature scaling.
 
-Please see :ref:`Tips on Practical Use <mlp_tips>` section that addresses
-some of these disadvantages.
+  Please see :ref:`Tips on Practical Use <mlp_tips>` section that addresses
+  some of these disadvantages.
 
-|details-end|
 
 Classification
 ==============
@@ -148,11 +145,11 @@ indices where the value is `1` represents the assigned classes of that sample::
 See the examples below and the docstring of
 :meth:`MLPClassifier.fit` for further information.
 
-.. topic:: Examples:
+.. rubric:: Examples
 
- * :ref:`sphx_glr_auto_examples_neural_networks_plot_mlp_training_curves.py`
- * See :ref:`sphx_glr_auto_examples_neural_networks_plot_mnist_filters.py` for
-   visualized representation of trained weights.
+* :ref:`sphx_glr_auto_examples_neural_networks_plot_mlp_training_curves.py`
+* See :ref:`sphx_glr_auto_examples_neural_networks_plot_mnist_filters.py` for
+  visualized representation of trained weights.
 
 Regression
 ==========
@@ -181,9 +178,9 @@ decision function with value of alpha.
 
 See the examples below for further information.
 
-.. topic:: Examples:
+.. rubric:: Examples
 
- * :ref:`sphx_glr_auto_examples_neural_networks_plot_mlp_alpha.py`
+* :ref:`sphx_glr_auto_examples_neural_networks_plot_mlp_alpha.py`
 
 Algorithms
 ==========
@@ -229,88 +226,83 @@ Complexity
 Suppose there are :math:`n` training samples, :math:`m` features, :math:`k`
 hidden layers, each containing :math:`h` neurons - for simplicity, and :math:`o`
 output neurons.  The time complexity of backpropagation is
-:math:`O(n\cdot m \cdot h^k \cdot o \cdot i)`, where :math:`i` is the number
+:math:`O(i \cdot n \cdot (m \cdot h + (k - 1) \cdot h \cdot h + h \cdot o))`, where :math:`i` is the number
 of iterations. Since backpropagation has a high time complexity, it is advisable
 to start with smaller number of hidden neurons and few hidden layers for
 training.
 
-|details-start|
-Mathematical formulation
-|details-split|
+.. dropdown:: Mathematical formulation
 
-Given a set of training examples :math:`(x_1, y_1), (x_2, y_2), \ldots, (x_n, y_n)`
-where :math:`x_i \in \mathbf{R}^n` and :math:`y_i \in \{0, 1\}`, a one hidden
-layer one hidden neuron MLP learns the function :math:`f(x) = W_2 g(W_1^T x + b_1) + b_2`
-where :math:`W_1 \in \mathbf{R}^m` and :math:`W_2, b_1, b_2 \in \mathbf{R}` are
-model parameters. :math:`W_1, W_2` represent the weights of the input layer and
-hidden layer, respectively; and :math:`b_1, b_2` represent the bias added to
-the hidden layer and the output layer, respectively.
-:math:`g(\cdot) : R \rightarrow R` is the activation function, set by default as
-the hyperbolic tan. It is given as,
+  Given a set of training examples :math:`(x_1, y_1), (x_2, y_2), \ldots, (x_n, y_n)`
+  where :math:`x_i \in \mathbf{R}^n` and :math:`y_i \in \{0, 1\}`, a one hidden
+  layer one hidden neuron MLP learns the function :math:`f(x) = W_2 g(W_1^T x + b_1) + b_2`
+  where :math:`W_1 \in \mathbf{R}^m` and :math:`W_2, b_1, b_2 \in \mathbf{R}` are
+  model parameters. :math:`W_1, W_2` represent the weights of the input layer and
+  hidden layer, respectively; and :math:`b_1, b_2` represent the bias added to
+  the hidden layer and the output layer, respectively.
+  :math:`g(\cdot) : R \rightarrow R` is the activation function, set by default as
+  the hyperbolic tan. It is given as,
 
-.. math::
-      g(z)= \frac{e^z-e^{-z}}{e^z+e^{-z}}
-
-For binary classification, :math:`f(x)` passes through the logistic function
-:math:`g(z)=1/(1+e^{-z})` to obtain output values between zero and one. A
-threshold, set to 0.5, would assign samples of outputs larger or equal 0.5
-to the positive class, and the rest to the negative class.
+  .. math::
+        g(z)= \frac{e^z-e^{-z}}{e^z+e^{-z}}
 
-If there are more than two classes, :math:`f(x)` itself would be a vector of
-size (n_classes,). Instead of passing through logistic function, it passes
-through the softmax function, which is written as,
-
-.. math::
-      \text{softmax}(z)_i = \frac{\exp(z_i)}{\sum_{l=1}^k\exp(z_l)}
+  For binary classification, :math:`f(x)` passes through the logistic function
+  :math:`g(z)=1/(1+e^{-z})` to obtain output values between zero and one. A
+  threshold, set to 0.5, would assign samples of outputs larger or equal 0.5
+  to the positive class, and the rest to the negative class.
 
-where :math:`z_i` represents the :math:`i` th element of the input to softmax,
-which corresponds to class :math:`i`, and :math:`K` is the number of classes.
-The result is a vector containing the probabilities that sample :math:`x`
-belong to each class. The output is the class with the highest probability.
+  If there are more than two classes, :math:`f(x)` itself would be a vector of
+  size (n_classes,). Instead of passing through logistic function, it passes
+  through the softmax function, which is written as,
 
-In regression, the output remains as :math:`f(x)`; therefore, output activation
-function is just the identity function.
+  .. math::
+        \text{softmax}(z)_i = \frac{\exp(z_i)}{\sum_{l=1}^k\exp(z_l)}
 
-MLP uses different loss functions depending on the problem type. The loss
-function for classification is Average Cross-Entropy, which in binary case is
-given as,
+  where :math:`z_i` represents the :math:`i` th element of the input to softmax,
+  which corresponds to class :math:`i`, and :math:`K` is the number of classes.
+  The result is a vector containing the probabilities that sample :math:`x`
+  belong to each class. The output is the class with the highest probability.
 
-.. math::
+  In regression, the output remains as :math:`f(x)`; therefore, output activation
+  function is just the identity function.
 
-    Loss(\hat{y},y,W) = -\dfrac{1}{n}\sum_{i=0}^n(y_i \ln {\hat{y_i}} + (1-y_i) \ln{(1-\hat{y_i})}) + \dfrac{\alpha}{2n} ||W||_2^2
+  MLP uses different loss functions depending on the problem type. The loss
+  function for classification is Average Cross-Entropy, which in binary case is
+  given as,
 
-where :math:`\alpha ||W||_2^2` is an L2-regularization term (aka penalty)
-that penalizes complex models; and :math:`\alpha > 0` is a non-negative
-hyperparameter that controls the magnitude of the penalty.
+  .. math::
 
-For regression, MLP uses the Mean Square Error loss function; written as,
+      Loss(\hat{y},y,W) = -\dfrac{1}{n}\sum_{i=0}^n(y_i \ln {\hat{y_i}} + (1-y_i) \ln{(1-\hat{y_i})}) + \dfrac{\alpha}{2n} ||W||_2^2
 
-.. math::
+  where :math:`\alpha ||W||_2^2` is an L2-regularization term (aka penalty)
+  that penalizes complex models; and :math:`\alpha > 0` is a non-negative
+  hyperparameter that controls the magnitude of the penalty.
 
-    Loss(\hat{y},y,W) = \frac{1}{2n}\sum_{i=0}^n||\hat{y}_i - y_i ||_2^2 + \frac{\alpha}{2n} ||W||_2^2
+  For regression, MLP uses the Mean Square Error loss function; written as,
 
+  .. math::
 
-Starting from initial random weights, multi-layer perceptron (MLP) minimizes
-the loss function by repeatedly updating these weights. After computing the
-loss, a backward pass propagates it from the output layer to the previous
-layers, providing each weight parameter with an update value meant to decrease
-the loss.
+      Loss(\hat{y},y,W) = \frac{1}{2n}\sum_{i=0}^n||\hat{y}_i - y_i ||_2^2 + \frac{\alpha}{2n} ||W||_2^2
 
-In gradient descent, the gradient :math:`\nabla Loss_{W}` of the loss with respect
-to the weights is computed and deducted from :math:`W`.
-More formally, this is expressed as,
+  Starting from initial random weights, multi-layer perceptron (MLP) minimizes
+  the loss function by repeatedly updating these weights. After computing the
+  loss, a backward pass propagates it from the output layer to the previous
+  layers, providing each weight parameter with an update value meant to decrease
+  the loss.
 
-.. math::
-    W^{i+1} = W^i - \epsilon \nabla {Loss}_{W}^{i}
+  In gradient descent, the gradient :math:`\nabla Loss_{W}` of the loss with respect
+  to the weights is computed and deducted from :math:`W`.
+  More formally, this is expressed as,
 
+  .. math::
+      W^{i+1} = W^i - \epsilon \nabla {Loss}_{W}^{i}
 
-where :math:`i` is the iteration step, and :math:`\epsilon` is the learning rate
-with a value larger than 0.
+  where :math:`i` is the iteration step, and :math:`\epsilon` is the learning rate
+  with a value larger than 0.
 
-The algorithm stops when it reaches a preset maximum number of iterations; or
-when the improvement in loss is below a certain, small number.
+  The algorithm stops when it reaches a preset maximum number of iterations; or
+  when the improvement in loss is below a certain, small number.
 
-|details-end|
 
 .. _mlp_tips:
 
@@ -361,25 +353,19 @@ or want to do additional monitoring, using ``warm_start=True`` and
     ...     # additional monitoring / inspection
     MLPClassifier(...
 
-|details-start|
-**References**
-|details-split|
-
-    * `"Learning representations by back-propagating errors."
-      <https://www.iro.umontreal.ca/~pift6266/A06/refs/backprop_old.pdf>`_
-      Rumelhart, David E., Geoffrey E. Hinton, and Ronald J. Williams.
+.. dropdown:: References
 
-    * `"Stochastic Gradient Descent" <https://leon.bottou.org/projects/sgd>`_ L. Bottou - Website, 2010.
+  * `"Learning representations by back-propagating errors."
+    <https://www.iro.umontreal.ca/~pift6266/A06/refs/backprop_old.pdf>`_
+    Rumelhart, David E., Geoffrey E. Hinton, and Ronald J. Williams.
 
-    * `"Backpropagation" <http://ufldl.stanford.edu/wiki/index.php/Backpropagation_Algorithm>`_
-      Andrew Ng, Jiquan Ngiam, Chuan Yu Foo, Yifan Mai, Caroline Suen - Website, 2011.
+  * `"Stochastic Gradient Descent" <https://leon.bottou.org/projects/sgd>`_ L. Bottou - Website, 2010.
 
-    * `"Efficient BackProp" <http://yann.lecun.com/exdb/publis/pdf/lecun-98b.pdf>`_
-      Y. LeCun, L. Bottou, G. Orr, K. Müller - In Neural Networks: Tricks
-      of the Trade 1998.
+  * `"Backpropagation" <http://ufldl.stanford.edu/wiki/index.php/Backpropagation_Algorithm>`_
+    Andrew Ng, Jiquan Ngiam, Chuan Yu Foo, Yifan Mai, Caroline Suen - Website, 2011.
 
-    *  :arxiv:`"Adam: A method for stochastic optimization."
-       <1412.6980>`
-       Kingma, Diederik, and Jimmy Ba (2014)
+  * `"Efficient BackProp" <http://yann.lecun.com/exdb/publis/pdf/lecun-98b.pdf>`_
+    Y. LeCun, L. Bottou, G. Orr, K. Müller - In Neural Networks: Tricks of the Trade 1998.
 
-|details-end|
+  * :arxiv:`"Adam: A method for stochastic optimization." <1412.6980>`
+    Kingma, Diederik, and Jimmy Ba (2014)
diff --git a/doc/modules/neural_networks_unsupervised.rst b/doc/modules/neural_networks_unsupervised.rst
index aca56ae8aaf2e..7f6c0016d183b 100644
--- a/doc/modules/neural_networks_unsupervised.rst
+++ b/doc/modules/neural_networks_unsupervised.rst
@@ -37,9 +37,9 @@ weights of independent RBMs. This method is known as unsupervised pre-training.
    :align: center
    :scale: 100%
 
-.. topic:: Examples:
+.. rubric:: Examples
 
-   * :ref:`sphx_glr_auto_examples_neural_networks_plot_rbm_logistic_classification.py`
+* :ref:`sphx_glr_auto_examples_neural_networks_plot_rbm_logistic_classification.py`
 
 
 Graphical model and parametrization
@@ -57,7 +57,7 @@ visible and hidden unit, omitted from the image for simplicity.
 
 The energy function measures the quality of a joint assignment:
 
-.. math:: 
+.. math::
 
    E(\mathbf{v}, \mathbf{h}) = -\sum_i \sum_j w_{ij}v_ih_j - \sum_i b_iv_i
      - \sum_j c_jh_j
@@ -149,13 +149,13 @@ step, in PCD we keep a number of chains (fantasy particles) that are updated
 :math:`k` Gibbs steps after each weight update. This allows the particles to
 explore the space more thoroughly.
 
-.. topic:: References:
+.. rubric:: References
 
-    * `"A fast learning algorithm for deep belief nets"
-      <https://www.cs.toronto.edu/~hinton/absps/fastnc.pdf>`_
-      G. Hinton, S. Osindero, Y.-W. Teh, 2006
+* `"A fast learning algorithm for deep belief nets"
+  <https://www.cs.toronto.edu/~hinton/absps/fastnc.pdf>`_,
+  G. Hinton, S. Osindero, Y.-W. Teh, 2006
 
-    * `"Training Restricted Boltzmann Machines using Approximations to
-      the Likelihood Gradient"
-      <https://www.cs.toronto.edu/~tijmen/pcd/pcd.pdf>`_
-      T. Tieleman, 2008
+* `"Training Restricted Boltzmann Machines using Approximations to
+  the Likelihood Gradient"
+  <https://www.cs.toronto.edu/~tijmen/pcd/pcd.pdf>`_,
+  T. Tieleman, 2008
diff --git a/doc/modules/outlier_detection.rst b/doc/modules/outlier_detection.rst
index d003b645eb19c..0c6891ed119bd 100644
--- a/doc/modules/outlier_detection.rst
+++ b/doc/modules/outlier_detection.rst
@@ -123,19 +123,19 @@ refer to the example
 :ref:`sphx_glr_auto_examples_miscellaneous_plot_anomaly_comparison.py` and the
 sections hereunder.
 
-.. topic:: Examples:
+.. rubric:: Examples
 
-  * See :ref:`sphx_glr_auto_examples_miscellaneous_plot_anomaly_comparison.py`
-    for a comparison of the :class:`svm.OneClassSVM`, the
-    :class:`ensemble.IsolationForest`, the
-    :class:`neighbors.LocalOutlierFactor` and
-    :class:`covariance.EllipticEnvelope`.
+* See :ref:`sphx_glr_auto_examples_miscellaneous_plot_anomaly_comparison.py`
+  for a comparison of the :class:`svm.OneClassSVM`, the
+  :class:`ensemble.IsolationForest`, the
+  :class:`neighbors.LocalOutlierFactor` and
+  :class:`covariance.EllipticEnvelope`.
 
-  * See :ref:`sphx_glr_auto_examples_miscellaneous_plot_outlier_detection_bench.py`
-    for an example showing how to evaluate outlier detection estimators,
-    the :class:`neighbors.LocalOutlierFactor` and the
-    :class:`ensemble.IsolationForest`, using ROC curves from
-    :class:`metrics.RocCurveDisplay`.
+* See :ref:`sphx_glr_auto_examples_miscellaneous_plot_outlier_detection_bench.py`
+  for an example showing how to evaluate outlier detection estimators,
+  the :class:`neighbors.LocalOutlierFactor` and the
+  :class:`ensemble.IsolationForest`, using ROC curves from
+  :class:`metrics.RocCurveDisplay`.
 
 Novelty Detection
 =================
@@ -167,18 +167,18 @@ implementation. The `nu` parameter, also known as the margin of
 the One-Class SVM, corresponds to the probability of finding a new,
 but regular, observation outside the frontier.
 
-.. topic:: References:
+.. rubric:: References
 
-    * `Estimating the support of a high-dimensional distribution
-      <https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/tr-99-87.pdf>`_
-      Schölkopf, Bernhard, et al. Neural computation 13.7 (2001): 1443-1471.
+* `Estimating the support of a high-dimensional distribution
+  <https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/tr-99-87.pdf>`_
+  Schölkopf, Bernhard, et al. Neural computation 13.7 (2001): 1443-1471.
 
-.. topic:: Examples:
+.. rubric:: Examples
 
-   * See :ref:`sphx_glr_auto_examples_svm_plot_oneclass.py` for visualizing the
-     frontier learned around some data by a
-     :class:`svm.OneClassSVM` object.
-   * :ref:`sphx_glr_auto_examples_applications_plot_species_distribution_modeling.py`
+* See :ref:`sphx_glr_auto_examples_svm_plot_oneclass.py` for visualizing the
+  frontier learned around some data by a :class:`svm.OneClassSVM` object.
+
+* :ref:`sphx_glr_auto_examples_applications_plot_species_distribution_modeling.py`
 
 .. figure:: ../auto_examples/svm/images/sphx_glr_plot_oneclass_001.png
    :target: ../auto_examples/svm/plot_oneclass.html
@@ -196,11 +196,11 @@ approximate the solution of a kernelized :class:`svm.OneClassSVM` whose
 complexity is at best quadratic in the number of samples. See section
 :ref:`sgd_online_one_class_svm` for more details.
 
-.. topic:: Examples:
+.. rubric:: Examples
 
-  * See :ref:`sphx_glr_auto_examples_linear_model_plot_sgdocsvm_vs_ocsvm.py`
-    for an illustration of the approximation of a kernelized One-Class SVM
-    with the `linear_model.SGDOneClassSVM` combined with kernel approximation.
+* See :ref:`sphx_glr_auto_examples_linear_model_plot_sgdocsvm_vs_ocsvm.py`
+  for an illustration of the approximation of a kernelized One-Class SVM
+  with the `linear_model.SGDOneClassSVM` combined with kernel approximation.
 
 
 Outlier Detection
@@ -238,18 +238,18 @@ This strategy is illustrated below.
    :align: center
    :scale: 75%
 
-.. topic:: Examples:
+.. rubric:: Examples
 
-   * See :ref:`sphx_glr_auto_examples_covariance_plot_mahalanobis_distances.py` for
-     an illustration of the difference between using a standard
-     (:class:`covariance.EmpiricalCovariance`) or a robust estimate
-     (:class:`covariance.MinCovDet`) of location and covariance to
-     assess the degree of outlyingness of an observation.
+* See :ref:`sphx_glr_auto_examples_covariance_plot_mahalanobis_distances.py` for
+  an illustration of the difference between using a standard
+  (:class:`covariance.EmpiricalCovariance`) or a robust estimate
+  (:class:`covariance.MinCovDet`) of location and covariance to
+  assess the degree of outlyingness of an observation.
 
-.. topic:: References:
+.. rubric:: References
 
-    * Rousseeuw, P.J., Van Driessen, K. "A fast algorithm for the minimum
-      covariance determinant estimator" Technometrics 41(3), 212 (1999)
+* Rousseeuw, P.J., Van Driessen, K. "A fast algorithm for the minimum
+  covariance determinant estimator" Technometrics 41(3), 212 (1999)
 
 .. _isolation_forest:
 
@@ -299,22 +299,22 @@ allows you to add more trees to an already fitted model::
   >>> clf.set_params(n_estimators=20)  # add 10 more trees  # doctest: +SKIP
   >>> clf.fit(X)  # fit the added trees  # doctest: +SKIP
 
-.. topic:: Examples:
+.. rubric:: Examples
 
-   * See :ref:`sphx_glr_auto_examples_ensemble_plot_isolation_forest.py` for
-     an illustration of the use of IsolationForest.
+* See :ref:`sphx_glr_auto_examples_ensemble_plot_isolation_forest.py` for
+  an illustration of the use of IsolationForest.
 
-   * See :ref:`sphx_glr_auto_examples_miscellaneous_plot_anomaly_comparison.py`
-     for a comparison of :class:`ensemble.IsolationForest` with
-     :class:`neighbors.LocalOutlierFactor`,
-     :class:`svm.OneClassSVM` (tuned to perform like an outlier detection
-     method), :class:`linear_model.SGDOneClassSVM`, and a covariance-based
-     outlier detection with :class:`covariance.EllipticEnvelope`.
+* See :ref:`sphx_glr_auto_examples_miscellaneous_plot_anomaly_comparison.py`
+  for a comparison of :class:`ensemble.IsolationForest` with
+  :class:`neighbors.LocalOutlierFactor`,
+  :class:`svm.OneClassSVM` (tuned to perform like an outlier detection
+  method), :class:`linear_model.SGDOneClassSVM`, and a covariance-based
+  outlier detection with :class:`covariance.EllipticEnvelope`.
 
-.. topic:: References:
+.. rubric:: References
 
-    * Liu, Fei Tony, Ting, Kai Ming and Zhou, Zhi-Hua. "Isolation forest."
-      Data Mining, 2008. ICDM'08. Eighth IEEE International Conference on.
+* Liu, Fei Tony, Ting, Kai Ming and Zhou, Zhi-Hua. "Isolation forest."
+  Data Mining, 2008. ICDM'08. Eighth IEEE International Conference on.
 
 .. _local_outlier_factor:
 
@@ -370,20 +370,20 @@ This strategy is illustrated below.
    :align: center
    :scale: 75%
 
-.. topic:: Examples:
+.. rubric:: Examples
 
-   * See :ref:`sphx_glr_auto_examples_neighbors_plot_lof_outlier_detection.py`
-     for an illustration of the use of :class:`neighbors.LocalOutlierFactor`.
+* See :ref:`sphx_glr_auto_examples_neighbors_plot_lof_outlier_detection.py`
+  for an illustration of the use of :class:`neighbors.LocalOutlierFactor`.
 
-   * See :ref:`sphx_glr_auto_examples_miscellaneous_plot_anomaly_comparison.py`
-     for a comparison with other anomaly detection methods.
+* See :ref:`sphx_glr_auto_examples_miscellaneous_plot_anomaly_comparison.py`
+  for a comparison with other anomaly detection methods.
 
-.. topic:: References:
+.. rubric:: References
 
-   *  Breunig, Kriegel, Ng, and Sander (2000)
-      `LOF: identifying density-based local outliers.
-      <https://www.dbs.ifi.lmu.de/Publikationen/Papers/LOF.pdf>`_
-      Proc. ACM SIGMOD
+* Breunig, Kriegel, Ng, and Sander (2000)
+  `LOF: identifying density-based local outliers.
+  <https://www.dbs.ifi.lmu.de/Publikationen/Papers/LOF.pdf>`_
+  Proc. ACM SIGMOD
 
 .. _novelty_with_lof:
 
diff --git a/doc/modules/partial_dependence.rst b/doc/modules/partial_dependence.rst
index 94f7206140b90..40f691a9e6dcc 100644
--- a/doc/modules/partial_dependence.rst
+++ b/doc/modules/partial_dependence.rst
@@ -79,25 +79,21 @@ parameter takes a list of indices, names of the categorical features or a boolea
 mask. The graphical representation of partial dependence for categorical features is
 a bar plot or a 2D heatmap.
 
-|details-start|
-**PDPs for multi-class classification**
-|details-split|
-
-For multi-class classification, you need to set the class label for which
-the PDPs should be created via the ``target`` argument::
-
-    >>> from sklearn.datasets import load_iris
-    >>> iris = load_iris()
-    >>> mc_clf = GradientBoostingClassifier(n_estimators=10,
-    ...     max_depth=1).fit(iris.data, iris.target)
-    >>> features = [3, 2, (3, 2)]
-    >>> PartialDependenceDisplay.from_estimator(mc_clf, X, features, target=0)
-    <...>
+.. dropdown:: PDPs for multi-class classification
+
+    For multi-class classification, you need to set the class label for which
+    the PDPs should be created via the ``target`` argument::
 
-The same parameter ``target`` is used to specify the target in multi-output
-regression settings.
+        >>> from sklearn.datasets import load_iris
+        >>> iris = load_iris()
+        >>> mc_clf = GradientBoostingClassifier(n_estimators=10,
+        ...     max_depth=1).fit(iris.data, iris.target)
+        >>> features = [3, 2, (3, 2)]
+        >>> PartialDependenceDisplay.from_estimator(mc_clf, X, features, target=0)
+        <...>
 
-|details-end|
+    The same parameter ``target`` is used to specify the target in multi-output
+    regression settings.
 
 If you need the raw values of the partial dependence function rather than
 the plots, you can use the
@@ -266,9 +262,9 @@ estimators that support it, and 'brute' is used for the rest.
     interpreting PDPs is that the features should be independent.
 
 
-.. topic:: Examples:
+.. rubric:: Examples
 
- * :ref:`sphx_glr_auto_examples_inspection_plot_partial_dependence.py`
+* :ref:`sphx_glr_auto_examples_inspection_plot_partial_dependence.py`
 
 .. rubric:: Footnotes
 
@@ -276,21 +272,20 @@ estimators that support it, and 'brute' is used for the rest.
    class (the positive class for binary classification), or the decision
    function.
 
-.. topic:: References
+.. rubric:: References
 
-    .. [H2009] T. Hastie, R. Tibshirani and J. Friedman,
-               `The Elements of Statistical Learning
-               <https://web.stanford.edu/~hastie/ElemStatLearn//>`_,
-               Second Edition, Section 10.13.2, Springer, 2009.
+.. [H2009] T. Hastie, R. Tibshirani and J. Friedman,
+    `The Elements of Statistical Learning
+    <https://web.stanford.edu/~hastie/ElemStatLearn//>`_,
+    Second Edition, Section 10.13.2, Springer, 2009.
 
-    .. [M2019] C. Molnar,
-               `Interpretable Machine Learning
-               <https://christophm.github.io/interpretable-ml-book/>`_,
-               Section 5.1, 2019.
+.. [M2019] C. Molnar,
+    `Interpretable Machine Learning
+    <https://christophm.github.io/interpretable-ml-book/>`_,
+    Section 5.1, 2019.
 
-    .. [G2015] :arxiv:`A. Goldstein, A. Kapelner, J. Bleich, and E. Pitkin,
-               "Peeking Inside the Black Box: Visualizing Statistical
-               Learning With Plots of Individual Conditional Expectation"
-               Journal of Computational and Graphical Statistics,
-               24(1): 44-65, Springer, 2015.
-               <1309.6392>`
+.. [G2015] :arxiv:`A. Goldstein, A. Kapelner, J. Bleich, and E. Pitkin,
+    "Peeking Inside the Black Box: Visualizing Statistical
+    Learning With Plots of Individual Conditional Expectation"
+    Journal of Computational and Graphical Statistics,
+    24(1): 44-65, Springer, 2015. <1309.6392>`
diff --git a/doc/modules/permutation_importance.rst b/doc/modules/permutation_importance.rst
index 368c6a6409aa0..12a20a8bcaa6c 100644
--- a/doc/modules/permutation_importance.rst
+++ b/doc/modules/permutation_importance.rst
@@ -110,48 +110,44 @@ which is more computationally efficient than sequentially calling
 :func:`permutation_importance` several times with a different scorer, as it
 reuses model predictions.
 
-|details-start|
-**Example of permutation feature importance using multiple scorers**
-|details-split|
-
-In the example below we use a list of metrics, but more input formats are
-possible, as documented in :ref:`multimetric_scoring`.
-
-  >>> scoring = ['r2', 'neg_mean_absolute_percentage_error', 'neg_mean_squared_error']
-  >>> r_multi = permutation_importance(
-  ...     model, X_val, y_val, n_repeats=30, random_state=0, scoring=scoring)
-  ...
-  >>> for metric in r_multi:
-  ...     print(f"{metric}")
-  ...     r = r_multi[metric]
-  ...     for i in r.importances_mean.argsort()[::-1]:
-  ...         if r.importances_mean[i] - 2 * r.importances_std[i] > 0:
-  ...             print(f"    {diabetes.feature_names[i]:<8}"
-  ...                   f"{r.importances_mean[i]:.3f}"
-  ...                   f" +/- {r.importances_std[i]:.3f}")
-  ...
-  r2
-      s5      0.204 +/- 0.050
-      bmi     0.176 +/- 0.048
-      bp      0.088 +/- 0.033
-      sex     0.056 +/- 0.023
-  neg_mean_absolute_percentage_error
-      s5      0.081 +/- 0.020
-      bmi     0.064 +/- 0.015
-      bp      0.029 +/- 0.010
-  neg_mean_squared_error
-      s5      1013.866 +/- 246.445
-      bmi     872.726 +/- 240.298
-      bp      438.663 +/- 163.022
-      sex     277.376 +/- 115.123
-
-The ranking of the features is approximately the same for different metrics even
-if the scales of the importance values are very different. However, this is not
-guaranteed and different metrics might lead to significantly different feature
-importances, in particular for models trained for imbalanced classification problems,
-for which **the choice of the classification metric can be critical**.
-
-|details-end|
+.. dropdown:: Example of permutation feature importance using multiple scorers
+
+  In the example below we use a list of metrics, but more input formats are
+  possible, as documented in :ref:`multimetric_scoring`.
+
+    >>> scoring = ['r2', 'neg_mean_absolute_percentage_error', 'neg_mean_squared_error']
+    >>> r_multi = permutation_importance(
+    ...     model, X_val, y_val, n_repeats=30, random_state=0, scoring=scoring)
+    ...
+    >>> for metric in r_multi:
+    ...     print(f"{metric}")
+    ...     r = r_multi[metric]
+    ...     for i in r.importances_mean.argsort()[::-1]:
+    ...         if r.importances_mean[i] - 2 * r.importances_std[i] > 0:
+    ...             print(f"    {diabetes.feature_names[i]:<8}"
+    ...                   f"{r.importances_mean[i]:.3f}"
+    ...                   f" +/- {r.importances_std[i]:.3f}")
+    ...
+    r2
+        s5      0.204 +/- 0.050
+        bmi     0.176 +/- 0.048
+        bp      0.088 +/- 0.033
+        sex     0.056 +/- 0.023
+    neg_mean_absolute_percentage_error
+        s5      0.081 +/- 0.020
+        bmi     0.064 +/- 0.015
+        bp      0.029 +/- 0.010
+    neg_mean_squared_error
+        s5      1013.866 +/- 246.445
+        bmi     872.726 +/- 240.298
+        bp      438.663 +/- 163.022
+        sex     277.376 +/- 115.123
+
+  The ranking of the features is approximately the same for different metrics even
+  if the scales of the importance values are very different. However, this is not
+  guaranteed and different metrics might lead to significantly different feature
+  importances, in particular for models trained for imbalanced classification problems,
+  for which **the choice of the classification metric can be critical**.
 
 Outline of the permutation importance algorithm
 -----------------------------------------------
@@ -228,12 +224,12 @@ keep one feature from each cluster.
 For more details on such strategy, see the example
 :ref:`sphx_glr_auto_examples_inspection_plot_permutation_importance_multicollinear.py`.
 
-.. topic:: Examples:
+.. rubric:: Examples
 
-  * :ref:`sphx_glr_auto_examples_inspection_plot_permutation_importance.py`
-  * :ref:`sphx_glr_auto_examples_inspection_plot_permutation_importance_multicollinear.py`
+* :ref:`sphx_glr_auto_examples_inspection_plot_permutation_importance.py`
+* :ref:`sphx_glr_auto_examples_inspection_plot_permutation_importance_multicollinear.py`
 
-.. topic:: References:
+.. rubric:: References
 
-   .. [1] L. Breiman, :doi:`"Random Forests" <10.1023/A:1010933404324>`,
-      Machine Learning, 45(1), 5-32, 2001.
+.. [1] L. Breiman, :doi:`"Random Forests" <10.1023/A:1010933404324>`,
+  Machine Learning, 45(1), 5-32, 2001.
diff --git a/doc/modules/preprocessing.rst b/doc/modules/preprocessing.rst
index 99678f2b3e45b..90889ad5af7e0 100644
--- a/doc/modules/preprocessing.rst
+++ b/doc/modules/preprocessing.rst
@@ -219,28 +219,22 @@ of the data is likely to not work very well. In these cases, you can use
 :class:`RobustScaler` as a drop-in replacement instead. It uses
 more robust estimates for the center and range of your data.
 
-|details-start|
-**References**
-|details-split|
 
-Further discussion on the importance of centering and scaling data is
-available on this FAQ: `Should I normalize/standardize/rescale the data?
-<http://www.faqs.org/faqs/ai-faq/neural-nets/part2/section-16.html>`_
+.. dropdown:: References
 
-|details-end|
+  Further discussion on the importance of centering and scaling data is
+  available on this FAQ: `Should I normalize/standardize/rescale the data?
+  <http://www.faqs.org/faqs/ai-faq/neural-nets/part2/section-16.html>`_
 
-|details-start|
-**Scaling vs Whitening**
-|details-split|
+.. dropdown:: Scaling vs Whitening
 
-It is sometimes not enough to center and scale the features
-independently, since a downstream model can further make some assumption
-on the linear independence of the features.
+  It is sometimes not enough to center and scale the features
+  independently, since a downstream model can further make some assumption
+  on the linear independence of the features.
 
-To address this issue you can use :class:`~sklearn.decomposition.PCA` with
-``whiten=True`` to further remove the linear correlation across features.
+  To address this issue you can use :class:`~sklearn.decomposition.PCA` with
+  ``whiten=True`` to further remove the linear correlation across features.
 
-|details-end|
 
 .. _kernel_centering:
 
@@ -255,63 +249,59 @@ followed by the removal of the mean in that space. In other words,
 :class:`KernelCenterer` computes the centered Gram matrix associated to a
 positive semidefinite kernel :math:`K`.
 
-|details-start|
-**Mathematical formulation**
-|details-split|
+.. dropdown:: Mathematical formulation
 
-We can have a look at the mathematical formulation now that we have the
-intuition. Let :math:`K` be a kernel matrix of shape `(n_samples, n_samples)`
-computed from :math:`X`, a data matrix of shape `(n_samples, n_features)`,
-during the `fit` step. :math:`K` is defined by
+  We can have a look at the mathematical formulation now that we have the
+  intuition. Let :math:`K` be a kernel matrix of shape `(n_samples, n_samples)`
+  computed from :math:`X`, a data matrix of shape `(n_samples, n_features)`,
+  during the `fit` step. :math:`K` is defined by
 
-.. math::
-  K(X, X) = \phi(X) . \phi(X)^{T}
+  .. math::
+    K(X, X) = \phi(X) . \phi(X)^{T}
 
-:math:`\phi(X)` is a function mapping of :math:`X` to a Hilbert space. A
-centered kernel :math:`\tilde{K}` is defined as:
+  :math:`\phi(X)` is a function mapping of :math:`X` to a Hilbert space. A
+  centered kernel :math:`\tilde{K}` is defined as:
 
-.. math::
-  \tilde{K}(X, X) = \tilde{\phi}(X) . \tilde{\phi}(X)^{T}
+  .. math::
+    \tilde{K}(X, X) = \tilde{\phi}(X) . \tilde{\phi}(X)^{T}
 
-where :math:`\tilde{\phi}(X)` results from centering :math:`\phi(X)` in the
-Hilbert space.
+  where :math:`\tilde{\phi}(X)` results from centering :math:`\phi(X)` in the
+  Hilbert space.
 
-Thus, one could compute :math:`\tilde{K}` by mapping :math:`X` using the
-function :math:`\phi(\cdot)` and center the data in this new space. However,
-kernels are often used because they allows some algebra calculations that
-avoid computing explicitly this mapping using :math:`\phi(\cdot)`. Indeed, one
-can implicitly center as shown in Appendix B in [Scholkopf1998]_:
+  Thus, one could compute :math:`\tilde{K}` by mapping :math:`X` using the
+  function :math:`\phi(\cdot)` and center the data in this new space. However,
+  kernels are often used because they allows some algebra calculations that
+  avoid computing explicitly this mapping using :math:`\phi(\cdot)`. Indeed, one
+  can implicitly center as shown in Appendix B in [Scholkopf1998]_:
 
-.. math::
-  \tilde{K} = K - 1_{\text{n}_{samples}} K - K 1_{\text{n}_{samples}} + 1_{\text{n}_{samples}} K 1_{\text{n}_{samples}}
+  .. math::
+    \tilde{K} = K - 1_{\text{n}_{samples}} K - K 1_{\text{n}_{samples}} + 1_{\text{n}_{samples}} K 1_{\text{n}_{samples}}
 
-:math:`1_{\text{n}_{samples}}` is a matrix of `(n_samples, n_samples)` where
-all entries are equal to :math:`\frac{1}{\text{n}_{samples}}`. In the
-`transform` step, the kernel becomes :math:`K_{test}(X, Y)` defined as:
+  :math:`1_{\text{n}_{samples}}` is a matrix of `(n_samples, n_samples)` where
+  all entries are equal to :math:`\frac{1}{\text{n}_{samples}}`. In the
+  `transform` step, the kernel becomes :math:`K_{test}(X, Y)` defined as:
 
-.. math::
-  K_{test}(X, Y) = \phi(Y) . \phi(X)^{T}
+  .. math::
+    K_{test}(X, Y) = \phi(Y) . \phi(X)^{T}
 
-:math:`Y` is the test dataset of shape `(n_samples_test, n_features)` and thus
-:math:`K_{test}` is of shape `(n_samples_test, n_samples)`. In this case,
-centering :math:`K_{test}` is done as:
+  :math:`Y` is the test dataset of shape `(n_samples_test, n_features)` and thus
+  :math:`K_{test}` is of shape `(n_samples_test, n_samples)`. In this case,
+  centering :math:`K_{test}` is done as:
 
-.. math::
-  \tilde{K}_{test}(X, Y) = K_{test} - 1'_{\text{n}_{samples}} K - K_{test} 1_{\text{n}_{samples}} + 1'_{\text{n}_{samples}} K 1_{\text{n}_{samples}}
+  .. math::
+    \tilde{K}_{test}(X, Y) = K_{test} - 1'_{\text{n}_{samples}} K - K_{test} 1_{\text{n}_{samples}} + 1'_{\text{n}_{samples}} K 1_{\text{n}_{samples}}
 
-:math:`1'_{\text{n}_{samples}}` is a matrix of shape
-`(n_samples_test, n_samples)` where all entries are equal to
-:math:`\frac{1}{\text{n}_{samples}}`.
+  :math:`1'_{\text{n}_{samples}}` is a matrix of shape
+  `(n_samples_test, n_samples)` where all entries are equal to
+  :math:`\frac{1}{\text{n}_{samples}}`.
 
-.. topic:: References
+  .. rubric:: References
 
   .. [Scholkopf1998] B. Schölkopf, A. Smola, and K.R. Müller,
     `"Nonlinear component analysis as a kernel eigenvalue problem."
     <https://www.mlpack.org/papers/kpca.pdf>`_
     Neural computation 10.5 (1998): 1299-1319.
 
-|details-end|
-
 .. _preprocessing_transformer:
 
 Non-linear transformation
@@ -383,54 +373,46 @@ possible in order to stabilize variance and minimize skewness.
 :class:`PowerTransformer` currently provides two such power transformations,
 the Yeo-Johnson transform and the Box-Cox transform.
 
-|details-start|
-**Yeo-Johnson transform**
-|details-split|
-
-.. math::
-    x_i^{(\lambda)} =
-    \begin{cases}
-     [(x_i + 1)^\lambda - 1] / \lambda & \text{if } \lambda \neq 0, x_i \geq 0, \\[8pt]
-    \ln{(x_i + 1)} & \text{if } \lambda = 0, x_i \geq 0 \\[8pt]
-    -[(-x_i + 1)^{2 - \lambda} - 1] / (2 - \lambda) & \text{if } \lambda \neq 2, x_i < 0, \\[8pt]
-     - \ln (- x_i + 1) & \text{if } \lambda = 2, x_i < 0
-    \end{cases}
-
-|details-end|
-
-|details-start|
-**Box-Cox transform**
-|details-split|
-
-.. math::
-    x_i^{(\lambda)} =
-    \begin{cases}
-    \dfrac{x_i^\lambda - 1}{\lambda} & \text{if } \lambda \neq 0, \\[8pt]
-    \ln{(x_i)} & \text{if } \lambda = 0,
-    \end{cases}
-
-
-Box-Cox can only be applied to strictly positive data. In both methods, the
-transformation is parameterized by :math:`\lambda`, which is determined through
-maximum likelihood estimation. Here is an example of using Box-Cox to map
-samples drawn from a lognormal distribution to a normal distribution::
-
-  >>> pt = preprocessing.PowerTransformer(method='box-cox', standardize=False)
-  >>> X_lognormal = np.random.RandomState(616).lognormal(size=(3, 3))
-  >>> X_lognormal
-  array([[1.28..., 1.18..., 0.84...],
-         [0.94..., 1.60..., 0.38...],
-         [1.35..., 0.21..., 1.09...]])
-  >>> pt.fit_transform(X_lognormal)
-  array([[ 0.49...,  0.17..., -0.15...],
-         [-0.05...,  0.58..., -0.57...],
-         [ 0.69..., -0.84...,  0.10...]])
-
-While the above example sets the `standardize` option to `False`,
-:class:`PowerTransformer` will apply zero-mean, unit-variance normalization
-to the transformed output by default.
-
-|details-end|
+.. dropdown:: Yeo-Johnson transform
+
+  .. math::
+      x_i^{(\lambda)} =
+      \begin{cases}
+      [(x_i + 1)^\lambda - 1] / \lambda & \text{if } \lambda \neq 0, x_i \geq 0, \\[8pt]
+      \ln{(x_i + 1)} & \text{if } \lambda = 0, x_i \geq 0 \\[8pt]
+      -[(-x_i + 1)^{2 - \lambda} - 1] / (2 - \lambda) & \text{if } \lambda \neq 2, x_i < 0, \\[8pt]
+      - \ln (- x_i + 1) & \text{if } \lambda = 2, x_i < 0
+      \end{cases}
+
+.. dropdown:: Box-Cox transform
+
+  .. math::
+      x_i^{(\lambda)} =
+      \begin{cases}
+      \dfrac{x_i^\lambda - 1}{\lambda} & \text{if } \lambda \neq 0, \\[8pt]
+      \ln{(x_i)} & \text{if } \lambda = 0,
+      \end{cases}
+
+  Box-Cox can only be applied to strictly positive data. In both methods, the
+  transformation is parameterized by :math:`\lambda`, which is determined through
+  maximum likelihood estimation. Here is an example of using Box-Cox to map
+  samples drawn from a lognormal distribution to a normal distribution::
+
+    >>> pt = preprocessing.PowerTransformer(method='box-cox', standardize=False)
+    >>> X_lognormal = np.random.RandomState(616).lognormal(size=(3, 3))
+    >>> X_lognormal
+    array([[1.28..., 1.18..., 0.84...],
+          [0.94..., 1.60..., 0.38...],
+          [1.35..., 0.21..., 1.09...]])
+    >>> pt.fit_transform(X_lognormal)
+    array([[ 0.49...,  0.17..., -0.15...],
+          [-0.05...,  0.58..., -0.57...],
+          [ 0.69..., -0.84...,  0.10...]])
+
+  While the above example sets the `standardize` option to `False`,
+  :class:`PowerTransformer` will apply zero-mean, unit-variance normalization
+  to the transformed output by default.
+
 
 Below are examples of Box-Cox and Yeo-Johnson applied to various probability
 distributions.  Note that when applied to certain distributions, the power
@@ -518,9 +500,8 @@ The normalizer instance can then be used on sample vectors as any transformer::
 
 Note: L2 normalization is also known as spatial sign preprocessing.
 
-|details-start|
-**Sparse input**
-|details-split|
+.. dropdown:: Sparse input
+
   :func:`normalize` and :class:`Normalizer` accept **both dense array-like
   and sparse matrices from scipy.sparse as input**.
 
@@ -529,12 +510,11 @@ Note: L2 normalization is also known as spatial sign preprocessing.
   efficient Cython routines. To avoid unnecessary memory copies, it is
   recommended to choose the CSR representation upstream.
 
-|details-end|
-
 .. _preprocessing_categorical_features:
 
 Encoding categorical features
 =============================
+
 Often features are not given as continuous values but categorical.
 For example a person could have features ``["male", "female"]``,
 ``["from Europe", "from US", "from Asia"]``,
@@ -721,42 +701,39 @@ not dropped::
     >>> drop_enc.inverse_transform(X_trans)
     array([['female', None, None]], dtype=object)
 
-|details-start|
-**Support of categorical features with missing values**
-|details-split|
+.. dropdown:: Support of categorical features with missing values
 
-:class:`OneHotEncoder` supports categorical features with missing values by
-considering the missing values as an additional category::
+  :class:`OneHotEncoder` supports categorical features with missing values by
+  considering the missing values as an additional category::
 
-    >>> X = [['male', 'Safari'],
-    ...      ['female', None],
-    ...      [np.nan, 'Firefox']]
-    >>> enc = preprocessing.OneHotEncoder(handle_unknown='error').fit(X)
-    >>> enc.categories_
-    [array(['female', 'male', nan], dtype=object),
-     array(['Firefox', 'Safari', None], dtype=object)]
-    >>> enc.transform(X).toarray()
-    array([[0., 1., 0., 0., 1., 0.],
-           [1., 0., 0., 0., 0., 1.],
-           [0., 0., 1., 1., 0., 0.]])
-
-If a feature contains both `np.nan` and `None`, they will be considered
-separate categories::
-
-    >>> X = [['Safari'], [None], [np.nan], ['Firefox']]
-    >>> enc = preprocessing.OneHotEncoder(handle_unknown='error').fit(X)
-    >>> enc.categories_
-    [array(['Firefox', 'Safari', None, nan], dtype=object)]
-    >>> enc.transform(X).toarray()
-    array([[0., 1., 0., 0.],
-           [0., 0., 1., 0.],
-           [0., 0., 0., 1.],
-           [1., 0., 0., 0.]])
+      >>> X = [['male', 'Safari'],
+      ...      ['female', None],
+      ...      [np.nan, 'Firefox']]
+      >>> enc = preprocessing.OneHotEncoder(handle_unknown='error').fit(X)
+      >>> enc.categories_
+      [array(['female', 'male', nan], dtype=object),
+      array(['Firefox', 'Safari', None], dtype=object)]
+      >>> enc.transform(X).toarray()
+      array([[0., 1., 0., 0., 1., 0.],
+            [1., 0., 0., 0., 0., 1.],
+            [0., 0., 1., 1., 0., 0.]])
+
+  If a feature contains both `np.nan` and `None`, they will be considered
+  separate categories::
+
+      >>> X = [['Safari'], [None], [np.nan], ['Firefox']]
+      >>> enc = preprocessing.OneHotEncoder(handle_unknown='error').fit(X)
+      >>> enc.categories_
+      [array(['Firefox', 'Safari', None, nan], dtype=object)]
+      >>> enc.transform(X).toarray()
+      array([[0., 1., 0., 0.],
+            [0., 0., 1., 0.],
+            [0., 0., 0., 1.],
+            [1., 0., 0., 0.]])
 
-See :ref:`dict_feature_extraction` for categorical features that are
-represented as a dict, not as scalars.
+  See :ref:`dict_feature_extraction` for categorical features that are
+  represented as a dict, not as scalars.
 
-|details-end|
 
 .. _encoder_infrequent_categories:
 
@@ -910,66 +887,55 @@ cardinality, where one-hot encoding would inflate the feature space making it
 more expensive for a downstream model to process. A classical example of high
 cardinality categories are location based such as zip code or region.
 
-|details-start|
-**Binary classification targets**
-|details-split|
-
-For the binary classification target, the target encoding is given by:
-
-.. math::
-    S_i = \lambda_i\frac{n_{iY}}{n_i} + (1 - \lambda_i)\frac{n_Y}{n}
+.. dropdown:: Binary classification targets
 
-where :math:`S_i` is the encoding for category :math:`i`, :math:`n_{iY}` is the
-number of observations with :math:`Y=1` and category :math:`i`, :math:`n_i` is
-the number of observations with category :math:`i`, :math:`n_Y` is the number of
-observations with :math:`Y=1`, :math:`n` is the number of observations, and
-:math:`\lambda_i` is a shrinkage factor for category :math:`i`. The shrinkage
-factor is given by:
+  For the binary classification target, the target encoding is given by:
 
-.. math::
-    \lambda_i = \frac{n_i}{m + n_i}
+  .. math::
+      S_i = \lambda_i\frac{n_{iY}}{n_i} + (1 - \lambda_i)\frac{n_Y}{n}
 
-where :math:`m` is a smoothing factor, which is controlled with the `smooth`
-parameter in :class:`TargetEncoder`. Large smoothing factors will put more
-weight on the global mean. When `smooth="auto"`, the smoothing factor is
-computed as an empirical Bayes estimate: :math:`m=\sigma_i^2/\tau^2`, where
-:math:`\sigma_i^2` is the variance of `y` with category :math:`i` and
-:math:`\tau^2` is the global variance of `y`.
+  where :math:`S_i` is the encoding for category :math:`i`, :math:`n_{iY}` is the
+  number of observations with :math:`Y=1` and category :math:`i`, :math:`n_i` is
+  the number of observations with category :math:`i`, :math:`n_Y` is the number of
+  observations with :math:`Y=1`, :math:`n` is the number of observations, and
+  :math:`\lambda_i` is a shrinkage factor for category :math:`i`. The shrinkage
+  factor is given by:
 
-|details-end|
+  .. math::
+      \lambda_i = \frac{n_i}{m + n_i}
 
-|details-start|
-**Multiclass classification targets**
-|details-split|
+  where :math:`m` is a smoothing factor, which is controlled with the `smooth`
+  parameter in :class:`TargetEncoder`. Large smoothing factors will put more
+  weight on the global mean. When `smooth="auto"`, the smoothing factor is
+  computed as an empirical Bayes estimate: :math:`m=\sigma_i^2/\tau^2`, where
+  :math:`\sigma_i^2` is the variance of `y` with category :math:`i` and
+  :math:`\tau^2` is the global variance of `y`.
 
-For multiclass classification targets, the formulation is similar to binary
-classification:
+.. dropdown:: Multiclass classification targets
 
-.. math::
-    S_{ij} = \lambda_i\frac{n_{iY_j}}{n_i} + (1 - \lambda_i)\frac{n_{Y_j}}{n}
+  For multiclass classification targets, the formulation is similar to binary
+  classification:
 
-where :math:`S_{ij}` is the encoding for category :math:`i` and class :math:`j`,
-:math:`n_{iY_j}` is the number of observations with :math:`Y=j` and category
-:math:`i`, :math:`n_i` is the number of observations with category :math:`i`,
-:math:`n_{Y_j}` is the number of observations with :math:`Y=j`, :math:`n` is the
-number of observations, and :math:`\lambda_i` is a shrinkage factor for category
-:math:`i`.
+  .. math::
+      S_{ij} = \lambda_i\frac{n_{iY_j}}{n_i} + (1 - \lambda_i)\frac{n_{Y_j}}{n}
 
-|details-end|
+  where :math:`S_{ij}` is the encoding for category :math:`i` and class :math:`j`,
+  :math:`n_{iY_j}` is the number of observations with :math:`Y=j` and category
+  :math:`i`, :math:`n_i` is the number of observations with category :math:`i`,
+  :math:`n_{Y_j}` is the number of observations with :math:`Y=j`, :math:`n` is the
+  number of observations, and :math:`\lambda_i` is a shrinkage factor for category
+  :math:`i`.
 
-|details-start|
-**Continuous targets**
-|details-split|
+.. dropdown:: Continuous targets
 
-For continuous targets, the formulation is similar to binary classification:
+  For continuous targets, the formulation is similar to binary classification:
 
-.. math::
-    S_i = \lambda_i\frac{\sum_{k\in L_i}Y_k}{n_i} + (1 - \lambda_i)\frac{\sum_{k=1}^{n}Y_k}{n}
+  .. math::
+      S_i = \lambda_i\frac{\sum_{k\in L_i}Y_k}{n_i} + (1 - \lambda_i)\frac{\sum_{k=1}^{n}Y_k}{n}
 
-where :math:`L_i` is the set of observations with category :math:`i` and
-:math:`n_i` is the number of observations with category :math:`i`.
+  where :math:`L_i` is the set of observations with category :math:`i` and
+  :math:`n_i` is the number of observations with category :math:`i`.
 
-|details-end|
 
 :meth:`~TargetEncoder.fit_transform` internally relies on a :term:`cross fitting`
 scheme to prevent target information from leaking into the train-time
@@ -1005,21 +971,21 @@ encoding learned in :meth:`~TargetEncoder.fit_transform`.
   that are not seen during `fit` are encoded with the target mean, i.e.
   `target_mean_`.
 
-.. topic:: Examples:
+.. rubric:: Examples
 
-  * :ref:`sphx_glr_auto_examples_preprocessing_plot_target_encoder.py`
-  * :ref:`sphx_glr_auto_examples_preprocessing_plot_target_encoder_cross_val.py`
+* :ref:`sphx_glr_auto_examples_preprocessing_plot_target_encoder.py`
+* :ref:`sphx_glr_auto_examples_preprocessing_plot_target_encoder_cross_val.py`
 
-.. topic:: References
+.. rubric:: References
 
-  .. [MIC] :doi:`Micci-Barreca, Daniele. "A preprocessing scheme for high-cardinality
-     categorical attributes in classification and prediction problems"
-     SIGKDD Explor. Newsl. 3, 1 (July 2001), 27–32. <10.1145/507533.507538>`
+.. [MIC] :doi:`Micci-Barreca, Daniele. "A preprocessing scheme for high-cardinality
+    categorical attributes in classification and prediction problems"
+    SIGKDD Explor. Newsl. 3, 1 (July 2001), 27-32. <10.1145/507533.507538>`
 
-  .. [PAR] :doi:`Pargent, F., Pfisterer, F., Thomas, J. et al. "Regularized target
-     encoding outperforms traditional methods in supervised machine learning with
-     high cardinality features" Comput Stat 37, 2671–2692 (2022)
-     <10.1007/s00180-022-01207-6>`
+.. [PAR] :doi:`Pargent, F., Pfisterer, F., Thomas, J. et al. "Regularized target
+    encoding outperforms traditional methods in supervised machine learning with
+    high cardinality features" Comput Stat 37, 2671-2692 (2022)
+    <10.1007/s00180-022-01207-6>`
 
 .. _preprocessing_discretization:
 
@@ -1097,11 +1063,11 @@ For instance, we can use the Pandas function :func:`pandas.cut`::
   ['infant', 'kid', 'teen', 'adult', 'senior citizen']
   Categories (5, object): ['infant' < 'kid' < 'teen' < 'adult' < 'senior citizen']
 
-.. topic:: Examples:
+.. rubric:: Examples
 
-  * :ref:`sphx_glr_auto_examples_preprocessing_plot_discretization.py`
-  * :ref:`sphx_glr_auto_examples_preprocessing_plot_discretization_classification.py`
-  * :ref:`sphx_glr_auto_examples_preprocessing_plot_discretization_strategies.py`
+* :ref:`sphx_glr_auto_examples_preprocessing_plot_discretization.py`
+* :ref:`sphx_glr_auto_examples_preprocessing_plot_discretization_classification.py`
+* :ref:`sphx_glr_auto_examples_preprocessing_plot_discretization_strategies.py`
 
 .. _preprocessing_binarization:
 
@@ -1294,23 +1260,20 @@ Interestingly, a :class:`SplineTransformer` of ``degree=0`` is the same as
 ``encode='onehot-dense'`` and ``n_bins = n_knots - 1`` if
 ``knots = strategy``.
 
-.. topic:: Examples:
+.. rubric:: Examples
 
-    * :ref:`sphx_glr_auto_examples_linear_model_plot_polynomial_interpolation.py`
-    * :ref:`sphx_glr_auto_examples_applications_plot_cyclical_feature_engineering.py`
+* :ref:`sphx_glr_auto_examples_linear_model_plot_polynomial_interpolation.py`
+* :ref:`sphx_glr_auto_examples_applications_plot_cyclical_feature_engineering.py`
 
-|details-start|
-**References**
-|details-split|
+.. dropdown:: References
 
-    * Eilers, P., & Marx, B. (1996). :doi:`Flexible Smoothing with B-splines and
-      Penalties <10.1214/ss/1038425655>`. Statist. Sci. 11 (1996), no. 2, 89--121.
+  * Eilers, P., & Marx, B. (1996). :doi:`Flexible Smoothing with B-splines and
+    Penalties <10.1214/ss/1038425655>`. Statist. Sci. 11 (1996), no. 2, 89--121.
 
-    * Perperoglou, A., Sauerbrei, W., Abrahamowicz, M. et al. :doi:`A review of
-      spline function procedures in R <10.1186/s12874-019-0666-3>`.
-      BMC Med Res Methodol 19, 46 (2019).
+  * Perperoglou, A., Sauerbrei, W., Abrahamowicz, M. et al. :doi:`A review of
+    spline function procedures in R <10.1186/s12874-019-0666-3>`.
+    BMC Med Res Methodol 19, 46 (2019).
 
-|details-end|
 
 .. _function_transformer:
 
diff --git a/doc/modules/random_projection.rst b/doc/modules/random_projection.rst
index 6931feb34ad1d..173aee434576c 100644
--- a/doc/modules/random_projection.rst
+++ b/doc/modules/random_projection.rst
@@ -19,19 +19,19 @@ samples of the dataset. Thus random projection is a suitable approximation
 technique for distance based method.
 
 
-.. topic:: References:
+.. rubric:: References
 
- * Sanjoy Dasgupta. 2000.
-   `Experiments with random projection. <https://cseweb.ucsd.edu/~dasgupta/papers/randomf.pdf>`_
-   In Proceedings of the Sixteenth conference on Uncertainty in artificial
-   intelligence (UAI'00), Craig Boutilier and Moisés Goldszmidt (Eds.). Morgan
-   Kaufmann Publishers Inc., San Francisco, CA, USA, 143-151.
+* Sanjoy Dasgupta. 2000.
+  `Experiments with random projection. <https://cseweb.ucsd.edu/~dasgupta/papers/randomf.pdf>`_
+  In Proceedings of the Sixteenth conference on Uncertainty in artificial
+  intelligence (UAI'00), Craig Boutilier and Moisés Goldszmidt (Eds.). Morgan
+  Kaufmann Publishers Inc., San Francisco, CA, USA, 143-151.
 
- * Ella Bingham and Heikki Mannila. 2001.
-   `Random projection in dimensionality reduction: applications to image and text data. <https://citeseerx.ist.psu.edu/doc_view/pid/aed77346f737b0ed5890b61ad02e5eb4ab2f3dc6>`_
-   In Proceedings of the seventh ACM SIGKDD international conference on
-   Knowledge discovery and data mining (KDD '01). ACM, New York, NY, USA,
-   245-250.
+* Ella Bingham and Heikki Mannila. 2001.
+  `Random projection in dimensionality reduction: applications to image and text data. <https://citeseerx.ist.psu.edu/doc_view/pid/aed77346f737b0ed5890b61ad02e5eb4ab2f3dc6>`_
+  In Proceedings of the seventh ACM SIGKDD international conference on
+  Knowledge discovery and data mining (KDD '01). ACM, New York, NY, USA,
+  245-250.
 
 
 .. _johnson_lindenstrauss:
@@ -74,17 +74,17 @@ bounded distortion introduced by the random projection::
    :scale: 75
    :align: center
 
-.. topic:: Example:
+.. rubric:: Examples
 
-  * See :ref:`sphx_glr_auto_examples_miscellaneous_plot_johnson_lindenstrauss_bound.py`
-    for a theoretical explication on the Johnson-Lindenstrauss lemma and an
-    empirical validation using sparse random matrices.
+* See :ref:`sphx_glr_auto_examples_miscellaneous_plot_johnson_lindenstrauss_bound.py`
+  for a theoretical explication on the Johnson-Lindenstrauss lemma and an
+  empirical validation using sparse random matrices.
 
-.. topic:: References:
+.. rubric:: References
 
-  * Sanjoy Dasgupta and Anupam Gupta, 1999.
-    `An elementary proof of the Johnson-Lindenstrauss Lemma.
-    <https://citeseerx.ist.psu.edu/doc_view/pid/95cd464d27c25c9c8690b378b894d337cdf021f9>`_
+* Sanjoy Dasgupta and Anupam Gupta, 1999.
+  `An elementary proof of the Johnson-Lindenstrauss Lemma.
+  <https://citeseerx.ist.psu.edu/doc_view/pid/95cd464d27c25c9c8690b378b894d337cdf021f9>`_
 
 .. _gaussian_random_matrix:
 
@@ -148,18 +148,17 @@ projection transformer::
   (100, 3947)
 
 
-.. topic:: References:
+.. rubric:: References
 
- * D. Achlioptas. 2003.
-   `Database-friendly random projections: Johnson-Lindenstrauss  with binary
-   coins <https://www.sciencedirect.com/science/article/pii/S0022000003000254>`_.
-   Journal of Computer and System Sciences 66 (2003) 671–687
+* D. Achlioptas. 2003.
+  `Database-friendly random projections: Johnson-Lindenstrauss  with binary
+  coins <https://www.sciencedirect.com/science/article/pii/S0022000003000254>`_.
+  Journal of Computer and System Sciences 66 (2003) 671-687.
 
- * Ping Li, Trevor J. Hastie, and Kenneth W. Church. 2006.
-   `Very sparse random projections. <https://web.stanford.edu/~hastie/Papers/Ping/KDD06_rp.pdf>`_
-   In Proceedings of the 12th ACM SIGKDD international conference on
-   Knowledge discovery and data mining (KDD '06). ACM, New York, NY, USA,
-   287-296.
+* Ping Li, Trevor J. Hastie, and Kenneth W. Church. 2006.
+  `Very sparse random projections. <https://web.stanford.edu/~hastie/Papers/Ping/KDD06_rp.pdf>`_
+  In Proceedings of the 12th ACM SIGKDD international conference on
+  Knowledge discovery and data mining (KDD '06). ACM, New York, NY, USA, 287-296.
 
 
 .. _random_projection_inverse_transform:
diff --git a/doc/modules/semi_supervised.rst b/doc/modules/semi_supervised.rst
index f8cae0a9ddcdf..8ba33638c6eec 100644
--- a/doc/modules/semi_supervised.rst
+++ b/doc/modules/semi_supervised.rst
@@ -60,18 +60,18 @@ until all samples have labels or no new samples are selected in that iteration.
    When using the self-training classifier, the
    :ref:`calibration <calibration>` of the classifier is important.
 
-.. topic:: Examples
+.. rubric:: Examples
 
-  * :ref:`sphx_glr_auto_examples_semi_supervised_plot_self_training_varying_threshold.py`
-  * :ref:`sphx_glr_auto_examples_semi_supervised_plot_semi_supervised_versus_svm_iris.py`
+* :ref:`sphx_glr_auto_examples_semi_supervised_plot_self_training_varying_threshold.py`
+* :ref:`sphx_glr_auto_examples_semi_supervised_plot_semi_supervised_versus_svm_iris.py`
 
-.. topic:: References
+.. rubric:: References
 
-    .. [1] :doi:`"Unsupervised word sense disambiguation rivaling supervised methods"
-       <10.3115/981658.981684>`
-       David Yarowsky, Proceedings of the 33rd annual meeting on Association for
-       Computational Linguistics (ACL '95). Association for Computational Linguistics,
-       Stroudsburg, PA, USA, 189-196.
+.. [1] :doi:`"Unsupervised word sense disambiguation rivaling supervised methods"
+    <10.3115/981658.981684>`
+    David Yarowsky, Proceedings of the 33rd annual meeting on Association for
+    Computational Linguistics (ACL '95). Association for Computational Linguistics,
+    Stroudsburg, PA, USA, 189-196.
 
 .. _label_propagation:
 
@@ -134,18 +134,18 @@ algorithm can lead to prohibitively long running times. On the other hand,
 the KNN kernel will produce a much more memory-friendly sparse matrix
 which can drastically reduce running times.
 
-.. topic:: Examples
+.. rubric:: Examples
 
-  * :ref:`sphx_glr_auto_examples_semi_supervised_plot_semi_supervised_versus_svm_iris.py`
-  * :ref:`sphx_glr_auto_examples_semi_supervised_plot_label_propagation_structure.py`
-  * :ref:`sphx_glr_auto_examples_semi_supervised_plot_label_propagation_digits.py`
-  * :ref:`sphx_glr_auto_examples_semi_supervised_plot_label_propagation_digits_active_learning.py`
+* :ref:`sphx_glr_auto_examples_semi_supervised_plot_semi_supervised_versus_svm_iris.py`
+* :ref:`sphx_glr_auto_examples_semi_supervised_plot_label_propagation_structure.py`
+* :ref:`sphx_glr_auto_examples_semi_supervised_plot_label_propagation_digits.py`
+* :ref:`sphx_glr_auto_examples_semi_supervised_plot_label_propagation_digits_active_learning.py`
 
-.. topic:: References
+.. rubric:: References
 
-    [2] Yoshua Bengio, Olivier Delalleau, Nicolas Le Roux. In Semi-Supervised
-    Learning (2006), pp. 193-216
+[2] Yoshua Bengio, Olivier Delalleau, Nicolas Le Roux. In Semi-Supervised
+Learning (2006), pp. 193-216
 
-    [3] Olivier Delalleau, Yoshua Bengio, Nicolas Le Roux. Efficient
-    Non-Parametric Function Induction in Semi-Supervised Learning. AISTAT 2005
-    https://www.gatsby.ucl.ac.uk/aistats/fullpapers/204.pdf
+[3] Olivier Delalleau, Yoshua Bengio, Nicolas Le Roux. Efficient
+Non-Parametric Function Induction in Semi-Supervised Learning. AISTAT 2005
+https://www.gatsby.ucl.ac.uk/aistats/fullpapers/204.pdf
diff --git a/doc/modules/sgd.rst b/doc/modules/sgd.rst
index a7981e9d4ec28..73df123b4ed19 100644
--- a/doc/modules/sgd.rst
+++ b/doc/modules/sgd.rst
@@ -189,14 +189,14 @@ For classification with a logistic loss, another variant of SGD with an
 averaging strategy is available with Stochastic Average Gradient (SAG)
 algorithm, available as a solver in :class:`LogisticRegression`.
 
-.. topic:: Examples:
+.. rubric:: Examples
 
- - :ref:`sphx_glr_auto_examples_linear_model_plot_sgd_separating_hyperplane.py`,
- - :ref:`sphx_glr_auto_examples_linear_model_plot_sgd_iris.py`
- - :ref:`sphx_glr_auto_examples_linear_model_plot_sgd_weighted_samples.py`
- - :ref:`sphx_glr_auto_examples_linear_model_plot_sgd_comparison.py`
- - :ref:`sphx_glr_auto_examples_svm_plot_separating_hyperplane_unbalanced.py`
-   (See the Note in the example)
+- :ref:`sphx_glr_auto_examples_linear_model_plot_sgd_separating_hyperplane.py`
+- :ref:`sphx_glr_auto_examples_linear_model_plot_sgd_iris.py`
+- :ref:`sphx_glr_auto_examples_linear_model_plot_sgd_weighted_samples.py`
+- :ref:`sphx_glr_auto_examples_linear_model_plot_sgd_comparison.py`
+- :ref:`sphx_glr_auto_examples_svm_plot_separating_hyperplane_unbalanced.py`
+  (See the Note in the example)
 
 Regression
 ==========
@@ -249,44 +249,40 @@ quadratic in the number of samples.
 with a large number of training samples (> 10,000) for which the SGD
 variant can be several orders of magnitude faster.
 
-|details-start|
-**Mathematical details**
-|details-split|
+.. dropdown:: Mathematical details
 
-Its implementation is based on the implementation of the stochastic
-gradient descent. Indeed, the original optimization problem of the One-Class
-SVM is given by
+  Its implementation is based on the implementation of the stochastic
+  gradient descent. Indeed, the original optimization problem of the One-Class
+  SVM is given by
 
-.. math::
-
-  \begin{aligned}
-  \min_{w, \rho, \xi} & \quad \frac{1}{2}\Vert w \Vert^2 - \rho + \frac{1}{\nu n} \sum_{i=1}^n \xi_i \\
-  \text{s.t.} & \quad \langle w, x_i \rangle \geq \rho - \xi_i \quad 1 \leq i \leq n \\
-  & \quad \xi_i \geq 0 \quad 1 \leq i \leq n
-  \end{aligned}
+  .. math::
 
-where :math:`\nu \in (0, 1]` is the user-specified parameter controlling the
-proportion of outliers and the proportion of support vectors. Getting rid of
-the slack variables :math:`\xi_i` this problem is equivalent to
+    \begin{aligned}
+    \min_{w, \rho, \xi} & \quad \frac{1}{2}\Vert w \Vert^2 - \rho + \frac{1}{\nu n} \sum_{i=1}^n \xi_i \\
+    \text{s.t.} & \quad \langle w, x_i \rangle \geq \rho - \xi_i \quad 1 \leq i \leq n \\
+    & \quad \xi_i \geq 0 \quad 1 \leq i \leq n
+    \end{aligned}
 
-.. math::
+  where :math:`\nu \in (0, 1]` is the user-specified parameter controlling the
+  proportion of outliers and the proportion of support vectors. Getting rid of
+  the slack variables :math:`\xi_i` this problem is equivalent to
 
-  \min_{w, \rho} \frac{1}{2}\Vert w \Vert^2 - \rho + \frac{1}{\nu n} \sum_{i=1}^n \max(0, \rho - \langle w, x_i \rangle) \, .
+  .. math::
 
-Multiplying by the constant :math:`\nu` and introducing the intercept
-:math:`b = 1 - \rho` we obtain the following equivalent optimization problem
+    \min_{w, \rho} \frac{1}{2}\Vert w \Vert^2 - \rho + \frac{1}{\nu n} \sum_{i=1}^n \max(0, \rho - \langle w, x_i \rangle) \, .
 
-.. math::
+  Multiplying by the constant :math:`\nu` and introducing the intercept
+  :math:`b = 1 - \rho` we obtain the following equivalent optimization problem
 
-  \min_{w, b} \frac{\nu}{2}\Vert w \Vert^2 + b\nu + \frac{1}{n} \sum_{i=1}^n \max(0, 1 - (\langle w, x_i \rangle + b)) \, .
+  .. math::
 
-This is similar to the optimization problems studied in section
-:ref:`sgd_mathematical_formulation` with :math:`y_i = 1, 1 \leq i \leq n` and
-:math:`\alpha = \nu/2`, :math:`L` being the hinge loss function and :math:`R`
-being the L2 norm. We just need to add the term :math:`b\nu` in the
-optimization loop.
+    \min_{w, b} \frac{\nu}{2}\Vert w \Vert^2 + b\nu + \frac{1}{n} \sum_{i=1}^n \max(0, 1 - (\langle w, x_i \rangle + b)) \, .
 
-|details-end|
+  This is similar to the optimization problems studied in section
+  :ref:`sgd_mathematical_formulation` with :math:`y_i = 1, 1 \leq i \leq n` and
+  :math:`\alpha = \nu/2`, :math:`L` being the hinge loss function and :math:`R`
+  being the L2 norm. We just need to add the term :math:`b\nu` in the
+  optimization loop.
 
 As :class:`SGDClassifier` and :class:`SGDRegressor`, :class:`SGDOneClassSVM`
 supports averaged SGD. Averaging can be enabled by setting ``average=True``.
@@ -305,9 +301,9 @@ efficiency, however, use the CSR
 matrix format as defined in `scipy.sparse.csr_matrix
 <https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.csr_matrix.html>`_.
 
-.. topic:: Examples:
+.. rubric:: Examples
 
- - :ref:`sphx_glr_auto_examples_text_plot_document_classification_20newsgroups.py`
+- :ref:`sphx_glr_auto_examples_text_plot_document_classification_20newsgroups.py`
 
 Complexity
 ==========
@@ -385,11 +381,11 @@ Tips on Practical Use
 * We found that Averaged SGD works best with a larger number of features
   and a higher eta0.
 
-.. topic:: References:
+.. rubric:: References
 
- * `"Efficient BackProp" <http://yann.lecun.com/exdb/publis/pdf/lecun-98b.pdf>`_
-   Y. LeCun, L. Bottou, G. Orr, K. Müller - In Neural Networks: Tricks
-   of the Trade 1998.
+* `"Efficient BackProp" <http://yann.lecun.com/exdb/publis/pdf/lecun-98b.pdf>`_
+  Y. LeCun, L. Bottou, G. Orr, K. Müller - In Neural Networks: Tricks
+  of the Trade 1998.
 
 .. _sgd_mathematical_formulation:
 
@@ -416,32 +412,28 @@ where :math:`L` is a loss function that measures model (mis)fit and
 complexity; :math:`\alpha > 0` is a non-negative hyperparameter that controls
 the regularization strength.
 
-|details-start|
-**Loss functions details**
-|details-split|
-
-Different choices for :math:`L` entail different classifiers or regressors:
-
-- Hinge (soft-margin): equivalent to Support Vector Classification.
-  :math:`L(y_i, f(x_i)) = \max(0, 1 - y_i f(x_i))`.
-- Perceptron:
-  :math:`L(y_i, f(x_i)) = \max(0, - y_i f(x_i))`.
-- Modified Huber:
-  :math:`L(y_i, f(x_i)) = \max(0, 1 - y_i f(x_i))^2` if :math:`y_i f(x_i) >
-  -1`, and :math:`L(y_i, f(x_i)) = -4 y_i f(x_i)` otherwise.
-- Log Loss: equivalent to Logistic Regression.
-  :math:`L(y_i, f(x_i)) = \log(1 + \exp (-y_i f(x_i)))`.
-- Squared Error: Linear regression (Ridge or Lasso depending on
-  :math:`R`).
-  :math:`L(y_i, f(x_i)) = \frac{1}{2}(y_i - f(x_i))^2`.
-- Huber: less sensitive to outliers than least-squares. It is equivalent to
-  least squares when :math:`|y_i - f(x_i)| \leq \varepsilon`, and
-  :math:`L(y_i, f(x_i)) = \varepsilon |y_i - f(x_i)| - \frac{1}{2}
-  \varepsilon^2` otherwise.
-- Epsilon-Insensitive: (soft-margin) equivalent to Support Vector Regression.
-  :math:`L(y_i, f(x_i)) = \max(0, |y_i - f(x_i)| - \varepsilon)`.
-
-|details-end|
+.. dropdown:: Loss functions details
+
+  Different choices for :math:`L` entail different classifiers or regressors:
+
+  - Hinge (soft-margin): equivalent to Support Vector Classification.
+    :math:`L(y_i, f(x_i)) = \max(0, 1 - y_i f(x_i))`.
+  - Perceptron:
+    :math:`L(y_i, f(x_i)) = \max(0, - y_i f(x_i))`.
+  - Modified Huber:
+    :math:`L(y_i, f(x_i)) = \max(0, 1 - y_i f(x_i))^2` if :math:`y_i f(x_i) >
+    -1`, and :math:`L(y_i, f(x_i)) = -4 y_i f(x_i)` otherwise.
+  - Log Loss: equivalent to Logistic Regression.
+    :math:`L(y_i, f(x_i)) = \log(1 + \exp (-y_i f(x_i)))`.
+  - Squared Error: Linear regression (Ridge or Lasso depending on
+    :math:`R`).
+    :math:`L(y_i, f(x_i)) = \frac{1}{2}(y_i - f(x_i))^2`.
+  - Huber: less sensitive to outliers than least-squares. It is equivalent to
+    least squares when :math:`|y_i - f(x_i)| \leq \varepsilon`, and
+    :math:`L(y_i, f(x_i)) = \varepsilon |y_i - f(x_i)| - \frac{1}{2}
+    \varepsilon^2` otherwise.
+  - Epsilon-Insensitive: (soft-margin) equivalent to Support Vector Regression.
+    :math:`L(y_i, f(x_i)) = \max(0, |y_i - f(x_i)| - \varepsilon)`.
 
 All of the above loss functions can be regarded as an upper bound on the
 misclassification error (Zero-one loss) as shown in the Figure below.
@@ -553,32 +545,29 @@ We use the truncated gradient algorithm proposed in [#3]_
 for L1 regularization (and the Elastic Net).
 The code is written in Cython.
 
-.. topic:: References:
+.. rubric:: References
 
-   .. [#1] `"Stochastic Gradient Descent"
-       <https://leon.bottou.org/projects/sgd>`_ L. Bottou - Website, 2010.
+.. [#1] `"Stochastic Gradient Descent"
+  <https://leon.bottou.org/projects/sgd>`_ L. Bottou - Website, 2010.
 
-   .. [#2] :doi:`"Pegasos: Primal estimated sub-gradient solver for svm"
-      <10.1145/1273496.1273598>`
-      S. Shalev-Shwartz, Y. Singer, N. Srebro - In Proceedings of ICML '07.
+.. [#2] :doi:`"Pegasos: Primal estimated sub-gradient solver for svm"
+  <10.1145/1273496.1273598>`
+  S. Shalev-Shwartz, Y. Singer, N. Srebro - In Proceedings of ICML '07.
 
-   .. [#3] `"Stochastic gradient descent training for l1-regularized
-      log-linear models with cumulative penalty"
-      <https://www.aclweb.org/anthology/P/P09/P09-1054.pdf>`_
-      Y. Tsuruoka, J. Tsujii, S. Ananiadou - In Proceedings of the AFNLP/ACL
-      '09.
+.. [#3] `"Stochastic gradient descent training for l1-regularized
+  log-linear models with cumulative penalty"
+  <https://www.aclweb.org/anthology/P/P09/P09-1054.pdf>`_
+  Y. Tsuruoka, J. Tsujii, S. Ananiadou - In Proceedings of the AFNLP/ACL'09.
 
-   .. [#4] :arxiv:`"Towards Optimal One Pass Large Scale Learning with
-      Averaged Stochastic Gradient Descent"
-      <1107.2490v2>`
-      Xu, Wei (2011)
+.. [#4] :arxiv:`"Towards Optimal One Pass Large Scale Learning with
+  Averaged Stochastic Gradient Descent"
+  <1107.2490v2>`. Xu, Wei (2011)
 
-   .. [#5] :doi:`"Regularization and variable selection via the elastic net"
-      <10.1111/j.1467-9868.2005.00503.x>`
-      H. Zou, T. Hastie - Journal of the Royal Statistical Society Series B,
-      67 (2), 301-320.
+.. [#5] :doi:`"Regularization and variable selection via the elastic net"
+  <10.1111/j.1467-9868.2005.00503.x>`
+  H. Zou, T. Hastie - Journal of the Royal Statistical Society Series B,
+  67 (2), 301-320.
 
-   .. [#6] :doi:`"Solving large scale linear prediction problems using stochastic
-      gradient descent algorithms"
-      <10.1145/1015330.1015332>`
-      T. Zhang - In Proceedings of ICML '04.
+.. [#6] :doi:`"Solving large scale linear prediction problems using stochastic
+  gradient descent algorithms" <10.1145/1015330.1015332>`
+  T. Zhang - In Proceedings of ICML '04.
diff --git a/doc/modules/svm.rst b/doc/modules/svm.rst
index e3bc1395819e9..47115e43a89e0 100644
--- a/doc/modules/svm.rst
+++ b/doc/modules/svm.rst
@@ -108,11 +108,10 @@ properties of these support vectors can be found in attributes
     >>> clf.n_support_
     array([1, 1]...)
 
-.. topic:: Examples:
+.. rubric:: Examples
 
- * :ref:`sphx_glr_auto_examples_svm_plot_separating_hyperplane.py`,
- * :ref:`sphx_glr_auto_examples_svm_plot_svm_nonlinear.py`
- * :ref:`sphx_glr_auto_examples_svm_plot_svm_anova.py`,
+* :ref:`sphx_glr_auto_examples_svm_plot_separating_hyperplane.py`
+* :ref:`sphx_glr_auto_examples_svm_plot_svm_anova.py`
 
 .. _svm_multi_class:
 
@@ -154,65 +153,61 @@ multi-class strategy, thus training `n_classes` models.
 See :ref:`svm_mathematical_formulation` for a complete description of
 the decision function.
 
-|details-start|
-**Details on multi-class strategies**
-|details-split|
-
-Note that the :class:`LinearSVC` also implements an alternative multi-class
-strategy, the so-called multi-class SVM formulated by Crammer and Singer
-[#8]_, by using the option ``multi_class='crammer_singer'``. In practice,
-one-vs-rest classification is usually preferred, since the results are mostly
-similar, but the runtime is significantly less.
-
-For "one-vs-rest" :class:`LinearSVC` the attributes ``coef_`` and ``intercept_``
-have the shape ``(n_classes, n_features)`` and ``(n_classes,)`` respectively.
-Each row of the coefficients corresponds to one of the ``n_classes``
-"one-vs-rest" classifiers and similar for the intercepts, in the
-order of the "one" class.
-
-In the case of "one-vs-one" :class:`SVC` and :class:`NuSVC`, the layout of
-the attributes is a little more involved. In the case of a linear
-kernel, the attributes ``coef_`` and ``intercept_`` have the shape
-``(n_classes * (n_classes - 1) / 2, n_features)`` and ``(n_classes *
-(n_classes - 1) / 2)`` respectively. This is similar to the layout for
-:class:`LinearSVC` described above, with each row now corresponding
-to a binary classifier. The order for classes
-0 to n is "0 vs 1", "0 vs 2" , ... "0 vs n", "1 vs 2", "1 vs 3", "1 vs n", . .
-. "n-1 vs n".
-
-The shape of ``dual_coef_`` is ``(n_classes-1, n_SV)`` with
-a somewhat hard to grasp layout.
-The columns correspond to the support vectors involved in any
-of the ``n_classes * (n_classes - 1) / 2`` "one-vs-one" classifiers.
-Each support vector ``v`` has a dual coefficient in each of the
-``n_classes - 1`` classifiers comparing the class of ``v`` against another class.
-Note that some, but not all, of these dual coefficients, may be zero.
-The ``n_classes - 1`` entries in each column are these dual coefficients,
-ordered by the opposing class.
-
-This might be clearer with an example: consider a three class problem with
-class 0 having three support vectors
-:math:`v^{0}_0, v^{1}_0, v^{2}_0` and class 1 and 2 having two support vectors
-:math:`v^{0}_1, v^{1}_1` and :math:`v^{0}_2, v^{1}_2` respectively.  For each
-support vector :math:`v^{j}_i`, there are two dual coefficients.  Let's call
-the coefficient of support vector :math:`v^{j}_i` in the classifier between
-classes :math:`i` and :math:`k` :math:`\alpha^{j}_{i,k}`.
-Then ``dual_coef_`` looks like this:
-
-+------------------------+------------------------+------------------------+------------------------+------------------------+------------------------+------------------------+
-|:math:`\alpha^{0}_{0,1}`|:math:`\alpha^{1}_{0,1}`|:math:`\alpha^{2}_{0,1}`|:math:`\alpha^{0}_{1,0}`|:math:`\alpha^{1}_{1,0}`|:math:`\alpha^{0}_{2,0}`|:math:`\alpha^{1}_{2,0}`|
-+------------------------+------------------------+------------------------+------------------------+------------------------+------------------------+------------------------+
-|:math:`\alpha^{0}_{0,2}`|:math:`\alpha^{1}_{0,2}`|:math:`\alpha^{2}_{0,2}`|:math:`\alpha^{0}_{1,2}`|:math:`\alpha^{1}_{1,2}`|:math:`\alpha^{0}_{2,1}`|:math:`\alpha^{1}_{2,1}`|
-+------------------------+------------------------+------------------------+------------------------+------------------------+------------------------+------------------------+
-|Coefficients                                                              |Coefficients                                     |Coefficients                                     |
-|for SVs of class 0                                                        |for SVs of class 1                               |for SVs of class 2                               |
-+--------------------------------------------------------------------------+-------------------------------------------------+-------------------------------------------------+
-
-|details-end|
-
-.. topic:: Examples:
-
- * :ref:`sphx_glr_auto_examples_svm_plot_iris_svc.py`,
+.. dropdown:: Details on multi-class strategies
+
+  Note that the :class:`LinearSVC` also implements an alternative multi-class
+  strategy, the so-called multi-class SVM formulated by Crammer and Singer
+  [#8]_, by using the option ``multi_class='crammer_singer'``. In practice,
+  one-vs-rest classification is usually preferred, since the results are mostly
+  similar, but the runtime is significantly less.
+
+  For "one-vs-rest" :class:`LinearSVC` the attributes ``coef_`` and ``intercept_``
+  have the shape ``(n_classes, n_features)`` and ``(n_classes,)`` respectively.
+  Each row of the coefficients corresponds to one of the ``n_classes``
+  "one-vs-rest" classifiers and similar for the intercepts, in the
+  order of the "one" class.
+
+  In the case of "one-vs-one" :class:`SVC` and :class:`NuSVC`, the layout of
+  the attributes is a little more involved. In the case of a linear
+  kernel, the attributes ``coef_`` and ``intercept_`` have the shape
+  ``(n_classes * (n_classes - 1) / 2, n_features)`` and ``(n_classes *
+  (n_classes - 1) / 2)`` respectively. This is similar to the layout for
+  :class:`LinearSVC` described above, with each row now corresponding
+  to a binary classifier. The order for classes
+  0 to n is "0 vs 1", "0 vs 2" , ... "0 vs n", "1 vs 2", "1 vs 3", "1 vs n", . .
+  . "n-1 vs n".
+
+  The shape of ``dual_coef_`` is ``(n_classes-1, n_SV)`` with
+  a somewhat hard to grasp layout.
+  The columns correspond to the support vectors involved in any
+  of the ``n_classes * (n_classes - 1) / 2`` "one-vs-one" classifiers.
+  Each support vector ``v`` has a dual coefficient in each of the
+  ``n_classes - 1`` classifiers comparing the class of ``v`` against another class.
+  Note that some, but not all, of these dual coefficients, may be zero.
+  The ``n_classes - 1`` entries in each column are these dual coefficients,
+  ordered by the opposing class.
+
+  This might be clearer with an example: consider a three class problem with
+  class 0 having three support vectors
+  :math:`v^{0}_0, v^{1}_0, v^{2}_0` and class 1 and 2 having two support vectors
+  :math:`v^{0}_1, v^{1}_1` and :math:`v^{0}_2, v^{1}_2` respectively.  For each
+  support vector :math:`v^{j}_i`, there are two dual coefficients.  Let's call
+  the coefficient of support vector :math:`v^{j}_i` in the classifier between
+  classes :math:`i` and :math:`k` :math:`\alpha^{j}_{i,k}`.
+  Then ``dual_coef_`` looks like this:
+
+  +------------------------+------------------------+------------------------+------------------------+------------------------+------------------------+------------------------+
+  |:math:`\alpha^{0}_{0,1}`|:math:`\alpha^{1}_{0,1}`|:math:`\alpha^{2}_{0,1}`|:math:`\alpha^{0}_{1,0}`|:math:`\alpha^{1}_{1,0}`|:math:`\alpha^{0}_{2,0}`|:math:`\alpha^{1}_{2,0}`|
+  +------------------------+------------------------+------------------------+------------------------+------------------------+------------------------+------------------------+
+  |:math:`\alpha^{0}_{0,2}`|:math:`\alpha^{1}_{0,2}`|:math:`\alpha^{2}_{0,2}`|:math:`\alpha^{0}_{1,2}`|:math:`\alpha^{1}_{1,2}`|:math:`\alpha^{0}_{2,1}`|:math:`\alpha^{1}_{2,1}`|
+  +------------------------+------------------------+------------------------+------------------------+------------------------+------------------------+------------------------+
+  |Coefficients                                                              |Coefficients                                     |Coefficients                                     |
+  |for SVs of class 0                                                        |for SVs of class 1                               |for SVs of class 2                               |
+  +--------------------------------------------------------------------------+-------------------------------------------------+-------------------------------------------------+
+
+.. rubric:: Examples
+
+* :ref:`sphx_glr_auto_examples_svm_plot_iris_svc.py`
 
 .. _scores_probabilities:
 
@@ -295,10 +290,10 @@ to the sample weights:
    :align: center
    :scale: 75
 
-.. topic:: Examples:
+.. rubric:: Examples
 
- * :ref:`sphx_glr_auto_examples_svm_plot_separating_hyperplane_unbalanced.py`
- * :ref:`sphx_glr_auto_examples_svm_plot_weighted_samples.py`,
+* :ref:`sphx_glr_auto_examples_svm_plot_separating_hyperplane_unbalanced.py`
+* :ref:`sphx_glr_auto_examples_svm_plot_weighted_samples.py`
 
 
 .. _svm_regression:
@@ -343,9 +338,9 @@ floating point values instead of integer values::
     array([1.5])
 
 
-.. topic:: Examples:
+.. rubric:: Examples
 
- * :ref:`sphx_glr_auto_examples_svm_plot_svm_regression.py`
+* :ref:`sphx_glr_auto_examples_svm_plot_svm_regression.py`
 
 .. _svm_outlier_detection:
 
@@ -516,11 +511,10 @@ Proper choice of ``C`` and ``gamma`` is critical to the SVM's performance.  One
 is advised to use :class:`~sklearn.model_selection.GridSearchCV` with
 ``C`` and ``gamma`` spaced exponentially far apart to choose good values.
 
-.. topic:: Examples:
+.. rubric:: Examples
 
- * :ref:`sphx_glr_auto_examples_svm_plot_rbf_parameters.py`
- * :ref:`sphx_glr_auto_examples_svm_plot_svm_nonlinear.py`
- * :ref:`sphx_glr_auto_examples_svm_plot_svm_scale_c.py`
+* :ref:`sphx_glr_auto_examples_svm_plot_rbf_parameters.py`
+* :ref:`sphx_glr_auto_examples_svm_plot_svm_scale_c.py`
 
 Custom Kernels
 --------------
@@ -539,60 +533,52 @@ classifiers, except that:
   use of ``fit()`` and ``predict()`` you will have unexpected results.
 
 
-|details-start|
-**Using Python functions as kernels**
-|details-split|
+.. dropdown:: Using Python functions as kernels
 
-You can use your own defined kernels by passing a function to the
-``kernel`` parameter.
+  You can use your own defined kernels by passing a function to the
+  ``kernel`` parameter.
 
-Your kernel must take as arguments two matrices of shape
-``(n_samples_1, n_features)``, ``(n_samples_2, n_features)``
-and return a kernel matrix of shape ``(n_samples_1, n_samples_2)``.
+  Your kernel must take as arguments two matrices of shape
+  ``(n_samples_1, n_features)``, ``(n_samples_2, n_features)``
+  and return a kernel matrix of shape ``(n_samples_1, n_samples_2)``.
 
-The following code defines a linear kernel and creates a classifier
-instance that will use that kernel::
+  The following code defines a linear kernel and creates a classifier
+  instance that will use that kernel::
 
-    >>> import numpy as np
-    >>> from sklearn import svm
-    >>> def my_kernel(X, Y):
-    ...     return np.dot(X, Y.T)
-    ...
-    >>> clf = svm.SVC(kernel=my_kernel)
-
-|details-end|
+      >>> import numpy as np
+      >>> from sklearn import svm
+      >>> def my_kernel(X, Y):
+      ...     return np.dot(X, Y.T)
+      ...
+      >>> clf = svm.SVC(kernel=my_kernel)
 
 
-|details-start|
-**Using the Gram matrix**
-|details-split|
+.. dropdown:: Using the Gram matrix
 
-You can pass pre-computed kernels by using the ``kernel='precomputed'``
-option. You should then pass Gram matrix instead of X to the `fit` and
-`predict` methods. The kernel values between *all* training vectors and the
-test vectors must be provided:
+  You can pass pre-computed kernels by using the ``kernel='precomputed'``
+  option. You should then pass Gram matrix instead of X to the `fit` and
+  `predict` methods. The kernel values between *all* training vectors and the
+  test vectors must be provided:
 
-    >>> import numpy as np
-    >>> from sklearn.datasets import make_classification
-    >>> from sklearn.model_selection import train_test_split
-    >>> from sklearn import svm
-    >>> X, y = make_classification(n_samples=10, random_state=0)
-    >>> X_train , X_test , y_train, y_test = train_test_split(X, y, random_state=0)
-    >>> clf = svm.SVC(kernel='precomputed')
-    >>> # linear kernel computation
-    >>> gram_train = np.dot(X_train, X_train.T)
-    >>> clf.fit(gram_train, y_train)
-    SVC(kernel='precomputed')
-    >>> # predict on training examples
-    >>> gram_test = np.dot(X_test, X_train.T)
-    >>> clf.predict(gram_test)
-    array([0, 1, 0])
+      >>> import numpy as np
+      >>> from sklearn.datasets import make_classification
+      >>> from sklearn.model_selection import train_test_split
+      >>> from sklearn import svm
+      >>> X, y = make_classification(n_samples=10, random_state=0)
+      >>> X_train , X_test , y_train, y_test = train_test_split(X, y, random_state=0)
+      >>> clf = svm.SVC(kernel='precomputed')
+      >>> # linear kernel computation
+      >>> gram_train = np.dot(X_train, X_train.T)
+      >>> clf.fit(gram_train, y_train)
+      SVC(kernel='precomputed')
+      >>> # predict on training examples
+      >>> gram_test = np.dot(X_test, X_train.T)
+      >>> clf.predict(gram_test)
+      array([0, 1, 0])
 
-|details-end|
+.. rubric:: Examples
 
-.. topic:: Examples:
-
- * :ref:`sphx_glr_auto_examples_svm_plot_custom_kernel.py`.
+* :ref:`sphx_glr_auto_examples_svm_plot_custom_kernel.py`
 
 .. _svm_mathematical_formulation:
 
@@ -689,43 +675,35 @@ term :math:`b`
     estimator used is :class:`~sklearn.linear_model.Ridge` regression,
     the relation between them is given as :math:`C = \frac{1}{alpha}`.
 
-|details-start|
-**LinearSVC**
-|details-split|
+.. dropdown:: LinearSVC
 
-The primal problem can be equivalently formulated as
+  The primal problem can be equivalently formulated as
 
-.. math::
+  .. math::
 
-    \min_ {w, b} \frac{1}{2} w^T w + C \sum_{i=1}^{n}\max(0, 1 - y_i (w^T \phi(x_i) + b)),
+      \min_ {w, b} \frac{1}{2} w^T w + C \sum_{i=1}^{n}\max(0, 1 - y_i (w^T \phi(x_i) + b)),
 
-where we make use of the `hinge loss
-<https://en.wikipedia.org/wiki/Hinge_loss>`_. This is the form that is
-directly optimized by :class:`LinearSVC`, but unlike the dual form, this one
-does not involve inner products between samples, so the famous kernel trick
-cannot be applied. This is why only the linear kernel is supported by
-:class:`LinearSVC` (:math:`\phi` is the identity function).
-
-|details-end|
+  where we make use of the `hinge loss
+  <https://en.wikipedia.org/wiki/Hinge_loss>`_. This is the form that is
+  directly optimized by :class:`LinearSVC`, but unlike the dual form, this one
+  does not involve inner products between samples, so the famous kernel trick
+  cannot be applied. This is why only the linear kernel is supported by
+  :class:`LinearSVC` (:math:`\phi` is the identity function).
 
 .. _nu_svc:
 
-|details-start|
-**NuSVC**
-|details-split|
-
-The :math:`\nu`-SVC formulation [#7]_ is a reparameterization of the
-:math:`C`-SVC and therefore mathematically equivalent.
+.. dropdown:: NuSVC
 
-We introduce a new parameter :math:`\nu` (instead of :math:`C`) which
-controls the number of support vectors and *margin errors*:
-:math:`\nu \in (0, 1]` is an upper bound on the fraction of margin errors and
-a lower bound of the fraction of support vectors. A margin error corresponds
-to a sample that lies on the wrong side of its margin boundary: it is either
-misclassified, or it is correctly classified but does not lie beyond the
-margin.
+  The :math:`\nu`-SVC formulation [#7]_ is a reparameterization of the
+  :math:`C`-SVC and therefore mathematically equivalent.
 
-|details-end|
+  We introduce a new parameter :math:`\nu` (instead of :math:`C`) which
+  controls the number of support vectors and *margin errors*:
+  :math:`\nu \in (0, 1]` is an upper bound on the fraction of margin errors and
+  a lower bound of the fraction of support vectors. A margin error corresponds
+  to a sample that lies on the wrong side of its margin boundary: it is either
+  misclassified, or it is correctly classified but does not lie beyond the
+  margin.
 
 SVR
 ---
@@ -774,21 +752,17 @@ which holds the difference :math:`\alpha_i - \alpha_i^*`, ``support_vectors_`` w
 holds the support vectors, and ``intercept_`` which holds the independent
 term :math:`b`
 
-|details-start|
-**LinearSVR**
-|details-split|
+.. dropdown:: LinearSVR
 
-The primal problem can be equivalently formulated as
-
-.. math::
+  The primal problem can be equivalently formulated as
 
-    \min_ {w, b} \frac{1}{2} w^T w + C \sum_{i=1}^{n}\max(0, |y_i - (w^T \phi(x_i) + b)| - \varepsilon),
+  .. math::
 
-where we make use of the epsilon-insensitive loss, i.e. errors of less than
-:math:`\varepsilon` are ignored. This is the form that is directly optimized
-by :class:`LinearSVR`.
+      \min_ {w, b} \frac{1}{2} w^T w + C \sum_{i=1}^{n}\max(0, |y_i - (w^T \phi(x_i) + b)| - \varepsilon),
 
-|details-end|
+  where we make use of the epsilon-insensitive loss, i.e. errors of less than
+  :math:`\varepsilon` are ignored. This is the form that is directly optimized
+  by :class:`LinearSVR`.
 
 .. _svm_implementation_details:
 
@@ -804,38 +778,37 @@ used, please refer to their respective papers.
 .. _`libsvm`: https://www.csie.ntu.edu.tw/~cjlin/libsvm/
 .. _`liblinear`: https://www.csie.ntu.edu.tw/~cjlin/liblinear/
 
-.. topic:: References:
+.. rubric:: References
 
-   .. [#1] Platt `"Probabilistic outputs for SVMs and comparisons to
-      regularized likelihood methods"
-      <https://www.cs.colorado.edu/~mozer/Teaching/syllabi/6622/papers/Platt1999.pdf>`_.
+.. [#1] Platt `"Probabilistic outputs for SVMs and comparisons to
+  regularized likelihood methods"
+  <https://www.cs.colorado.edu/~mozer/Teaching/syllabi/6622/papers/Platt1999.pdf>`_.
 
-   .. [#2] Wu, Lin and Weng, `"Probability estimates for multi-class
-      classification by pairwise coupling"
-      <https://www.csie.ntu.edu.tw/~cjlin/papers/svmprob/svmprob.pdf>`_, JMLR
-      5:975-1005, 2004.
+.. [#2] Wu, Lin and Weng, `"Probability estimates for multi-class
+  classification by pairwise coupling"
+  <https://www.csie.ntu.edu.tw/~cjlin/papers/svmprob/svmprob.pdf>`_,
+  JMLR 5:975-1005, 2004.
 
-   .. [#3] Fan, Rong-En, et al.,
-      `"LIBLINEAR: A library for large linear classification."
-      <https://www.csie.ntu.edu.tw/~cjlin/papers/liblinear.pdf>`_,
-      Journal of machine learning research 9.Aug (2008): 1871-1874.
+.. [#3] Fan, Rong-En, et al.,
+  `"LIBLINEAR: A library for large linear classification."
+  <https://www.csie.ntu.edu.tw/~cjlin/papers/liblinear.pdf>`_,
+  Journal of machine learning research 9.Aug (2008): 1871-1874.
 
-   .. [#4] Chang and Lin, `LIBSVM: A Library for Support Vector Machines
-      <https://www.csie.ntu.edu.tw/~cjlin/papers/libsvm.pdf>`_.
+.. [#4] Chang and Lin, `LIBSVM: A Library for Support Vector Machines
+  <https://www.csie.ntu.edu.tw/~cjlin/papers/libsvm.pdf>`_.
 
-   .. [#5] Bishop, `Pattern recognition and machine learning
-      <https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf>`_,
-      chapter 7 Sparse Kernel Machines
+.. [#5] Bishop, `Pattern recognition and machine learning
+  <https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf>`_,
+  chapter 7 Sparse Kernel Machines
 
-   .. [#6] :doi:`"A Tutorial on Support Vector Regression"
-      <10.1023/B:STCO.0000035301.49549.88>`
-      Alex J. Smola, Bernhard Schölkopf - Statistics and Computing archive
-      Volume 14 Issue 3, August 2004, p. 199-222.
+.. [#6] :doi:`"A Tutorial on Support Vector Regression"
+  <10.1023/B:STCO.0000035301.49549.88>`
+  Alex J. Smola, Bernhard Schölkopf - Statistics and Computing archive
+  Volume 14 Issue 3, August 2004, p. 199-222.
 
-   .. [#7] Schölkopf et. al `New Support Vector Algorithms
-      <https://www.stat.purdue.edu/~yuzhu/stat598m3/Papers/NewSVM.pdf>`_
+.. [#7] Schölkopf et. al `New Support Vector Algorithms
+  <https://www.stat.purdue.edu/~yuzhu/stat598m3/Papers/NewSVM.pdf>`_
 
-   .. [#8] Crammer and Singer `On the Algorithmic Implementation ofMulticlass
-      Kernel-based Vector Machines
-      <http://jmlr.csail.mit.edu/papers/volume2/crammer01a/crammer01a.pdf>`_,
-      JMLR 2001.
+.. [#8] Crammer and Singer `On the Algorithmic Implementation ofMulticlass
+  Kernel-based Vector Machines
+  <http://jmlr.csail.mit.edu/papers/volume2/crammer01a/crammer01a.pdf>`_, JMLR 2001.
diff --git a/doc/modules/tree.rst b/doc/modules/tree.rst
index b54b913573a34..9b475d6c09f5f 100644
--- a/doc/modules/tree.rst
+++ b/doc/modules/tree.rst
@@ -146,82 +146,78 @@ Once trained, you can plot the tree with the :func:`plot_tree` function::
    :scale: 75
    :align: center
 
-|details-start|
-**Alternative ways to export trees**
-|details-split|
-
-We can also export the tree in `Graphviz
-<https://www.graphviz.org/>`_ format using the :func:`export_graphviz`
-exporter. If you use the `conda <https://conda.io>`_ package manager, the graphviz binaries
-and the python package can be installed with `conda install python-graphviz`.
-
-Alternatively binaries for graphviz can be downloaded from the graphviz project homepage,
-and the Python wrapper installed from pypi with `pip install graphviz`.
-
-Below is an example graphviz export of the above tree trained on the entire
-iris dataset; the results are saved in an output file `iris.pdf`::
-
-
-    >>> import graphviz # doctest: +SKIP
-    >>> dot_data = tree.export_graphviz(clf, out_file=None) # doctest: +SKIP
-    >>> graph = graphviz.Source(dot_data) # doctest: +SKIP
-    >>> graph.render("iris") # doctest: +SKIP
-
-The :func:`export_graphviz` exporter also supports a variety of aesthetic
-options, including coloring nodes by their class (or value for regression) and
-using explicit variable and class names if desired. Jupyter notebooks also
-render these plots inline automatically::
-
-    >>> dot_data = tree.export_graphviz(clf, out_file=None, # doctest: +SKIP
-    ...                      feature_names=iris.feature_names,  # doctest: +SKIP
-    ...                      class_names=iris.target_names,  # doctest: +SKIP
-    ...                      filled=True, rounded=True,  # doctest: +SKIP
-    ...                      special_characters=True)  # doctest: +SKIP
-    >>> graph = graphviz.Source(dot_data)  # doctest: +SKIP
-    >>> graph # doctest: +SKIP
-
-.. only:: html
-
-    .. figure:: ../images/iris.svg
-       :align: center
-
-.. only:: latex
-
-    .. figure:: ../images/iris.pdf
-       :align: center
-
-.. figure:: ../auto_examples/tree/images/sphx_glr_plot_iris_dtc_001.png
-   :target: ../auto_examples/tree/plot_iris_dtc.html
-   :align: center
-   :scale: 75
-
-Alternatively, the tree can also be exported in textual format with the
-function :func:`export_text`. This method doesn't require the installation
-of external libraries and is more compact:
-
-    >>> from sklearn.datasets import load_iris
-    >>> from sklearn.tree import DecisionTreeClassifier
-    >>> from sklearn.tree import export_text
-    >>> iris = load_iris()
-    >>> decision_tree = DecisionTreeClassifier(random_state=0, max_depth=2)
-    >>> decision_tree = decision_tree.fit(iris.data, iris.target)
-    >>> r = export_text(decision_tree, feature_names=iris['feature_names'])
-    >>> print(r)
-    |--- petal width (cm) <= 0.80
-    |   |--- class: 0
-    |--- petal width (cm) >  0.80
-    |   |--- petal width (cm) <= 1.75
-    |   |   |--- class: 1
-    |   |--- petal width (cm) >  1.75
-    |   |   |--- class: 2
-    <BLANKLINE>
-
-|details-end|
-
-.. topic:: Examples:
-
- * :ref:`sphx_glr_auto_examples_tree_plot_iris_dtc.py`
- * :ref:`sphx_glr_auto_examples_tree_plot_unveil_tree_structure.py`
+.. dropdown:: Alternative ways to export trees
+
+  We can also export the tree in `Graphviz
+  <https://www.graphviz.org/>`_ format using the :func:`export_graphviz`
+  exporter. If you use the `conda <https://conda.io>`_ package manager, the graphviz binaries
+  and the python package can be installed with `conda install python-graphviz`.
+
+  Alternatively binaries for graphviz can be downloaded from the graphviz project homepage,
+  and the Python wrapper installed from pypi with `pip install graphviz`.
+
+  Below is an example graphviz export of the above tree trained on the entire
+  iris dataset; the results are saved in an output file `iris.pdf`::
+
+
+      >>> import graphviz # doctest: +SKIP
+      >>> dot_data = tree.export_graphviz(clf, out_file=None) # doctest: +SKIP
+      >>> graph = graphviz.Source(dot_data) # doctest: +SKIP
+      >>> graph.render("iris") # doctest: +SKIP
+
+  The :func:`export_graphviz` exporter also supports a variety of aesthetic
+  options, including coloring nodes by their class (or value for regression) and
+  using explicit variable and class names if desired. Jupyter notebooks also
+  render these plots inline automatically::
+
+      >>> dot_data = tree.export_graphviz(clf, out_file=None, # doctest: +SKIP
+      ...                      feature_names=iris.feature_names,  # doctest: +SKIP
+      ...                      class_names=iris.target_names,  # doctest: +SKIP
+      ...                      filled=True, rounded=True,  # doctest: +SKIP
+      ...                      special_characters=True)  # doctest: +SKIP
+      >>> graph = graphviz.Source(dot_data)  # doctest: +SKIP
+      >>> graph # doctest: +SKIP
+
+  .. only:: html
+
+      .. figure:: ../images/iris.svg
+        :align: center
+
+  .. only:: latex
+
+      .. figure:: ../images/iris.pdf
+        :align: center
+
+  .. figure:: ../auto_examples/tree/images/sphx_glr_plot_iris_dtc_001.png
+    :target: ../auto_examples/tree/plot_iris_dtc.html
+    :align: center
+    :scale: 75
+
+  Alternatively, the tree can also be exported in textual format with the
+  function :func:`export_text`. This method doesn't require the installation
+  of external libraries and is more compact:
+
+      >>> from sklearn.datasets import load_iris
+      >>> from sklearn.tree import DecisionTreeClassifier
+      >>> from sklearn.tree import export_text
+      >>> iris = load_iris()
+      >>> decision_tree = DecisionTreeClassifier(random_state=0, max_depth=2)
+      >>> decision_tree = decision_tree.fit(iris.data, iris.target)
+      >>> r = export_text(decision_tree, feature_names=iris['feature_names'])
+      >>> print(r)
+      |--- petal width (cm) <= 0.80
+      |   |--- class: 0
+      |--- petal width (cm) >  0.80
+      |   |--- petal width (cm) <= 1.75
+      |   |   |--- class: 1
+      |   |--- petal width (cm) >  1.75
+      |   |   |--- class: 2
+      <BLANKLINE>
+
+.. rubric:: Examples
+
+* :ref:`sphx_glr_auto_examples_tree_plot_iris_dtc.py`
+* :ref:`sphx_glr_auto_examples_tree_plot_unveil_tree_structure.py`
 
 .. _tree_regression:
 
@@ -248,9 +244,9 @@ instead of integer values::
     >>> clf.predict([[1, 1]])
     array([0.5])
 
-.. topic:: Examples:
+.. rubric:: Examples
 
- * :ref:`sphx_glr_auto_examples_tree_plot_tree_regression.py`
+* :ref:`sphx_glr_auto_examples_tree_plot_tree_regression.py`
 
 
 .. _tree_multioutput:
@@ -306,21 +302,17 @@ the lower half of those faces.
    :scale: 75
    :align: center
 
-.. topic:: Examples:
+.. rubric:: Examples
 
-  * :ref:`sphx_glr_auto_examples_tree_plot_tree_regression_multioutput.py`
-  * :ref:`sphx_glr_auto_examples_miscellaneous_plot_multioutput_face_completion.py`
+* :ref:`sphx_glr_auto_examples_tree_plot_tree_regression_multioutput.py`
+* :ref:`sphx_glr_auto_examples_miscellaneous_plot_multioutput_face_completion.py`
 
-|details-start|
-**References**
-|details-split|
+.. rubric:: References
 
 * M. Dumont et al,  `Fast multi-class image annotation with random subwindows
   and multiple output randomized trees
-  <http://www.montefiore.ulg.ac.be/services/stochastic/pubs/2009/DMWG09/dumont-visapp09-shortpaper.pdf>`_, International Conference on
-  Computer Vision Theory and Applications 2009
-
-|details-end|
+  <http://www.montefiore.ulg.ac.be/services/stochastic/pubs/2009/DMWG09/dumont-visapp09-shortpaper.pdf>`_,
+  International Conference on Computer Vision Theory and Applications 2009
 
 .. _tree_complexity:
 
@@ -412,36 +404,32 @@ Tree algorithms: ID3, C4.5, C5.0 and CART
 What are all the various decision tree algorithms and how do they differ
 from each other? Which one is implemented in scikit-learn?
 
-|details-start|
-**Various decision tree algorithms**
-|details-split|
-
-ID3_ (Iterative Dichotomiser 3) was developed in 1986 by Ross Quinlan.
-The algorithm creates a multiway tree, finding for each node (i.e. in
-a greedy manner) the categorical feature that will yield the largest
-information gain for categorical targets. Trees are grown to their
-maximum size and then a pruning step is usually applied to improve the
-ability of the tree to generalize to unseen data.
-
-C4.5 is the successor to ID3 and removed the restriction that features
-must be categorical by dynamically defining a discrete attribute (based
-on numerical variables) that partitions the continuous attribute value
-into a discrete set of intervals. C4.5 converts the trained trees
-(i.e. the output of the ID3 algorithm) into sets of if-then rules.
-The accuracy of each rule is then evaluated to determine the order
-in which they should be applied. Pruning is done by removing a rule's
-precondition if the accuracy of the rule improves without it.
-
-C5.0 is Quinlan's latest version release under a proprietary license.
-It uses less memory and builds smaller rulesets than C4.5 while being
-more accurate.
-
-CART (Classification and Regression Trees) is very similar to C4.5, but
-it differs in that it supports numerical target variables (regression) and
-does not compute rule sets. CART constructs binary trees using the feature
-and threshold that yield the largest information gain at each node.
-
-|details-end|
+.. dropdown:: Various decision tree algorithms
+
+  ID3_ (Iterative Dichotomiser 3) was developed in 1986 by Ross Quinlan.
+  The algorithm creates a multiway tree, finding for each node (i.e. in
+  a greedy manner) the categorical feature that will yield the largest
+  information gain for categorical targets. Trees are grown to their
+  maximum size and then a pruning step is usually applied to improve the
+  ability of the tree to generalize to unseen data.
+
+  C4.5 is the successor to ID3 and removed the restriction that features
+  must be categorical by dynamically defining a discrete attribute (based
+  on numerical variables) that partitions the continuous attribute value
+  into a discrete set of intervals. C4.5 converts the trained trees
+  (i.e. the output of the ID3 algorithm) into sets of if-then rules.
+  The accuracy of each rule is then evaluated to determine the order
+  in which they should be applied. Pruning is done by removing a rule's
+  precondition if the accuracy of the rule improves without it.
+
+  C5.0 is Quinlan's latest version release under a proprietary license.
+  It uses less memory and builds smaller rulesets than C4.5 while being
+  more accurate.
+
+  CART (Classification and Regression Trees) is very similar to C4.5, but
+  it differs in that it supports numerical target variables (regression) and
+  does not compute rule sets. CART constructs binary trees using the feature
+  and threshold that yield the largest information gain at each node.
 
 scikit-learn uses an optimized version of the CART algorithm; however, the
 scikit-learn implementation does not support categorical variables for now.
@@ -515,39 +503,35 @@ Log Loss or Entropy:
 
     H(Q_m) = - \sum_k p_{mk} \log(p_{mk})
 
-|details-start|
-**Shannon entropy**
-|details-split|
-
-The entropy criterion computes the Shannon entropy of the possible classes. It
-takes the class frequencies of the training data points that reached a given
-leaf :math:`m` as their probability. Using the **Shannon entropy as tree node
-splitting criterion is equivalent to minimizing the log loss** (also known as
-cross-entropy and multinomial deviance) between the true labels :math:`y_i`
-and the probabilistic predictions :math:`T_k(x_i)` of the tree model :math:`T` for class :math:`k`.
+.. dropdown:: Shannon entropy
 
-To see this, first recall that the log loss of a tree model :math:`T`
-computed on a dataset :math:`D` is defined as follows:
+  The entropy criterion computes the Shannon entropy of the possible classes. It
+  takes the class frequencies of the training data points that reached a given
+  leaf :math:`m` as their probability. Using the **Shannon entropy as tree node
+  splitting criterion is equivalent to minimizing the log loss** (also known as
+  cross-entropy and multinomial deviance) between the true labels :math:`y_i`
+  and the probabilistic predictions :math:`T_k(x_i)` of the tree model :math:`T` for class :math:`k`.
 
-.. math::
+  To see this, first recall that the log loss of a tree model :math:`T`
+  computed on a dataset :math:`D` is defined as follows:
 
-    \mathrm{LL}(D, T) = -\frac{1}{n} \sum_{(x_i, y_i) \in D} \sum_k I(y_i = k) \log(T_k(x_i))
+  .. math::
 
-where :math:`D` is a training dataset of :math:`n` pairs :math:`(x_i, y_i)`.
+      \mathrm{LL}(D, T) = -\frac{1}{n} \sum_{(x_i, y_i) \in D} \sum_k I(y_i = k) \log(T_k(x_i))
 
-In a classification tree, the predicted class probabilities within leaf nodes
-are constant, that is: for all :math:`(x_i, y_i) \in Q_m`, one has:
-:math:`T_k(x_i) = p_{mk}` for each class :math:`k`.
+  where :math:`D` is a training dataset of :math:`n` pairs :math:`(x_i, y_i)`.
 
-This property makes it possible to rewrite :math:`\mathrm{LL}(D, T)` as the
-sum of the Shannon entropies computed for each leaf of :math:`T` weighted by
-the number of training data points that reached each leaf:
+  In a classification tree, the predicted class probabilities within leaf nodes
+  are constant, that is: for all :math:`(x_i, y_i) \in Q_m`, one has:
+  :math:`T_k(x_i) = p_{mk}` for each class :math:`k`.
 
-.. math::
+  This property makes it possible to rewrite :math:`\mathrm{LL}(D, T)` as the
+  sum of the Shannon entropies computed for each leaf of :math:`T` weighted by
+  the number of training data points that reached each leaf:
 
-    \mathrm{LL}(D, T) = \sum_{m \in T} \frac{n_m}{n} H(Q_m)
+  .. math::
 
-|details-end|
+      \mathrm{LL}(D, T) = \sum_{m \in T} \frac{n_m}{n} H(Q_m)
 
 Regression criteria
 -------------------
@@ -685,13 +669,11 @@ with the smallest value of :math:`\alpha_{eff}` is the weakest link and will
 be pruned. This process stops when the pruned tree's minimal
 :math:`\alpha_{eff}` is greater than the ``ccp_alpha`` parameter.
 
-.. topic:: Examples:
+.. rubric:: Examples
 
-    * :ref:`sphx_glr_auto_examples_tree_plot_cost_complexity_pruning.py`
+* :ref:`sphx_glr_auto_examples_tree_plot_cost_complexity_pruning.py`
 
-|details-start|
-**References**
-|details-split|
+.. rubric:: References
 
 .. [BRE] L. Breiman, J. Friedman, R. Olshen, and C. Stone. Classification
   and Regression Trees. Wadsworth, Belmont, CA, 1984.
@@ -705,5 +687,3 @@ be pruned. This process stops when the pruned tree's minimal
 
 * T. Hastie, R. Tibshirani and J. Friedman. Elements of Statistical
   Learning, Springer, 2009.
-
-|details-end|
diff --git a/doc/modules/unsupervised_reduction.rst b/doc/modules/unsupervised_reduction.rst
index 90c80714c3131..f94d6ac301e47 100644
--- a/doc/modules/unsupervised_reduction.rst
+++ b/doc/modules/unsupervised_reduction.rst
@@ -24,9 +24,9 @@ PCA: principal component analysis
 :class:`decomposition.PCA` looks for a combination of features that
 capture well the variance of the original features. See :ref:`decompositions`.
 
-.. topic:: **Examples**
+.. rubric:: Examples
 
-   * :ref:`sphx_glr_auto_examples_applications_plot_face_recognition.py`
+* :ref:`sphx_glr_auto_examples_applications_plot_face_recognition.py`
 
 Random projections
 -------------------
@@ -35,9 +35,9 @@ The module: :mod:`~sklearn.random_projection` provides several tools for data
 reduction by random projections. See the relevant section of the
 documentation: :ref:`random_projection`.
 
-.. topic:: **Examples**
+.. rubric:: Examples
 
-   * :ref:`sphx_glr_auto_examples_miscellaneous_plot_johnson_lindenstrauss_bound.py`
+* :ref:`sphx_glr_auto_examples_miscellaneous_plot_johnson_lindenstrauss_bound.py`
 
 Feature agglomeration
 ------------------------
@@ -46,10 +46,10 @@ Feature agglomeration
 :ref:`hierarchical_clustering` to group together features that behave
 similarly.
 
-.. topic:: **Examples**
+.. rubric:: Examples
 
-   * :ref:`sphx_glr_auto_examples_cluster_plot_feature_agglomeration_vs_univariate_selection.py`
-   * :ref:`sphx_glr_auto_examples_cluster_plot_digits_agglomeration.py`
+* :ref:`sphx_glr_auto_examples_cluster_plot_feature_agglomeration_vs_univariate_selection.py`
+* :ref:`sphx_glr_auto_examples_cluster_plot_digits_agglomeration.py`
 
 .. topic:: **Feature scaling**
 
diff --git a/doc/preface.rst b/doc/preface.rst
deleted file mode 100644
index 447083a3a8136..0000000000000
--- a/doc/preface.rst
+++ /dev/null
@@ -1,32 +0,0 @@
-.. This helps define the TOC ordering for "about us" sections. Particularly
-   useful for PDF output as this section is not linked from elsewhere.
-
-.. Places global toc into the sidebar
-
-:globalsidebartoc: True
-
-.. _preface_menu:
-
-.. include:: includes/big_toc_css.rst
-.. include:: tune_toc.rst
-
-=======================
-Welcome to scikit-learn
-=======================
-
-|
-
-.. toctree::
-    :maxdepth: 2
-
-    install
-    faq
-    support
-    related_projects
-    about
-    testimonials/testimonials
-    whats_new
-    roadmap
-    governance
-
-|
diff --git a/doc/scss/api-search.scss b/doc/scss/api-search.scss
new file mode 100644
index 0000000000000..51cf15f92c1cb
--- /dev/null
+++ b/doc/scss/api-search.scss
@@ -0,0 +1,111 @@
+/**
+ * This is the styling for the API index page (`api/index`), in particular for the API
+ * search table. It involves overriding the style sheet of DataTables which does not
+ * fit well into the theme, especially in dark theme; see https://datatables.net/
+ */
+
+.dt-container {
+  margin-bottom: 2rem;
+
+  // Fix the selection box for entries per page
+  select.dt-input {
+    padding: 0 !important;
+    margin-right: 0.4rem !important;
+
+    > option {
+      color: var(--pst-color-text-base);
+      background-color: var(--pst-color-background);
+    }
+  }
+
+  // Fix the search box
+  input.dt-input {
+    width: 50%;
+    line-height: normal;
+    padding: 0.1rem 0.3rem !important;
+    margin-left: 0.4rem !important;
+  }
+
+  table.dataTable {
+    th {
+      // Avoid table header being too tall
+      p {
+        margin-bottom: 0;
+      }
+
+      // Fix the ascending/descending order buttons in the header
+      span.dt-column-order {
+        &::before,
+        &::after {
+          color: var(--pst-color-text-base);
+          line-height: 0.7rem !important;
+        }
+      }
+    }
+
+    td {
+      // Fix color of text warning no records found
+      &.dt-empty {
+        color: var(--pst-color-text-base) !important;
+      }
+    }
+
+    // Unset bottom border of the last row
+    tr:last-child > * {
+      border-bottom: unset !important;
+    }
+  }
+
+  div.dt-paging button.dt-paging-button {
+    padding: 0 0.5rem;
+
+    &.disabled {
+      color: var(--pst-color-border) !important;
+
+      // Overwrite the !important color assigned by DataTables because we must keep
+      // the color of disabled buttons consistent with and without hovering
+      &:hover {
+        color: var(--pst-color-border) !important;
+      }
+    }
+
+    // Fix colors of paging buttons
+    &.current,
+    &:not(.disabled):not(.current):hover {
+      color: var(--pst-color-on-surface) !important;
+      border-color: var(--pst-color-surface) !important;
+      background: var(--pst-color-surface) !important;
+    }
+
+    // Highlight the border of the current selected paging button
+    &.current {
+      border-color: var(--pst-color-text-base) !important;
+    }
+  }
+}
+
+// Styling the object description cells in the table
+div.sk-apisearch-desc {
+  p {
+    margin-bottom: 0;
+  }
+
+  div.caption > p {
+    a,
+    code {
+      color: var(--pst-color-text-muted);
+    }
+
+    code {
+      padding: 0;
+      font-size: 0.7rem;
+      font-weight: var(--pst-font-weight-caption);
+      background-color: transparent;
+    }
+
+    .sd-badge {
+      font-size: 0.7rem;
+      margin-left: 0.3rem;
+    }
+  }
+}
diff --git a/doc/scss/api.scss b/doc/scss/api.scss
new file mode 100644
index 0000000000000..d7110def4ac09
--- /dev/null
+++ b/doc/scss/api.scss
@@ -0,0 +1,52 @@
+/**
+ * This is the styling for API reference pages, currently under `modules/generated`.
+ * Note that it should be applied *ONLY* to API reference pages, as the selectors are
+ * designed based on how `autodoc` and `autosummary` generate the stuff.
+ */
+
+// Make the admonitions more compact
+div.versionadded,
+div.versionchanged,
+div.deprecated {
+  margin: 1rem auto;
+
+  > p {
+    margin: 0.3rem auto;
+  }
+}
+
+// Make docstrings more compact
+dd {
+  p:not(table *) {
+    margin-bottom: 0.5rem !important;
+  }
+
+  ul {
+    margin-bottom: 0.5rem !important;
+    padding-left: 2rem !important;
+  }
+}
+
+// The first method is too close the the docstring above
+dl.py.method:first-of-type {
+  margin-top: 2rem;
+}
+
+// https://github.com/pydata/pydata-sphinx-theme/blob/8cf45f835bfdafc5f3821014a18f3b7e0fc2d44b/src/pydata_sphinx_theme/assets/styles/content/_api.scss
+dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.glossary):not(.simple) {
+  margin-bottom: 1.5rem;
+
+  dd {
+    margin-left: 1.2rem;
+  }
+
+  // "Parameters", "Returns", etc. in the docstring
+  dt.field-odd,
+  dt.field-even {
+    margin: 0.5rem 0;
+
+    + dd > dl {
+      margin-bottom: 0.5rem;
+    }
+  }
+}
diff --git a/doc/scss/colors.scss b/doc/scss/colors.scss
new file mode 100644
index 0000000000000..bbc6aa6c2a3d6
--- /dev/null
+++ b/doc/scss/colors.scss
@@ -0,0 +1,51 @@
+/**
+ * This is the style sheet for customized colors of scikit-learn.
+ * Tints and shades are generated by https://colorkit.co/color-shades-generator/
+ *
+ * This file is compiled into styles/colors.css by sphinxcontrib.sass, see:
+ * https://sass-lang.com/guide/
+ */
+
+:root {
+  /* scikit-learn cyan */
+  --sk-cyan-tint-9: #edf7fd;
+  --sk-cyan-tint-8: #daeffa;
+  --sk-cyan-tint-7: #c8e6f8;
+  --sk-cyan-tint-6: #b5def5;
+  --sk-cyan-tint-5: #a2d6f2;
+  --sk-cyan-tint-4: #8fcdef;
+  --sk-cyan-tint-3: #7ac5ec;
+  --sk-cyan-tint-2: #64bce9;
+  --sk-cyan-tint-1: #4bb4e5;
+  --sk-cyan: #29abe2;
+  --sk-cyan-shades-1: #2294c4;
+  --sk-cyan-shades-2: #1c7ea8;
+  --sk-cyan-shades-3: #15688c;
+  --sk-cyan-shades-4: #0f5471;
+  --sk-cyan-shades-5: #094057;
+  --sk-cyan-shades-6: #052d3e;
+  --sk-cyan-shades-7: #021b27;
+  --sk-cyan-shades-8: #010b12;
+  --sk-cyan-shades-9: #000103;
+
+  /* scikit-learn orange */
+  --sk-orange-tint-9: #fff5ec;
+  --sk-orange-tint-8: #ffead9;
+  --sk-orange-tint-7: #ffe0c5;
+  --sk-orange-tint-6: #ffd5b2;
+  --sk-orange-tint-5: #fecb9e;
+  --sk-orange-tint-4: #fdc08a;
+  --sk-orange-tint-3: #fcb575;
+  --sk-orange-tint-2: #fbaa5e;
+  --sk-orange-tint-1: #f99f44;
+  --sk-orange: #f7931e;
+  --sk-orange-shades-1: #d77f19;
+  --sk-orange-shades-2: #b76c13;
+  --sk-orange-shades-3: #99590e;
+  --sk-orange-shades-4: #7c4709;
+  --sk-orange-shades-5: #603605;
+  --sk-orange-shades-6: #452503;
+  --sk-orange-shades-7: #2c1601;
+  --sk-orange-shades-8: #150800;
+  --sk-orange-shades-9: #030100;
+}
diff --git a/doc/scss/custom.scss b/doc/scss/custom.scss
new file mode 100644
index 0000000000000..ce4451fce4467
--- /dev/null
+++ b/doc/scss/custom.scss
@@ -0,0 +1,192 @@
+/**
+ * This is a general styling sheet.
+ * It should be used for customizations that affect multiple pages.
+ *
+ * This file is compiled into styles/custom.css by sphinxcontrib.sass, see:
+ * https://sass-lang.com/guide/
+ */
+
+/* Global */
+
+code.literal {
+  border: 0;
+}
+
+/* Version switcher */
+
+.version-switcher__menu a.list-group-item.sk-avail-docs-link {
+  display: flex;
+  align-items: center;
+
+  &:after {
+    content: var(--pst-icon-external-link);
+    font: var(--fa-font-solid);
+    font-size: 0.75rem;
+    margin-left: 0.5rem;
+  }
+}
+
+/* Primary sidebar */
+
+.bd-sidebar-primary {
+  width: 22.5%;
+  min-width: 16rem;
+
+  // The version switcher button in the sidebar is ill-styled
+  button.version-switcher__button {
+    margin-bottom: unset;
+    margin-left: 0.3rem;
+    font-size: 1rem;
+  }
+
+  // The section navigation part is to close to the right boundary (originally an even
+  // larger negative right margin was used)
+  nav.bd-links {
+    margin-right: -0.5rem;
+  }
+}
+
+/* Article content */
+
+.bd-article {
+  h1 {
+    font-weight: 500;
+    margin-bottom: 2rem;
+  }
+
+  h2 {
+    font-weight: 500;
+    margin-bottom: 1.5rem;
+  }
+
+  // Avoid changing the aspect ratio of images; add some padding so that at least
+  // there is some space between image and background in dark mode
+  img {
+    height: unset !important;
+    padding: 1%;
+  }
+
+  // Resize table of contents to make the top few levels of headings more visible
+  li.toctree-l1 {
+    padding-bottom: 0.5em;
+
+    > a {
+      font-size: 150%;
+      font-weight: bold;
+    }
+  }
+
+  li.toctree-l2,
+  li.toctree-l3,
+  li.toctree-l4 {
+    margin-left: 15px;
+  }
+}
+
+/* Dropdowns (sphinx-design) */
+
+details.sd-dropdown {
+  &:hover > summary.sd-summary-title > a.headerlink {
+    visibility: visible;
+  }
+
+  > summary.sd-summary-title {
+    > a.headerlink {
+      font-size: 1rem;
+    }
+
+    // See `js/scripts/dropdown.js`: this is styling the "expand/collapse all" button
+    > button.sk-toggle-all {
+      color: var(--pst-sd-dropdown-color);
+      top: 0.9rem !important;
+      right: 3rem !important;
+      pointer-events: auto !important;
+      display: none;
+      border: none;
+      background: transparent;
+    }
+  }
+
+  &[open] > summary.sd-summary-title:hover > .sd-summary-up.sk-toggle-all,
+  &:not([open])
+    > summary.sd-summary-title:hover
+    > .sd-summary-down.sk-toggle-all {
+    display: block;
+  }
+}
+
+/* scikit-learn buttons */
+
+a.btn {
+  &.sk-btn-orange {
+    background-color: var(--sk-orange-tint-1);
+    color: black !important;
+
+    &:hover {
+      background-color: var(--sk-orange-tint-3);
+    }
+  }
+
+  &.sk-btn-cyan {
+    background-color: var(--sk-cyan-shades-2);
+    color: white !important;
+
+    &:hover {
+      background-color: var(--sk-cyan-shades-1);
+    }
+  }
+}
+
+/* scikit-learn avatar grid, see build_tools/generate_authors_table.py */
+
+div.sk-authors-container {
+  display: flex;
+  flex-wrap: wrap;
+  justify-content: center;
+
+  > div {
+    width: 6rem;
+    margin: 0.5rem;
+    font-size: 0.9rem;
+  }
+}
+
+/* scikit-learn text-image grid, used in testimonials and sponsors pages */
+
+@mixin sk-text-image-grid($img-max-height) {
+  display: flex;
+  align-items: center;
+  flex-wrap: wrap;
+
+  div.text-box,
+  div.image-box {
+    width: 50%;
+
+    @media screen and (max-width: 500px) {
+      width: 100%;
+    }
+  }
+
+  div.text-box .annotation {
+    font-size: 0.9rem;
+    font-style: italic;
+    color: var(--pst-color-text-muted);
+  }
+
+  div.image-box {
+    text-align: center;
+
+    img {
+      max-height: $img-max-height;
+      max-width: 50%;
+    }
+  }
+}
+
+div.sk-text-image-grid-small {
+  @include sk-text-image-grid(60px);
+}
+
+div.sk-text-image-grid-large {
+  @include sk-text-image-grid(100px);
+}
diff --git a/doc/scss/index.scss b/doc/scss/index.scss
new file mode 100644
index 0000000000000..c3bb8e86b41c6
--- /dev/null
+++ b/doc/scss/index.scss
@@ -0,0 +1,176 @@
+/**
+ * Styling sheet for the scikit-learn landing page. This should be loaded only for the
+ * landing page.
+ *
+ * This file is compiled into styles/index.css by sphinxcontrib.sass, see:
+ * https://sass-lang.com/guide/
+ */
+
+/* Theme-aware colors for the landing page */
+
+html {
+  &[data-theme="light"] {
+    --sk-landing-bg-1: var(--sk-cyan-shades-3);
+    --sk-landing-bg-2: var(--sk-cyan);
+    --sk-landing-bg-3: var(--sk-orange-tint-8);
+    --sk-landing-bg-4: var(--sk-orange-tint-3);
+  }
+
+  &[data-theme="dark"] {
+    --sk-landing-bg-1: var(--sk-cyan-shades-5);
+    --sk-landing-bg-2: var(--sk-cyan-shades-2);
+    --sk-landing-bg-3: var(--sk-orange-tint-4);
+    --sk-landing-bg-4: var(--sk-orange-tint-1);
+  }
+}
+
+/* General */
+
+div.sk-landing-container {
+  max-width: 1400px;
+}
+
+/* Top bar */
+
+div.sk-landing-top-bar {
+  background-image: linear-gradient(
+    160deg,
+    var(--sk-landing-bg-1) 0%,
+    var(--sk-landing-bg-2) 17%,
+    var(--sk-landing-bg-3) 59%,
+    var(--sk-landing-bg-4) 100%
+  );
+
+  .sk-landing-header,
+  .sk-landing-subheader {
+    color: white;
+    text-shadow: 0px 0px 8px var(--sk-landing-bg-1);
+  }
+
+  .sk-landing-header {
+    font-size: 3.2rem;
+    margin-bottom: 0.5rem;
+  }
+
+  .sk-landing-subheader {
+    letter-spacing: 0.17rem;
+    margin-top: 0;
+    font-weight: 500;
+  }
+
+  a.sk-btn-orange {
+    font-size: 1.1rem;
+    font-weight: 500;
+  }
+
+  ul.sk-landing-header-body {
+    margin-top: auto;
+    margin-bottom: auto;
+    font-size: 1.2rem;
+    font-weight: 500;
+    color: black;
+  }
+}
+
+/* Body */
+
+div.sk-landing-body {
+  div.card {
+    background-color: var(--pst-color-background);
+    border-color: var(--pst-color-border);
+  }
+
+  .sk-px-xl-4 {
+    @media screen and (min-width: 1200px) {
+      padding-left: 1.3rem !important;
+      padding-right: 1.3rem !important;
+    }
+  }
+
+  .card-body {
+    p {
+      margin-bottom: 0.8rem;
+      color: var(--pst-color-text-base);
+    }
+
+    .sk-card-title {
+      font-weight: 700;
+      margin: 0 0 1rem 0;
+    }
+  }
+
+  .sk-card-img-container {
+    display: flex;
+    justify-content: center;
+    align-items: end;
+    margin-bottom: 1rem;
+
+    img {
+      max-width: unset;
+      height: 15rem;
+    }
+  }
+}
+
+/* More info */
+
+div.sk-landing-more-info {
+  font-size: 0.96rem;
+  background-color: var(--pst-color-surface);
+
+  .sk-landing-call-header {
+    font-weight: 700;
+    margin-top: 0;
+
+    html[data-theme="light"] & {
+      color: var(--sk-orange-shades-1);
+    }
+
+    html[data-theme="dark"] & {
+      color: var(--sk-orange);
+    }
+  }
+
+  ul.sk-landing-call-list > li {
+    margin-bottom: 0.25rem;
+  }
+
+  .sk-who-uses-carousel {
+    min-height: 200px;
+
+    .carousel-item img {
+      max-height: 100px;
+      max-width: 50%;
+      margin: 0.5rem;
+    }
+  }
+
+  .sk-more-testimonials {
+    text-align: right !important;
+  }
+}
+
+/* Footer */
+
+div.sk-landing-footer {
+  a.sk-footer-funding-link {
+    text-decoration: none;
+
+    p.sk-footer-funding-text {
+      color: var(--pst-color-link);
+
+      &:hover {
+        color: var(--pst-color-secondary);
+      }
+    }
+
+    div.sk-footer-funding-logos > img {
+      max-height: 40px;
+      max-width: 85px;
+      margin: 0 8px 8px 8px;
+      padding: 5px;
+      border-radius: 3px;
+      background-color: white;
+    }
+  }
+}
diff --git a/doc/scss/install.scss b/doc/scss/install.scss
new file mode 100644
index 0000000000000..92e201f00a107
--- /dev/null
+++ b/doc/scss/install.scss
@@ -0,0 +1,20 @@
+/**
+ * Styling for the installation page, including overriding some default styling of
+ * sphinx-design. This style sheet should be included only for the install page.
+ *
+ * This file is compiled into styles/install.css by sphinxcontrib.sass, see:
+ * https://sass-lang.com/guide/
+ */
+
+.install-instructions .sd-tab-set > label.sd-tab-label {
+  margin: 0;
+  text-align: center;
+
+  &.tab-6 {
+    width: 50% !important;
+  }
+
+  &.tab-4 {
+    width: calc(100% / 3) !important;
+  }
+}
diff --git a/doc/sphinxext/add_toctree_functions.py b/doc/sphinxext/add_toctree_functions.py
deleted file mode 100644
index 4459ab971f4c4..0000000000000
--- a/doc/sphinxext/add_toctree_functions.py
+++ /dev/null
@@ -1,160 +0,0 @@
-"""Inspired by https://github.com/pandas-dev/pydata-sphinx-theme
-
-BSD 3-Clause License
-
-Copyright (c) 2018, pandas
-All rights reserved.
-
-Redistribution and use in source and binary forms, with or without
-modification, are permitted provided that the following conditions are met:
-
-* Redistributions of source code must retain the above copyright notice, this
-  list of conditions and the following disclaimer.
-
-* Redistributions in binary form must reproduce the above copyright notice,
-  this list of conditions and the following disclaimer in the documentation
-  and/or other materials provided with the distribution.
-
-* Neither the name of the copyright holder nor the names of its
-  contributors may be used to endorse or promote products derived from
-  this software without specific prior written permission.
-
-THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
-AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
-DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
-FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
-DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
-SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
-CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
-OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
-OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-"""
-
-import docutils
-
-
-def add_toctree_functions(app, pagename, templatename, context, doctree):
-    """Add functions so Jinja templates can add toctree objects.
-
-    This converts the docutils nodes into a nested dictionary that Jinja can
-    use in our templating.
-    """
-    from sphinx.environment.adapters.toctree import TocTree
-
-    def get_nav_object(maxdepth=None, collapse=True, numbered=False, **kwargs):
-        """Return a list of nav links that can be accessed from Jinja.
-
-        Parameters
-        ----------
-        maxdepth: int
-            How many layers of TocTree will be returned
-        collapse: bool
-            Whether to only include sub-pages of the currently-active page,
-            instead of sub-pages of all top-level pages of the site.
-        numbered: bool
-            Whether to add section number to title
-        kwargs: key/val pairs
-            Passed to the `TocTree.get_toctree_for` Sphinx method
-        """
-        # The TocTree will contain the full site TocTree including sub-pages.
-        # "collapse=True" collapses sub-pages of non-active TOC pages.
-        # maxdepth controls how many TOC levels are returned
-        toctree = TocTree(app.env).get_toctree_for(
-            pagename, app.builder, collapse=collapse, maxdepth=maxdepth, **kwargs
-        )
-        # If no toctree is defined (AKA a single-page site), skip this
-        if toctree is None:
-            return []
-
-        # toctree has this structure
-        #   <caption>
-        #   <bullet_list>
-        #       <list_item classes="toctree-l1">
-        #       <list_item classes="toctree-l1">
-        # `list_item`s are the actual TOC links and are the only thing we want
-        toc_items = [
-            item
-            for child in toctree.children
-            for item in child
-            if isinstance(item, docutils.nodes.list_item)
-        ]
-
-        # Now convert our docutils nodes into dicts that Jinja can use
-        nav = [
-            docutils_node_to_jinja(child, only_pages=True, numbered=numbered)
-            for child in toc_items
-        ]
-
-        return nav
-
-    context["get_nav_object"] = get_nav_object
-
-
-def docutils_node_to_jinja(list_item, only_pages=False, numbered=False):
-    """Convert a docutils node to a structure that can be read by Jinja.
-
-    Parameters
-    ----------
-    list_item : docutils list_item node
-        A parent item, potentially with children, corresponding to the level
-        of a TocTree.
-    only_pages : bool
-        Only include items for full pages in the output dictionary. Exclude
-        anchor links (TOC items with a URL that starts with #)
-    numbered: bool
-        Whether to add section number to title
-
-    Returns
-    -------
-    nav : dict
-        The TocTree, converted into a dictionary with key/values that work
-        within Jinja.
-    """
-    if not list_item.children:
-        return None
-
-    # We assume this structure of a list item:
-    # <list_item>
-    #     <compact_paragraph >
-    #         <reference> <-- the thing we want
-    reference = list_item.children[0].children[0]
-    title = reference.astext()
-    url = reference.attributes["refuri"]
-    active = "current" in list_item.attributes["classes"]
-
-    secnumber = reference.attributes.get("secnumber", None)
-    if numbered and secnumber is not None:
-        secnumber = ".".join(str(n) for n in secnumber)
-        title = f"{secnumber}. {title}"
-
-    # If we've got an anchor link, skip it if we wish
-    if only_pages and "#" in url:
-        return None
-
-    # Converting the docutils attributes into jinja-friendly objects
-    nav = {}
-    nav["title"] = title
-    nav["url"] = url
-    nav["active"] = active
-
-    # Recursively convert children as well
-    # If there are sub-pages for this list_item, there should be two children:
-    # a paragraph, and a bullet_list.
-    nav["children"] = []
-    if len(list_item.children) > 1:
-        # The `.children` of the bullet_list has the nodes of the sub-pages.
-        subpage_list = list_item.children[1].children
-        for sub_page in subpage_list:
-            child_nav = docutils_node_to_jinja(
-                sub_page, only_pages=only_pages, numbered=numbered
-            )
-            if child_nav is not None:
-                nav["children"].append(child_nav)
-    return nav
-
-
-def setup(app):
-    app.connect("html-page-context", add_toctree_functions)
-
-    return {"parallel_read_safe": True, "parallel_write_safe": True}
diff --git a/doc/sphinxext/autoshortsummary.py b/doc/sphinxext/autoshortsummary.py
new file mode 100644
index 0000000000000..8451f3133d05b
--- /dev/null
+++ b/doc/sphinxext/autoshortsummary.py
@@ -0,0 +1,53 @@
+from sphinx.ext.autodoc import ModuleLevelDocumenter
+
+
+class ShortSummaryDocumenter(ModuleLevelDocumenter):
+    """An autodocumenter that only renders the short summary of the object."""
+
+    # Defines the usage: .. autoshortsummary:: {{ object }}
+    objtype = "shortsummary"
+
+    # Disable content indentation
+    content_indent = ""
+
+    # Avoid being selected as the default documenter for some objects, because we are
+    # returning `can_document_member` as True for all objects
+    priority = -99
+
+    @classmethod
+    def can_document_member(cls, member, membername, isattr, parent):
+        """Allow documenting any object."""
+        return True
+
+    def get_object_members(self, want_all):
+        """Document no members."""
+        return (False, [])
+
+    def add_directive_header(self, sig):
+        """Override default behavior to add no directive header or options."""
+        pass
+
+    def add_content(self, more_content):
+        """Override default behavior to add only the first line of the docstring.
+
+        Modified based on the part of processing docstrings in the original
+        implementation of this method.
+
+        https://github.com/sphinx-doc/sphinx/blob/faa33a53a389f6f8bc1f6ae97d6015fa92393c4a/sphinx/ext/autodoc/__init__.py#L609-L622
+        """
+        sourcename = self.get_sourcename()
+        docstrings = self.get_doc()
+
+        if docstrings is not None:
+            if not docstrings:
+                docstrings.append([])
+            # Get the first non-empty line of the processed docstring; this could lead
+            # to unexpected results if the object does not have a short summary line.
+            short_summary = next(
+                (s for s in self.process_doc(docstrings) if s), "<no summary>"
+            )
+            self.add_line(short_summary, sourcename, 0)
+
+
+def setup(app):
+    app.add_autodocumenter(ShortSummaryDocumenter)
diff --git a/doc/sphinxext/dropdown_anchors.py b/doc/sphinxext/dropdown_anchors.py
new file mode 100644
index 0000000000000..eb0b414de6ae8
--- /dev/null
+++ b/doc/sphinxext/dropdown_anchors.py
@@ -0,0 +1,78 @@
+import re
+
+from docutils import nodes
+from sphinx.transforms.post_transforms import SphinxPostTransform
+from sphinx_design.dropdown import dropdown_main, dropdown_title
+
+
+class DropdownAnchorAdder(SphinxPostTransform):
+    """Insert anchor links to the sphinx-design dropdowns.
+
+    Some of the dropdowns were originally headers that had automatic anchors, so we
+    need to make sure that the old anchors still work. See the original implementation
+    (in JS): https://github.com/scikit-learn/scikit-learn/pull/27409
+
+    The structure of each sphinx-design dropdown node is expected to be:
+
+    <dropdown_main ...>
+        <dropdown_title ...>
+            ...icon      <-- This exists if the "icon" option of the sphinx-design
+                             dropdown is set; we do not use it in our documentation
+
+            ...title     <-- This may contain multiple nodes, e.g. literal nodes if
+                             there are inline codes; we use the concatenated text of
+                             all these nodes to generate the anchor ID
+
+            Here we insert the anchor link!
+
+            <container ...>  <-- The "dropdown closed" marker
+            <container ...>  <-- The "dropdown open" marker
+        </dropdown_title>
+        <container...>
+            ...main contents
+        </container>
+    </dropdown_main>
+    """
+
+    default_priority = 9999  # Apply later than everything else
+    formats = ["html"]
+
+    def run(self):
+        """Run the post transformation."""
+        # Counter to store the duplicated summary text to add it as a suffix in the
+        # anchor ID
+        anchor_id_counters = {}
+
+        for sd_dropdown in self.document.findall(dropdown_main):
+            # Grab the dropdown title
+            sd_dropdown_title = sd_dropdown.next_node(dropdown_title)
+
+            # Concatenate the text of relevant nodes as the title text
+            # Since we do not have the prefix icon, the relevant nodes are the very
+            # first child node until the third last node (last two are markers)
+            title_text = "".join(
+                node.astext() for node in sd_dropdown_title.children[:-2]
+            )
+
+            # The ID uses the first line, lowercased, with spaces replaced by dashes;
+            # suffix the anchor ID with a counter if it already exists
+            anchor_id = re.sub(r"\s+", "-", title_text.strip().split("\n")[0]).lower()
+            if anchor_id in anchor_id_counters:
+                anchor_id_counters[anchor_id] += 1
+                anchor_id = f"{anchor_id}-{anchor_id_counters[anchor_id]}"
+            else:
+                anchor_id_counters[anchor_id] = 1
+            sd_dropdown["ids"].append(anchor_id)
+
+            # Create the anchor element and insert after the title text; we do this
+            # directly with raw HTML
+            anchor_html = (
+                f'<a class="headerlink" href="#{anchor_id}" '
+                'title="Link to this dropdown">#</a>'
+            )
+            anchor_node = nodes.raw("", anchor_html, format="html")
+            sd_dropdown_title.insert(-2, anchor_node)  # before the two markers
+
+
+def setup(app):
+    app.add_post_transform(DropdownAnchorAdder)
diff --git a/doc/sphinxext/move_gallery_links.py b/doc/sphinxext/move_gallery_links.py
new file mode 100644
index 0000000000000..dff27f7358c7f
--- /dev/null
+++ b/doc/sphinxext/move_gallery_links.py
@@ -0,0 +1,193 @@
+"""
+This script intends to better integrate sphinx-gallery into pydata-sphinx-theme. In
+particular, it moves the download links and badge links in the footer of each generated
+example page into the secondary sidebar, then removes the footer and the top note
+pointing to the footer.
+
+The download links are for Python source code and Jupyter notebook respectively, and
+the badge links are for JupyterLite and Binder.
+
+Currently this is achieved via post-processing the HTML generated by sphinx-gallery.
+This hack can be removed if the following upstream issue is resolved:
+https://github.com/sphinx-gallery/sphinx-gallery/issues/1258
+"""
+
+from pathlib import Path
+
+from bs4 import BeautifulSoup
+from sphinx.util.display import status_iterator
+from sphinx.util.logging import getLogger
+
+logger = getLogger(__name__)
+
+
+def move_gallery_links(app, exception):
+    if exception is not None:
+        return
+
+    for gallery_dir in app.config.sphinx_gallery_conf["gallery_dirs"]:
+        html_gallery_dir = Path(app.builder.outdir, gallery_dir)
+
+        # Get all gallery example files to be tweaked; tuples (file, docname)
+        flat = []
+        for file in html_gallery_dir.rglob("*.html"):
+            if file.name in ("index.html", "sg_execution_times.html"):
+                # These are not gallery example pages, skip
+                continue
+
+            # Extract the documentation name from the path
+            docname = file.relative_to(app.builder.outdir).with_suffix("").as_posix()
+            if docname in app.config.html_context["redirects"]:
+                # This is a redirected page, skip
+                continue
+            if docname not in app.project.docnames:
+                # This should not happen, warn
+                logger.warning(f"Document {docname} not found but {file} exists")
+                continue
+            flat.append((file, docname))
+
+        for html_file, _ in status_iterator(
+            flat,
+            length=len(flat),
+            summary="Tweaking gallery links... ",
+            verbosity=app.verbosity,
+            stringify_func=lambda x: x[1],  # display docname
+        ):
+            with html_file.open("r", encoding="utf-8") as f:
+                html = f.read()
+            soup = BeautifulSoup(html, "html.parser")
+
+            # Find the secondary sidebar; it should exist in all gallery example pages
+            secondary_sidebar = soup.find("div", class_="sidebar-secondary-items")
+            if secondary_sidebar is None:
+                logger.warning(f"Secondary sidebar not found in {html_file}")
+                continue
+
+            def _create_secondary_sidebar_component(items):
+                """Create a new component in the secondary sidebar.
+
+                `items` should be a list of dictionaries with "element" being the bs4
+                tag of the component and "title" being the title (None if not needed).
+                """
+                component = soup.new_tag("div", **{"class": "sidebar-secondary-item"})
+                for item in items:
+                    item_wrapper = soup.new_tag("div")
+                    item_wrapper.append(item["element"])
+                    if item["title"]:
+                        item_wrapper["title"] = item["title"]
+                    component.append(item_wrapper)
+                secondary_sidebar.append(component)
+
+            def _create_download_link(link, is_jupyter=False):
+                """Create a download link to be appended to a component.
+
+                `link` should be the bs4 tag of the original download link, either for
+                the Python source code (is_jupyter=False) of for the Jupyter notebook
+                (is_jupyter=True). `link` will not be removed; instead the whole
+                footnote would be removed where `link` is located.
+
+                This returns a dictionary with "element" being the bs4 tag of the new
+                download link and "title" being the name of the file to download.
+                """
+                new_link = soup.new_tag("a", href=link["href"], download="")
+
+                # Place a download icon at the beginning of the new link
+                download_icon = soup.new_tag("i", **{"class": "fa-solid fa-download"})
+                new_link.append(download_icon)
+
+                # Create the text of the new link; it is shortend to fit better into
+                # the secondary sidebar. The leading space before "Download ..." is
+                # intentional to create a small gap between the icon and the text,
+                # being consistent with the other pydata-sphinx-theme components
+                link_type = "Jupyter notebook" if is_jupyter else "source code"
+                new_text = soup.new_string(f" Download {link_type}")
+                new_link.append(new_text)
+
+                # Get the file name to download and use it as the title of the new link
+                # which will show up when hovering over the link; the file name is
+                # expected to be in the last span of `link`
+                link_spans = link.find_all("span")
+                title = link_spans[-1].text if link_spans else None
+
+                return {"element": new_link, "title": title}
+
+            def _create_badge_link(link):
+                """Create a badge link to be appended to a component.
+
+                `link` should be the bs4 tag of the original badge link, either for
+                binder or JupyterLite. `link` will not be removed; instead the whole
+                footnote would be removed where `link` is located.
+
+                This returns a dictionary with "element" being the bs4 tag of the new
+                download link and "title" being `None` (no need).
+                """
+                new_link = soup.new_tag("a", href=link["href"])
+
+                # The link would essentially be an anchor wrapper outside the image of
+                # the badge; we get the src and alt attributes by finding the original
+                # image and limit the height to 20px (fixed) so that the secondary
+                # sidebar will appear neater
+                badge_img = link.find("img")
+                new_img = soup.new_tag(
+                    "img", src=badge_img["src"], alt=badge_img["alt"], height=20
+                )
+                new_link.append(new_img)
+
+                return {"element": new_link, "title": None}
+
+            try:
+                # `sg_note` is the "go to the end" note at the top of the page
+                # `sg_footer` is the footer with the download links and badge links
+                # These will be removed at the end if new links are successfully created
+                sg_note = soup.find("div", class_="sphx-glr-download-link-note")
+                sg_footer = soup.find("div", class_="sphx-glr-footer")
+
+                # If any one of these two is not found, we directly give up tweaking
+                if sg_note is None or sg_footer is None:
+                    continue
+
+                # Move the download links into the secondary sidebar
+                py_link_div = sg_footer.find("div", class_="sphx-glr-download-python")
+                ipy_link_div = sg_footer.find("div", class_="sphx-glr-download-jupyter")
+                _create_secondary_sidebar_component(
+                    [
+                        _create_download_link(py_link_div.a, is_jupyter=False),
+                        _create_download_link(ipy_link_div.a, is_jupyter=True),
+                    ]
+                )
+
+                # Move the badge links into the secondary sidebar
+                lite_link_div = sg_footer.find("div", class_="lite-badge")
+                binder_link_div = sg_footer.find("div", class_="binder-badge")
+                _create_secondary_sidebar_component(
+                    [
+                        _create_badge_link(lite_link_div.a),
+                        _create_badge_link(binder_link_div.a),
+                    ]
+                )
+
+                # Remove the sourcelink component from the secondary sidebar; the reason
+                # we do not remove it by configuration is that we need the secondary
+                # sidebar to be present for this script to work, while in-page toc alone
+                # could have been empty
+                sourcelink = secondary_sidebar.find("div", class_="sourcelink")
+                if sourcelink is not None:
+                    sourcelink.parent.extract()  # because sourcelink has a wrapper div
+
+                # Remove the the top note and the whole footer
+                sg_note.extract()
+                sg_footer.extract()
+
+            except Exception:
+                # If any step fails we directly skip the file
+                continue
+
+            # Write the modified file back
+            with html_file.open("w", encoding="utf-8") as f:
+                f.write(str(soup))
+
+
+def setup(app):
+    # Default priority is 500 which sphinx-gallery uses for its build-finished events;
+    # we need a larger priority to run after sphinx-gallery (larger is later)
+    app.connect("build-finished", move_gallery_links, priority=900)
diff --git a/doc/sphinxext/override_pst_pagetoc.py b/doc/sphinxext/override_pst_pagetoc.py
new file mode 100644
index 0000000000000..f5697de8ef155
--- /dev/null
+++ b/doc/sphinxext/override_pst_pagetoc.py
@@ -0,0 +1,84 @@
+from functools import cache
+
+from sphinx.util.logging import getLogger
+
+logger = getLogger(__name__)
+
+
+def override_pst_pagetoc(app, pagename, templatename, context, doctree):
+    """Overrides the `generate_toc_html` function of pydata-sphinx-theme for API."""
+
+    @cache
+    def generate_api_toc_html(kind="html"):
+        """Generate the in-page toc for an API page.
+
+        This relies on the `generate_toc_html` function added by pydata-sphinx-theme
+        into the context. We save the original function into `pst_generate_toc_html`
+        and override `generate_toc_html` with this function for generated API pages.
+
+        The pagetoc of an API page would look like the following:
+
+        <ul class="visible ...">               <-- Unwrap
+         <li class="toc-h1 ...">               <-- Unwrap
+          <a class="..." href="#">{{obj}}</a>  <-- Decompose
+
+          <ul class="visible ...">
+           <li class="toc-h2 ...">
+            ...object
+            <ul class="...">                          <-- Set visible if exists
+             <li class="toc-h3 ...">...method 1</li>  <-- Shorten
+             <li class="toc-h3 ...">...method 2</li>  <-- Shorten
+             ...more methods                          <-- Shorten
+            </ul>
+           </li>
+           <li class="toc-h2 ...">...gallery examples</li>
+          </ul>
+
+         </li>                                 <-- Unwrapped
+        </ul>                                  <-- Unwrapped
+        """
+        soup = context["pst_generate_toc_html"](kind="soup")
+
+        try:
+            # Unwrap the outermost level
+            soup.ul.unwrap()
+            soup.li.unwrap()
+            soup.a.decompose()
+
+            # Get all toc-h2 level entries, where the first one should be the function
+            # or class, and the second one, if exists, should be the examples; there
+            # should be no more than two entries at this level for generated API pages
+            lis = soup.ul.select("li.toc-h2")
+            main_li = lis[0]
+            meth_list = main_li.ul
+
+            if meth_list is not None:
+                # This is a class API page, we remove the class name from the method
+                # names to make them better fit into the secondary sidebar; also we
+                # make the toc-h3 level entries always visible to more easily navigate
+                # through the methods
+                meth_list["class"].append("visible")
+                for meth in meth_list.find_all("li", {"class": "toc-h3"}):
+                    target = meth.a.code.span
+                    target.string = target.string.split(".", 1)[1]
+
+            # This corresponds to the behavior of `generate_toc_html`
+            return str(soup) if kind == "html" else soup
+
+        except Exception as e:
+            # Upon any failure we return the original pagetoc
+            logger.warning(
+                f"Failed to generate API pagetoc for {pagename}: {e}; falling back"
+            )
+            return context["pst_generate_toc_html"](kind=kind)
+
+    # Override the pydata-sphinx-theme implementation for generate API pages
+    if pagename.startswith("modules/generated/"):
+        context["pst_generate_toc_html"] = context["generate_toc_html"]
+        context["generate_toc_html"] = generate_api_toc_html
+
+
+def setup(app):
+    # Need to be triggered after `pydata_sphinx_theme.toctree.add_toctree_functions`,
+    # and since default priority is 500 we set 900 for safety
+    app.connect("html-page-context", override_pst_pagetoc, priority=900)
diff --git a/doc/supervised_learning.rst b/doc/supervised_learning.rst
index 71fb3007c2e3c..ba24e8ee23c6f 100644
--- a/doc/supervised_learning.rst
+++ b/doc/supervised_learning.rst
@@ -1,9 +1,3 @@
-.. Places parent toc into the sidebar
-
-:parenttoc: True
-
-.. include:: includes/big_toc_css.rst
-
 .. _supervised-learning:
 
 Supervised learning
diff --git a/doc/templates/base.rst b/doc/templates/base.rst
new file mode 100644
index 0000000000000..ee86bd8a18dbe
--- /dev/null
+++ b/doc/templates/base.rst
@@ -0,0 +1,36 @@
+{{ objname | escape | underline(line="=") }}
+
+{% if objtype == "module" -%}
+
+.. automodule:: {{ fullname }}
+
+{%- elif objtype == "function" -%}
+
+.. currentmodule:: {{ module }}
+
+.. autofunction:: {{ objname }}
+
+.. minigallery:: {{ module }}.{{ objname }}
+   :add-heading: Gallery examples
+   :heading-level: -
+
+{%- elif objtype == "class" -%}
+
+.. currentmodule:: {{ module }}
+
+.. autoclass:: {{ objname }}
+   :members:
+   :inherited-members:
+   :special-members: __call__
+
+.. minigallery:: {{ module }}.{{ objname }} {% for meth in methods %}{{ module }}.{{ objname }}.{{ meth }} {% endfor %}
+   :add-heading: Gallery examples
+   :heading-level: -
+
+{%- else -%}
+
+.. currentmodule:: {{ module }}
+
+.. auto{{ objtype }}:: {{ objname }}
+
+{%- endif -%}
diff --git a/doc/templates/class.rst b/doc/templates/class.rst
deleted file mode 100644
index 1e98be4099b73..0000000000000
--- a/doc/templates/class.rst
+++ /dev/null
@@ -1,17 +0,0 @@
-..
-    The empty line below should not be removed. It is added such that the `rst_prolog`
-    is added before the :mod: directive. Otherwise, the rendering will show as a
-    paragraph instead of a header.
-
-:mod:`{{module}}`.{{objname}}
-{{ underline }}==============
-
-.. currentmodule:: {{ module }}
-
-.. autoclass:: {{ objname }}
-
-.. include:: {{module}}.{{objname}}.examples
-
-.. raw:: html
-
-    <div class="clearer"></div>
diff --git a/doc/templates/class_with_call.rst b/doc/templates/class_with_call.rst
deleted file mode 100644
index bc1567709c9d3..0000000000000
--- a/doc/templates/class_with_call.rst
+++ /dev/null
@@ -1,21 +0,0 @@
-..
-    The empty line below should not be removed. It is added such that the `rst_prolog`
-    is added before the :mod: directive. Otherwise, the rendering will show as a
-    paragraph instead of a header.
-
-:mod:`{{module}}`.{{objname}}
-{{ underline }}===============
-
-.. currentmodule:: {{ module }}
-
-.. autoclass:: {{ objname }}
-
-   {% block methods %}
-   .. automethod:: __call__
-   {% endblock %}
-
-.. include:: {{module}}.{{objname}}.examples
-
-.. raw:: html
-
-    <div class="clearer"></div>
diff --git a/doc/templates/deprecated_class.rst b/doc/templates/deprecated_class.rst
deleted file mode 100644
index 5c31936f6fc36..0000000000000
--- a/doc/templates/deprecated_class.rst
+++ /dev/null
@@ -1,28 +0,0 @@
-..
-    The empty line below should not be removed. It is added such that the `rst_prolog`
-    is added before the :mod: directive. Otherwise, the rendering will show as a
-    paragraph instead of a header.
-
-:mod:`{{module}}`.{{objname}}
-{{ underline }}==============
-
-.. meta::
-   :robots: noindex
-
-.. warning::
-   **DEPRECATED**
-
-
-.. currentmodule:: {{ module }}
-
-.. autoclass:: {{ objname }}
-
-   {% block methods %}
-   .. automethod:: __init__
-   {% endblock %}
-
-.. include:: {{module}}.{{objname}}.examples
-
-.. raw:: html
-
-    <div class="clearer"></div>
diff --git a/doc/templates/deprecated_class_with_call.rst b/doc/templates/deprecated_class_with_call.rst
deleted file mode 100644
index 072a31112be50..0000000000000
--- a/doc/templates/deprecated_class_with_call.rst
+++ /dev/null
@@ -1,29 +0,0 @@
-..
-    The empty line below should not be removed. It is added such that the `rst_prolog`
-    is added before the :mod: directive. Otherwise, the rendering will show as a
-    paragraph instead of a header.
-
-:mod:`{{module}}`.{{objname}}
-{{ underline }}===============
-
-.. meta::
-   :robots: noindex
-
-.. warning::
-   **DEPRECATED**
-
-
-.. currentmodule:: {{ module }}
-
-.. autoclass:: {{ objname }}
-
-   {% block methods %}
-   .. automethod:: __init__
-   .. automethod:: __call__
-   {% endblock %}
-
-.. include:: {{module}}.{{objname}}.examples
-
-.. raw:: html
-
-    <div class="clearer"></div>
diff --git a/doc/templates/deprecated_class_without_init.rst b/doc/templates/deprecated_class_without_init.rst
deleted file mode 100644
index a26afbead5451..0000000000000
--- a/doc/templates/deprecated_class_without_init.rst
+++ /dev/null
@@ -1,24 +0,0 @@
-..
-    The empty line below should not be removed. It is added such that the `rst_prolog`
-    is added before the :mod: directive. Otherwise, the rendering will show as a
-    paragraph instead of a header.
-
-:mod:`{{module}}`.{{objname}}
-{{ underline }}==============
-
-.. meta::
-   :robots: noindex
-
-.. warning::
-   **DEPRECATED**
-
-
-.. currentmodule:: {{ module }}
-
-.. autoclass:: {{ objname }}
-
-.. include:: {{module}}.{{objname}}.examples
-
-.. raw:: html
-
-    <div class="clearer"></div>
diff --git a/doc/templates/deprecated_function.rst b/doc/templates/deprecated_function.rst
deleted file mode 100644
index ead5abec27076..0000000000000
--- a/doc/templates/deprecated_function.rst
+++ /dev/null
@@ -1,24 +0,0 @@
-..
-    The empty line below should not be removed. It is added such that the `rst_prolog`
-    is added before the :mod: directive. Otherwise, the rendering will show as a
-    paragraph instead of a header.
-
-:mod:`{{module}}`.{{objname}}
-{{ underline }}====================
-
-.. meta::
-   :robots: noindex
-
-.. warning::
-   **DEPRECATED**
-
-
-.. currentmodule:: {{ module }}
-
-.. autofunction:: {{ objname }}
-
-.. include:: {{module}}.{{objname}}.examples
-
-.. raw:: html
-
-    <div class="clearer"></div>
diff --git a/doc/templates/display_all_class_methods.rst b/doc/templates/display_all_class_methods.rst
deleted file mode 100644
index b179473cf841e..0000000000000
--- a/doc/templates/display_all_class_methods.rst
+++ /dev/null
@@ -1,19 +0,0 @@
-..
-    The empty line below should not be removed. It is added such that the `rst_prolog`
-    is added before the :mod: directive. Otherwise, the rendering will show as a
-    paragraph instead of a header.
-
-:mod:`{{module}}`.{{objname}}
-{{ underline }}==============
-
-.. currentmodule:: {{ module }}
-
-.. autoclass:: {{ objname }}
-
-.. include:: {{module}}.{{objname}}.examples
-.. include:: {{module}}.{{objname}}.from_estimator.examples
-.. include:: {{module}}.{{objname}}.from_predictions.examples
-
-.. raw:: html
-
-    <div class="clearer"></div>
diff --git a/doc/templates/display_only_from_estimator.rst b/doc/templates/display_only_from_estimator.rst
deleted file mode 100644
index 9981910dc8be7..0000000000000
--- a/doc/templates/display_only_from_estimator.rst
+++ /dev/null
@@ -1,18 +0,0 @@
-..
-    The empty line below should not be removed. It is added such that the `rst_prolog`
-    is added before the :mod: directive. Otherwise, the rendering will show as a
-    paragraph instead of a header.
-
-:mod:`{{module}}`.{{objname}}
-{{ underline }}==============
-
-.. currentmodule:: {{ module }}
-
-.. autoclass:: {{ objname }}
-
-.. include:: {{module}}.{{objname}}.examples
-.. include:: {{module}}.{{objname}}.from_estimator.examples
-
-.. raw:: html
-
-    <div class="clearer"></div>
diff --git a/doc/templates/function.rst b/doc/templates/function.rst
deleted file mode 100644
index 93d368ecfe6d5..0000000000000
--- a/doc/templates/function.rst
+++ /dev/null
@@ -1,17 +0,0 @@
-..
-    The empty line below should not be removed. It is added such that the `rst_prolog`
-    is added before the :mod: directive. Otherwise, the rendering will show as a
-    paragraph instead of a header.
-
-:mod:`{{module}}`.{{objname}}
-{{ underline }}====================
-
-.. currentmodule:: {{ module }}
-
-.. autofunction:: {{ objname }}
-
-.. include:: {{module}}.{{objname}}.examples
-
-.. raw:: html
-
-    <div class="clearer"></div>
diff --git a/doc/templates/generate_deprecated.sh b/doc/templates/generate_deprecated.sh
deleted file mode 100755
index a7301fb5dc419..0000000000000
--- a/doc/templates/generate_deprecated.sh
+++ /dev/null
@@ -1,8 +0,0 @@
-#!/bin/bash
-for f in [^d]*; do (head -n2 < $f; echo '
-.. meta::
-   :robots: noindex
-
-.. warning::
-   **DEPRECATED**
-'; tail -n+3 $f) > deprecated_$f; done
diff --git a/doc/templates/index.html b/doc/templates/index.html
index a3c91c30ca765..61457be2494ea 100644
--- a/doc/templates/index.html
+++ b/doc/templates/index.html
@@ -1,25 +1,27 @@
 {% extends "layout.html" %}
 {% set title = 'scikit-learn: machine learning in Python' %}
 
-{% if theme_link_to_live_contributing_page|tobool %}
+{% if is_devrelease|tobool %}
+  {%- set contributing_link = pathto("developers/contributing") %}
+  {%- set contributing_attrs = "" %}
+{%- else %}
   {%- set contributing_link = "https://scikit-learn.org/dev/developers/contributing.html" %}
   {%- set contributing_attrs = 'target="_blank" rel="noopener noreferrer"' %}
-{%- else %}
-  {%- set contributing_link = pathto('developers/contributing') %}
-  {%- set contributing_attrs = '' %}
 {%- endif %}
 
+{%- import "static/webpack-macros.html" as _webpack with context %}
 
-{% block content %}
-<div class="container-fluid sk-landing-bg py-3">
+{% block docs_navbar %}
+{{ super() }}
+
+<div class="container-fluid sk-landing-top-bar py-4">
   <div class="container sk-landing-container">
     <div class="row">
       <div class="col-md-6 mb-3 mb-md-0">
-        <h1 class="sk-landing-header text-white text-monospace">scikit-learn</h1>
-        <h4 class="sk-landing-subheader text-white font-italic mb-3">Machine Learning in Python</h4>
-        <a class="btn sk-landing-btn mb-1" href="{{ pathto('getting_started') }}" role="button">Getting Started</a>
-        <a class="btn sk-landing-btn mb-1" href="{{ pathto(release_highlights) }}" role="button">Release Highlights for {{ release_highlights_version }}</a>
-        <a class="btn sk-landing-btn mb-1" href="https://github.com/scikit-learn/scikit-learn" role="button">GitHub</a>
+        <h1 class="sk-landing-header font-monospace">scikit-learn</h1>
+        <h4 class="sk-landing-subheader fst-italic mb-3">Machine Learning in Python</h4>
+        <a class="btn sk-btn-orange mb-1" href="{{ pathto('getting_started') }}" role="button">Getting Started</a>
+        <a class="btn sk-btn-orange mb-1" href="{{ pathto(release_highlights) }}" role="button">Release Highlights for {{ release_highlights_version }}</a>
       </div>
       <div class="col-md-6 d-flex">
         <ul class="sk-landing-header-body">
@@ -33,239 +35,279 @@ <h4 class="sk-landing-subheader text-white font-italic mb-3">Machine Learning in
   </div>
 </div>
 
-<div class="container sk-landing-container pt-3 body" role="main">
+{% endblock docs_navbar %}
+
+{% block docs_main %}
+
+<div class="container sk-landing-container pt-3 sk-landing-body" role="main">
   <div class="row no-gutters">
+    <!-- Classification -->
     <div class="col-md-4 mb-3 px-md-2 sk-px-xl-4">
-      <div class="card h-100">
+      <div class="card h-100" sk-align-group="1">
         <div class="card-body">
-          <a href="supervised_learning.html#supervised-learning"><h4 class="sk-card-title card-title">Classification</h4></a>
-          <p class="card-text">Identifying which category an object belongs to.</p>
-          <p class="card-text"><strong>Applications:</strong> Spam detection, image recognition.</br>
-          <strong>Algorithms:</strong>
-          <a href="modules/ensemble.html#histogram-based-gradient-boosting">Gradient boosting</a>,
-          <a href="modules/neighbors.html#classification">nearest neighbors</a>,
-          <a href="modules/ensemble.html#forest">random forest</a>,
-          <a href="modules/linear_model.html#logistic-regression">logistic regression</a>,
-          and <a href="supervised_learning.html#supervised-learning">more...</a></p>
+          <h4 class="sk-card-title card-title sk-vert-align" sk-align-name="title">
+            <a href="supervised_learning.html">Classification</a>
+          </h4>
+          <p class="sk-vert-align" sk-align-name="desc">Identifying which category an object belongs to.</p>
+          <p>
+            <strong>Applications:</strong> Spam detection, image recognition.</br>
+            <strong>Algorithms:</strong>
+            <a href="modules/ensemble.html#histogram-based-gradient-boosting">Gradient boosting</a>,
+            <a href="modules/neighbors.html#classification">nearest neighbors</a>,
+            <a href="modules/ensemble.html#forest">random forest</a>,
+            <a href="modules/linear_model.html#logistic-regression">logistic regression</a>,
+            and <a href="supervised_learning.html">more...</a>
+          </p>
         </div>
-        <div class="overflow-hidden mx-2 text-center flex-fill">
-          <a href="auto_examples/classification/plot_classifier_comparison.html"  aria-label="Classification">
-          <img src="_images/sphx_glr_plot_classifier_comparison_001_carousel.png" class="sk-index-img" style="width:initial;max-width:initial" alt="Classifier comparison">
+        <div class="sk-card-img-container overflow-hidden mx-2 flex-fill">
+          <a href="auto_examples/classification/plot_classifier_comparison.html" aria-label="Classification">
+            <img src="_images/sphx_glr_plot_classifier_comparison_001_carousel.png" alt="Classifier comparison">
           </a>
         </div>
-          <a href="auto_examples/index.html#classification" class="sk-btn-primary btn text-white btn-block" role="button">Examples</a>
+        <a href="auto_examples/classification/index.html" class="sk-btn-cyan btn" role="button">Examples</a>
       </div>
     </div>
+    <!-- Regression -->
     <div class="col-md-4 mb-3 px-md-2 sk-px-xl-4">
-      <div class="card h-100">
+      <div class="card h-100" sk-align-group="1">
         <div class="card-body">
-          <a href="supervised_learning.html#supervised-learning"><h4 class="sk-card-title card-title">Regression</h4></a>
-          <p class="card-text">Predicting a continuous-valued attribute associated with an object.</p>
-          <p class="card-text"><strong>Applications:</strong> Drug response, Stock prices.</br>
-          <strong>Algorithms:</strong>
-          <a href="modules/ensemble.html#histogram-based-gradient-boosting">Gradient boosting</a>,
-          <a href="modules/neighbors.html#regression">nearest neighbors</a>,
-          <a href="modules/ensemble.html#forest">random forest</a>,
-          <a href="modules/linear_model.html#ridge-regression-and-classification">ridge</a>,
-          and <a href="supervised_learning.html#supervised-learning">more...</a></p>
+          <h4 class="sk-card-title card-title sk-vert-align" sk-align-name="title">
+            <a href="supervised_learning.html">Regression</a>
+          </h4>
+          <p class="sk-vert-align" sk-align-name="desc">Predicting a continuous-valued attribute associated with an object.</p>
+          <p>
+            <strong>Applications:</strong> Drug response, stock prices.</br>
+            <strong>Algorithms:</strong>
+            <a href="modules/ensemble.html#histogram-based-gradient-boosting">Gradient boosting</a>,
+            <a href="modules/neighbors.html#regression">nearest neighbors</a>,
+            <a href="modules/ensemble.html#forest">random forest</a>,
+            <a href="modules/linear_model.html#ridge-regression-and-classification">ridge</a>,
+            and <a href="supervised_learning.html">more...</a>
+          </p>
         </div>
-        <div class="overflow-hidden mx-2 text-center flex-fill">
-          <a href="auto_examples/ensemble/plot_hgbt_regression.html"  aria-label="Regression">
-          <img src="_images/sphx_glr_plot_hgbt_regression_002.png" class="sk-index-img" alt="Decision Tree Regression with HGBT">
+        <div class="sk-card-img-container overflow-hidden mx-2 flex-fill">
+          <a href="auto_examples/ensemble/plot_hgbt_regression.html" aria-label="Regression">
+            <img src="_images/sphx_glr_plot_hgbt_regression_002.png" alt="Decision Tree Regression with HGBT">
           </a>
         </div>
-          <a href="auto_examples/index.html#examples" class="sk-btn-primary btn text-white btn-block" role="button">Examples</a>
+        <a href="auto_examples/index.html" class="sk-btn-cyan btn" role="button">Examples</a>
       </div>
     </div>
+    <!-- Clustering -->
     <div class="col-md-4 mb-3 px-md-2 sk-px-xl-4">
-      <div class="card h-100">
+      <div class="card h-100" sk-align-group="1">
         <div class="card-body">
-          <a href="modules/clustering.html#clustering"><h4 class="sk-card-title card-title">Clustering</h4></a>
-          <p class="card-text">Automatic grouping of similar objects into sets.</p>
-          <p class="card-text"><strong>Applications:</strong> Customer segmentation, Grouping experiment outcomes</br>
-          <strong>Algorithms:</strong>
-          <a href="modules/clustering.html#k-means">k-Means</a>,
-          <a href="modules/clustering.html#hdbscan">HDBSCAN</a>,
-          <a href="modules/clustering.html#hierarchical-clustering">hierarchical
-	  clustering</a>,
-          and <a href="modules/clustering.html#clustering">more...</a></p>
+          <h4 class="sk-card-title card-title sk-vert-align" sk-align-name="title">
+            <a href="modules/clustering.html">Clustering</a>
+          </h4>
+          <p class="sk-vert-align" sk-align-name="desc">Automatic grouping of similar objects into sets.</p>
+          <p>
+            <strong>Applications:</strong> Customer segmentation, grouping experiment outcomes.</br>
+            <strong>Algorithms:</strong>
+            <a href="modules/clustering.html#k-means">k-Means</a>,
+            <a href="modules/clustering.html#hdbscan">HDBSCAN</a>,
+            <a href="modules/clustering.html#hierarchical-clustering">hierarchical clustering</a>,
+            and <a href="modules/clustering.html">more...</a>
+          </p>
         </div>
-        <div class="overflow-hidden mx-2 text-center flex-fill">
-          <a href="auto_examples/cluster/plot_kmeans_digits.html"  aria-label="Clustering">
-          <img src="_images/sphx_glr_plot_kmeans_digits_thumb.png" class="sk-index-img" alt="A demo of K-Means clustering on the handwritten digits data">
+        <div class="sk-card-img-container overflow-hidden mx-2 flex-fill">
+          <a href="auto_examples/cluster/plot_kmeans_digits.html" aria-label="Clustering">
+            <img src="_images/sphx_glr_plot_kmeans_digits_thumb.png" alt="A demo of K-Means clustering on the handwritten digits data">
           </a>
         </div>
-          <a href="auto_examples/index.html#cluster-examples" class="sk-btn-primary btn text-white btn-block" role="button">Examples</a>
+        <a href="auto_examples/cluster/index.html" class="sk-btn-cyan btn" role="button">Examples</a>
       </div>
     </div>
+    <!-- Dimensionality reduction -->
     <div class="col-md-4 mb-3 px-md-2 sk-px-xl-4">
-      <div class="card h-100">
+      <div class="card h-100" sk-align-group="2">
         <div class="card-body">
-          <a href="modules/decomposition.html#decompositions"><h4 class="sk-card-title card-title">Dimensionality reduction</h4></a>
-          <p class="card-text">Reducing the number of random variables to consider.</p>
-          <p class="card-text"><strong>Applications:</strong> Visualization, Increased efficiency</br>
-          <strong>Algorithms:</strong>
-          <a href="modules/decomposition.html#pca">PCA</a>,
-          <a href="modules/feature_selection.html#feature-selection">feature selection</a>,
-          <a href="modules/decomposition.html#nmf">non-negative matrix factorization</a>,
-          and <a href="modules/decomposition.html#decompositions">more...</a></p>
+          <h4 class="sk-card-title card-title sk-vert-align" sk-align-name="title">
+            <a href="modules/decomposition.html">Dimensionality reduction</a>
+          </h4>
+          <p class="sk-vert-align" sk-align-name="desc">Reducing the number of random variables to consider.</p>
+          <p>
+            <strong>Applications:</strong> Visualization, increased efficiency.</br>
+            <strong>Algorithms:</strong>
+            <a href="modules/decomposition.html#pca">PCA</a>,
+            <a href="modules/feature_selection.html#feature-selection">feature selection</a>,
+            <a href="modules/decomposition.html#nmf">non-negative matrix factorization</a>,
+            and <a href="modules/decomposition.html">more...</a>
+          </p>
         </div>
-        <div class="overflow-hidden mx-2 text-center flex-fill">
-          <a href="auto_examples/decomposition/plot_pca_iris.html"  aria-label="Dimensionality reduction">
-          <img src="_images/sphx_glr_plot_pca_iris_thumb.png" class="sk-index-img" alt="PCA example with Iris Data-set">
+        <div class="sk-card-img-container overflow-hidden mx-2 flex-fill">
+          <a href="auto_examples/decomposition/plot_pca_iris.html" aria-label="Dimensionality reduction">
+            <img src="_images/sphx_glr_plot_pca_iris_thumb.png" alt="PCA example with Iris Data-set">
           </a>
         </div>
-          <a href="auto_examples/index.html#decomposition-examples" class="sk-btn-primary btn text-white btn-block" role="button">Examples</a>
+        <a href="auto_examples/decomposition/index.html" class="sk-btn-cyan btn" role="button">Examples</a>
       </div>
     </div>
+    <!-- Model selection -->
     <div class="col-md-4 mb-3 px-md-2 sk-px-xl-4">
-      <div class="card h-100">
+      <div class="card h-100" sk-align-group="2">
         <div class="card-body">
-          <a href="model_selection.html#model-selection"><h4 class="sk-card-title card-title">Model selection</h4></a>
-          <p class="card-text">Comparing, validating and choosing parameters and models.</p>
-          <p class="card-text"><strong>Applications:</strong> Improved accuracy via parameter tuning</br>
-          <strong>Algorithms:</strong>
-          <a href="modules/grid_search.html#grid-search">grid search</a>,
-          <a href="modules/cross_validation.html#cross-validation">cross validation</a>,
-          <a href="modules/model_evaluation.html#model-evaluation">metrics</a>,
-          and <a href="model_selection.html">more...</a></p>
+          <h4 class="sk-card-title card-title sk-vert-align" sk-align-name="title">
+            <a href="model_selection.html">Model selection</a>
+          </h4>
+          <p class="sk-vert-align" sk-align-name="desc">Comparing, validating and choosing parameters and models.</p>
+          <p>
+            <strong>Applications:</strong> Improved accuracy via parameter tuning.</br>
+            <strong>Algorithms:</strong>
+            <a href="modules/grid_search.html">Grid search</a>,
+            <a href="modules/cross_validation.html">cross validation</a>,
+            <a href="modules/model_evaluation.html">metrics</a>,
+            and <a href="model_selection.html">more...</a>
+          </p>
         </div>
-        <div class="overflow-hidden mx-2 text-center flex-fill">
-          <a href="auto_examples/model_selection/plot_multi_metric_evaluation.html"  aria-label="Model selection">
-            <img src="_images/sphx_glr_plot_multi_metric_evaluation_thumb.png" class="sk-index-img" alt="Demonstration of multi-metric evaluation on cross_val_score and GridSearchCV">
+        <div class="sk-card-img-container overflow-hidden mx-2 flex-fill">
+          <a href="auto_examples/model_selection/plot_multi_metric_evaluation.html" aria-label="Model selection">
+            <img src="_images/sphx_glr_plot_multi_metric_evaluation_thumb.png" alt="Demonstration of multi-metric evaluation on cross_val_score and GridSearchCV">
           </a>
         </div>
-          <a href="auto_examples/index.html#model-selection" class="sk-btn-primary btn text-white btn-block" role="button">Examples</a>
+        <a href="auto_examples/model_selection/index.html" class="sk-btn-cyan btn" role="button">Examples</a>
       </div>
     </div>
+    <!-- Preprocessing -->
     <div class="col-md-4 mb-3 px-md-2 sk-px-xl-4">
-      <div class="card h-100">
+      <div class="card h-100" sk-align-group="2">
         <div class="card-body">
-          <a href="modules/preprocessing.html#preprocessing"><h4 class="sk-card-title card-title">Preprocessing</h4></a>
-          <p class="card-text">Feature extraction and normalization.</p>
-          <p class="card-text"><strong>Applications:</strong>  Transforming input data such as text for use with machine learning algorithms.</br>
-          <strong>Algorithms:</strong>
-          <a href="modules/preprocessing.html#preprocessing">preprocessing</a>,
-          <a href="modules/feature_extraction.html#feature-extraction">feature extraction</a>,
-          and <a href="modules/preprocessing.html#preprocessing">more...</a></p>
+          <h4 class="sk-card-title card-title sk-vert-align" sk-align-name="title">
+            <a href="modules/preprocessing.html">Preprocessing</a>
+          </h4>
+          <p class="sk-vert-align" sk-align-name="desc">Feature extraction and normalization.</p>
+          <p>
+            <strong>Applications:</strong> Transforming input data such as text for use with machine learning algorithms.</br>
+            <strong>Algorithms:</strong>
+            <a href="modules/preprocessing.html">Preprocessing</a>,
+            <a href="modules/feature_extraction.html">feature extraction</a>,
+            and <a href="modules/preprocessing.html">more...</a>
+          </p>
         </div>
-        <div class="overflow-hidden mx-2 text-center flex-fill">
-          <a href="auto_examples/preprocessing/plot_discretization_strategies.html"  aria-label="Preprocessing">
-          <img src="_images/sphx_glr_plot_discretization_strategies_thumb.png" class="sk-index-img" alt="Demonstrating the different strategies of KBinsDiscretizer">
+        <div class="sk-card-img-container overflow-hidden mx-2 flex-fill">
+          <a href="auto_examples/preprocessing/plot_discretization_strategies.html" aria-label="Preprocessing">
+            <img src="_images/sphx_glr_plot_discretization_strategies_thumb.png" alt="Demonstrating the different strategies of KBinsDiscretizer">
           </a>
         </div>
-          <a href="auto_examples/index.html#preprocessing" class="sk-btn-primary btn text-white btn-block" role="button">Examples</a>
+        <a href="auto_examples/preprocessing/index.html" class="sk-btn-cyan btn" role="button">Examples</a>
       </div>
     </div>
   </div>
 </div>
 
-<div class="container-fluid sk-landing-bg-more-info py-3">
-  <div class="container sk-landing-container">
+{% endblock docs_main %}
+
+{% block footer %}
+
+<div class="container-fluid sk-landing-more-info py-3">
+  <div class="container sk-landing-container bd-page-width">
     <div class="row">
+      <!-- News -->
       <div class="col-md-4">
         <h4 class="sk-landing-call-header">News</h4>
         <ul class="sk-landing-call-list list-unstyled">
-        <li><strong>On-going development:</strong>
-        <a href="whats_new/v1.5.html#version-1-5-0">scikit-learn 1.5 (Changelog)</a>
-        </li>
-        <li><strong>April 2024.</strong> scikit-learn 1.4.2 is available for download (<a href="whats_new/v1.4.html#version-1-4-2">Changelog</a>).
-        </li>
-        <li><strong>February 2024.</strong> scikit-learn 1.4.1.post1 is available for download (<a href="whats_new/v1.4.html#version-1-4-1-post1">Changelog</a>).
-        </li>
-        <li><strong>January 2024.</strong> scikit-learn 1.4.0 is available for download (<a href="whats_new/v1.4.html#version-1-4-0">Changelog</a>).
-        </li>
-        <li><strong>October 2023.</strong> scikit-learn 1.3.2 is available for download (<a href="whats_new/v1.3.html#version-1-3-2">Changelog</a>).
-        </li>
-	      <li><strong>September 2023.</strong> scikit-learn 1.3.1 is available for download (<a href="whats_new/v1.3.html#version-1-3-1">Changelog</a>).
-        </li>
-        <li><strong>June 2023.</strong> scikit-learn 1.3.0 is available for download (<a href="whats_new/v1.3.html#version-1-3-0">Changelog</a>).
-	      </li>
-        <li><strong>All releases:</strong>
-        <a href="https://scikit-learn.org/dev/whats_new.html"><strong>What's new</strong> (Changelog)</a>
-        </li>
+          <li><strong>On-going development:</strong> <a href="whats_new/v1.6.html#version-1-6-0">scikit-learn 1.6 (Changelog)</a>.</li>
+          <li><strong>May 2024.</strong> scikit-learn 1.5.0 is available for download (<a href="whats_new/v1.5.html#version-1-5-0">Changelog</a>).</li>
+          <li><strong>April 2024.</strong> scikit-learn 1.4.2 is available for download (<a href="whats_new/v1.4.html#version-1-4-2">Changelog</a>).</li>
+          <li><strong>February 2024.</strong> scikit-learn 1.4.1.post1 is available for download (<a href="whats_new/v1.4.html#version-1-4-1-post1">Changelog</a>).</li>
+          <li><strong>January 2024.</strong> scikit-learn 1.4.0 is available for download (<a href="whats_new/v1.4.html#version-1-4-0">Changelog</a>).</li>
+          <li><strong>October 2023.</strong> scikit-learn 1.3.2 is available for download (<a href="whats_new/v1.3.html#version-1-3-2">Changelog</a>).</li>
+          <li><strong>September 2023.</strong> scikit-learn 1.3.1 is available for download (<a href="whats_new/v1.3.html#version-1-3-1">Changelog</a>).</li>
+          <li><strong>June 2023.</strong> scikit-learn 1.3.0 is available for download (<a href="whats_new/v1.3.html#version-1-3-0">Changelog</a>).</li>
+          <li><strong>All releases:</strong> <a href="https://scikit-learn.org/dev/whats_new.html"><strong>What's new</strong> (Changelog)</a>.</li>
         </ul>
       </div>
+      <!-- Community -->
       <div class="col-md-4">
         <h4 class="sk-landing-call-header">Community</h4>
         <ul class="sk-landing-call-list list-unstyled">
-        <li><strong>About us:</strong> See <a href="about.html#people">authors</a> and <a href="{{ contributing_link }}" {{ contributing_attrs }}>contributing</a></li>
-        <li><strong>More Machine Learning:</strong> Find <a href="related_projects.html">related projects</a></li>
-        <li><strong>Questions?</strong> See <a href="faq.html">FAQ</a>, <a href="support.html">support</a>, and <a href="https://stackoverflow.com/questions/tagged/scikit-learn">stackoverflow</a></li>
-        <li><strong>Subscribe to the</strong> <a href="https://mail.python.org/mailman/listinfo/scikit-learn">mailing list</a></li>
-        <li><strong>Blog:</strong> <a href="https://blog.scikit-learn.org">blog.scikit-learn.org</a></li>
-        <li><strong>Logos & Branding:</strong> <a href="https://github.com/scikit-learn/scikit-learn/tree/main/doc/logos">logos and branding</a></li>
-        <li><strong>Calendar:</strong> <a href="https://blog.scikit-learn.org/calendar/">calendar</a></li>
-        <li><strong>Twitter:</strong> <a href="https://twitter.com/scikit_learn">@scikit_learn</a></li>
-        <li><strong>LinkedIn:</strong> <a href="https://www.linkedin.com/company/scikit-learn">linkedin/scikit-learn</a></li>
-        <li><strong>YouTube:</strong> <a href="https://www.youtube.com/channel/UCJosFjYm0ZYVUARxuOZqnnw/playlists">youtube.com/scikit-learn</a></li>
-        <li><strong>Facebook:</strong> <a href="https://www.facebook.com/scikitlearnofficial/">@scikitlearnofficial</a></li>
-        <li><strong>Instagram:</strong> <a href="https://www.instagram.com/scikitlearnofficial/">@scikitlearnofficial</a></li>
-        <li><strong>TikTok:</strong> <a href="https://www.tiktok.com/@scikit.learn">@scikit.learn</a></li>
-        <li><strong>Mastodon:</strong> <a href="https://mastodon.social/@sklearn@fosstodon.org">@sklearn</a></li>
-        <li><strong>Discord:</strong> <a href="https://discord.gg/h9qyrK8Jc8">@scikit-learn</a></li>
-        <li>Communication on all channels should respect <a href="https://www.python.org/psf/conduct/">PSF's code of conduct.</a></li>
+          <li><strong>About us:</strong> See <a href="about.html#the-people-behind-scikit-learn">people</a> and <a href="{{ contributing_link }}" {{ contributing_attrs }}>contributing</a></li>
+          <li><strong>More Machine Learning:</strong> Find <a href="related_projects.html">related projects</a></li>
+          <li><strong>Questions?</strong> See <a href="faq.html">FAQ</a>, <a href="support.html">support</a>, and <a href="https://stackoverflow.com/questions/tagged/scikit-learn">stackoverflow</a></li>
+          <li><strong>Subscribe to the</strong> <a href="https://mail.python.org/mailman/listinfo/scikit-learn">mailing list</a></li>
+          <li><strong>Blog:</strong> <a href="https://blog.scikit-learn.org">blog.scikit-learn.org</a></li>
+          <li><strong>Logos & Branding:</strong> <a href="https://github.com/scikit-learn/scikit-learn/tree/main/doc/logos">logos and branding</a></li>
+          <li><strong>Calendar:</strong> <a href="https://blog.scikit-learn.org/calendar/">calendar</a></li>
+          <li><strong>Twitter:</strong> <a href="https://twitter.com/scikit_learn">@scikit_learn</a></li>
+          <li><strong>LinkedIn:</strong> <a href="https://www.linkedin.com/company/scikit-learn">linkedin/scikit-learn</a></li>
+          <li><strong>YouTube:</strong> <a href="https://www.youtube.com/channel/UCJosFjYm0ZYVUARxuOZqnnw/playlists">youtube.com/scikit-learn</a></li>
+          <li><strong>Facebook:</strong> <a href="https://www.facebook.com/scikitlearnofficial/">@scikitlearnofficial</a></li>
+          <li><strong>Instagram:</strong> <a href="https://www.instagram.com/scikitlearnofficial/">@scikitlearnofficial</a></li>
+          <li><strong>TikTok:</strong> <a href="https://www.tiktok.com/@scikit.learn">@scikit.learn</a></li>
+          <li><strong>Mastodon:</strong> <a href="https://mastodon.social/@sklearn@fosstodon.org">@sklearn</a></li>
+          <li><strong>Discord:</strong> <a href="https://discord.gg/h9qyrK8Jc8">@scikit-learn</a></li>
+          <li>Communication on all channels should respect <a href="https://www.python.org/psf/conduct/">PSF's code of conduct.</a></li>
         </ul>
-
-        <a class="btn btn-warning btn-big sk-donate-btn mb-1" href="https://numfocus.org/donate-to-scikit-learn">Help us, <strong>donate!</strong></a>
-        <a class="btn btn-warning btn-big mb-1" href="about.html#citing-scikit-learn"><strong>Cite us!</strong></a>
+        <p>
+          <a class="btn sk-btn-orange mb-1" href="https://numfocus.org/donate-to-scikit-learn">Help us, <strong>donate!</strong></a>
+          <a class="btn sk-btn-orange mb-1" href="about.html#citing-scikit-learn"><strong>Cite us!</strong></a>
+        </p>
       </div>
+      <!--Testimonials -->
       <div class="col-md-4">
         <h4 class="sk-landing-call-header">Who uses scikit-learn?</h4>
-        <div id="carouselExampleSlidesOnly" class="carousel slide" data-ride="carousel">
-        <div class="carousel-inner">
+        <div id="skWhoUsesCarousel" class="carousel slide sk-who-uses-carousel" data-bs-ride="carousel" data-bs-interval="5000">
+          <div class="carousel-inner">
             <div class="carousel-item active">
-            <img class="d-block mx-auto sk-who-uses-carousel-img img-thumbnail" src="_images/inria.png" alt="inria">
-            <em>"We use scikit-learn to support leading-edge basic research [...]"</em>
+              <img class="d-block mx-auto img-thumbnail" src="_images/inria.png" alt="inria">
+              <em>"We use scikit-learn to support leading-edge basic research [...]"</em>
             </div>
             <div class="carousel-item">
-            <img class="d-block mx-auto sk-who-uses-carousel-img img-thumbnail" src="_images/spotify.png" alt="spotify">
-            <em>"I think it's the most well-designed ML package I've seen so far."</em>
+              <img class="d-block mx-auto img-thumbnail" src="_images/spotify.png" alt="spotify">
+              <em>"I think it's the most well-designed ML package I've seen so far."</em>
             </div>
             <div class="carousel-item">
-            <img class="d-block mx-auto sk-who-uses-carousel-img img-thumbnail" src="_images/change-logo.png" alt="change-logo">
-            <em>"scikit-learn's ease-of-use, performance and overall variety of algorithms implemented has proved invaluable [...]."</em>
+              <img class="d-block mx-auto img-thumbnail" src="_images/change-logo.png" alt="change-logo">
+              <em>"scikit-learn's ease-of-use, performance and overall variety of algorithms implemented has proved invaluable [...]"</em>
             </div>
             <div class="carousel-item">
-            <img class="d-block mx-auto sk-who-uses-carousel-img img-thumbnail" src="_images/telecomparistech.jpg" alt="telecomparistech">
-            <em>"The great benefit of scikit-learn is its fast learning curve [...]"</em>
+              <img class="d-block mx-auto img-thumbnail" src="_images/telecomparistech.jpg" alt="telecomparistech">
+              <em>"The great benefit of scikit-learn is its fast learning curve [...]"</em>
             </div>
             <div class="carousel-item">
-            <img class="d-block mx-auto sk-who-uses-carousel-img img-thumbnail" src="_images/aweber.png" alt="aweber">
-            <em>"It allows us to do AWesome stuff we would not otherwise accomplish"</em>
+              <img class="d-block mx-auto img-thumbnail" src="_images/aweber.png" alt="aweber">
+              <em>"It allows us to do AWesome stuff we would not otherwise accomplish."</em>
             </div>
             <div class="carousel-item">
-            <img class="d-block mx-auto sk-who-uses-carousel-img img-thumbnail" src="_images/yhat.png" alt="yhat">
-            <em>"scikit-learn makes doing advanced analysis in Python accessible to anyone."</em>
+              <img class="d-block mx-auto img-thumbnail" src="_images/yhat.png" alt="yhat">
+              <em>"scikit-learn makes doing advanced analysis in Python accessible to anyone."</em>
             </div>
           </div>
         </div>
-        <p class="text-right">
-            <a href="testimonials/testimonials.html">More testimonials</a>
+        <p class="sk-more-testimonials">
+          <a href="testimonials/testimonials.html">More testimonials...</a>
         </p>
       </div>
     </div>
   </div>
 </div>
-<div class="container-fluid py-3">
+
+<div class="container-fluid sk-landing-footer py-3">
   <div class="container sk-landing-container">
-        <a class="sk-footer-funding-link" href="about.html#funding">
-        <div class="text-center">
-                <p class="mt-2">
-                  scikit-learn development and maintenance are financially supported by
-                </p>
-                <img class="sk-footer-funding-logo" src="_static/probabl.png" title="Probabl">
-                <img class="sk-footer-funding-logo" src="_static/inria-small.png" title="INRIA">
-                <img class="sk-footer-funding-logo" src="_static/chanel-small.png" title="Chanel" >
-                <img class="sk-footer-funding-logo" src="_static/axa-small.png" title="AXA Assurances" >
-                <img class="sk-footer-funding-logo" src="_static/bnp-small.png" title="BNP Paris Bas Cardif" >
-                <img class="sk-footer-funding-logo" src="_static/microsoft-small.png" title="Microsoft" >
-                <img class="sk-footer-funding-logo" src="_static/dataiku-small.png" title="Dataiku" >
-                <img class="sk-footer-funding-logo" src="_static/huggingface_logo-noborder.png" title="Hugging Face" >
-                <img class="sk-footer-funding-logo" src="_static/nvidia-small.png" title="Nvidia" >
-                <img class="sk-footer-funding-logo" src="_static/quansight-labs-small.png" title="Quansight Labs" >
+    <a class="sk-footer-funding-link" href="about.html#funding">
+      <div class="text-center">
+        <p class="mt-2 sk-footer-funding-text">
+          scikit-learn development and maintenance are financially supported by
+        </p>
+        <div class="sk-footer-funding-logos">
+          <img src="_static/probabl.png" title="Probabl">
+          <img src="_static/inria-small.png" title="INRIA">
+          <img src="_static/chanel-small.png" title="Chanel">
+          <img src="_static/axa-small.png" title="AXA Assurances">
+          <img src="_static/bnp-small.png" title="BNP Paris Bas Cardif">
+          <img src="_static/microsoft-small.png" title="Microsoft">
+          <img src="_static/dataiku-small.png" title="Dataiku">
+          <img src="_static/huggingface_logo-noborder.png" title="Hugging Face">
+          <img src="_static/nvidia-small.png" title="Nvidia">
+          <img src="_static/quansight-labs-small.png" title="Quansight Labs">
         </div>
-        </a>
+      </div>
+    </a>
   </div>
 </div>
-{% endblock %}
+
+{% endblock footer %}
+
+{%- block scripts_end %}
+{{ _webpack.body_post() }}
+{%- endblock scripts_end %}
diff --git a/doc/testimonials/testimonials.rst b/doc/testimonials/testimonials.rst
index fbf53ae36ef2c..3c8c15b2e25ee 100644
--- a/doc/testimonials/testimonials.rst
+++ b/doc/testimonials/testimonials.rst
@@ -1,1151 +1,752 @@
-.. _testimonials:
-
-================================================================================
-Who is using scikit-learn?
-================================================================================
+:orphan:
 
-.. raw:: html
-
-  <div class="testimonial">
+.. title:: Testimonials
 
+.. _testimonials:
 
-.. to add a testimonials, just XXX
+==========================
+Who is using scikit-learn?
+==========================
 
 `J.P.Morgan <https://www.jpmorgan.com>`_
-------------------------------------------
-
-.. raw:: html
-
-   <div class="sk-testimonial-div">
-   <div class="sk-testimonial-div-box">
-
-Scikit-learn is an indispensable part of the Python machine learning
-toolkit at JPMorgan. It is very widely used across all parts of the bank
-for classification, predictive analytics, and very many other machine
-learning tasks. Its straightforward API, its breadth of algorithms, and
-the quality of its documentation combine to make scikit-learn
-simultaneously very approachable and very powerful.
+----------------------------------------
 
-.. raw:: html
+.. div:: sk-text-image-grid-large
 
-   <span class="testimonial-author">
+  .. div:: text-box
 
-Stephen Simmons, VP, Athena Research, JPMorgan
+    Scikit-learn is an indispensable part of the Python machine learning
+    toolkit at JPMorgan. It is very widely used across all parts of the bank
+    for classification, predictive analytics, and very many other machine
+    learning tasks. Its straightforward API, its breadth of algorithms, and
+    the quality of its documentation combine to make scikit-learn
+    simultaneously very approachable and very powerful.
 
-.. raw:: html
+    .. rst-class:: annotation
 
-   </span>
-    </div>
-    <div class="sk-testimonial-div-box">
+      Stephen Simmons, VP, Athena Research, JPMorgan
 
-.. image:: images/jpmorgan.png
-    :width: 120pt
-    :align: center
-    :target: https://www.jpmorgan.com
+  .. div:: image-box
 
-.. raw:: html
+    .. image:: images/jpmorgan.png
+      :target: https://www.jpmorgan.com
 
-   </div>
-   </div>
 
 `Spotify <https://www.spotify.com>`_
 ------------------------------------
 
-.. raw:: html
-
-   <div class="sk-testimonial-div">
-   <div class="sk-testimonial-div-box">
-
-Scikit-learn provides a toolbox with solid implementations of a bunch of
-state-of-the-art models and makes it easy to plug them into existing
-applications. We've been using it quite a lot for music recommendations at
-Spotify and I think it's the most well-designed ML package I've seen so
-far.
-
-.. raw:: html
+.. div:: sk-text-image-grid-large
 
-   <span class="testimonial-author">
+  .. div:: text-box
 
-Erik Bernhardsson, Engineering Manager Music Discovery & Machine Learning, Spotify
+    Scikit-learn provides a toolbox with solid implementations of a bunch of
+    state-of-the-art models and makes it easy to plug them into existing
+    applications. We've been using it quite a lot for music recommendations at
+    Spotify and I think it's the most well-designed ML package I've seen so far.
 
-.. raw:: html
+    .. rst-class:: annotation
 
-   </span>
-    </div>
-    <div class="sk-testimonial-div-box">
+      Erik Bernhardsson, Engineering Manager Music Discovery & Machine Learning, Spotify
 
-.. image:: images/spotify.png
-    :width: 120pt
-    :align: center
-    :target: https://www.spotify.com
+  .. div:: image-box
 
-.. raw:: html
+    .. image:: images/spotify.png
+      :target: https://www.spotify.com
 
-   </div>
-   </div>
 
 `Inria <https://www.inria.fr/>`_
 --------------------------------
 
-.. raw:: html
-
-   <div class="sk-testimonial-div">
-   <div class="sk-testimonial-div-box">
-
-.. title Scikit-learn for efficient and easier machine learning research
-.. Author: Gaël Varoquaux
-
-
-At INRIA, we use scikit-learn to support leading-edge basic research in many
-teams: `Parietal <https://team.inria.fr/parietal/>`_ for neuroimaging, `Lear
-<https://lear.inrialpes.fr/>`_ for computer vision, `Visages
-<https://team.inria.fr/visages/>`_ for medical image analysis, `Privatics
-<https://team.inria.fr/privatics>`_ for security. The project is a fantastic
-tool to address difficult applications of machine learning in an academic
-environment as it is performant and versatile, but all easy-to-use and well
-documented, which makes it well suited to grad students.
+.. div:: sk-text-image-grid-large
 
+  .. div:: text-box
 
-.. raw:: html
+    At INRIA, we use scikit-learn to support leading-edge basic research in many
+    teams: `Parietal <https://team.inria.fr/parietal/>`_ for neuroimaging, `Lear
+    <https://lear.inrialpes.fr/>`_ for computer vision, `Visages
+    <https://team.inria.fr/visages/>`_ for medical image analysis, `Privatics
+    <https://team.inria.fr/privatics>`_ for security. The project is a fantastic
+    tool to address difficult applications of machine learning in an academic
+    environment as it is performant and versatile, but all easy-to-use and well
+    documented, which makes it well suited to grad students.
 
-   <span class="testimonial-author">
+    .. rst-class:: annotation
 
-Gaël Varoquaux, research at Parietal
+      Gaël Varoquaux, research at Parietal
 
-.. raw:: html
+  .. div:: image-box
 
-   </span>
-   </div>
-   <div class="sk-testimonial-div-box">
-
-.. image:: images/inria.png
-    :width: 120pt
-    :align: center
-    :target: https://www.inria.fr/
-
-.. raw:: html
-
-   </div>
-   </div>
+    .. image:: images/inria.png
+      :target: https://www.inria.fr/
 
 
 `betaworks <https://betaworks.com>`_
 ------------------------------------
 
-.. raw:: html
-
-   <div class="sk-testimonial-div">
-   <div class="sk-testimonial-div-box">
-
-Betaworks is a NYC-based startup studio that builds new products, grows
-companies, and invests in others. Over the past 8 years we've launched a
-handful of social data analytics-driven services, such as Bitly, Chartbeat,
-digg and Scale Model. Consistently the betaworks data science team uses
-Scikit-learn for a variety of tasks. From exploratory analysis, to product
-development, it is an essential part of our toolkit. Recent uses are included
-in `digg's new video recommender system
-<https://medium.com/i-data/the-digg-video-recommender-2f9ade7c4ba3>`_,
-and Poncho's `dynamic heuristic subspace clustering
-<https://medium.com/@DiggData/scaling-poncho-using-data-ca24569d56fd>`_.
+.. div:: sk-text-image-grid-large
 
-.. raw:: html
+  .. div:: text-box
 
-   <span class="testimonial-author">
+    Betaworks is a NYC-based startup studio that builds new products, grows
+    companies, and invests in others. Over the past 8 years we've launched a
+    handful of social data analytics-driven services, such as Bitly, Chartbeat,
+    digg and Scale Model. Consistently the betaworks data science team uses
+    Scikit-learn for a variety of tasks. From exploratory analysis, to product
+    development, it is an essential part of our toolkit. Recent uses are included
+    in `digg's new video recommender system
+    <https://medium.com/i-data/the-digg-video-recommender-2f9ade7c4ba3>`_,
+    and Poncho's `dynamic heuristic subspace clustering
+    <https://medium.com/@DiggData/scaling-poncho-using-data-ca24569d56fd>`_.
 
-Gilad Lotan, Chief Data Scientist
+    .. rst-class:: annotation
 
-.. raw:: html
+      Gilad Lotan, Chief Data Scientist
 
-   </span>
-   </div>
-   <div class="sk-testimonial-div-box">
+  .. div:: image-box
 
-.. image:: images/betaworks.png
-    :width: 120pt
-    :align: center
-    :target: https://betaworks.com
-
-.. raw:: html
-
-   </div>
-   </div>
+    .. image:: images/betaworks.png
+      :target: https://betaworks.com
 
 
 `Hugging Face <https://huggingface.co>`_
 ----------------------------------------
 
-.. raw:: html
-
-   <div class="sk-testimonial-div">
-   <div class="sk-testimonial-div-box">
-
-At Hugging Face we're using NLP and probabilistic models to generate
-conversational Artificial intelligences that are fun to chat with. Despite using
-deep neural nets for `a few <https://medium.com/huggingface/understanding-emotions-from-keras-to-pytorch-3ccb61d5a983>`_
-of our `NLP tasks <https://huggingface.co/coref/>`_, scikit-learn is still the bread-and-butter of
-our daily machine learning routine. The ease of use and predictability of the
-interface, as well as the straightforward mathematical explanations that are
-here when you need them, is the killer feature. We use a variety of scikit-learn
-models in production and they are also operationally very pleasant to work with.
+.. div:: sk-text-image-grid-large
 
-.. raw:: html
+  .. div:: text-box
 
-   <span class="testimonial-author">
+    At Hugging Face we're using NLP and probabilistic models to generate
+    conversational Artificial intelligences that are fun to chat with. Despite using
+    deep neural nets for `a few <https://medium.com/huggingface/understanding-emotions-from-keras-to-pytorch-3ccb61d5a983>`_
+    of our `NLP tasks <https://huggingface.co/coref/>`_, scikit-learn is still the
+    bread-and-butter of our daily machine learning routine. The ease of use and
+    predictability of the interface, as well as the straightforward mathematical
+    explanations that are here when you need them, is the killer feature. We use a
+    variety of scikit-learn models in production and they are also operationally very
+    pleasant to work with.
 
-Julien Chaumond, Chief Technology Officer
+    .. rst-class:: annotation
 
-.. raw:: html
+      Julien Chaumond, Chief Technology Officer
 
-   </span>
-   </div>
-   <div class="sk-testimonial-div-box">
+  .. div:: image-box
 
-.. image:: images/huggingface.png
-    :width: 120pt
-    :align: center
-    :target: https://huggingface.co
-
-.. raw:: html
-
-   </div>
-   </div>
+    .. image:: images/huggingface.png
+      :target: https://huggingface.co
 
 
 `Evernote <https://evernote.com>`_
 ----------------------------------
 
-.. raw:: html
-
-   <div class="sk-testimonial-div">
-   <div class="sk-testimonial-div-box">
-
-Building a classifier is typically an iterative process of exploring
-the data, selecting the features (the attributes of the data believed
-to be predictive in some way), training the models, and finally
-evaluating them. For many of these tasks, we relied on the excellent
-scikit-learn package for Python.
+.. div:: sk-text-image-grid-large
 
-`Read more <http://blog.evernote.com/tech/2013/01/22/stay-classified/>`_
+  .. div:: text-box
 
-.. raw:: html
+    Building a classifier is typically an iterative process of exploring
+    the data, selecting the features (the attributes of the data believed
+    to be predictive in some way), training the models, and finally
+    evaluating them. For many of these tasks, we relied on the excellent
+    scikit-learn package for Python.
 
-   <span class="testimonial-author">
+    `Read more <http://blog.evernote.com/tech/2013/01/22/stay-classified/>`_
 
-Mark Ayzenshtat, VP, Augmented Intelligence
+    .. rst-class:: annotation
 
-.. raw:: html
+      Mark Ayzenshtat, VP, Augmented Intelligence
 
-   </span>
-   </div>
-   <div class="sk-testimonial-div-box">
+  .. div:: image-box
 
-.. image:: images/evernote.png
-    :width: 120pt
-    :align: center
-    :target: https://evernote.com
+    .. image:: images/evernote.png
+      :target: https://evernote.com
 
-.. raw:: html
-
-   </div>
-   </div>
 
 `Télécom ParisTech <https://www.telecom-paristech.fr/>`_
 --------------------------------------------------------
 
-.. raw:: html
-
-   <div class="sk-testimonial-div">
-   <div class="sk-testimonial-div-box">
-
-At Telecom ParisTech, scikit-learn is used for hands-on sessions and home
-assignments in introductory and advanced machine learning courses. The classes
-are for undergrads and masters students. The great benefit of scikit-learn is
-its fast learning curve that allows students to quickly start working on
-interesting and motivating problems.
-
-.. raw:: html
+.. div:: sk-text-image-grid-large
 
-   <span class="testimonial-author">
+  .. div:: text-box
 
-Alexandre Gramfort, Assistant Professor
+    At Telecom ParisTech, scikit-learn is used for hands-on sessions and home
+    assignments in introductory and advanced machine learning courses. The classes
+    are for undergrads and masters students. The great benefit of scikit-learn is
+    its fast learning curve that allows students to quickly start working on
+    interesting and motivating problems.
 
-.. raw:: html
+    .. rst-class:: annotation
 
-   </span>
-   </div>
-   <div class="sk-testimonial-div-box">
+      Alexandre Gramfort, Assistant Professor
 
-.. image:: images/telecomparistech.jpg
-    :width: 120pt
-    :align: center
-    :target: https://www.telecom-paristech.fr/
+  .. div:: image-box
 
-.. raw:: html
-
-   </div>
-   </div>
+    .. image:: images/telecomparistech.jpg
+      :target: https://www.telecom-paristech.fr/
 
 
 `Booking.com <https://www.booking.com>`_
------------------------------------------
-.. raw:: html
-
-   <div class="sk-testimonial-div">
-   <div class="sk-testimonial-div-box">
-
-At Booking.com, we use machine learning algorithms for many different
-applications, such as recommending hotels and destinations to our customers,
-detecting fraudulent reservations, or scheduling our customer service agents.
-Scikit-learn is one of the tools we use when implementing standard algorithms
-for prediction tasks. Its API and documentations are excellent and make it easy
-to use. The scikit-learn developers do a great job of incorporating state of
-the art implementations and new algorithms into the package. Thus, scikit-learn
-provides convenient access to a wide spectrum of algorithms, and allows us to
-readily find the right tool for the right job.
-
+----------------------------------------
 
-.. raw:: html
+.. div:: sk-text-image-grid-large
 
-   <span class="testimonial-author">
+  .. div:: text-box
 
-Melanie Mueller, Data Scientist
+    At Booking.com, we use machine learning algorithms for many different
+    applications, such as recommending hotels and destinations to our customers,
+    detecting fraudulent reservations, or scheduling our customer service agents.
+    Scikit-learn is one of the tools we use when implementing standard algorithms
+    for prediction tasks. Its API and documentations are excellent and make it easy
+    to use. The scikit-learn developers do a great job of incorporating state of
+    the art implementations and new algorithms into the package. Thus, scikit-learn
+    provides convenient access to a wide spectrum of algorithms, and allows us to
+    readily find the right tool for the right job.
 
-.. raw:: html
+    .. rst-class:: annotation
 
-   </span>
-   </div>
-   <div class="sk-testimonial-div-box">
+      Melanie Mueller, Data Scientist
 
-.. image:: images/booking.png
-    :width: 120pt
-    :align: center
-    :target: https://www.booking.com
+  .. div:: image-box
 
-.. raw:: html
+    .. image:: images/booking.png
+      :target: https://www.booking.com
 
-   </div>
-   </div>
 
 `AWeber <https://www.aweber.com/>`_
-------------------------------------------
-
-.. raw:: html
-
-   <div class="sk-testimonial-div">
-   <div class="sk-testimonial-div-box">
-
-The scikit-learn toolkit is indispensable for the Data Analysis and Management
-team at AWeber.  It allows us to do AWesome stuff we would not otherwise have
-the time or resources to accomplish. The documentation is excellent, allowing
-new engineers to quickly evaluate and apply many different algorithms to our
-data. The text feature extraction utilities are useful when working with the
-large volume of email content we have at AWeber. The RandomizedPCA
-implementation, along with Pipelining and FeatureUnions, allows us to develop
-complex machine learning algorithms efficiently and reliably.
+-----------------------------------
 
-Anyone interested in learning more about how AWeber deploys scikit-learn in a
-production environment should check out talks from PyData Boston by AWeber's
-Michael Becker available at https://github.com/mdbecker/pydata_2013
+.. div:: sk-text-image-grid-large
 
-.. raw:: html
+  .. div:: text-box
 
-   <span class="testimonial-author">
+    The scikit-learn toolkit is indispensable for the Data Analysis and Management
+    team at AWeber.  It allows us to do AWesome stuff we would not otherwise have
+    the time or resources to accomplish. The documentation is excellent, allowing
+    new engineers to quickly evaluate and apply many different algorithms to our
+    data. The text feature extraction utilities are useful when working with the
+    large volume of email content we have at AWeber. The RandomizedPCA
+    implementation, along with Pipelining and FeatureUnions, allows us to develop
+    complex machine learning algorithms efficiently and reliably.
 
-Michael Becker, Software Engineer, Data Analysis and Management Ninjas
+    Anyone interested in learning more about how AWeber deploys scikit-learn in a
+    production environment should check out talks from PyData Boston by AWeber's
+    Michael Becker available at https://github.com/mdbecker/pydata_2013.
 
-.. raw:: html
+    .. rst-class:: annotation
 
-   </span>
-   </div>
-   <div class="sk-testimonial-div-box">
+      Michael Becker, Software Engineer, Data Analysis and Management Ninjas
 
-.. image:: images/aweber.png
-    :width: 120pt
-    :align: center
-    :target: https://www.aweber.com/
+  .. div:: image-box
 
-.. raw:: html
+    .. image:: images/aweber.png
+      :target: https://www.aweber.com
 
-   </div>
-   </div>
 
 `Yhat <https://www.yhat.com>`_
-------------------------------------------
-
-.. raw:: html
+------------------------------
 
-   <div class="sk-testimonial-div">
-   <div class="sk-testimonial-div-box">
+.. div:: sk-text-image-grid-large
 
-The combination of consistent APIs, thorough documentation, and top notch
-implementation make scikit-learn our favorite machine learning package in
-Python. scikit-learn makes doing advanced analysis in Python accessible to
-anyone. At Yhat, we make it easy to integrate these models into your production
-applications. Thus eliminating the unnecessary dev time encountered
-productionizing analytical work.
+  .. div:: text-box
 
+    The combination of consistent APIs, thorough documentation, and top notch
+    implementation make scikit-learn our favorite machine learning package in
+    Python. scikit-learn makes doing advanced analysis in Python accessible to
+    anyone. At Yhat, we make it easy to integrate these models into your production
+    applications. Thus eliminating the unnecessary dev time encountered
+    productionizing analytical work.
 
-.. raw:: html
+    .. rst-class:: annotation
 
-   <span class="testimonial-author">
+      Greg Lamp, Co-founder
 
-Greg Lamp, Co-founder Yhat
+  .. div:: image-box
 
-.. raw:: html
+    .. image:: images/yhat.png
+      :target: https://www.yhat.com
 
-   </span>
-   </div>
-   <div class="sk-testimonial-div-box">
-
-.. image:: images/yhat.png
-    :width: 120pt
-    :align: center
-    :target: https://www.yhat.com
-
-.. raw:: html
-
-   </div>
-   </div>
 
 `Rangespan <http://www.rangespan.com>`_
-----------------------------------------
+---------------------------------------
 
-.. raw:: html
+.. div:: sk-text-image-grid-large
 
-   <div class="sk-testimonial-div">
-   <div class="sk-testimonial-div-box">
+  .. div:: text-box
 
-The Python scikit-learn toolkit is a core tool in the data science
-group at Rangespan. Its large collection of well documented models and
-algorithms allow our team of data scientists to prototype fast and
-quickly iterate to find the right solution to our learning problems.
-We find that scikit-learn is not only the right tool for prototyping,
-but its careful and well tested implementation give us the confidence
-to run scikit-learn models in production.
+    The Python scikit-learn toolkit is a core tool in the data science
+    group at Rangespan. Its large collection of well documented models and
+    algorithms allow our team of data scientists to prototype fast and
+    quickly iterate to find the right solution to our learning problems.
+    We find that scikit-learn is not only the right tool for prototyping,
+    but its careful and well tested implementation give us the confidence
+    to run scikit-learn models in production.
 
-.. raw:: html
+    .. rst-class:: annotation
 
-   <span class="testimonial-author">
+      Jurgen Van Gael, Data Science Director
 
-Jurgen Van Gael, Data Science Director at Rangespan Ltd
+  .. div:: image-box
 
-.. raw:: html
+    .. image:: images/rangespan.png
+      :target: http://www.rangespan.com
 
-   </span>
-   </div>
-   <div class="sk-testimonial-div-box">
-
-.. image:: images/rangespan.png
-    :width: 120pt
-    :align: center
-    :target: http://www.rangespan.com
-
-.. raw:: html
-
-   </div>
-   </div>
 
 `Birchbox <https://www.birchbox.com>`_
-------------------------------------------
-
-.. raw:: html
-
-   <div class="sk-testimonial-div">
-   <div class="sk-testimonial-div-box">
-
-At Birchbox, we face a range of machine learning problems typical to
-E-commerce: product recommendation, user clustering, inventory prediction,
-trends detection, etc. Scikit-learn lets us experiment with many models,
-especially in the exploration phase of a new project: the data can be passed
-around in a consistent way; models are easy to save and reuse; updates keep us
-informed of new developments from the pattern discovery research community.
-Scikit-learn is an important tool for our team, built the right way in the
-right language.
-
-.. raw:: html
+--------------------------------------
 
-   <span class="testimonial-author">
+.. div:: sk-text-image-grid-large
 
-Thierry Bertin-Mahieux, Birchbox, Data Scientist
+  .. div:: text-box
 
-.. raw:: html
+    At Birchbox, we face a range of machine learning problems typical to
+    E-commerce: product recommendation, user clustering, inventory prediction,
+    trends detection, etc. Scikit-learn lets us experiment with many models,
+    especially in the exploration phase of a new project: the data can be passed
+    around in a consistent way; models are easy to save and reuse; updates keep us
+    informed of new developments from the pattern discovery research community.
+    Scikit-learn is an important tool for our team, built the right way in the
+    right language.
 
-   </span>
-   </div>
-   <div class="sk-testimonial-div-box">
+    .. rst-class:: annotation
 
-.. image:: images/birchbox.jpg
-    :width: 120pt
-    :align: center
-    :target: https://www.birchbox.com
+      Thierry Bertin-Mahieux, Data Scientist
 
-.. raw:: html
+  .. div:: image-box
 
-   </div>
-   </div>
+    .. image:: images/birchbox.jpg
+      :target: https://www.birchbox.com
 
 
 `Bestofmedia Group <http://www.bestofmedia.com>`_
---------------------------------------------------
+-------------------------------------------------
 
-.. raw:: html
+.. div:: sk-text-image-grid-large
 
-   <div class="sk-testimonial-div">
-   <div class="sk-testimonial-div-box">
+  .. div:: text-box
 
-Scikit-learn is our #1 toolkit for all things machine learning
-at Bestofmedia. We use it for a variety of tasks (e.g. spam fighting,
-ad click prediction, various ranking models) thanks to the varied,
-state-of-the-art algorithm implementations packaged into it.
-In the lab it accelerates prototyping of complex pipelines. In
-production I can say it has proven to be robust and efficient enough
-to be deployed for business critical components.
+    Scikit-learn is our #1 toolkit for all things machine learning
+    at Bestofmedia. We use it for a variety of tasks (e.g. spam fighting,
+    ad click prediction, various ranking models) thanks to the varied,
+    state-of-the-art algorithm implementations packaged into it.
+    In the lab it accelerates prototyping of complex pipelines. In
+    production I can say it has proven to be robust and efficient enough
+    to be deployed for business critical components.
 
-.. raw:: html
+    .. rst-class:: annotation
 
-   <span class="testimonial-author">
+      Eustache Diemert, Lead Scientist
 
-Eustache Diemert, Lead Scientist Bestofmedia Group
+  .. div:: image-box
 
-.. raw:: html
+    .. image:: images/bestofmedia-logo.png
+      :target: http://www.bestofmedia.com
 
-   </span>
-   </div>
-   <div class="sk-testimonial-div-box">
-
-.. image:: images/bestofmedia-logo.png
-    :width: 120pt
-    :align: center
-    :target: http://www.bestofmedia.com
-
-.. raw:: html
-
-   </div>
-   </div>
 
 `Change.org <https://www.change.org>`_
 --------------------------------------
 
-.. raw:: html
-
-   <div class="sk-testimonial-div">
-   <div class="sk-testimonial-div-box">
+.. div:: sk-text-image-grid-large
 
-At change.org we automate the use of scikit-learn's RandomForestClassifier
-in our production systems to drive email targeting that reaches millions
-of users across the world each week. In the lab, scikit-learn's ease-of-use,
-performance, and overall variety of algorithms implemented has proved invaluable
-in giving us a single reliable source to turn to for our machine-learning needs.
+  .. div:: text-box
 
-.. raw:: html
+    At change.org we automate the use of scikit-learn's RandomForestClassifier
+    in our production systems to drive email targeting that reaches millions
+    of users across the world each week. In the lab, scikit-learn's ease-of-use,
+    performance, and overall variety of algorithms implemented has proved invaluable
+    in giving us a single reliable source to turn to for our machine-learning needs.
 
-   <span class="testimonial-author">
+    .. rst-class:: annotation
 
-Vijay Ramesh, Software Engineer in Data/science at Change.org
+      Vijay Ramesh, Software Engineer in Data/science at Change.org
 
-.. raw:: html
+  .. div:: image-box
 
-   </span>
-   </div>
-   <div class="sk-testimonial-div-box">
+    .. image:: images/change-logo.png
+      :target: https://www.change.org
 
-.. image:: images/change-logo.png
-    :width: 120pt
-    :align: center
-    :target: https://www.change.org
-
-.. raw:: html
-
-   </div>
-   </div>
 
 `PHIMECA Engineering <https://www.phimeca.com/?lang=en>`_
-----------------------------------------------------------
-
-.. raw:: html
-
-   <div class="sk-testimonial-div">
-   <div class="sk-testimonial-div-box">
-
-At PHIMECA Engineering, we use scikit-learn estimators as surrogates for
-expensive-to-evaluate numerical models (mostly but not exclusively
-finite-element mechanical models) for speeding up the intensive post-processing
-operations involved in our simulation-based decision making framework.
-Scikit-learn's fit/predict API together with its efficient cross-validation
-tools considerably eases the task of selecting the best-fit estimator. We are
-also using scikit-learn for illustrating concepts in our training sessions.
-Trainees are always impressed by the ease-of-use of scikit-learn despite the
-apparent theoretical complexity of machine learning.
+---------------------------------------------------------
 
-.. raw:: html
+.. div:: sk-text-image-grid-large
 
-   <span class="testimonial-author">
+  .. div:: text-box
 
-Vincent Dubourg, PHIMECA Engineering, PhD Engineer
+    At PHIMECA Engineering, we use scikit-learn estimators as surrogates for
+    expensive-to-evaluate numerical models (mostly but not exclusively
+    finite-element mechanical models) for speeding up the intensive post-processing
+    operations involved in our simulation-based decision making framework.
+    Scikit-learn's fit/predict API together with its efficient cross-validation
+    tools considerably eases the task of selecting the best-fit estimator. We are
+    also using scikit-learn for illustrating concepts in our training sessions.
+    Trainees are always impressed by the ease-of-use of scikit-learn despite the
+    apparent theoretical complexity of machine learning.
 
-.. raw:: html
+    .. rst-class:: annotation
 
-   </span>
-   </div>
-   <div class="sk-testimonial-div-box">
+      Vincent Dubourg, PHIMECA Engineering, PhD Engineer
 
-.. image:: images/phimeca.png
-    :width: 120pt
-    :align: center
-    :target: https://www.phimeca.com/?lang=en
+  .. div:: image-box
 
-.. raw:: html
+    .. image:: images/phimeca.png
+      :target: https://www.phimeca.com/?lang=en
 
-   </div>
-   </div>
 
 `HowAboutWe <http://www.howaboutwe.com/>`_
-----------------------------------------------------------
-
-.. raw:: html
-
-   <div class="sk-testimonial-div">
-   <div class="sk-testimonial-div-box">
-
-At HowAboutWe, scikit-learn lets us implement a wide array of machine learning
-techniques in analysis and in production, despite having a small team.  We use
-scikit-learn's classification algorithms to predict user behavior, enabling us
-to (for example) estimate the value of leads from a given traffic source early
-in the lead's tenure on our site. Also, our users' profiles consist of
-primarily unstructured data (answers to open-ended questions), so we use
-scikit-learn's feature extraction and dimensionality reduction tools to
-translate these unstructured data into inputs for our matchmaking system.
-
-.. raw:: html
+------------------------------------------
 
-   <span class="testimonial-author">
+.. div:: sk-text-image-grid-large
 
-Daniel Weitzenfeld, Senior Data Scientist at HowAboutWe
+  .. div:: text-box
 
-.. raw:: html
+    At HowAboutWe, scikit-learn lets us implement a wide array of machine learning
+    techniques in analysis and in production, despite having a small team.  We use
+    scikit-learn's classification algorithms to predict user behavior, enabling us
+    to (for example) estimate the value of leads from a given traffic source early
+    in the lead's tenure on our site. Also, our users' profiles consist of
+    primarily unstructured data (answers to open-ended questions), so we use
+    scikit-learn's feature extraction and dimensionality reduction tools to
+    translate these unstructured data into inputs for our matchmaking system.
 
-   </span>
-   </div>
-   <div class="sk-testimonial-div-box">
+    .. rst-class:: annotation
 
-.. image:: images/howaboutwe.png
-    :width: 120pt
-    :align: center
-    :target: http://www.howaboutwe.com/
+      Daniel Weitzenfeld, Senior Data Scientist at HowAboutWe
 
-.. raw:: html
+  .. div:: image-box
 
-   </div>
-   </div>
+    .. image:: images/howaboutwe.png
+      :target: http://www.howaboutwe.com/
 
 
 `PeerIndex <https://www.brandwatch.com/peerindex-and-brandwatch>`_
 ------------------------------------------------------------------
 
-.. raw:: html
-
-   <div class="sk-testimonial-div">
-   <div class="sk-testimonial-div-box">
-
-At PeerIndex we use scientific methodology to build the Influence Graph - a
-unique dataset that allows us to identify who's really influential and in which
-context. To do this, we have to tackle a range of machine learning and
-predictive modeling problems. Scikit-learn has emerged as our primary tool for
-developing prototypes and making quick progress. From predicting missing data
-and classifying tweets to clustering communities of social media users, scikit-
-learn proved useful in a variety of applications. Its very intuitive interface
-and excellent compatibility with other python tools makes it and indispensable
-tool in our daily research efforts.
+.. div:: sk-text-image-grid-large
 
-.. raw:: html
+  .. div:: text-box
 
-   <span class="testimonial-author">
+    At PeerIndex we use scientific methodology to build the Influence Graph - a
+    unique dataset that allows us to identify who's really influential and in which
+    context. To do this, we have to tackle a range of machine learning and
+    predictive modeling problems. Scikit-learn has emerged as our primary tool for
+    developing prototypes and making quick progress. From predicting missing data
+    and classifying tweets to clustering communities of social media users, scikit-
+    learn proved useful in a variety of applications. Its very intuitive interface
+    and excellent compatibility with other python tools makes it and indispensable
+    tool in our daily research efforts.
 
-Ferenc Huszar - Senior Data Scientist at Peerindex
+    .. rst-class:: annotation
 
-.. raw:: html
+      Ferenc Huszar, Senior Data Scientist at Peerindex
 
-   </span>
-   </div>
-   <div class="sk-testimonial-div-box">
+  .. div:: image-box
 
-.. image:: images/peerindex.png
-    :width: 120pt
-    :align: center
-    :target: https://www.brandwatch.com/peerindex-and-brandwatch
-
-.. raw:: html
-
-   </div>
-   </div>
+    .. image:: images/peerindex.png
+      :target: https://www.brandwatch.com/peerindex-and-brandwatch
 
 
 `DataRobot <https://www.datarobot.com>`_
 ----------------------------------------
 
-.. raw:: html
-
-   <div class="sk-testimonial-div">
-   <div class="sk-testimonial-div-box">
-
-DataRobot is building next generation predictive analytics software to make data scientists more productive, and scikit-learn is an integral part of our system. The variety of machine learning techniques in combination with the solid implementations that scikit-learn offers makes it a one-stop-shopping library for machine learning in Python. Moreover, its consistent API, well-tested code and permissive licensing allow us to use it in a production environment. Scikit-learn has literally saved us years of work we would have had to do ourselves to bring our product to market.
-
-.. raw:: html
+.. div:: sk-text-image-grid-large
 
-   <span class="testimonial-author">
+  .. div:: text-box
 
-Jeremy Achin, CEO & Co-founder DataRobot Inc.
+    DataRobot is building next generation predictive analytics software to make data
+    scientists more productive, and scikit-learn is an integral part of our system. The
+    variety of machine learning techniques in combination with the solid implementations
+    that scikit-learn offers makes it a one-stop-shopping library for machine learning
+    in Python. Moreover, its consistent API, well-tested code and permissive licensing
+    allow us to use it in a production environment. Scikit-learn has literally saved us
+    years of work we would have had to do ourselves to bring our product to market.
 
-.. raw:: html
+    .. rst-class:: annotation
 
-   </span>
-   </div>
-   <div class="sk-testimonial-div-box">
+      Jeremy Achin, CEO & Co-founder DataRobot Inc.
 
-.. image:: images/datarobot.png
-    :width: 120pt
-    :align: center
-    :target: https://www.datarobot.com
+  .. div:: image-box
 
-.. raw:: html
-
-   </div>
-   </div>
+    .. image:: images/datarobot.png
+      :target: https://www.datarobot.com
 
 
 `OkCupid <https://www.okcupid.com/>`_
---------------------------------------
-
-.. raw:: html
-
-   <div class="sk-testimonial-div">
-   <div class="sk-testimonial-div-box">
+-------------------------------------
 
-We're using scikit-learn at OkCupid to evaluate and improve our matchmaking
-system. The range of features it has, especially preprocessing utilities, means
-we can use it for a wide variety of projects, and it's performant enough to
-handle the volume of data that we need to sort through. The documentation is
-really thorough, as well, which makes the library quite easy to use.
+.. div:: sk-text-image-grid-large
 
-.. raw:: html
+  .. div:: text-box
 
-   <span class="testimonial-author">
+    We're using scikit-learn at OkCupid to evaluate and improve our matchmaking
+    system. The range of features it has, especially preprocessing utilities, means
+    we can use it for a wide variety of projects, and it's performant enough to
+    handle the volume of data that we need to sort through. The documentation is
+    really thorough, as well, which makes the library quite easy to use.
 
-David Koh - Senior Data Scientist at OkCupid
+    .. rst-class:: annotation
 
-.. raw:: html
+      David Koh - Senior Data Scientist at OkCupid
 
-   </span>
-   </div>
-   <div class="sk-testimonial-div-box">
+  .. div:: image-box
 
-.. image:: images/okcupid.png
-    :width: 120pt
-    :align: center
-    :target: https://www.okcupid.com
-
-.. raw:: html
-
-    </div>
-    </div>
+    .. image:: images/okcupid.png
+      :target: https://www.okcupid.com
 
 
 `Lovely <https://livelovely.com/>`_
 -----------------------------------
 
-.. raw:: html
-
-   <div class="sk-testimonial-div">
-   <div class="sk-testimonial-div-box">
-
-At Lovely, we strive to deliver the best apartment marketplace, with respect to
-our users and our listings. From understanding user behavior, improving data
-quality, and detecting fraud, scikit-learn is a regular tool for gathering
-insights, predictive modeling and improving our product. The easy-to-read
-documentation and intuitive architecture of the API makes machine learning both
-explorable and accessible to a wide range of python developers. I'm constantly
-recommending that more developers and scientists try scikit-learn.
-
-.. raw:: html
+.. div:: sk-text-image-grid-large
 
-   <span class="testimonial-author">
+  .. div:: text-box
 
-Simon Frid - Data Scientist, Lead at Lovely
+    At Lovely, we strive to deliver the best apartment marketplace, with respect to
+    our users and our listings. From understanding user behavior, improving data
+    quality, and detecting fraud, scikit-learn is a regular tool for gathering
+    insights, predictive modeling and improving our product. The easy-to-read
+    documentation and intuitive architecture of the API makes machine learning both
+    explorable and accessible to a wide range of python developers. I'm constantly
+    recommending that more developers and scientists try scikit-learn.
 
-.. raw:: html
+    .. rst-class:: annotation
 
-   </span>
-   </div>
-   <div class="sk-testimonial-div-box">
+      Simon Frid - Data Scientist, Lead at Lovely
 
-.. image:: images/lovely.png
-    :width: 120pt
-    :align: center
-    :target: https://livelovely.com
-
-.. raw:: html
-
-   </div>
-   </div>
+  .. div:: image-box
 
+    .. image:: images/lovely.png
+      :target: https://livelovely.com
 
 
 `Data Publica <http://www.data-publica.com/>`_
 ----------------------------------------------
 
-.. raw:: html
-
-   <div class="sk-testimonial-div">
-   <div class="sk-testimonial-div-box">
-
-Data Publica builds a new predictive sales tool for commercial and marketing teams called C-Radar.
-We extensively use scikit-learn to build segmentations of customers through clustering, and to predict future customers based on past partnerships success or failure.
-We also categorize companies using their website communication thanks to scikit-learn and its machine learning algorithm implementations.
-Eventually, machine learning makes it possible to detect weak signals that traditional tools cannot see.
-All these complex tasks are performed in an easy and straightforward way thanks to the great quality of the scikit-learn framework.
+.. div:: sk-text-image-grid-large
 
-.. raw:: html
+  .. div:: text-box
 
-   <span class="testimonial-author">
+    Data Publica builds a new predictive sales tool for commercial and marketing teams
+    called C-Radar. We extensively use scikit-learn to build segmentations of customers
+    through clustering, and to predict future customers based on past partnerships
+    success or failure. We also categorize companies using their website communication
+    thanks to scikit-learn and its machine learning algorithm implementations.
+    Eventually, machine learning makes it possible to detect weak signals that
+    traditional tools cannot see. All these complex tasks are performed in an easy and
+    straightforward way thanks to the great quality of the scikit-learn framework.
 
-Guillaume Lebourgeois & Samuel Charron - Data Scientists at Data Publica
+    .. rst-class:: annotation
 
-.. raw:: html
+      Guillaume Lebourgeois & Samuel Charron - Data Scientists at Data Publica
 
-   </span>
-   </div>
-   <div class="sk-testimonial-div-box">
-
-.. image:: images/datapublica.png
-    :width: 120pt
-    :align: center
-    :target: http://www.data-publica.com/
-
-.. raw:: html
-
-   </div>
-   </div>
+  .. div:: image-box
 
+    .. image:: images/datapublica.png
+      :target: http://www.data-publica.com/
 
 
 `Machinalis <https://www.machinalis.com/>`_
 -------------------------------------------
 
-.. raw:: html
-
-   <div class="sk-testimonial-div">
-   <div class="sk-testimonial-div-box">
+.. div:: sk-text-image-grid-large
 
-Scikit-learn is the cornerstone of all the machine learning projects carried at
-Machinalis. It has a consistent API, a wide selection of algorithms and lots
-of auxiliary tools to deal with the boilerplate.
-We have used it in production environments on a variety of projects
-including click-through rate prediction, `information extraction <https://github.com/machinalis/iepy>`_,
-and even counting sheep!
+  .. div:: text-box
 
-In fact, we use it so much that we've started to freeze our common use cases
-into Python packages, some of them open-sourced, like
-`FeatureForge <https://github.com/machinalis/featureforge>`_ .
-Scikit-learn in one word: Awesome.
+    Scikit-learn is the cornerstone of all the machine learning projects carried at
+    Machinalis. It has a consistent API, a wide selection of algorithms and lots of
+    auxiliary tools to deal with the boilerplate. We have used it in production
+    environments on a variety of projects including click-through rate prediction,
+    `information extraction <https://github.com/machinalis/iepy>`_, and even counting
+    sheep!
 
-.. raw:: html
+    In fact, we use it so much that we've started to freeze our common use cases
+    into Python packages, some of them open-sourced, like `FeatureForge
+    <https://github.com/machinalis/featureforge>`_. Scikit-learn in one word: Awesome.
 
-   <span class="testimonial-author">
+    .. rst-class:: annotation
 
-Rafael Carrascosa, Lead developer
+      Rafael Carrascosa, Lead developer
 
-.. raw:: html
+  .. div:: image-box
 
-   </span>
-   </div>
-   <div class="sk-testimonial-div-box">
-
-.. image:: images/machinalis.png
-    :width: 120pt
-    :align: center
-    :target: https://www.machinalis.com/
-
-.. raw:: html
-
-   </div>
-   </div>
+    .. image:: images/machinalis.png
+      :target: https://www.machinalis.com/
 
 
 `solido <https://www.solidodesign.com/>`_
 -----------------------------------------
 
-.. raw:: html
-
-   <div class="sk-testimonial-div">
-   <div class="sk-testimonial-div-box">
+.. div:: sk-text-image-grid-large
 
-Scikit-learn is helping to drive Moore's Law, via Solido. Solido creates
-computer-aided design tools used by the majority of top-20 semiconductor
-companies and fabs, to design the bleeding-edge chips inside smartphones,
-automobiles, and more. Scikit-learn helps to power Solido's algorithms for
-rare-event estimation, worst-case verification, optimization, and more. At
-Solido, we are particularly fond of scikit-learn's libraries for Gaussian
-Process models, large-scale regularized linear regression, and classification.
-Scikit-learn has increased our productivity, because for many ML problems we no
-longer need to “roll our own” code. `This PyData 2014 talk <https://www.youtube.com/watch?v=Jm-eBD9xR3w>`_ has details.
+  .. div:: text-box
 
+    Scikit-learn is helping to drive Moore's Law, via Solido. Solido creates
+    computer-aided design tools used by the majority of top-20 semiconductor
+    companies and fabs, to design the bleeding-edge chips inside smartphones,
+    automobiles, and more. Scikit-learn helps to power Solido's algorithms for
+    rare-event estimation, worst-case verification, optimization, and more. At
+    Solido, we are particularly fond of scikit-learn's libraries for Gaussian
+    Process models, large-scale regularized linear regression, and classification.
+    Scikit-learn has increased our productivity, because for many ML problems we no
+    longer need to “roll our own” code. `This PyData 2014 talk
+    <https://www.youtube.com/watch?v=Jm-eBD9xR3w>`_ has details.
 
-.. raw:: html
+    .. rst-class:: annotation
 
-  <span class="testimonial-author">
+      Trent McConaghy, founder, Solido Design Automation Inc.
 
-Trent McConaghy, founder, Solido Design Automation Inc.
-
-.. raw:: html
-
-   </span>
-   </div>
-   <div class="sk-testimonial-div-box">
-
-.. image:: images/solido_logo.png
-    :width: 120pt
-    :align: center
-    :target: https://www.solidodesign.com/
-
-.. raw:: html
-
-   </div>
-   </div>
+  .. div:: image-box
 
+    .. image:: images/solido_logo.png
+      :target: https://www.solidodesign.com/
 
 
 `INFONEA <http://www.infonea.com/en/>`_
------------------------------------------
-
-.. raw:: html
-
-   <div class="sk-testimonial-div">
-   <div class="sk-testimonial-div-box">
-
-We employ scikit-learn for rapid prototyping and custom-made Data Science
-solutions within our in-memory based Business Intelligence Software
-INFONEA®. As a well-documented and comprehensive collection of
-state-of-the-art algorithms and pipelining methods, scikit-learn enables
-us to provide flexible and scalable scientific analysis solutions. Thus,
-scikit-learn is immensely valuable in realizing a powerful integration of
-Data Science technology within self-service business analytics.
+---------------------------------------
 
-.. raw:: html
+.. div:: sk-text-image-grid-large
 
-  <span class="testimonial-author">
+  .. div:: text-box
 
-Thorsten Kranz, Data Scientist, Coma Soft AG.
+    We employ scikit-learn for rapid prototyping and custom-made Data Science
+    solutions within our in-memory based Business Intelligence Software
+    INFONEA®. As a well-documented and comprehensive collection of
+    state-of-the-art algorithms and pipelining methods, scikit-learn enables
+    us to provide flexible and scalable scientific analysis solutions. Thus,
+    scikit-learn is immensely valuable in realizing a powerful integration of
+    Data Science technology within self-service business analytics.
 
-.. raw:: html
+    .. rst-class:: annotation
 
-   </span>
-   </div>
-   <div class="sk-testimonial-div-box">
+      Thorsten Kranz, Data Scientist, Coma Soft AG.
 
-.. image:: images/infonea.jpg
-    :width: 120pt
-    :align: center
-    :target: http://www.infonea.com/en/
+  .. div:: image-box
 
-.. raw:: html
-
-   </div>
-   </div>
+    .. image:: images/infonea.jpg
+      :target: http://www.infonea.com/en/
 
 
 `Dataiku <https://www.dataiku.com/>`_
------------------------------------------
-
-.. raw:: html
-
-   <div class="sk-testimonial-div">
-   <div class="sk-testimonial-div-box">
+-------------------------------------
 
-Our software, Data Science Studio (DSS), enables users to create data services
-that combine `ETL <https://en.wikipedia.org/wiki/Extract,_transform,_load>`_ with
-Machine Learning. Our Machine Learning module integrates
-many scikit-learn algorithms. The scikit-learn library is a perfect integration
-with DSS because it offers algorithms for virtually all business cases. Our goal
-is to offer a transparent and flexible tool that makes it easier to optimize
-time consuming aspects of building a data service, preparing data, and training
-machine learning algorithms on all types of data.
+.. div:: sk-text-image-grid-large
 
+  .. div:: text-box
 
-.. raw:: html
+    Our software, Data Science Studio (DSS), enables users to create data services
+    that combine `ETL <https://en.wikipedia.org/wiki/Extract,_transform,_load>`_ with
+    Machine Learning. Our Machine Learning module integrates
+    many scikit-learn algorithms. The scikit-learn library is a perfect integration
+    with DSS because it offers algorithms for virtually all business cases. Our goal
+    is to offer a transparent and flexible tool that makes it easier to optimize
+    time consuming aspects of building a data service, preparing data, and training
+    machine learning algorithms on all types of data.
 
-  <span class="testimonial-author">
+    .. rst-class:: annotation
 
-Florian Douetteau, CEO, Dataiku
+      Florian Douetteau, CEO, Dataiku
 
-.. raw:: html
+  .. div:: image-box
 
-   </span>
-   </div>
-   <div class="sk-testimonial-div-box">
+    .. image:: images/dataiku_logo.png
+      :target: https://www.dataiku.com/
 
-.. image:: images/dataiku_logo.png
-    :width: 120pt
-    :align: center
-    :target: https://www.dataiku.com/
-
-.. raw:: html
-
-   </div>
-   </div>
 
 `Otto Group <https://ottogroup.com/>`_
------------------------------------------
-
-.. raw:: html
-
-   <div class="sk-testimonial-div">
-   <div class="sk-testimonial-div-box">
-
-Here at Otto Group, one of global Big Five B2C online retailers, we are using
-scikit-learn in all aspects of our daily work from data exploration to development
-of machine learning application to the productive deployment of those services.
-It helps us to tackle machine learning problems ranging from e-commerce to logistics.
-It consistent APIs enabled us to build the `Palladium REST-API framework
-<https://github.com/ottogroup/palladium/>`_ around it and continuously deliver
-scikit-learn based services.
-
+--------------------------------------
 
-.. raw:: html
+.. div:: sk-text-image-grid-large
 
-  <span class="testimonial-author">
+  .. div:: text-box
 
-Christian Rammig, Head of Data Science, Otto Group
+    Here at Otto Group, one of global Big Five B2C online retailers, we are using
+    scikit-learn in all aspects of our daily work from data exploration to development
+    of machine learning application to the productive deployment of those services.
+    It helps us to tackle machine learning problems ranging from e-commerce to logistics.
+    It consistent APIs enabled us to build the `Palladium REST-API framework
+    <https://github.com/ottogroup/palladium/>`_ around it and continuously deliver
+    scikit-learn based services.
 
-.. raw:: html
+    .. rst-class:: annotation
 
-   </span>
-   </div>
-   <div class="sk-testimonial-div-box">
+      Christian Rammig, Head of Data Science, Otto Group
 
-.. image:: images/ottogroup_logo.png
-    :width: 120pt
-    :align: center
-    :target: https://ottogroup.com
+  .. div:: image-box
 
-.. raw:: html
+    .. image:: images/ottogroup_logo.png
+      :target: https://ottogroup.com
 
-   </div>
-   </div>
 
 `Zopa <https://zopa.com/>`_
------------------------------------------
+---------------------------
 
-.. raw:: html
+.. div:: sk-text-image-grid-large
 
-   <div class="sk-testimonial-div">
-   <div class="sk-testimonial-div-box"-->
+  .. div:: text-box
 
-At Zopa, the first ever Peer-to-Peer lending platform, we extensively use scikit-learn
-to run the business and optimize our users' experience. It powers our
-Machine Learning models involved in credit risk, fraud risk, marketing, and pricing,
-and has been used for originating at least 1 billion GBP worth of Zopa loans.
-It is very well documented, powerful, and simple to use. We are grateful for the
-capabilities it has provided, and for allowing us to deliver on our mission of making
-money simple and fair.
+    At Zopa, the first ever Peer-to-Peer lending platform, we extensively use
+    scikit-learn to run the business and optimize our users' experience. It powers our
+    Machine Learning models involved in credit risk, fraud risk, marketing, and pricing,
+    and has been used for originating at least 1 billion GBP worth of Zopa loans. It is
+    very well documented, powerful, and simple to use. We are grateful for the
+    capabilities it has provided, and for allowing us to deliver on our mission of
+    making money simple and fair.
 
-.. raw:: html
+    .. rst-class:: annotation
 
-  <span class="testimonial-author">
+      Vlasios Vasileiou, Head of Data Science, Zopa
 
-Vlasios Vasileiou, Head of Data Science, Zopa
+  .. div:: image-box
 
-.. raw:: html
+    .. image:: images/zopa.png
+      :target: https://zopa.com
 
-   </span>
-   </div>
-   <div class="sk-testimonial-div-box"-->
-
-.. image:: images/zopa.png
-    :width: 120pt
-    :align: center
-    :target: https://zopa.com
-
-.. raw:: html
-
-   </div>
-   </div>
 
 `MARS <https://www.mars.com/global>`_
---------------------------------------
+-------------------------------------
 
-.. raw:: html
+.. div:: sk-text-image-grid-large
 
-   <div class="sk-testimonial-div">
-   <div class="sk-testimonial-div-box">
+  .. div:: text-box
 
-Scikit-Learn is integral to the Machine Learning Ecosystem at Mars. Whether
-we're designing better recipes for petfood or closely analysing our cocoa
-supply chain, Scikit-Learn is used as a tool for rapidly prototyping ideas
-and taking them to production. This allows us to better understand and meet
-the needs of our consumers worldwide. Scikit-Learn's feature-rich toolset is
-easy to use and equips our associates with the capabilities they need to
-solve the business challenges they face every day.
+    Scikit-Learn is integral to the Machine Learning Ecosystem at Mars. Whether
+    we're designing better recipes for petfood or closely analysing our cocoa
+    supply chain, Scikit-Learn is used as a tool for rapidly prototyping ideas
+    and taking them to production. This allows us to better understand and meet
+    the needs of our consumers worldwide. Scikit-Learn's feature-rich toolset is
+    easy to use and equips our associates with the capabilities they need to
+    solve the business challenges they face every day.
 
-.. raw:: html
+    .. rst-class:: annotation
 
-   <span class="testimonial-author">
+      Michael Fitzke, Next Generation Technologies Sr Leader, Mars Inc.
 
-Michael Fitzke Next Generation Technologies Sr Leader, Mars Inc.
+  .. div:: image-box
 
-.. raw:: html
-
-   </span>
-   </div>
-   <div class="sk-testimonial-div-box">
-
-.. image:: images/mars.png
-    :width: 120pt
-    :align: center
-    :target: https://www.mars.com/global
-
-.. raw:: html
-
-   </div>
-   </div>
+    .. image:: images/mars.png
+      :target: https://www.mars.com/global
 
 
 `BNP Paribas Cardif <https://www.bnpparibascardif.com/>`_
 ---------------------------------------------------------
 
-.. raw:: html
-
-   <div class="sk-testimonial-div">
-   <div class="sk-testimonial-div-box">
-
-BNP Paribas Cardif uses scikit-learn for several of its machine learning models
-in production. Our internal community of developers and data scientists has
-been using scikit-learn since 2015, for several reasons: the quality of the
-developments, documentation and contribution governance, and the sheer size of
-the contributing community. We even explicitly mention the use of
-scikit-learn's pipelines in our internal model risk governance as one of our
-good practices to decrease operational risks and overfitting risk. As a way to
-support open source software development and in particular scikit-learn
-project, we decided to participate to scikit-learn's consortium at La Fondation
-Inria since its creation in 2018.
-
-.. raw:: html
-
-   <span class="testimonial-author">
+.. div:: sk-text-image-grid-large
 
-Sébastien Conort, Chief Data Scientist, BNP Paribas Cardif
+  .. div:: text-box
 
-.. raw:: html
+    BNP Paribas Cardif uses scikit-learn for several of its machine learning models
+    in production. Our internal community of developers and data scientists has
+    been using scikit-learn since 2015, for several reasons: the quality of the
+    developments, documentation and contribution governance, and the sheer size of
+    the contributing community. We even explicitly mention the use of
+    scikit-learn's pipelines in our internal model risk governance as one of our
+    good practices to decrease operational risks and overfitting risk. As a way to
+    support open source software development and in particular scikit-learn
+    project, we decided to participate to scikit-learn's consortium at La Fondation
+    Inria since its creation in 2018.
 
-   </span>
-   </div>
-   <div class="sk-testimonial-div-box">
+    .. rst-class:: annotation
 
-.. image:: images/bnp_paribas_cardif.png
-    :width: 120pt
-    :align: center
-    :target: https://www.bnpparibascardif.com/
+      Sébastien Conort, Chief Data Scientist, BNP Paribas Cardif
 
-.. raw:: html
+  .. div:: image-box
 
-   </div>
-   </div>
+    .. image:: images/bnp_paribas_cardif.png
+      :target: https://www.bnpparibascardif.com/
diff --git a/doc/themes/scikit-learn-modern/javascript.html b/doc/themes/scikit-learn-modern/javascript.html
deleted file mode 100644
index be4cf26073441..0000000000000
--- a/doc/themes/scikit-learn-modern/javascript.html
+++ /dev/null
@@ -1,56 +0,0 @@
-{% if theme_legacy_google_analytics|tobool %}
-<script>
-    window.ga=window.ga||function(){(ga.q=ga.q||[]).push(arguments)};ga.l=+new Date;
-    ga('create', 'UA-22606712-2', 'auto');
-    ga('set', 'anonymizeIp', true);
-    ga('send', 'pageview');
-</script>
-<script async src='https://www.google-analytics.com/analytics.js'></script>
-{% endif %}
-
-{% if theme_analytics|tobool %}
-<script defer data-domain="scikit-learn.org" src="https://views.scientific-python.org/js/script.js">
-</script>
-{% endif %}
-
-<script src="{{ pathto('_static/clipboard.min.js', 1) }}"></script>
-<script src="{{ pathto('_static/copybutton.js', 1) }}"></script>
-
-<script>
-$(document).ready(function() {
-    /* Add a [>>>] button on the top-right corner of code samples to hide
-     * the >>> and ... prompts and the output and thus make the code
-     * copyable. */
-    var div = $('.highlight-python .highlight,' +
-                '.highlight-python3 .highlight,' +
-                '.highlight-pycon .highlight,' +
-		'.highlight-default .highlight')
-    var pre = div.find('pre');
-
-    // get the styles from the current theme
-    pre.parent().parent().css('position', 'relative');
-
-    // create and add the button to all the code blocks that contain >>>
-    div.each(function(index) {
-        var jthis = $(this);
-        // tracebacks (.gt) contain bare text elements that need to be
-        // wrapped in a span to work with .nextUntil() (see later)
-        jthis.find('pre:has(.gt)').contents().filter(function() {
-            return ((this.nodeType == 3) && (this.data.trim().length > 0));
-        }).wrap('<span>');
-    });
-
-	/*** Add permalink buttons next to glossary terms ***/
-	$('dl.glossary > dt[id]').append(function() {
-		return ('<a class="headerlink" href="#' +
-			    this.getAttribute('id') +
-			    '" title="Permalink to this term">¶</a>');
-	});
-});
-
-</script>
-{%- if pagename != 'index' and pagename != 'documentation' %}
-    {% if theme_mathjax_path %}
-<script id="MathJax-script" async src="{{ theme_mathjax_path }}"></script>
-    {% endif %}
-{%- endif %}
diff --git a/doc/themes/scikit-learn-modern/layout.html b/doc/themes/scikit-learn-modern/layout.html
deleted file mode 100644
index c95184d42c671..0000000000000
--- a/doc/themes/scikit-learn-modern/layout.html
+++ /dev/null
@@ -1,150 +0,0 @@
-{# TEMPLATE VAR SETTINGS #}
-{%- set url_root = pathto('', 1) %}
-{%- if url_root == '#' %}{% set url_root = '' %}{% endif %}
-{%- if not embedded and docstitle %}
-  {%- set titlesuffix = " &mdash; "|safe + docstitle|e %}
-{%- else %}
-  {%- set titlesuffix = "" %}
-{%- endif %}
-{%- set lang_attr = 'en' %}
-
-<!DOCTYPE html>
-<!-- data-theme below is forced to be "light" but should be changed if we use pydata-theme-sphinx in the future -->
-<!--[if IE 8]><html class="no-js lt-ie9" lang="{{ lang_attr }}" data-content_root="{{ url_root }}" data-theme="light"> <![endif]-->
-<!--[if gt IE 8]><!--> <html class="no-js" lang="{{ lang_attr }}" data-content_root="{{ url_root }}" data-theme="light"> <!--<![endif]-->
-<head>
-  <meta charset="utf-8">
-  {{ metatags }}
-  <meta name="viewport" content="width=device-width, initial-scale=1.0">
-
-  {% block htmltitle %}
-  <title>{{ title|striptags|e }}{{ titlesuffix }}</title>
-  {% endblock %}
-  <link rel="canonical" href="https://scikit-learn.org/stable/{{pagename}}.html" />
-
-  {% if favicon_url %}
-  <link rel="shortcut icon" href="{{ favicon_url|e }}"/>
-  {% endif %}
-
-  <link rel="stylesheet" href="{{ pathto('_static/css/vendor/bootstrap.min.css', 1) }}" type="text/css" />
-  {%- for css in css_files %}
-    {%- if css|attr("rel") %}
-  <link rel="{{ css.rel }}" href="{{ pathto(css.filename, 1) }}" type="text/css"{% if css.title is not none %} title="{{ css.title }}"{% endif %} />
-    {%- else %}
-  <link rel="stylesheet" href="{{ pathto(css, 1) }}" type="text/css" />
-    {%- endif %}
-  {%- endfor %}
-  <link rel="stylesheet" href="{{ pathto('_static/' + styles[0], 1) }}" type="text/css" />
-<script id="documentation_options" data-url_root="{{ url_root }}" src="{{ pathto('_static/documentation_options.js', 1) }}"></script>
-<script src="{{ pathto('_static/js/vendor/jquery-3.6.3.slim.min.js', 1) }}"></script>
-<script src="{{ pathto('_static/js/details-permalink.js', 1) }}"></script>
-{%- block extrahead %} {% endblock %}
-</head>
-<body>
-{% include "nav.html" %}
-{%- block content %}
-<div class="d-flex" id="sk-doc-wrapper">
-    <input type="checkbox" name="sk-toggle-checkbox" id="sk-toggle-checkbox">
-    <label id="sk-sidemenu-toggle" class="sk-btn-toggle-toc btn sk-btn-primary" for="sk-toggle-checkbox">Toggle Menu</label>
-    <div id="sk-sidebar-wrapper" class="border-right">
-      <div class="sk-sidebar-toc-wrapper">
-        <div class="btn-group w-100 mb-2" role="group" aria-label="rellinks">
-          {%- if prev %}
-            <a href="{{ prev.link|e }}" role="button" class="btn sk-btn-rellink py-1" sk-rellink-tooltip="{{ prev.title|striptags }}">Prev</a>
-          {%- else %}
-            <a href="#" role="button" class="btn sk-btn-rellink py-1 disabled"">Prev</a>
-          {%- endif %}
-          {%- if parents -%}
-            <a href="{{ parents[-1].link|e }}" role="button" class="btn sk-btn-rellink py-1" sk-rellink-tooltip="{{ parents[-1].title|striptags }}">Up</a>
-          {%- else %}
-            <a href="#" role="button" class="btn sk-btn-rellink disabled py-1">Up</a>
-          {%- endif %}
-          {%- if next %}
-            <a href="{{ next.link|e }}" role="button" class="btn sk-btn-rellink py-1" sk-rellink-tooltip="{{ next.title|striptags }}">Next</a>
-          {%- else %}
-            <a href="#" role="button" class="btn sk-btn-rellink py-1 disabled"">Next</a>
-          {%- endif %}
-        </div>
-        {%- if pagename != "install" %}
-        <div class="alert alert-danger p-1 mb-2" role="alert">
-          <p class="text-center mb-0">
-          <strong>scikit-learn {{ release }}</strong><br/>
-          <a href="https://scikit-learn.org/dev/versions.html">Other versions</a>
-          </p>
-        </div>
-        {%- endif %}
-        <div class="alert alert-warning p-1 mb-2" role="alert">
-          <p class="text-center mb-0">
-            Please <a class="font-weight-bold" href="{{ pathto('about').replace('#', '') }}#citing-scikit-learn"><string>cite us</string></a> if you use the software.
-          </p>
-        </div>
-            {%- if meta and meta['parenttoc']|tobool %}
-            <div class="sk-sidebar-toc">
-            {% set nav = get_nav_object(maxdepth=3, collapse=True, numbered=True) %}
-              <ul>
-              {% for main_nav_item in nav %}
-              {% if main_nav_item.active %}
-              <li>
-                <a href="{{ main_nav_item.url }}" class="sk-toc-active">{{ main_nav_item.title }}</a>
-              </li>
-              <ul>
-              {% for nav_item in main_nav_item.children %}
-                <li>
-                  <a href="{{ nav_item.url }}" class="{% if nav_item.active %}sk-toc-active{% endif %}">{{ nav_item.title }}</a>
-                  {% if nav_item.children %}
-                  <ul>
-                    {% for inner_child in nav_item.children %}
-                      <li class="sk-toctree-l3">
-                        <a href="{{ inner_child.url }}">{{ inner_child.title }}</a>
-                      </li>
-                    {% endfor %}
-                  </ul>
-                  {% endif %}
-                </li>
-              {% endfor %}
-              </ul>
-              {% endif %}
-              {% endfor %}
-              </ul>
-            </div>
-            {%- elif meta and meta['globalsidebartoc']|tobool %}
-            <div class="sk-sidebar-toc sk-sidebar-global-toc">
-              {{ toctree(maxdepth=2, titles_only=True) }}
-            </div>
-            {%- else %}
-            <div class="sk-sidebar-toc">
-              {{ toc }}
-            </div>
-            {%- endif %}
-      </div>
-    </div>
-    <div id="sk-page-content-wrapper">
-      <div class="sk-page-content container-fluid body px-md-3" role="main">
-        {% block body %}{% endblock %}
-      </div>
-    <div class="container">
-      <footer class="sk-content-footer">
-        {%- if pagename != 'index' %}
-        {%- if show_copyright %}
-          {%- if hasdoc('copyright') %}
-            {% trans path=pathto('copyright'), copyright=copyright|e %}&copy; {{ copyright }}.{% endtrans %}
-          {%- else %}
-            {% trans copyright=copyright|e %}&copy; {{ copyright }}.{% endtrans %}
-          {%- endif %}
-        {%- endif %}
-        {%- if last_updated %}
-          {% trans last_updated=last_updated|e %}Last updated on {{ last_updated }}.{% endtrans %}
-        {%- endif %}
-        {%- if show_source and has_source and sourcename %}
-          <a href="{{ pathto('_sources/' + sourcename, true)|e }}" rel="nofollow">{{ _('Show this page source') }}</a>
-        {%- endif %}
-        {%- endif %}
-      </footer>
-    </div>
-  </div>
-</div>
-{%- endblock %}
-<script src="{{ pathto('_static/js/vendor/bootstrap.min.js', 1) }}"></script>
-{% include "javascript.html" %}
-</body>
-</html>
diff --git a/doc/themes/scikit-learn-modern/nav.html b/doc/themes/scikit-learn-modern/nav.html
deleted file mode 100644
index 14d82e2e46e95..0000000000000
--- a/doc/themes/scikit-learn-modern/nav.html
+++ /dev/null
@@ -1,102 +0,0 @@
-{%- if pagename != 'index' and pagename != 'documentation' %}
-  {%- set nav_bar_class = "sk-docs-navbar" %}
-  {%- set top_container_cls = "sk-docs-container" %}
-{%- else %}
-  {%- set nav_bar_class = "sk-landing-navbar" %}
-  {%- set top_container_cls = "sk-landing-container" %}
-{%- endif %}
-
-{% if theme_link_to_live_contributing_page|tobool %}
-{# Link to development page for live builds #}
-  {%- set development_link = "https://scikit-learn.org/dev/developers/index.html" %}
-{# Open on a new development page in new window/tab for live builds #}
-  {%- set development_attrs = 'target="_blank" rel="noopener noreferrer"' %}
-{%- else %}
-  {%- set development_link = pathto('developers/index') %}
-  {%- set development_attrs = '' %}
-{%- endif %}
-
-{# title, link, link_attrs #}
-{%- set drop_down_navigation = [
-  ('Getting Started', pathto('getting_started'), ''),
-  ('Tutorial', pathto('tutorial/index'), ''),
-  ("What's new", pathto('whats_new/v' + version), ''),
-  ('Glossary', pathto('glossary'), ''),
-  ('Development', development_link, development_attrs),
-  ('FAQ', pathto('faq'), ''),
-  ('Support', pathto('support'), ''),
-  ('Related packages', pathto('related_projects'), ''),
-  ('Roadmap', pathto('roadmap'), ''),
-  ('Governance', pathto('governance'), ''),
-  ('About us', pathto('about'), ''),
-  ('GitHub', 'https://github.com/scikit-learn/scikit-learn', ''),
-  ('Other Versions and Download', 'https://scikit-learn.org/dev/versions.html', '')]
--%}
-
-<nav id="navbar" class="{{ nav_bar_class }} navbar navbar-expand-md navbar-light bg-light py-0">
-  <div class="container-fluid {{ top_container_cls }} px-0">
-    {%- if logo_url %}
-      <a class="navbar-brand py-0" href="{{ pathto('index') }}">
-        <img
-          class="sk-brand-img"
-          src="{{ logo_url|e }}"
-          alt="logo"/>
-      </a>
-    {%- endif %}
-    <button
-      id="sk-navbar-toggler"
-      class="navbar-toggler"
-      type="button"
-      data-toggle="collapse"
-      data-target="#navbarSupportedContent"
-      aria-controls="navbarSupportedContent"
-      aria-expanded="false"
-      aria-label="Toggle navigation"
-    >
-      <span class="navbar-toggler-icon"></span>
-    </button>
-
-    <div class="sk-navbar-collapse collapse navbar-collapse" id="navbarSupportedContent">
-      <ul class="navbar-nav mr-auto">
-        <li class="nav-item">
-          <a class="sk-nav-link nav-link" href="{{ pathto('install') }}">Install</a>
-        </li>
-        <li class="nav-item">
-          <a class="sk-nav-link nav-link" href="{{ pathto('user_guide') }}">User Guide</a>
-        </li>
-        <li class="nav-item">
-          <a class="sk-nav-link nav-link" href="{{ pathto('modules/classes') }}">API</a>
-        </li>
-        <li class="nav-item">
-          <a class="sk-nav-link nav-link" href="{{ pathto('auto_examples/index') }}">Examples</a>
-        </li>
-        <li class="nav-item">
-          <a class="sk-nav-link nav-link" target="_blank" rel="noopener noreferrer" href="https://blog.scikit-learn.org/">Community</a>
-        </li>
-        {%- for title, link, link_attrs in drop_down_navigation %}
-        <li class="nav-item">
-          <a class="sk-nav-link nav-link nav-more-item-mobile-items" href="{{ link }}" {{ link_attrs }}>{{ title }}</a>
-        </li>
-        {%- endfor %}
-        <li class="nav-item dropdown nav-more-item-dropdown">
-          <a class="sk-nav-link nav-link dropdown-toggle" href="#" id="navbarDropdown" role="button" data-toggle="dropdown" aria-haspopup="true" aria-expanded="false">More</a>
-          <div class="dropdown-menu" aria-labelledby="navbarDropdown">
-            {%- for title, link, link_attrs in drop_down_navigation %}
-              <a class="sk-nav-dropdown-item dropdown-item" href="{{ link }}" {{ link_attrs }}>{{ title}}</a>
-            {%- endfor %}
-          </div>
-        </li>
-      </ul>
-      {%- if pagename != "search"%}
-      <div id="searchbox" role="search">
-          <div class="searchformwrapper">
-          <form class="search" action="{{ pathto('search') }}" method="get">
-            <input class="sk-search-text-input" type="text" name="q" aria-labelledby="searchlabel" />
-            <input class="sk-search-text-btn" type="submit" value="{{ _('Go') }}" />
-          </form>
-          </div>
-      </div>
-      {%- endif %}
-    </div>
-  </div>
-</nav>
diff --git a/doc/themes/scikit-learn-modern/search.html b/doc/themes/scikit-learn-modern/search.html
deleted file mode 100644
index 81e000bf9e5c4..0000000000000
--- a/doc/themes/scikit-learn-modern/search.html
+++ /dev/null
@@ -1,8 +0,0 @@
-{%- extends "basic/search.html" %}
-{% block extrahead %}
-  <script type="text/javascript" src="{{ pathto('searchindex.js', 1) }}" defer></script>
-  <script src="{{ pathto('_static/doctools.js', 1) }}"></script>
-  <script src="{{ pathto('_static/language_data.js', 1) }}"></script>
-  <script src="{{ pathto('_static/searchtools.js', 1) }}"></script>
-  <script src="{{ pathto('_static/sphinx_highlight.js', 1) }}"></script>
-{% endblock %}
diff --git a/doc/themes/scikit-learn-modern/static/css/theme.css b/doc/themes/scikit-learn-modern/static/css/theme.css
deleted file mode 100644
index bd447d88e0b3b..0000000000000
--- a/doc/themes/scikit-learn-modern/static/css/theme.css
+++ /dev/null
@@ -1,1412 +0,0 @@
-/* Elements */
-a {
-  color: #2878A2;
-  word-wrap: break-word;
-}
-
-a:focus {
-  outline: none;
-}
-
-/* Anchor links */
-
-a.headerlink {
-  color: #c60f0f;
-  font-size: 0.8em;
-  padding: 0 4px 0 4px;
-  text-decoration: none;
-  visibility: hidden;
-}
-
-a.headerlink:hover {
-  background-color: #c60f0f;
-  color: white;
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-  hyphens: auto;
-}
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-  color: #222;
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-  padding: 0.15rem;
-  word-break: normal;
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-  padding: 0;
-  white-space: nowrap;
-}
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-img {
-   max-width: 100%;
-}
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-    background-color: #fbe54e;
-}
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-div.highlight {
-  border: 1px solid #ddd;
-  margin-bottom: 1rem;
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-div.highlight pre {
-  padding: 0.2rem 0.5rem;
-  margin-bottom: 0;
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-.versionmodified {
-  font-style: italic;
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-  color: black;
-  cursor: pointer;
-  font-size: 1.1rem;
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-.sk-donate-btn {
-  cursor: pointer;
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-.sk-page-content div.logo {
-  float: left;
-  width: 200px;
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-@media screen and (min-width: 992px) {
-  .sk-page-content {
-    padding-left: 2rem!important;
-    padding-right: 2rem!important;
-  }
-}
-
-@media screen and (min-width: 1200px) {
-  .sk-px-xl-4 {
-    padding-left: 1.3rem!important;
-    padding-right: 1.3rem!important;
-  }
-}
-
-/* clearfix */
-
-div.clearer {
-  clear: both;
-}
-
-/* details / summary */
-
-/* Enables section links to be visible when anchor-linked */
-div.sk-page-content details::before {
-  display: block;
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-div.sk-page-content details {
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-div.sk-page-content summary.btn {
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-    padding: 0pt .5ex;
-    margin: 1ex 0pt;
-    border: 1px solid #e9ecef;
-    border-left-width: .25rem;
-    border-radius: .25rem;
-    background: rgb(250, 252, 253)
-}
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-div.sk-page-content summary {
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-div.sk-page-content summary .tooltiptext {
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-div.sk-page-content summary:hover .tooltiptext {
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-/* Button */
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-.sk-btn-primary {
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-.line-block {
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-.sk-search-text-input {
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-.sk-search-text-btn {
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-@media screen and (min-width: 768px) {
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-@media screen and (min-width: 886px) {
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-.navbar-light .navbar-nav a.nav-link, a.sk-dropdown-item  {
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-.navbar-light .navbar-nav a.nav-link:hover, a.sk-dropdown-item:hover {
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-.nav-more-item-mobile-items {
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-.nav-more-item-dropdown {
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-div.sk-landing-container {
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diff --git a/doc/themes/scikit-learn-modern/static/js/details-permalink.js b/doc/themes/scikit-learn-modern/static/js/details-permalink.js
deleted file mode 100644
index 62392e9836f64..0000000000000
--- a/doc/themes/scikit-learn-modern/static/js/details-permalink.js
+++ /dev/null
@@ -1,47 +0,0 @@
-// Function to create permalink into <details> elements to be able to link them
-// The assumption is that such a block will be defined as follows:
-//     <details id="summary-anchor">
-//     <summary class="btn btn-light">
-//     Some title
-//     <span class="tooltiptext">Click for more details</span>
-//     <a class="headerlink" href="#summary-anchor" title="Permalink to this heading">¶</a>
-//     </summary>
-//     <div class="card">
-//     Some details
-//     </div>
-//     </details>
-// We seek to replace `#summary-anchor` with a unique identifier based on the
-// summary text.
-// This syntax is defined in `doc/conf.py` in the `rst_prolog` variable.
-function updateIdAndHrefBasedOnSummaryText() {
-    var allDetailsElements = document.querySelectorAll('details');
-    // Counter to store the duplicated summary text to add it as a suffix in the
-    // anchor ID
-    var anchorIDCounters = {};
-
-    allDetailsElements.forEach(function (detailsElement) {
-        // Get the <summary> element within the current <details>
-        var summaryElement = detailsElement.querySelector('summary');
-
-        // The ID uses the first line, lowercased, and spaces replaced with dashes
-        var anchorID = summaryElement.textContent.trim().split("\n")[0].replace(/\s+/g, '-').toLowerCase();
-
-        // Suffix the anchor ID with a counter if it already exists
-        if (anchorIDCounters[anchorID]) {
-            anchorIDCounters[anchorID] += 1;
-            anchorID = anchorID + '-' + anchorIDCounters[anchorID];
-        } else {
-            anchorIDCounters[anchorID] = 1;
-        }
-
-        detailsElement.setAttribute('id', anchorID);
-
-        var anchorElement = summaryElement.querySelector('a.headerlink');
-        anchorElement.setAttribute('href', '#' + anchorID);
-    });
-}
-
-// Add an event listener to execute the function when the page is loaded
-document.addEventListener('DOMContentLoaded', function () {
-    updateIdAndHrefBasedOnSummaryText();
-});
diff --git a/doc/themes/scikit-learn-modern/static/js/vendor/bootstrap.min.js b/doc/themes/scikit-learn-modern/static/js/vendor/bootstrap.min.js
deleted file mode 100644
index 4955aeec1142c..0000000000000
--- a/doc/themes/scikit-learn-modern/static/js/vendor/bootstrap.min.js
+++ /dev/null
@@ -1,6 +0,0 @@
-/*!
-  * Bootstrap v4.3.1 (https://getbootstrap.com/)
-  * Copyright 2011-2019 The Bootstrap Authors (https://github.com/twbs/bootstrap/graphs/contributors)
-  * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)
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diff --git a/doc/themes/scikit-learn-modern/static/js/vendor/jquery-3.6.3.slim.min.js b/doc/themes/scikit-learn-modern/static/js/vendor/jquery-3.6.3.slim.min.js
deleted file mode 100644
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diff --git a/doc/themes/scikit-learn-modern/theme.conf b/doc/themes/scikit-learn-modern/theme.conf
deleted file mode 100644
index f86c74b1b1686..0000000000000
--- a/doc/themes/scikit-learn-modern/theme.conf
+++ /dev/null
@@ -1,10 +0,0 @@
-[theme]
-inherit = basic
-pygments_style = default
-stylesheet = css/theme.css
-
-[options]
-legacy_google_analytics = true
-analytics = true
-link_to_live_contributing_page = false
-mathjax_path =
diff --git a/doc/tune_toc.rst b/doc/tune_toc.rst
deleted file mode 100644
index 0310f0e59b4e4..0000000000000
--- a/doc/tune_toc.rst
+++ /dev/null
@@ -1,131 +0,0 @@
-.. raw:: html
-
-   <script>
-   window.addEventListener('DOMContentLoaded', function() {
-        (function($) {
-   //Function to make the index toctree collapsible
-   $(function () {
-       $('div.body .toctree-l2')
-           .click(function(event){
-               if (event.target.tagName.toLowerCase() != "a") {
-                   if ($(this).children('ul').length > 0) {
-                        $(this).attr('data-content',
-                            (!$(this).children('ul').is(':hidden')) ? '\u25ba' : '\u25bc');
-                       $(this).children('ul').toggle();
-                   }
-                   return true; //Makes links clickable
-               }
-           })
-           .mousedown(function(event){ return false; }) //Firefox highlighting fix
-           .children('ul').hide();
-       // Initialize the values
-       $('div.body li.toctree-l2:not(:has(ul))').attr('data-content', '-');
-       $('div.body li.toctree-l2:has(ul)').attr('data-content', '\u25ba');
-       $('div.body li.toctree-l2:has(ul)').css('cursor', 'pointer');
-
-       $('div.body .toctree-l2').hover(
-           function () {
-               if ($(this).children('ul').length > 0) {
-                   $(this).css('background-color', '#e5e5e5').children('ul').css('background-color', '#F0F0F0');
-                   $(this).attr('data-content',
-                       (!$(this).children('ul').is(':hidden')) ? '\u25bc' : '\u25ba');
-               }
-               else {
-                   $(this).css('background-color', '#F9F9F9');
-               }
-           },
-           function () {
-               $(this).css('background-color', 'white').children('ul').css('background-color', 'white');
-               if ($(this).children('ul').length > 0) {
-                   $(this).attr('data-content',
-                       (!$(this).children('ul').is(':hidden')) ? '\u25bc' : '\u25ba');
-               }
-           }
-       );
-   });
-        })(jQuery);
-    });
-   </script>
-
-  <style type="text/css">
-    div.body li, div.body ul {
-        transition-duration: 0.2s;
-    }
-
-    div.body li.toctree-l1 {
-        padding: 5px 0 0;
-        list-style-type: none;
-        font-size: 150%;
-        background-color: #f2f2f2;
-        font-weight: normal;
-        color: #20435c;
-        margin-left: 0;
-        margin-bottom: 1.2em;
-        font-weight: bold;
-        }
-
-    div.body li.toctree-l1 a {
-        color: #314F64;
-    }
-
-    div.body li.toctree-l1 > a {
-        margin-left: 0.75rem;
-    }
-
-    div.body li.toctree-l2 {
-        padding: 0.25em 0 0.25em 0 ;
-        list-style-type: none;
-        background-color: #FFFFFF;
-        font-size: 85% ;
-        font-weight: normal;
-        margin-left: 0;
-    }
-
-    div.body li.toctree-l2 ul {
-        padding-left: 40px ;
-    }
-
-    div.body li.toctree-l2:before {
-        content: attr(data-content);
-        font-size: 1rem;
-        color: #777;
-        display: inline-block;
-        width: 1.5rem;
-    }
-
-    div.body li.toctree-l3 {
-        font-size: 88% ;
-        list-style-type: square;
-        font-weight: normal;
-        margin-left: 0;
-    }
-
-    div.body li.toctree-l4 {
-        font-size: 93% ;
-        list-style-type: circle;
-        font-weight: normal;
-        margin-left: 0;
-    }
-
-    div.body div.topic li.toctree-l1 {
-        font-size: 100% ;
-        font-weight: bold;
-        background-color: transparent;
-        margin-bottom: 0;
-        margin-left: 1.5em;
-        display:inline;
-    }
-
-    div.body div.topic p {
-        font-size: 90% ;
-        margin: 0.4ex;
-    }
-
-    div.body div.topic p.topic-title {
-        display:inline;
-        font-size: 100% ;
-        margin-bottom: 0;
-    }
-  </style>
-
-
diff --git a/doc/tutorial/index.rst b/doc/tutorial/index.rst
index cfd63719321f2..bd4b8997f5f39 100644
--- a/doc/tutorial/index.rst
+++ b/doc/tutorial/index.rst
@@ -1,19 +1,9 @@
-.. Places global toc into the sidebar
-
-:globalsidebartoc: True
-
 .. _tutorial_menu:
 
-
-.. include:: ../includes/big_toc_css.rst
-.. include:: ../tune_toc.rst
-
 ======================
 scikit-learn Tutorials
 ======================
 
-|
-
 .. toctree::
    :maxdepth: 2
 
@@ -23,8 +13,6 @@ scikit-learn Tutorials
    machine_learning_map/index
    ../presentations
 
-|
-
 .. note:: **Doctest Mode**
 
    The code-examples in the above tutorials are written in a
diff --git a/doc/tutorial/machine_learning_map/ML_MAPS_README.txt b/doc/tutorial/machine_learning_map/ML_MAPS_README.txt
deleted file mode 100644
index 114ecb2d13f59..0000000000000
--- a/doc/tutorial/machine_learning_map/ML_MAPS_README.txt
+++ /dev/null
@@ -1,93 +0,0 @@
-Machine Learning Cheat Sheet (for scikit-learn)
-===============================================
-
-This document is intended to explain how to edit
-the machine learning cheat sheet, originally created
-by Andreas Mueller:
-
-(https://peekaboo-vision.blogspot.de/2013/01/machine-learning-cheat-sheet-for-scikit.html)
-
-The image is made interactive using an imagemap, and uses the jQuery Map Highlight plugin module
-by David Lynch (https://davidlynch.org/projects/maphilight/docs/) to highlight
-the different items on the image upon mouseover.
-
-Modifying the map on the docs is currently a little bit tedious,
-so I'll try to make it as simple as possible.
-
-1. Editing the layout of the map and its paths.
-------------------------------------------------
-
-Use a Graphics editor like Inkscape Vector Graphics Editor
-to open the ml_map.svg file, in this folder. From there
-you can move objects around, etc. as you need.
-
-Save when done, and make sure to export a .PNG file
-to replace the old-outdated ml_map.png, as that file
-is used as a background image.
-
-2. Accessing the paths of the SVG file and exporting them.
-----------------------------------------------------------
-
-Use an image manipulation package like GIMP Image Editor to open
-the ml_map.svg file, in this folder. With GIMP, make sure
-to select 'Import paths'.
-
-Once the image has been opened, you can see all imported paths on the paths tab.
-You can edit these or create new paths. In GIMP, right-clicking one of the
-paths and choosing: Path Tool will allow you to see the paths on
-the image. The paths will be exported later and will be used to
-make the click able regions on our image map.
-
-3. Export paths as SVG files
-----------------------------
-
-After you've edited a path or created a new one, right click it on
-the paths menu and choose 'Export Path..'. This way we extract just
-that path on its own as 'new_area.svg' for example.
-
-4. Edit the SVG file
----------------------
-Using a script made by David Lynch, we will convert the svg files into
-html maps. To do this, open the svg file in question in any text editor.
-Make sure that the 'width' and 'height' are not in 'in' or 'px', i.e
-"100" is OK, but "100px" or "1.25in" are not.
-
-Then wrap the <path> tags in <g> and </g> tags.
-Then the file is ready for the script.
-
-5. From SVG to HTML map
------------------------
-
-Use the provided svg2imagemap.py script on your edited svg file:
-
-$ python svg2imagemap.py new_area.svg
-
-where new_area.svg is our file.
-
-6. Add the new map to the main html file
-------------------------------------------
-
-Copy the code from the newly created 'new_area.html'
-file. Open the ml_map.html file.
-
-Add the <area href=....... ></area> that you copied
-after the last </area> tag in the ml_map.html file.
-
-Add the link address to 'href' and a tooltip to
-'title' within your <area ...> tag.
-
-If you wish to add the green and blue hover effect
-to the area, add
-data-maphilight='{"strokeColor":"0000ff","strokeWidth":5,"fillColor":"66FF66","fillOpacity":0.4}'
-
-to your  area tag, as done in the other <area..> tags above.
-
-Save the file, and you're done.
-
------------------------------------------------------
-
-I'll take some time to make some scripts to automate this process
-a bit more at some point, as it is not difficult to do,
-but tedious.
-
--Jaques Grobler
diff --git a/doc/tutorial/machine_learning_map/README.md b/doc/tutorial/machine_learning_map/README.md
new file mode 100644
index 0000000000000..8d82c175dad58
--- /dev/null
+++ b/doc/tutorial/machine_learning_map/README.md
@@ -0,0 +1,20 @@
+The scikit-learn machine learning cheat sheet was originally created by Andreas Mueller:
+https://peekaboo-vision.blogspot.de/2013/01/machine-learning-cheat-sheet-for-scikit.html
+
+The current version of the chart is located at `doc/images/ml_map.svg` in SVG+XML
+format, created using [draw.io](https://draw.io/). To edit the chart, open the file in
+draw.io, make changes, and save. This should update the chart in-place. Another option
+would be to re-export the chart as SVG and replace the existing file. The options used
+for exporting the chart are:
+
+- Zoom: 100%
+- Border width: 15
+- Size: Diagram
+- Transparent Background: False
+- Appearance: Light
+
+Each node in the chart that contains an estimator should have a link, where the root
+directory is at `../../`. Note that after updating or re-exporting the SVG, the links
+may be prefixed with e.g. `https://app.diagrams.net/`. Remember to check and remove
+them, for instance by replacing all occurrences of `https://app.diagrams.net/../../`
+with `../../`.
diff --git a/doc/tutorial/machine_learning_map/index.rst b/doc/tutorial/machine_learning_map/index.rst
index 708f8bc43bf73..e3d6531d05bd5 100644
--- a/doc/tutorial/machine_learning_map/index.rst
+++ b/doc/tutorial/machine_learning_map/index.rst
@@ -1,55 +1,76 @@
+:html_theme.sidebar_secondary.remove:
+
 .. _ml_map:
 
+Choosing the right estimator
+============================
+
+Often the hardest part of solving a machine learning problem can be finding the right
+estimator for the job. Different estimators are better suited for different types of
+data and different problems.
 
-.. include:: ../../includes/big_toc_css.rst
+The flowchart below is designed to give users a bit of a rough guide on how to approach
+problems with regard to which estimators to try on your data. Click on any estimator in
+the chart below to see its documentation. The 😭 emoji is to be read as "if this
+estimator does not achieve the desired outcome, then follow the arrow and try the next
+one". Use scroll wheel to zoom in and out, and click and drag to pan around. You can
+also download the chart: :download:`ml_map.svg <../../images/ml_map.svg>`.
 
-Choosing the right estimator
-=======================================================
+.. raw:: html
+
+  <style>
+    #sk-ml-map {
+      height: 80vh;
+      margin: 1.5rem 0;
+    }
 
+    #sk-ml-map svg {
+      height: 100%;
+      width: 100%;
+      border: 2px solid var(--pst-color-border);
+      border-radius: 0.5rem;
+    }
 
-Often the hardest part of solving a machine learning problem can
-be finding the right estimator for the job.
+    html[data-theme="dark"] #sk-ml-map svg {
+      filter: invert(90%) hue-rotate(180deg);
+    }
+  </style>
 
-Different estimators are better suited for different types of data
-and different problems.
+  <script src="../../_static/scripts/vendor/svg-pan-zoom.min.js"></script>
+  <script>
+    document.addEventListener("DOMContentLoaded", function () {
+      const beforePan = function (oldPan, newPan) {
+        const gutterWidth = 100, gutterHeight = 100;
+        const sizes = this.getSizes();
 
-The flowchart below is designed to give users a bit of
-a rough guide on how to approach problems with regard to
-which estimators to try on your data.
+        // Compute pan limits
+        const leftLimit = -((sizes.viewBox.x + sizes.viewBox.width) * sizes.realZoom) + gutterWidth;
+        const rightLimit = sizes.width - gutterWidth - (sizes.viewBox.x * sizes.realZoom);
+        const topLimit = -((sizes.viewBox.y + sizes.viewBox.height) * sizes.realZoom) + gutterHeight;
+        const bottomLimit = sizes.height - gutterHeight - (sizes.viewBox.y * sizes.realZoom);
 
-Click on any estimator in the chart below to see its documentation.
+        return {
+          x: Math.max(leftLimit, Math.min(rightLimit, newPan.x)),
+          y: Math.max(topLimit, Math.min(bottomLimit, newPan.y))
+        };
+      };
 
+      // Limit the pan
+      svgPanZoom("#sk-ml-map svg", {
+        zoomEnabled: true,
+        controlIconsEnabled: true,
+        fit: 1,
+        center: 1,
+        beforePan: beforePan,
+      });
+    });
+  </script>
 
+  <div id="sk-ml-map">
+
+.. raw:: html
+  :file: ../../images/ml_map.svg
 
 .. raw:: html
 
-        <img src="../../_static/ml_map.png" class="map" alt="Move mouse over image" usemap="#imgmap">
-      	    <map name="imgmap">
-	    	<area href="../../documentation.html" title="Back to Documentation" shape="poly" coords="97,1094, 76,1097, 56,1105, 40,1120, 35,1132, 34,1145, 35,1153, 40,1162, 46,1171, 54,1177, 62,1182, 72,1187, 81,1188, 100,1189, 118,1186, 127,1182, 136,1177, 146,1170, 152,1162, 155,1158, 158,1146, 158,1126, 143,1110, 138,1105, 127,1100, 97,1094"></area>
-		<area href="../../modules/linear_model.html#elastic-net" title="Elastic Net Documentation" shape="poly" coords="1556,446, 1556,446, 1556,476, 1556,476, 1556,476, 1676,476, 1676,476, 1676,476, 1676,446, 1676,446, 1676,446, 1556,446, 1556,446" data-maphilight='{"strokeColor":"0000ff","strokeWidth":5,"fillColor":"66FF66","fillOpacity":0.4}'></area>
-		<area href="../../modules/ensemble.html" title="Ensemble Methods Documentation" shape="poly" coords="209,200, 209,200, 209,252, 209,252, 209,252, 332,252, 332,252, 332,252, 332,200, 332,200, 332,200, 209,200, 209,200" data-maphilight='{"strokeColor":"0000ff","strokeWidth":5,"fillColor":"66FF66","fillOpacity":0.4}'></area>
-		<area href="../../modules/ensemble.html" title="Ensemble Methods Documentation" shape="poly" coords="1828,506, 1828,506, 1828,544, 1828,544, 1828,544, 2054,544, 2054,544, 2054,544, 2054,506, 2054,506, 2054,506, 1828,506, 1828,506" data-maphilight='{"strokeColor":"0000ff","strokeWidth":5,"fillColor":"66FF66","fillOpacity":0.4}'></area>
-		<area href="../../modules/mixture.html" title="Gaussian mixture models Documentation" shape="poly" coords="142,637, 142,637, 142,667, 142,667, 142,667, 265,667, 265,667, 265,667, 265,637, 265,637, 265,637, 142,637, 142,637" data-maphilight='{"strokeColor":"0000ff","strokeWidth":5,"fillColor":"66FF66","fillOpacity":0.4}'></area>
-		<area href="../../modules/manifold.html#isomap" title="Isomap Documentation" shape="poly" coords="1500,799, 1500,799, 1500,844, 1500,844, 1500,844, 1618,844, 1618,844, 1618,844, 1618,800, 1618,800, 1618,800, 1500,799, 1500,799" data-maphilight='{"strokeColor":"0000ff","strokeWidth":5,"fillColor":"66FF66","fillOpacity":0.4}'></area>
-		<area href="../../modules/kernel_approximation.html" title="Kernel Approximation Documentation" shape="poly" coords="1477,982, 1477,982, 1477,1055, 1477,1055, 1477,1055, 1638,1055, 1638,1055, 1638,1055, 1638,982, 1638,982, 1638,982, 1477,982, 1477,982" data-maphilight='{"strokeColor":"0000ff","strokeWidth":5,"fillColor":"66FF66","fillOpacity":0.4}'></area>
-		<area href="../../modules/kernel_approximation.html" title="Kernel Approximation Documentation" shape="poly" coords="472,100, 472,100, 472,173, 472,173, 472,173, 634,173, 634,173, 634,173, 634,100, 634,100, 634,100, 472,100, 472,100" data-maphilight='{"strokeColor":"0000ff","strokeWidth":5,"fillColor":"66FF66","fillOpacity":0.4}'></area>
-		<area href="../../modules/clustering.html#k-means" title="KMeans Documentation" shape="poly" coords="377,605, 377,605, 377,655, 377,655, 377,655, 476,655, 476,655, 476,655, 476,605, 476,605, 476,605, 377,605, 377,605" data-maphilight='{"strokeColor":"0000ff","strokeWidth":5,"fillColor":"66FF66","fillOpacity":0.4}'></area>
-		<area href="../../modules/neighbors.html" title="Nearest Neighbors" shape="poly" coords="440,219, 440,219, 440,293, 440,293, 440,293, 574,293, 574,293, 574,293, 574,219, 574,219, 574,219, 440,219, 440,219" data-maphilight='{"strokeColor":"0000ff","strokeWidth":5,"fillColor":"66FF66","fillOpacity":0.4}'></area>
-		<area href="../../modules/linear_model.html#lasso" title="Lasso Documentation" shape="poly" coords="1550,408, 1550,408, 1550,436, 1550,436, 1550,436, 1671,436, 1671,436, 1671,436, 1671,408, 1671,408, 1671,408, 1550,408, 1550,408" data-maphilight='{"strokeColor":"0000ff","strokeWidth":5,"fillColor":"66FF66","fillOpacity":0.4}'></area>
-		<area href="../../modules/svm.html#classification" title="LinearSVC Documentation" shape="poly" coords="609,419, 609,419, 609,492, 609,492, 609,492, 693,492, 693,492, 693,492, 693,419, 693,419, 693,419, 609,419, 609,419" data-maphilight='{"strokeColor":"0000ff","strokeWidth":5,"fillColor":"66FF66","fillOpacity":0.4}'></area>
-		<area href="../../modules/manifold.html#locally-linear-embedding" title="Locally Linear Embedding Documentation" shape="poly" coords="1719,888, 1719,888, 1719,945, 1719,945, 1719,945, 1819,945, 1819,945, 1819,945, 1819,888, 1819,888, 1819,888, 1719,888, 1719,888" data-maphilight='{"strokeColor":"0000ff","strokeWidth":5,"fillColor":"66FF66","fillOpacity":0.4}'></area>
-		<area href="../../modules/clustering.html#mean-shift" title="Mean Shift Documentation" shape="poly" coords="562,949, 562,949, 562,981, 562,981, 562,981, 682,981, 682,981, 682,981, 682,949, 682,949, 682,949, 562,949, 562,949" data-maphilight='{"strokeColor":"0000ff","strokeWidth":5,"fillColor":"66FF66","fillOpacity":0.4}'></area>
-		<area href="../../modules/clustering.html#mini-batch-k-means" title="Mini Batch K-means Documentation" shape="poly" coords="343,917, 343,917, 343,990, 343,990, 343,990, 461,990, 461,990, 461,990, 461,917, 461,917, 461,917, 343,917, 343,917" data-maphilight='{"strokeColor":"0000ff","strokeWidth":5,"fillColor":"66FF66","fillOpacity":0.4}'></area>
-		<area href="../../modules/naive_bayes.html" title="Naive Bayes Documentation" shape="poly" coords="194,339, 194,339, 194,412, 194,412, 194,412, 294,412, 294,412, 294,412, 294,339, 294,339, 294,339, 194,339, 194,339" data-maphilight='{"strokeColor":"0000ff","strokeWidth":5,"fillColor":"66FF66","fillOpacity":0.4}'></area>
-		<area href="../../modules/decomposition.html#principal-component-analysis-pca" title="Principal Component Analysis Documentation" shape="poly" coords="1208,778, 1208,778, 1208,851, 1208,851, 1208,851, 1350,851, 1350,851, 1350,851, 1350,778, 1350,778, 1350,778, 1208,778, 1208,778" data-maphilight='{"strokeColor":"0000ff","strokeWidth":5,"fillColor":"66FF66","fillOpacity":0.4}'></area>
-		<area href="../../modules/linear_model.html#ridge-regression" title="Ridge Regression Documentation" shape="poly" coords="1696,648, 1696,648, 1696,687, 1696,687, 1696,687, 1890,687, 1890,687, 1890,687, 1890,648, 1890,648, 1890,648, 1696,648, 1696,648" data-maphilight='{"strokeColor":"0000ff","strokeWidth":5,"fillColor":"66FF66","fillOpacity":0.4}'></area>
-		<area href="../../modules/sgd.html#classification" title="SGD Classifier Documentation" shape="poly" coords="691,205, 691,205, 691,278, 691,278, 691,278, 803,278, 803,278, 803,278, 803,205, 803,205, 803,205, 691,205, 691,205" data-maphilight='{"strokeColor":"0000ff","strokeWidth":5,"fillColor":"66FF66","fillOpacity":0.4}'></area>
-		<area href="../../modules/sgd.html#regression" title="SGD Regression Documentation" shape="poly" coords="1317,425, 1317,425, 1317,498, 1317,498, 1317,498, 1436,498, 1436,498, 1436,498, 1436,425, 1436,425, 1436,425, 1317,425, 1317,425" data-maphilight='{"strokeColor":"0000ff","strokeWidth":5,"fillColor":"66FF66","fillOpacity":0.4}'></area>
-		<area href="../../modules/clustering.html#spectral-clustering" title="Spectral Clustering Documentation" shape="poly" coords="145,572, 145,572, 145,631, 145,631, 145,631, 267,631, 267,631, 267,631, 267,572, 267,572, 267,572, 145,572, 145,572" data-maphilight='{"strokeColor":"0000ff","strokeWidth":5,"fillColor":"66FF66","fillOpacity":0.4}'></area>
-		<area href="../../modules/manifold.html#spectral-embedding" title="Spectral Embedding Documentation" shape="poly" coords="1502,849, 1502,849, 1502,910, 1502,910, 1502,910, 1618,910, 1618,910, 1618,910, 1618,849, 1618,849, 1618,849, 1502,849, 1502,849" data-maphilight='{"strokeColor":"0000ff","strokeWidth":5,"fillColor":"66FF66","fillOpacity":0.4}'></area>
-		<area href="../../modules/svm.html#classification" title="SVC Documentation" shape="poly" coords="210,157, 210,157, 210,194, 210,194, 210,194, 333,194, 333,194, 333,194, 333,157, 333,157, 333,157, 210,157, 210,157" data-maphilight='{"strokeColor":"0000ff","strokeWidth":5,"fillColor":"66FF66","fillOpacity":0.4}'></area>
-		<area href="../../modules/svm.html#regression" title="SVR Documentation" shape="poly" coords="1696,692, 1696,692, 1696,732, 1696,732, 1696,732, 1890,732, 1890,732, 1890,732, 1890,692, 1890,692, 1890,692, 1696,692, 1696,692" data-maphilight='{"strokeColor":"0000ff","strokeWidth":5,"fillColor":"66FF66","fillOpacity":0.4}'></area>
-		<area href="../../modules/svm.html#regression" title="SVR Documentation" shape="poly" coords="1831,458, 1831,458, 1831,496, 1831,496, 1831,496, 2052,496, 2052,496, 2052,496, 2052,458, 2052,458, 2052,458, 1831,458, 1831,458" data-maphilight='{"strokeColor":"0000ff","strokeWidth":5,"fillColor":"66FF66","fillOpacity":0.4}'></area>
-		<area href="../../modules/mixture.html#bgmm" title=" Bayesian GMM Documentation" shape="poly" coords="562,994, 562,994, 562,1026, 562,1026, 562,1026, 682,1026, 682,1026, 682,1026, 682,994, 682,994, 682,994, 562,994, 562,994" data-maphilight='{"strokeColor":"0000ff","strokeWidth":5,"fillColor":"66FF66","fillOpacity":0.4}'></area>
-	    </map>
-	</img>
+  </div>
diff --git a/doc/tutorial/machine_learning_map/parse_path.py b/doc/tutorial/machine_learning_map/parse_path.py
deleted file mode 100644
index b1c68cec7f76b..0000000000000
--- a/doc/tutorial/machine_learning_map/parse_path.py
+++ /dev/null
@@ -1,192 +0,0 @@
-#!/usr/local/bin/python
-
-"""
-Based on: http://wxpsvg.googlecode.com/svn/trunk/svg/pathdata.py
-According to that project, this file is licensed under the LGPL
-"""
-
-try:
-    from pyparsing import (ParserElement, Literal, Word, CaselessLiteral, 
-        Optional, Combine, Forward, ZeroOrMore, nums, oneOf, Group, ParseException, OneOrMore)
-except ImportError:
-    import sys
-    sys.exit("pyparsing is required")
-    
-    
-#ParserElement.enablePackrat()
-
-def Command(char):
-    """ Case insensitive but case preserving"""
-    return CaselessPreservingLiteral(char)
-    
-def Arguments(token):
-    return Group(token)
-    
-    
-class CaselessPreservingLiteral(CaselessLiteral):
-    """ Like CaselessLiteral, but returns the match as found
-        instead of as defined.
-    """
-    def __init__( self, matchString ):
-        super().__init__(matchString.upper())
-        self.name = "'%s'" % matchString
-        self.errmsg = "Expected " + self.name
-        self.myException.msg = self.errmsg
-
-    def parseImpl( self, instring, loc, doActions=True ):
-        test = instring[ loc:loc+self.matchLen ]
-        if test.upper() == self.match:
-            return loc+self.matchLen, test
-        #~ raise ParseException( instring, loc, self.errmsg )
-        exc = self.myException
-        exc.loc = loc
-        exc.pstr = instring
-        raise exc   
-    
-def Sequence(token):
-    """ A sequence of the token"""
-    return OneOrMore(token+maybeComma)
-
-digit_sequence = Word(nums)
-
-sign = oneOf("+ -")
-
-def convertToFloat(s, loc, toks):
-    try:
-        return float(toks[0])
-    except BaseException as e:
-        raise ParseException(loc, "invalid float format %s" % toks[0]) from e
-
-exponent = CaselessLiteral("e")+Optional(sign)+Word(nums)
-
-#note that almost all these fields are optional, 
-#and this can match almost anything. We rely on Pythons built-in
-#float() function to clear out invalid values - loosely matching like this
-#speeds up parsing quite a lot
-floatingPointConstant = Combine(
-    Optional(sign) + 
-    Optional(Word(nums)) + 
-    Optional(Literal(".") + Optional(Word(nums)))+
-    Optional(exponent)
-)
-
-floatingPointConstant.setParseAction(convertToFloat)
-
-number = floatingPointConstant
-
-#same as FP constant but don't allow a - sign
-nonnegativeNumber = Combine(
-    Optional(Word(nums)) + 
-    Optional(Literal(".") + Optional(Word(nums)))+
-    Optional(exponent)
-)
-nonnegativeNumber.setParseAction(convertToFloat)
-
-coordinate = number
-
-#comma or whitespace can separate values all over the place in SVG
-maybeComma = Optional(Literal(',')).suppress()
-
-coordinateSequence = Sequence(coordinate)
-
-coordinatePair = (coordinate + maybeComma + coordinate).setParseAction(tuple)
-coordinatePairSequence = Sequence(coordinatePair)
-
-coordinatePairPair = coordinatePair + maybeComma + coordinatePair
-coordinatePairPairSequence = Sequence(Group(coordinatePairPair))
-
-coordinatePairTriple = coordinatePair + maybeComma + coordinatePair + maybeComma + coordinatePair
-coordinatePairTripleSequence = Sequence(Group(coordinatePairTriple))
-
-#commands
-lineTo = Group(Command("L") + Arguments(coordinatePairSequence))
-curve = Group(Command("C") + Arguments(coordinatePairSequence))
-
-moveTo = Group(Command("M") + Arguments(coordinatePairSequence))
-
-closePath = Group(Command("Z")).setParseAction(lambda t: ('Z', (None,)))
-
-flag = oneOf("1 0").setParseAction(lambda t: bool(int((t[0]))))
-
-arcRadius = (
-    nonnegativeNumber + maybeComma + #rx
-    nonnegativeNumber #ry
-).setParseAction(tuple)
-
-arcFlags = (flag + maybeComma + flag).setParseAction(tuple)
-
-ellipticalArcArgument = Group(
-    arcRadius + maybeComma + #rx, ry
-    number + maybeComma +#rotation
-    arcFlags + #large-arc-flag, sweep-flag
-    coordinatePair #(x,y)
-)
-
-ellipticalArc = Group(Command("A") + Arguments(Sequence(ellipticalArcArgument)))
-
-smoothQuadraticBezierCurveto = Group(Command("T") + Arguments(coordinatePairSequence))
-
-quadraticBezierCurveto = Group(Command("Q") + Arguments(coordinatePairPairSequence))
-
-smoothCurve = Group(Command("S") + Arguments(coordinatePairPairSequence))
-
-#curve = Group(Command("C") + Arguments(coordinatePairTripleSequence))
-
-horizontalLine = Group(Command("H") + Arguments(coordinateSequence))
-verticalLine = Group(Command("V") + Arguments(coordinateSequence))
-
-drawToCommand = (
-    lineTo | moveTo | closePath | ellipticalArc | smoothQuadraticBezierCurveto |
-    quadraticBezierCurveto | smoothCurve | curve | horizontalLine | verticalLine
-    )
-
-#~ number.debug = True
-moveToDrawToCommands = moveTo + ZeroOrMore(drawToCommand)
-
-path = ZeroOrMore(moveToDrawToCommands)
-path.keepTabs = True
-
-def get_points(d):
-    commands = path.parseString(d)
-    points = []
-    currentset = None
-    for command in commands:
-        if command[0] == 'M' or command[0] == 'm':
-            currentset = []
-            points.append(currentset)
-            currentset.append(command[1][-1])
-        elif command[0] == 'L' or command[0] == 'l':
-            currentset.extend(command[1])
-        elif command[0] == 'C' or command[0] == 'c':
-            currentset.extend(command[1])
-    return points
-
-if __name__ == "__main__":
-    s = ("M 242.96145,653.59282 L 244.83646,650.1553 L 247.02397,649.8428 "
-         "L 247.33647,650.62405 L 245.30521,653.59282 L 242.96145,653.59282 z "
-         "M 252.80525,649.99905 L 258.74278,652.49906 L 260.77404,652.18656 "
-         "L 262.33654,648.43654 L 261.71154,645.15528 L 257.64902,644.68653 "
-         "L 253.74275,646.40528 L 252.80525,649.99905 z M 282.49289,659.6866 "
-         "L 286.08665,664.99912 L 288.43041,664.68662 L 289.52417,664.21787 "
-         "L 290.93042,665.46787 L 294.52419,665.31162 L 295.4617,663.90537 "
-         "L 292.64918,662.18661 L 290.77417,658.59284 L 288.74291,655.15533 "
-         "L 283.11789,657.96784 L 282.49289,659.6866 z M 302.02423,668.28039 "
-         "L 303.27423,666.40538 L 307.8055,667.34288 L 308.43051,666.87413 "
-         "L 314.36803,667.49913 L 314.05553,668.74914 L 311.55552,670.15539 "
-         "L 307.33675,669.84289 L 302.02423,668.28039 z M 307.1805,673.28041 "
-         "L 309.05551,677.03043 L 312.02427,675.93667 L 312.33677,674.37416 "
-         "L 310.77427,672.3429 L 307.1805,672.0304 L 307.1805,673.28041 z "
-         "M 313.89928,672.18665 L 316.08679,669.37414 L 320.61806,671.7179 "
-         "L 324.83683,672.81166 L 329.0556,675.46792 L 329.0556,677.34293 "
-         "L 325.61809,679.06169 L 320.93056,679.99919 L 318.5868,678.59293 "
-         "L 313.89928,672.18665 z M 329.99311,687.18672 L 331.55561,685.93672 "
-         "L 334.83688,687.49923 L 342.18066,690.93674 L 345.46193,692.968 "
-         "L 347.02443,695.31176 L 348.89944,699.53053 L 352.80571,702.03054 "
-         "L 352.49321,703.28055 L 348.74319,706.40556 L 344.68067,707.81182 "
-         "L 343.27442,707.18682 L 340.30565,708.90557 L 337.96189,712.03059 "
-         "L 335.77438,714.8431 L 334.05562,714.68685 L 330.61811,712.18684 "
-         "L 330.30561,707.81182 L 330.93061,705.46806 L 329.3681,699.99928 "
-         "L 327.33684,698.28052 L 327.18059,695.78051 L 329.3681,694.84301 "
-         "L 331.39936,691.87425 L 331.86811,690.93674 L 330.30561,689.21798 "
-         "L 329.99311,687.18672 z ")
-    print(path.parseString(s))
diff --git a/doc/tutorial/machine_learning_map/pyparsing.py b/doc/tutorial/machine_learning_map/pyparsing.py
deleted file mode 100644
index 88d00e138d02c..0000000000000
--- a/doc/tutorial/machine_learning_map/pyparsing.py
+++ /dev/null
@@ -1,5715 +0,0 @@
-# module pyparsing.py
-#
-# Copyright (c) 2003-2016  Paul T. McGuire
-#
-# Permission is hereby granted, free of charge, to any person obtaining
-# a copy of this software and associated documentation files (the
-# "Software"), to deal in the Software without restriction, including
-# without limitation the rights to use, copy, modify, merge, publish,
-# distribute, sublicense, and/or sell copies of the Software, and to
-# permit persons to whom the Software is furnished to do so, subject to
-# the following conditions:
-#
-# The above copyright notice and this permission notice shall be
-# included in all copies or substantial portions of the Software.
-#
-# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
-# EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
-# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
-# IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY
-# CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
-# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
-# SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
-#
-# ruff: noqa
-
-__doc__ = \
-"""
-pyparsing module - Classes and methods to define and execute parsing grammars
-
-The pyparsing module is an alternative approach to creating and executing simple grammars,
-vs. the traditional lex/yacc approach, or the use of regular expressions.  With pyparsing, you
-don't need to learn a new syntax for defining grammars or matching expressions - the parsing module
-provides a library of classes that you use to construct the grammar directly in Python.
-
-Here is a program to parse "Hello, World!" (or any greeting of the form 
-C{"<salutation>, <addressee>!"}), built up using L{Word}, L{Literal}, and L{And} elements 
-(L{'+'<ParserElement.__add__>} operator gives L{And} expressions, strings are auto-converted to
-L{Literal} expressions)::
-
-    from pyparsing import Word, alphas
-
-    # define grammar of a greeting
-    greet = Word(alphas) + "," + Word(alphas) + "!"
-
-    hello = "Hello, World!"
-    print (hello, "->", greet.parseString(hello))
-
-The program outputs the following::
-
-    Hello, World! -> ['Hello', ',', 'World', '!']
-
-The Python representation of the grammar is quite readable, owing to the self-explanatory
-class names, and the use of '+', '|' and '^' operators.
-
-The L{ParseResults} object returned from L{ParserElement.parseString<ParserElement.parseString>} can be accessed as a nested list, a dictionary, or an
-object with named attributes.
-
-The pyparsing module handles some of the problems that are typically vexing when writing text parsers:
- - extra or missing whitespace (the above program will also handle "Hello,World!", "Hello  ,  World  !", etc.)
- - quoted strings
- - embedded comments
-"""
-
-__version__ = "2.2.0"
-__versionTime__ = "06 Mar 2017 02:06 UTC"
-__author__ = "Paul McGuire <ptmcg@users.sourceforge.net>"
-
-import string
-from weakref import ref as wkref
-import copy
-import sys
-import warnings
-import re
-import sre_constants
-import collections
-import pprint
-import traceback
-import types
-from datetime import datetime
-
-try:
-    from _thread import RLock
-except ImportError:
-    from threading import RLock
-
-try:
-    from collections import OrderedDict as _OrderedDict
-except ImportError:
-    try:
-        from ordereddict import OrderedDict as _OrderedDict
-    except ImportError:
-        _OrderedDict = None
-
-#~ sys.stderr.write( "testing pyparsing module, version %s, %s\n" % (__version__,__versionTime__ ) )
-
-__all__ = [
-'And', 'CaselessKeyword', 'CaselessLiteral', 'CharsNotIn', 'Combine', 'Dict', 'Each', 'Empty',
-'FollowedBy', 'Forward', 'GoToColumn', 'Group', 'Keyword', 'LineEnd', 'LineStart', 'Literal',
-'MatchFirst', 'NoMatch', 'NotAny', 'OneOrMore', 'OnlyOnce', 'Optional', 'Or',
-'ParseBaseException', 'ParseElementEnhance', 'ParseException', 'ParseExpression', 'ParseFatalException',
-'ParseResults', 'ParseSyntaxException', 'ParserElement', 'QuotedString', 'RecursiveGrammarException',
-'Regex', 'SkipTo', 'StringEnd', 'StringStart', 'Suppress', 'Token', 'TokenConverter', 
-'White', 'Word', 'WordEnd', 'WordStart', 'ZeroOrMore',
-'alphanums', 'alphas', 'alphas8bit', 'anyCloseTag', 'anyOpenTag', 'cStyleComment', 'col',
-'commaSeparatedList', 'commonHTMLEntity', 'countedArray', 'cppStyleComment', 'dblQuotedString',
-'dblSlashComment', 'delimitedList', 'dictOf', 'downcaseTokens', 'empty', 'hexnums',
-'htmlComment', 'javaStyleComment', 'line', 'lineEnd', 'lineStart', 'lineno',
-'makeHTMLTags', 'makeXMLTags', 'matchOnlyAtCol', 'matchPreviousExpr', 'matchPreviousLiteral',
-'nestedExpr', 'nullDebugAction', 'nums', 'oneOf', 'opAssoc', 'operatorPrecedence', 'printables',
-'punc8bit', 'pythonStyleComment', 'quotedString', 'removeQuotes', 'replaceHTMLEntity', 
-'replaceWith', 'restOfLine', 'sglQuotedString', 'srange', 'stringEnd',
-'stringStart', 'traceParseAction', 'unicodeString', 'upcaseTokens', 'withAttribute',
-'indentedBlock', 'originalTextFor', 'ungroup', 'infixNotation','locatedExpr', 'withClass',
-'CloseMatch', 'tokenMap', 'pyparsing_common',
-]
-
-system_version = tuple(sys.version_info)[:3]
-PY_3 = system_version[0] == 3
-if PY_3:
-    _MAX_INT = sys.maxsize
-    basestring = str
-    unichr = chr
-    _ustr = str
-
-    # build list of single arg builtins, that can be used as parse actions
-    singleArgBuiltins = [sum, len, sorted, reversed, list, tuple, set, any, all, min, max]
-
-else:
-    _MAX_INT = sys.maxint
-    range = xrange
-
-    def _ustr(obj):
-        """Drop-in replacement for str(obj) that tries to be Unicode friendly. It first tries
-           str(obj). If that fails with a UnicodeEncodeError, then it tries unicode(obj). It
-           then < returns the unicode object | encodes it with the default encoding | ... >.
-        """
-        if isinstance(obj,unicode):
-            return obj
-
-        try:
-            # If this works, then _ustr(obj) has the same behaviour as str(obj), so
-            # it won't break any existing code.
-            return str(obj)
-
-        except UnicodeEncodeError:
-            # Else encode it
-            ret = unicode(obj).encode(sys.getdefaultencoding(), 'xmlcharrefreplace')
-            xmlcharref = Regex(r'&#\d+;')
-            xmlcharref.setParseAction(lambda t: '\\u' + hex(int(t[0][2:-1]))[2:])
-            return xmlcharref.transformString(ret)
-
-    # build list of single arg builtins, tolerant of Python version, that can be used as parse actions
-    singleArgBuiltins = []
-    import __builtin__
-    for fname in "sum len sorted reversed list tuple set any all min max".split():
-        try:
-            singleArgBuiltins.append(getattr(__builtin__,fname))
-        except AttributeError:
-            continue
-            
-_generatorType = type((y for y in range(1)))
- 
-def _xml_escape(data):
-    """Escape &, <, >, ", ', etc. in a string of data."""
-
-    # ampersand must be replaced first
-    from_symbols = '&><"\''
-    to_symbols = ('&'+s+';' for s in "amp gt lt quot apos".split())
-    for from_,to_ in zip(from_symbols, to_symbols):
-        data = data.replace(from_, to_)
-    return data
-
-class _Constants(object):
-    pass
-
-alphas     = string.ascii_uppercase + string.ascii_lowercase
-nums       = "0123456789"
-hexnums    = nums + "ABCDEFabcdef"
-alphanums  = alphas + nums
-_bslash    = chr(92)
-printables = "".join(c for c in string.printable if c not in string.whitespace)
-
-class ParseBaseException(Exception):
-    """base exception class for all parsing runtime exceptions"""
-    # Performance tuning: we construct a *lot* of these, so keep this
-    # constructor as small and fast as possible
-    def __init__( self, pstr, loc=0, msg=None, elem=None ):
-        self.loc = loc
-        if msg is None:
-            self.msg = pstr
-            self.pstr = ""
-        else:
-            self.msg = msg
-            self.pstr = pstr
-        self.parserElement = elem
-        self.args = (pstr, loc, msg)
-
-    @classmethod
-    def _from_exception(cls, pe):
-        """
-        internal factory method to simplify creating one type of ParseException 
-        from another - avoids having __init__ signature conflicts among subclasses
-        """
-        return cls(pe.pstr, pe.loc, pe.msg, pe.parserElement)
-
-    def __getattr__( self, aname ):
-        """supported attributes by name are:
-            - lineno - returns the line number of the exception text
-            - col - returns the column number of the exception text
-            - line - returns the line containing the exception text
-        """
-        if( aname == "lineno" ):
-            return lineno( self.loc, self.pstr )
-        elif( aname in ("col", "column") ):
-            return col( self.loc, self.pstr )
-        elif( aname == "line" ):
-            return line( self.loc, self.pstr )
-        else:
-            raise AttributeError(aname)
-
-    def __str__( self ):
-        return "%s (at char %d), (line:%d, col:%d)" % \
-                ( self.msg, self.loc, self.lineno, self.column )
-    def __repr__( self ):
-        return _ustr(self)
-    def markInputline( self, markerString = ">!<" ):
-        """Extracts the exception line from the input string, and marks
-           the location of the exception with a special symbol.
-        """
-        line_str = self.line
-        line_column = self.column - 1
-        if markerString:
-            line_str = "".join((line_str[:line_column],
-                                markerString, line_str[line_column:]))
-        return line_str.strip()
-    def __dir__(self):
-        return "lineno col line".split() + dir(type(self))
-
-class ParseException(ParseBaseException):
-    """
-    Exception thrown when parse expressions don't match class;
-    supported attributes by name are:
-     - lineno - returns the line number of the exception text
-     - col - returns the column number of the exception text
-     - line - returns the line containing the exception text
-        
-    Example::
-        try:
-            Word(nums).setName("integer").parseString("ABC")
-        except ParseException as pe:
-            print(pe)
-            print("column: {}".format(pe.col))
-            
-    prints::
-       Expected integer (at char 0), (line:1, col:1)
-        column: 1
-    """
-    pass
-
-class ParseFatalException(ParseBaseException):
-    """user-throwable exception thrown when inconsistent parse content
-       is found; stops all parsing immediately"""
-    pass
-
-class ParseSyntaxException(ParseFatalException):
-    """just like L{ParseFatalException}, but thrown internally when an
-       L{ErrorStop<And._ErrorStop>} ('-' operator) indicates that parsing is to stop 
-       immediately because an unbacktrackable syntax error has been found"""
-    pass
-
-#~ class ReparseException(ParseBaseException):
-    #~ """Experimental class - parse actions can raise this exception to cause
-       #~ pyparsing to reparse the input string:
-        #~ - with a modified input string, and/or
-        #~ - with a modified start location
-       #~ Set the values of the ReparseException in the constructor, and raise the
-       #~ exception in a parse action to cause pyparsing to use the new string/location.
-       #~ Setting the values as None causes no change to be made.
-       #~ """
-    #~ def __init_( self, newstring, restartLoc ):
-        #~ self.newParseText = newstring
-        #~ self.reparseLoc = restartLoc
-
-class RecursiveGrammarException(Exception):
-    """exception thrown by L{ParserElement.validate} if the grammar could be improperly recursive"""
-    def __init__( self, parseElementList ):
-        self.parseElementTrace = parseElementList
-
-    def __str__( self ):
-        return "RecursiveGrammarException: %s" % self.parseElementTrace
-
-class _ParseResultsWithOffset(object):
-    def __init__(self,p1,p2):
-        self.tup = (p1,p2)
-    def __getitem__(self,i):
-        return self.tup[i]
-    def __repr__(self):
-        return repr(self.tup[0])
-    def setOffset(self,i):
-        self.tup = (self.tup[0],i)
-
-class ParseResults(object):
-    """
-    Structured parse results, to provide multiple means of access to the parsed data:
-       - as a list (C{len(results)})
-       - by list index (C{results[0], results[1]}, etc.)
-       - by attribute (C{results.<resultsName>} - see L{ParserElement.setResultsName})
-
-    Example::
-        integer = Word(nums)
-        date_str = (integer.setResultsName("year") + '/' 
-                        + integer.setResultsName("month") + '/' 
-                        + integer.setResultsName("day"))
-        # equivalent form:
-        # date_str = integer("year") + '/' + integer("month") + '/' + integer("day")
-
-        # parseString returns a ParseResults object
-        result = date_str.parseString("1999/12/31")
-
-        def test(s, fn=repr):
-            print("%s -> %s" % (s, fn(eval(s))))
-        test("list(result)")
-        test("result[0]")
-        test("result['month']")
-        test("result.day")
-        test("'month' in result")
-        test("'minutes' in result")
-        test("result.dump()", str)
-    prints::
-        list(result) -> ['1999', '/', '12', '/', '31']
-        result[0] -> '1999'
-        result['month'] -> '12'
-        result.day -> '31'
-        'month' in result -> True
-        'minutes' in result -> False
-        result.dump() -> ['1999', '/', '12', '/', '31']
-        - day: 31
-        - month: 12
-        - year: 1999
-    """
-    def __new__(cls, toklist=None, name=None, asList=True, modal=True ):
-        if isinstance(toklist, cls):
-            return toklist
-        retobj = object.__new__(cls)
-        retobj.__doinit = True
-        return retobj
-
-    # Performance tuning: we construct a *lot* of these, so keep this
-    # constructor as small and fast as possible
-    def __init__( self, toklist=None, name=None, asList=True, modal=True, isinstance=isinstance ):
-        if self.__doinit:
-            self.__doinit = False
-            self.__name = None
-            self.__parent = None
-            self.__accumNames = {}
-            self.__asList = asList
-            self.__modal = modal
-            if toklist is None:
-                toklist = []
-            if isinstance(toklist, list):
-                self.__toklist = toklist[:]
-            elif isinstance(toklist, _generatorType):
-                self.__toklist = list(toklist)
-            else:
-                self.__toklist = [toklist]
-            self.__tokdict = dict()
-
-        if name is not None and name:
-            if not modal:
-                self.__accumNames[name] = 0
-            if isinstance(name,int):
-                name = _ustr(name) # will always return a str, but use _ustr for consistency
-            self.__name = name
-            if not (isinstance(toklist, (type(None), basestring, list)) and toklist in (None,'',[])):
-                if isinstance(toklist,basestring):
-                    toklist = [ toklist ]
-                if asList:
-                    if isinstance(toklist,ParseResults):
-                        self[name] = _ParseResultsWithOffset(toklist.copy(),0)
-                    else:
-                        self[name] = _ParseResultsWithOffset(ParseResults(toklist[0]),0)
-                    self[name].__name = name
-                else:
-                    try:
-                        self[name] = toklist[0]
-                    except (KeyError,TypeError,IndexError):
-                        self[name] = toklist
-
-    def __getitem__( self, i ):
-        if isinstance( i, (int,slice) ):
-            return self.__toklist[i]
-        else:
-            if i not in self.__accumNames:
-                return self.__tokdict[i][-1][0]
-            else:
-                return ParseResults([ v[0] for v in self.__tokdict[i] ])
-
-    def __setitem__( self, k, v, isinstance=isinstance ):
-        if isinstance(v,_ParseResultsWithOffset):
-            self.__tokdict[k] = self.__tokdict.get(k,list()) + [v]
-            sub = v[0]
-        elif isinstance(k,(int,slice)):
-            self.__toklist[k] = v
-            sub = v
-        else:
-            self.__tokdict[k] = self.__tokdict.get(k,list()) + [_ParseResultsWithOffset(v,0)]
-            sub = v
-        if isinstance(sub,ParseResults):
-            sub.__parent = wkref(self)
-
-    def __delitem__( self, i ):
-        if isinstance(i,(int,slice)):
-            mylen = len( self.__toklist )
-            del self.__toklist[i]
-
-            # convert int to slice
-            if isinstance(i, int):
-                if i < 0:
-                    i += mylen
-                i = slice(i, i+1)
-            # get removed indices
-            removed = list(range(*i.indices(mylen)))
-            removed.reverse()
-            # fixup indices in token dictionary
-            for name,occurrences in self.__tokdict.items():
-                for j in removed:
-                    for k, (value, position) in enumerate(occurrences):
-                        occurrences[k] = _ParseResultsWithOffset(value, position - (position > j))
-        else:
-            del self.__tokdict[i]
-
-    def __contains__( self, k ):
-        return k in self.__tokdict
-
-    def __len__( self ): return len( self.__toklist )
-    def __bool__(self): return ( not not self.__toklist )
-    __nonzero__ = __bool__
-    def __iter__( self ): return iter( self.__toklist )
-    def __reversed__( self ): return iter( self.__toklist[::-1] )
-    def _iterkeys( self ):
-        if hasattr(self.__tokdict, "iterkeys"):
-            return self.__tokdict.iterkeys()
-        else:
-            return iter(self.__tokdict)
-
-    def _itervalues( self ):
-        return (self[k] for k in self._iterkeys())
-            
-    def _iteritems( self ):
-        return ((k, self[k]) for k in self._iterkeys())
-
-    if PY_3:
-        keys = _iterkeys       
-        """Returns an iterator of all named result keys (Python 3.x only)."""
-
-        values = _itervalues
-        """Returns an iterator of all named result values (Python 3.x only)."""
-
-        items = _iteritems
-        """Returns an iterator of all named result key-value tuples (Python 3.x only)."""
-
-    else:
-        iterkeys = _iterkeys
-        """Returns an iterator of all named result keys (Python 2.x only)."""
-
-        itervalues = _itervalues
-        """Returns an iterator of all named result values (Python 2.x only)."""
-
-        iteritems = _iteritems
-        """Returns an iterator of all named result key-value tuples (Python 2.x only)."""
-
-        def keys( self ):
-            """Returns all named result keys (as a list in Python 2.x, as an iterator in Python 3.x)."""
-            return list(self.iterkeys())
-
-        def values( self ):
-            """Returns all named result values (as a list in Python 2.x, as an iterator in Python 3.x)."""
-            return list(self.itervalues())
-                
-        def items( self ):
-            """Returns all named result key-values (as a list of tuples in Python 2.x, as an iterator in Python 3.x)."""
-            return list(self.iteritems())
-
-    def haskeys( self ):
-        """Since keys() returns an iterator, this method is helpful in bypassing
-           code that looks for the existence of any defined results names."""
-        return bool(self.__tokdict)
-        
-    def pop( self, *args, **kwargs):
-        """
-        Removes and returns item at specified index (default=C{last}).
-        Supports both C{list} and C{dict} semantics for C{pop()}. If passed no
-        argument or an integer argument, it will use C{list} semantics
-        and pop tokens from the list of parsed tokens. If passed a 
-        non-integer argument (most likely a string), it will use C{dict}
-        semantics and pop the corresponding value from any defined 
-        results names. A second default return value argument is 
-        supported, just as in C{dict.pop()}.
-
-        Example::
-            def remove_first(tokens):
-                tokens.pop(0)
-            print(OneOrMore(Word(nums)).parseString("0 123 321")) # -> ['0', '123', '321']
-            print(OneOrMore(Word(nums)).addParseAction(remove_first).parseString("0 123 321")) # -> ['123', '321']
-
-            label = Word(alphas)
-            patt = label("LABEL") + OneOrMore(Word(nums))
-            print(patt.parseString("AAB 123 321").dump())
-
-            # Use pop() in a parse action to remove named result (note that corresponding value is not
-            # removed from list form of results)
-            def remove_LABEL(tokens):
-                tokens.pop("LABEL")
-                return tokens
-            patt.addParseAction(remove_LABEL)
-            print(patt.parseString("AAB 123 321").dump())
-        prints::
-            ['AAB', '123', '321']
-            - LABEL: AAB
-
-            ['AAB', '123', '321']
-        """
-        if not args:
-            args = [-1]
-        for k,v in kwargs.items():
-            if k == 'default':
-                args = (args[0], v)
-            else:
-                raise TypeError("pop() got an unexpected keyword argument '%s'" % k)
-        if (isinstance(args[0], int) or 
-                        len(args) == 1 or 
-                        args[0] in self):
-            index = args[0]
-            ret = self[index]
-            del self[index]
-            return ret
-        else:
-            defaultvalue = args[1]
-            return defaultvalue
-
-    def get(self, key, defaultValue=None):
-        """
-        Returns named result matching the given key, or if there is no
-        such name, then returns the given C{defaultValue} or C{None} if no
-        C{defaultValue} is specified.
-
-        Similar to C{dict.get()}.
-        
-        Example::
-            integer = Word(nums)
-            date_str = integer("year") + '/' + integer("month") + '/' + integer("day")           
-
-            result = date_str.parseString("1999/12/31")
-            print(result.get("year")) # -> '1999'
-            print(result.get("hour", "not specified")) # -> 'not specified'
-            print(result.get("hour")) # -> None
-        """
-        if key in self:
-            return self[key]
-        else:
-            return defaultValue
-
-    def insert( self, index, insStr ):
-        """
-        Inserts new element at location index in the list of parsed tokens.
-        
-        Similar to C{list.insert()}.
-
-        Example::
-            print(OneOrMore(Word(nums)).parseString("0 123 321")) # -> ['0', '123', '321']
-
-            # use a parse action to insert the parse location in the front of the parsed results
-            def insert_locn(locn, tokens):
-                tokens.insert(0, locn)
-            print(OneOrMore(Word(nums)).addParseAction(insert_locn).parseString("0 123 321")) # -> [0, '0', '123', '321']
-        """
-        self.__toklist.insert(index, insStr)
-        # fixup indices in token dictionary
-        for name,occurrences in self.__tokdict.items():
-            for k, (value, position) in enumerate(occurrences):
-                occurrences[k] = _ParseResultsWithOffset(value, position + (position > index))
-
-    def append( self, item ):
-        """
-        Add single element to end of ParseResults list of elements.
-
-        Example::
-            print(OneOrMore(Word(nums)).parseString("0 123 321")) # -> ['0', '123', '321']
-            
-            # use a parse action to compute the sum of the parsed integers, and add it to the end
-            def append_sum(tokens):
-                tokens.append(sum(map(int, tokens)))
-            print(OneOrMore(Word(nums)).addParseAction(append_sum).parseString("0 123 321")) # -> ['0', '123', '321', 444]
-        """
-        self.__toklist.append(item)
-
-    def extend( self, itemseq ):
-        """
-        Add sequence of elements to end of ParseResults list of elements.
-
-        Example::
-            patt = OneOrMore(Word(alphas))
-            
-            # use a parse action to append the reverse of the matched strings, to make a palindrome
-            def make_palindrome(tokens):
-                tokens.extend(reversed([t[::-1] for t in tokens]))
-                return ''.join(tokens)
-            print(patt.addParseAction(make_palindrome).parseString("lskdj sdlkjf lksd")) # -> 'lskdjsdlkjflksddsklfjkldsjdksl'
-        """
-        if isinstance(itemseq, ParseResults):
-            self += itemseq
-        else:
-            self.__toklist.extend(itemseq)
-
-    def clear( self ):
-        """
-        Clear all elements and results names.
-        """
-        del self.__toklist[:]
-        self.__tokdict.clear()
-
-    def __getattr__( self, name ):
-        try:
-            return self[name]
-        except KeyError:
-            return ""
-            
-        if name in self.__tokdict:
-            if name not in self.__accumNames:
-                return self.__tokdict[name][-1][0]
-            else:
-                return ParseResults([ v[0] for v in self.__tokdict[name] ])
-        else:
-            return ""
-
-    def __add__( self, other ):
-        ret = self.copy()
-        ret += other
-        return ret
-
-    def __iadd__( self, other ):
-        if other.__tokdict:
-            offset = len(self.__toklist)
-            addoffset = lambda a: offset if a<0 else a+offset
-            otheritems = other.__tokdict.items()
-            otherdictitems = [(k, _ParseResultsWithOffset(v[0],addoffset(v[1])) )
-                                for (k,vlist) in otheritems for v in vlist]
-            for k,v in otherdictitems:
-                self[k] = v
-                if isinstance(v[0],ParseResults):
-                    v[0].__parent = wkref(self)
-            
-        self.__toklist += other.__toklist
-        self.__accumNames.update( other.__accumNames )
-        return self
-
-    def __radd__(self, other):
-        if isinstance(other,int) and other == 0:
-            # useful for merging many ParseResults using sum() builtin
-            return self.copy()
-        else:
-            # this may raise a TypeError - so be it
-            return other + self
-        
-    def __repr__( self ):
-        return "(%s, %s)" % ( repr( self.__toklist ), repr( self.__tokdict ) )
-
-    def __str__( self ):
-        return '[' + ', '.join(_ustr(i) if isinstance(i, ParseResults) else repr(i) for i in self.__toklist) + ']'
-
-    def _asStringList( self, sep='' ):
-        out = []
-        for item in self.__toklist:
-            if out and sep:
-                out.append(sep)
-            if isinstance( item, ParseResults ):
-                out += item._asStringList()
-            else:
-                out.append( _ustr(item) )
-        return out
-
-    def asList( self ):
-        """
-        Returns the parse results as a nested list of matching tokens, all converted to strings.
-
-        Example::
-            patt = OneOrMore(Word(alphas))
-            result = patt.parseString("sldkj lsdkj sldkj")
-            # even though the result prints in string-like form, it is actually a pyparsing ParseResults
-            print(type(result), result) # -> <class 'pyparsing.ParseResults'> ['sldkj', 'lsdkj', 'sldkj']
-            
-            # Use asList() to create an actual list
-            result_list = result.asList()
-            print(type(result_list), result_list) # -> <class 'list'> ['sldkj', 'lsdkj', 'sldkj']
-        """
-        return [res.asList() if isinstance(res,ParseResults) else res for res in self.__toklist]
-
-    def asDict( self ):
-        """
-        Returns the named parse results as a nested dictionary.
-
-        Example::
-            integer = Word(nums)
-            date_str = integer("year") + '/' + integer("month") + '/' + integer("day")
-            
-            result = date_str.parseString('12/31/1999')
-            print(type(result), repr(result)) # -> <class 'pyparsing.ParseResults'> (['12', '/', '31', '/', '1999'], {'day': [('1999', 4)], 'year': [('12', 0)], 'month': [('31', 2)]})
-            
-            result_dict = result.asDict()
-            print(type(result_dict), repr(result_dict)) # -> <class 'dict'> {'day': '1999', 'year': '12', 'month': '31'}
-
-            # even though a ParseResults supports dict-like access, sometime you just need to have a dict
-            import json
-            print(json.dumps(result)) # -> Exception: TypeError: ... is not JSON serializable
-            print(json.dumps(result.asDict())) # -> {"month": "31", "day": "1999", "year": "12"}
-        """
-        if PY_3:
-            item_fn = self.items
-        else:
-            item_fn = self.iteritems
-            
-        def toItem(obj):
-            if isinstance(obj, ParseResults):
-                if obj.haskeys():
-                    return obj.asDict()
-                else:
-                    return [toItem(v) for v in obj]
-            else:
-                return obj
-                
-        return dict((k,toItem(v)) for k,v in item_fn())
-
-    def copy( self ):
-        """
-        Returns a new copy of a C{ParseResults} object.
-        """
-        ret = ParseResults( self.__toklist )
-        ret.__tokdict = self.__tokdict.copy()
-        ret.__parent = self.__parent
-        ret.__accumNames.update( self.__accumNames )
-        ret.__name = self.__name
-        return ret
-
-    def asXML( self, doctag=None, namedItemsOnly=False, indent="", formatted=True ):
-        """
-        (Deprecated) Returns the parse results as XML. Tags are created for tokens and lists that have defined results names.
-        """
-        nl = "\n"
-        out = []
-        namedItems = dict((v[1],k) for (k,vlist) in self.__tokdict.items()
-                                                            for v in vlist)
-        nextLevelIndent = indent + "  "
-
-        # collapse out indents if formatting is not desired
-        if not formatted:
-            indent = ""
-            nextLevelIndent = ""
-            nl = ""
-
-        selfTag = None
-        if doctag is not None:
-            selfTag = doctag
-        else:
-            if self.__name:
-                selfTag = self.__name
-
-        if not selfTag:
-            if namedItemsOnly:
-                return ""
-            else:
-                selfTag = "ITEM"
-
-        out += [ nl, indent, "<", selfTag, ">" ]
-
-        for i,res in enumerate(self.__toklist):
-            if isinstance(res,ParseResults):
-                if i in namedItems:
-                    out += [ res.asXML(namedItems[i],
-                                        namedItemsOnly and doctag is None,
-                                        nextLevelIndent,
-                                        formatted)]
-                else:
-                    out += [ res.asXML(None,
-                                        namedItemsOnly and doctag is None,
-                                        nextLevelIndent,
-                                        formatted)]
-            else:
-                # individual token, see if there is a name for it
-                resTag = None
-                if i in namedItems:
-                    resTag = namedItems[i]
-                if not resTag:
-                    if namedItemsOnly:
-                        continue
-                    else:
-                        resTag = "ITEM"
-                xmlBodyText = _xml_escape(_ustr(res))
-                out += [ nl, nextLevelIndent, "<", resTag, ">",
-                                                xmlBodyText,
-                                                "</", resTag, ">" ]
-
-        out += [ nl, indent, "</", selfTag, ">" ]
-        return "".join(out)
-
-    def __lookup(self,sub):
-        for k,vlist in self.__tokdict.items():
-            for v,loc in vlist:
-                if sub is v:
-                    return k
-        return None
-
-    def getName(self):
-        r"""
-        Returns the results name for this token expression. Useful when several 
-        different expressions might match at a particular location.
-
-        Example::
-            integer = Word(nums)
-            ssn_expr = Regex(r"\d\d\d-\d\d-\d\d\d\d")
-            house_number_expr = Suppress('#') + Word(nums, alphanums)
-            user_data = (Group(house_number_expr)("house_number") 
-                        | Group(ssn_expr)("ssn")
-                        | Group(integer)("age"))
-            user_info = OneOrMore(user_data)
-            
-            result = user_info.parseString("22 111-22-3333 #221B")
-            for item in result:
-                print(item.getName(), ':', item[0])
-        prints::
-            age : 22
-            ssn : 111-22-3333
-            house_number : 221B
-        """
-        if self.__name:
-            return self.__name
-        elif self.__parent:
-            par = self.__parent()
-            if par:
-                return par.__lookup(self)
-            else:
-                return None
-        elif (len(self) == 1 and
-               len(self.__tokdict) == 1 and
-               next(iter(self.__tokdict.values()))[0][1] in (0,-1)):
-            return next(iter(self.__tokdict.keys()))
-        else:
-            return None
-
-    def dump(self, indent='', depth=0, full=True):
-        """
-        Diagnostic method for listing out the contents of a C{ParseResults}.
-        Accepts an optional C{indent} argument so that this string can be embedded
-        in a nested display of other data.
-
-        Example::
-            integer = Word(nums)
-            date_str = integer("year") + '/' + integer("month") + '/' + integer("day")
-            
-            result = date_str.parseString('12/31/1999')
-            print(result.dump())
-        prints::
-            ['12', '/', '31', '/', '1999']
-            - day: 1999
-            - month: 31
-            - year: 12
-        """
-        out = []
-        NL = '\n'
-        out.append( indent+_ustr(self.asList()) )
-        if full:
-            if self.haskeys():
-                items = sorted((str(k), v) for k,v in self.items())
-                for k,v in items:
-                    if out:
-                        out.append(NL)
-                    out.append( "%s%s- %s: " % (indent,('  '*depth), k) )
-                    if isinstance(v,ParseResults):
-                        if v:
-                            out.append( v.dump(indent,depth+1) )
-                        else:
-                            out.append(_ustr(v))
-                    else:
-                        out.append(repr(v))
-            elif any(isinstance(vv,ParseResults) for vv in self):
-                v = self
-                for i,vv in enumerate(v):
-                    if isinstance(vv,ParseResults):
-                        out.append("\n%s%s[%d]:\n%s%s%s" % (indent,('  '*(depth)),i,indent,('  '*(depth+1)),vv.dump(indent,depth+1) ))
-                    else:
-                        out.append("\n%s%s[%d]:\n%s%s%s" % (indent,('  '*(depth)),i,indent,('  '*(depth+1)),_ustr(vv)))
-            
-        return "".join(out)
-
-    def pprint(self, *args, **kwargs):
-        """
-        Pretty-printer for parsed results as a list, using the C{pprint} module.
-        Accepts additional positional or keyword args as defined for the 
-        C{pprint.pprint} method. (U{https://docs.python.org/3/library/pprint.html#pprint.pprint})
-
-        Example::
-            ident = Word(alphas, alphanums)
-            num = Word(nums)
-            func = Forward()
-            term = ident | num | Group('(' + func + ')')
-            func <<= ident + Group(Optional(delimitedList(term)))
-            result = func.parseString("fna a,b,(fnb c,d,200),100")
-            result.pprint(width=40)
-        prints::
-            ['fna',
-             ['a',
-              'b',
-              ['(', 'fnb', ['c', 'd', '200'], ')'],
-              '100']]
-        """
-        pprint.pprint(self.asList(), *args, **kwargs)
-
-    # add support for pickle protocol
-    def __getstate__(self):
-        return ( self.__toklist,
-                 ( self.__tokdict.copy(),
-                   self.__parent is not None and self.__parent() or None,
-                   self.__accumNames,
-                   self.__name ) )
-
-    def __setstate__(self,state):
-        self.__toklist = state[0]
-        (self.__tokdict,
-         par,
-         inAccumNames,
-         self.__name) = state[1]
-        self.__accumNames = {}
-        self.__accumNames.update(inAccumNames)
-        if par is not None:
-            self.__parent = wkref(par)
-        else:
-            self.__parent = None
-
-    def __getnewargs__(self):
-        return self.__toklist, self.__name, self.__asList, self.__modal
-
-    def __dir__(self):
-        return (dir(type(self)) + list(self.keys()))
-
-collections.MutableMapping.register(ParseResults)
-
-def col (loc,strg):
-    """Returns current column within a string, counting newlines as line separators.
-   The first column is number 1.
-
-   Note: the default parsing behavior is to expand tabs in the input string
-   before starting the parsing process.  See L{I{ParserElement.parseString}<ParserElement.parseString>} for more information
-   on parsing strings containing C{<TAB>}s, and suggested methods to maintain a
-   consistent view of the parsed string, the parse location, and line and column
-   positions within the parsed string.
-   """
-    s = strg
-    return 1 if 0<loc<len(s) and s[loc-1] == '\n' else loc - s.rfind("\n", 0, loc)
-
-def lineno(loc,strg):
-    """Returns current line number within a string, counting newlines as line separators.
-   The first line is number 1.
-
-   Note: the default parsing behavior is to expand tabs in the input string
-   before starting the parsing process.  See L{I{ParserElement.parseString}<ParserElement.parseString>} for more information
-   on parsing strings containing C{<TAB>}s, and suggested methods to maintain a
-   consistent view of the parsed string, the parse location, and line and column
-   positions within the parsed string.
-   """
-    return strg.count("\n",0,loc) + 1
-
-def line( loc, strg ):
-    """Returns the line of text containing loc within a string, counting newlines as line separators.
-       """
-    lastCR = strg.rfind("\n", 0, loc)
-    nextCR = strg.find("\n", loc)
-    if nextCR >= 0:
-        return strg[lastCR+1:nextCR]
-    else:
-        return strg[lastCR+1:]
-
-def _defaultStartDebugAction( instring, loc, expr ):
-    print (("Match " + _ustr(expr) + " at loc " + _ustr(loc) + "(%d,%d)" % ( lineno(loc,instring), col(loc,instring) )))
-
-def _defaultSuccessDebugAction( instring, startloc, endloc, expr, toks ):
-    print ("Matched " + _ustr(expr) + " -> " + str(toks.asList()))
-
-def _defaultExceptionDebugAction( instring, loc, expr, exc ):
-    print ("Exception raised:" + _ustr(exc))
-
-def nullDebugAction(*args):
-    """'Do-nothing' debug action, to suppress debugging output during parsing."""
-    pass
-
-# Only works on Python 3.x - nonlocal is toxic to Python 2 installs
-#~ 'decorator to trim function calls to match the arity of the target'
-#~ def _trim_arity(func, maxargs=3):
-    #~ if func in singleArgBuiltins:
-        #~ return lambda s,l,t: func(t)
-    #~ limit = 0
-    #~ foundArity = False
-    #~ def wrapper(*args):
-        #~ nonlocal limit,foundArity
-        #~ while 1:
-            #~ try:
-                #~ ret = func(*args[limit:])
-                #~ foundArity = True
-                #~ return ret
-            #~ except TypeError:
-                #~ if limit == maxargs or foundArity:
-                    #~ raise
-                #~ limit += 1
-                #~ continue
-    #~ return wrapper
-
-# this version is Python 2.x-3.x cross-compatible
-'decorator to trim function calls to match the arity of the target'
-def _trim_arity(func, maxargs=2):
-    if func in singleArgBuiltins:
-        return lambda s,l,t: func(t)
-    limit = [0]
-    foundArity = [False]
-    
-    def extract_stack(limit=0):
-        offset = -2
-        frame_summary = traceback.extract_stack(limit=-offset+limit-1)[offset]
-        return [(frame_summary.filename, frame_summary.lineno)]
-    def extract_tb(tb, limit=0):
-        frames = traceback.extract_tb(tb, limit=limit)
-        frame_summary = frames[-1]
-        return [(frame_summary.filename, frame_summary.lineno)]
-    
-    # synthesize what would be returned by traceback.extract_stack at the call to 
-    # user's parse action 'func', so that we don't incur call penalty at parse time
-    
-    LINE_DIFF = 6
-    # IF ANY CODE CHANGES, EVEN JUST COMMENTS OR BLANK LINES, BETWEEN THE NEXT LINE AND 
-    # THE CALL TO FUNC INSIDE WRAPPER, LINE_DIFF MUST BE MODIFIED!!!!
-    this_line = extract_stack(limit=2)[-1]
-    pa_call_line_synth = (this_line[0], this_line[1]+LINE_DIFF)
-
-    def wrapper(*args):
-        while 1:
-            try:
-                ret = func(*args[limit[0]:])
-                foundArity[0] = True
-                return ret
-            except TypeError:
-                # re-raise TypeErrors if they did not come from our arity testing
-                if foundArity[0]:
-                    raise
-                else:
-                    try:
-                        tb = sys.exc_info()[-1]
-                        if not extract_tb(tb, limit=2)[-1][:2] == pa_call_line_synth:
-                            raise
-                    finally:
-                        del tb
-
-                if limit[0] <= maxargs:
-                    limit[0] += 1
-                    continue
-                raise
-
-    # copy func name to wrapper for sensible debug output
-    func_name = "<parse action>"
-    try:
-        func_name = getattr(func, '__name__', 
-                            getattr(func, '__class__').__name__)
-    except Exception:
-        func_name = str(func)
-    wrapper.__name__ = func_name
-
-    return wrapper
-
-class ParserElement(object):
-    """Abstract base level parser element class."""
-    DEFAULT_WHITE_CHARS = " \n\t\r"
-    verbose_stacktrace = False
-
-    @staticmethod
-    def setDefaultWhitespaceChars( chars ):
-        r"""
-        Overrides the default whitespace chars
-
-        Example::
-            # default whitespace chars are space, <TAB> and newline
-            OneOrMore(Word(alphas)).parseString("abc def\nghi jkl")  # -> ['abc', 'def', 'ghi', 'jkl']
-            
-            # change to just treat newline as significant
-            ParserElement.setDefaultWhitespaceChars(" \t")
-            OneOrMore(Word(alphas)).parseString("abc def\nghi jkl")  # -> ['abc', 'def']
-        """
-        ParserElement.DEFAULT_WHITE_CHARS = chars
-
-    @staticmethod
-    def inlineLiteralsUsing(cls):
-        """
-        Set class to be used for inclusion of string literals into a parser.
-        
-        Example::
-            # default literal class used is Literal
-            integer = Word(nums)
-            date_str = integer("year") + '/' + integer("month") + '/' + integer("day")           
-
-            date_str.parseString("1999/12/31")  # -> ['1999', '/', '12', '/', '31']
-
-
-            # change to Suppress
-            ParserElement.inlineLiteralsUsing(Suppress)
-            date_str = integer("year") + '/' + integer("month") + '/' + integer("day")           
-
-            date_str.parseString("1999/12/31")  # -> ['1999', '12', '31']
-        """
-        ParserElement._literalStringClass = cls
-
-    def __init__( self, savelist=False ):
-        self.parseAction = list()
-        self.failAction = None
-        #~ self.name = "<unknown>"  # don't define self.name, let subclasses try/except upcall
-        self.strRepr = None
-        self.resultsName = None
-        self.saveAsList = savelist
-        self.skipWhitespace = True
-        self.whiteChars = ParserElement.DEFAULT_WHITE_CHARS
-        self.copyDefaultWhiteChars = True
-        self.mayReturnEmpty = False # used when checking for left-recursion
-        self.keepTabs = False
-        self.ignoreExprs = list()
-        self.debug = False
-        self.streamlined = False
-        self.mayIndexError = True # used to optimize exception handling for subclasses that don't advance parse index
-        self.errmsg = ""
-        self.modalResults = True # used to mark results names as modal (report only last) or cumulative (list all)
-        self.debugActions = ( None, None, None ) #custom debug actions
-        self.re = None
-        self.callPreparse = True # used to avoid redundant calls to preParse
-        self.callDuringTry = False
-
-    def copy( self ):
-        """
-        Make a copy of this C{ParserElement}.  Useful for defining different parse actions
-        for the same parsing pattern, using copies of the original parse element.
-        
-        Example::
-            integer = Word(nums).setParseAction(lambda toks: int(toks[0]))
-            integerK = integer.copy().addParseAction(lambda toks: toks[0]*1024) + Suppress("K")
-            integerM = integer.copy().addParseAction(lambda toks: toks[0]*1024*1024) + Suppress("M")
-            
-            print(OneOrMore(integerK | integerM | integer).parseString("5K 100 640K 256M"))
-        prints::
-            [5120, 100, 655360, 268435456]
-        Equivalent form of C{expr.copy()} is just C{expr()}::
-            integerM = integer().addParseAction(lambda toks: toks[0]*1024*1024) + Suppress("M")
-        """
-        cpy = copy.copy( self )
-        cpy.parseAction = self.parseAction[:]
-        cpy.ignoreExprs = self.ignoreExprs[:]
-        if self.copyDefaultWhiteChars:
-            cpy.whiteChars = ParserElement.DEFAULT_WHITE_CHARS
-        return cpy
-
-    def setName( self, name ):
-        """
-        Define name for this expression, makes debugging and exception messages clearer.
-        
-        Example::
-            Word(nums).parseString("ABC")  # -> Exception: Expected W:(0123...) (at char 0), (line:1, col:1)
-            Word(nums).setName("integer").parseString("ABC")  # -> Exception: Expected integer (at char 0), (line:1, col:1)
-        """
-        self.name = name
-        self.errmsg = "Expected " + self.name
-        if hasattr(self,"exception"):
-            self.exception.msg = self.errmsg
-        return self
-
-    def setResultsName( self, name, listAllMatches=False ):
-        """
-        Define name for referencing matching tokens as a nested attribute
-        of the returned parse results.
-        NOTE: this returns a *copy* of the original C{ParserElement} object;
-        this is so that the client can define a basic element, such as an
-        integer, and reference it in multiple places with different names.
-
-        You can also set results names using the abbreviated syntax,
-        C{expr("name")} in place of C{expr.setResultsName("name")} - 
-        see L{I{__call__}<__call__>}.
-
-        Example::
-            date_str = (integer.setResultsName("year") + '/' 
-                        + integer.setResultsName("month") + '/' 
-                        + integer.setResultsName("day"))
-
-            # equivalent form:
-            date_str = integer("year") + '/' + integer("month") + '/' + integer("day")
-        """
-        newself = self.copy()
-        if name.endswith("*"):
-            name = name[:-1]
-            listAllMatches=True
-        newself.resultsName = name
-        newself.modalResults = not listAllMatches
-        return newself
-
-    def setBreak(self,breakFlag = True):
-        """Method to invoke the Python pdb debugger when this element is
-           about to be parsed. Set C{breakFlag} to True to enable, False to
-           disable.
-        """
-        if breakFlag:
-            _parseMethod = self._parse
-            def breaker(instring, loc, doActions=True, callPreParse=True):
-                import pdb
-                pdb.set_trace()
-                return _parseMethod( instring, loc, doActions, callPreParse )
-            breaker._originalParseMethod = _parseMethod
-            self._parse = breaker
-        else:
-            if hasattr(self._parse,"_originalParseMethod"):
-                self._parse = self._parse._originalParseMethod
-        return self
-
-    def setParseAction( self, *fns, **kwargs ):
-        """
-        Define one or more actions to perform when successfully matching parse element definition.
-        Parse action fn is a callable method with 0-3 arguments, called as C{fn(s,loc,toks)},
-        C{fn(loc,toks)}, C{fn(toks)}, or just C{fn()}, where:
-         - s   = the original string being parsed (see note below)
-         - loc = the location of the matching substring
-         - toks = a list of the matched tokens, packaged as a C{L{ParseResults}} object
-        If the functions in fns modify the tokens, they can return them as the return
-        value from fn, and the modified list of tokens will replace the original.
-        Otherwise, fn does not need to return any value.
-
-        Optional keyword arguments:
-         - callDuringTry = (default=C{False}) indicate if parse action should be run during lookaheads and alternate testing
-
-        Note: the default parsing behavior is to expand tabs in the input string
-        before starting the parsing process.  See L{I{parseString}<parseString>} for more information
-        on parsing strings containing C{<TAB>}s, and suggested methods to maintain a
-        consistent view of the parsed string, the parse location, and line and column
-        positions within the parsed string.
-        
-        Example::
-            integer = Word(nums)
-            date_str = integer + '/' + integer + '/' + integer
-
-            date_str.parseString("1999/12/31")  # -> ['1999', '/', '12', '/', '31']
-
-            # use parse action to convert to ints at parse time
-            integer = Word(nums).setParseAction(lambda toks: int(toks[0]))
-            date_str = integer + '/' + integer + '/' + integer
-
-            # note that integer fields are now ints, not strings
-            date_str.parseString("1999/12/31")  # -> [1999, '/', 12, '/', 31]
-        """
-        self.parseAction = list(map(_trim_arity, list(fns)))
-        self.callDuringTry = kwargs.get("callDuringTry", False)
-        return self
-
-    def addParseAction( self, *fns, **kwargs ):
-        """
-        Add one or more parse actions to expression's list of parse actions. See L{I{setParseAction}<setParseAction>}.
-        
-        See examples in L{I{copy}<copy>}.
-        """
-        self.parseAction += list(map(_trim_arity, list(fns)))
-        self.callDuringTry = self.callDuringTry or kwargs.get("callDuringTry", False)
-        return self
-
-    def addCondition(self, *fns, **kwargs):
-        """Add a boolean predicate function to expression's list of parse actions. See 
-        L{I{setParseAction}<setParseAction>} for function call signatures. Unlike C{setParseAction}, 
-        functions passed to C{addCondition} need to return boolean success/fail of the condition.
-
-        Optional keyword arguments:
-         - message = define a custom message to be used in the raised exception
-         - fatal   = if True, will raise ParseFatalException to stop parsing immediately; otherwise will raise ParseException
-         
-        Example::
-            integer = Word(nums).setParseAction(lambda toks: int(toks[0]))
-            year_int = integer.copy()
-            year_int.addCondition(lambda toks: toks[0] >= 2000, message="Only support years 2000 and later")
-            date_str = year_int + '/' + integer + '/' + integer
-
-            result = date_str.parseString("1999/12/31")  # -> Exception: Only support years 2000 and later (at char 0), (line:1, col:1)
-        """
-        msg = kwargs.get("message", "failed user-defined condition")
-        exc_type = ParseFatalException if kwargs.get("fatal", False) else ParseException
-        for fn in fns:
-            def pa(s,l,t):
-                if not bool(_trim_arity(fn)(s,l,t)):
-                    raise exc_type(s,l,msg)
-            self.parseAction.append(pa)
-        self.callDuringTry = self.callDuringTry or kwargs.get("callDuringTry", False)
-        return self
-
-    def setFailAction( self, fn ):
-        """Define action to perform if parsing fails at this expression.
-           Fail action fn is a callable function that takes the arguments
-           C{fn(s,loc,expr,err)} where:
-            - s = string being parsed
-            - loc = location where expression match was attempted and failed
-            - expr = the parse expression that failed
-            - err = the exception thrown
-           The function returns no value.  It may throw C{L{ParseFatalException}}
-           if it is desired to stop parsing immediately."""
-        self.failAction = fn
-        return self
-
-    def _skipIgnorables( self, instring, loc ):
-        exprsFound = True
-        while exprsFound:
-            exprsFound = False
-            for e in self.ignoreExprs:
-                try:
-                    while 1:
-                        loc,dummy = e._parse( instring, loc )
-                        exprsFound = True
-                except ParseException:
-                    pass
-        return loc
-
-    def preParse( self, instring, loc ):
-        if self.ignoreExprs:
-            loc = self._skipIgnorables( instring, loc )
-
-        if self.skipWhitespace:
-            wt = self.whiteChars
-            instrlen = len(instring)
-            while loc < instrlen and instring[loc] in wt:
-                loc += 1
-
-        return loc
-
-    def parseImpl( self, instring, loc, doActions=True ):
-        return loc, []
-
-    def postParse( self, instring, loc, tokenlist ):
-        return tokenlist
-
-    #~ @profile
-    def _parseNoCache( self, instring, loc, doActions=True, callPreParse=True ):
-        debugging = ( self.debug ) #and doActions )
-
-        if debugging or self.failAction:
-            #~ print ("Match",self,"at loc",loc,"(%d,%d)" % ( lineno(loc,instring), col(loc,instring) ))
-            if (self.debugActions[0] ):
-                self.debugActions[0]( instring, loc, self )
-            if callPreParse and self.callPreparse:
-                preloc = self.preParse( instring, loc )
-            else:
-                preloc = loc
-            tokensStart = preloc
-            try:
-                try:
-                    loc,tokens = self.parseImpl( instring, preloc, doActions )
-                except IndexError:
-                    raise ParseException( instring, len(instring), self.errmsg, self )
-            except ParseBaseException as err:
-                #~ print ("Exception raised:", err)
-                if self.debugActions[2]:
-                    self.debugActions[2]( instring, tokensStart, self, err )
-                if self.failAction:
-                    self.failAction( instring, tokensStart, self, err )
-                raise
-        else:
-            if callPreParse and self.callPreparse:
-                preloc = self.preParse( instring, loc )
-            else:
-                preloc = loc
-            tokensStart = preloc
-            if self.mayIndexError or loc >= len(instring):
-                try:
-                    loc,tokens = self.parseImpl( instring, preloc, doActions )
-                except IndexError:
-                    raise ParseException( instring, len(instring), self.errmsg, self )
-            else:
-                loc,tokens = self.parseImpl( instring, preloc, doActions )
-
-        tokens = self.postParse( instring, loc, tokens )
-
-        retTokens = ParseResults( tokens, self.resultsName, asList=self.saveAsList, modal=self.modalResults )
-        if self.parseAction and (doActions or self.callDuringTry):
-            if debugging:
-                try:
-                    for fn in self.parseAction:
-                        tokens = fn( instring, tokensStart, retTokens )
-                        if tokens is not None:
-                            retTokens = ParseResults( tokens,
-                                                      self.resultsName,
-                                                      asList=self.saveAsList and isinstance(tokens,(ParseResults,list)),
-                                                      modal=self.modalResults )
-                except ParseBaseException as err:
-                    #~ print "Exception raised in user parse action:", err
-                    if (self.debugActions[2] ):
-                        self.debugActions[2]( instring, tokensStart, self, err )
-                    raise
-            else:
-                for fn in self.parseAction:
-                    tokens = fn( instring, tokensStart, retTokens )
-                    if tokens is not None:
-                        retTokens = ParseResults( tokens,
-                                                  self.resultsName,
-                                                  asList=self.saveAsList and isinstance(tokens,(ParseResults,list)),
-                                                  modal=self.modalResults )
-
-        if debugging:
-            #~ print ("Matched",self,"->",retTokens.asList())
-            if (self.debugActions[1] ):
-                self.debugActions[1]( instring, tokensStart, loc, self, retTokens )
-
-        return loc, retTokens
-
-    def tryParse( self, instring, loc ):
-        try:
-            return self._parse( instring, loc, doActions=False )[0]
-        except ParseFatalException:
-            raise ParseException( instring, loc, self.errmsg, self)
-    
-    def canParseNext(self, instring, loc):
-        try:
-            self.tryParse(instring, loc)
-        except (ParseException, IndexError):
-            return False
-        else:
-            return True
-
-    class _UnboundedCache(object):
-        def __init__(self):
-            cache = {}
-            self.not_in_cache = not_in_cache = object()
-
-            def get(self, key):
-                return cache.get(key, not_in_cache)
-
-            def set(self, key, value):
-                cache[key] = value
-
-            def clear(self):
-                cache.clear()
-                
-            def cache_len(self):
-                return len(cache)
-
-            self.get = types.MethodType(get, self)
-            self.set = types.MethodType(set, self)
-            self.clear = types.MethodType(clear, self)
-            self.__len__ = types.MethodType(cache_len, self)
-
-    if _OrderedDict is not None:
-        class _FifoCache(object):
-            def __init__(self, size):
-                self.not_in_cache = not_in_cache = object()
-
-                cache = _OrderedDict()
-
-                def get(self, key):
-                    return cache.get(key, not_in_cache)
-
-                def set(self, key, value):
-                    cache[key] = value
-                    while len(cache) > size:
-                        try:
-                            cache.popitem(False)
-                        except KeyError:
-                            pass
-
-                def clear(self):
-                    cache.clear()
-
-                def cache_len(self):
-                    return len(cache)
-
-                self.get = types.MethodType(get, self)
-                self.set = types.MethodType(set, self)
-                self.clear = types.MethodType(clear, self)
-                self.__len__ = types.MethodType(cache_len, self)
-
-    else:
-        class _FifoCache(object):
-            def __init__(self, size):
-                self.not_in_cache = not_in_cache = object()
-
-                cache = {}
-                key_fifo = collections.deque([], size)
-
-                def get(self, key):
-                    return cache.get(key, not_in_cache)
-
-                def set(self, key, value):
-                    cache[key] = value
-                    while len(key_fifo) > size:
-                        cache.pop(key_fifo.popleft(), None)
-                    key_fifo.append(key)
-
-                def clear(self):
-                    cache.clear()
-                    key_fifo.clear()
-
-                def cache_len(self):
-                    return len(cache)
-
-                self.get = types.MethodType(get, self)
-                self.set = types.MethodType(set, self)
-                self.clear = types.MethodType(clear, self)
-                self.__len__ = types.MethodType(cache_len, self)
-
-    # argument cache for optimizing repeated calls when backtracking through recursive expressions
-    packrat_cache = {} # this is set later by enabledPackrat(); this is here so that resetCache() doesn't fail
-    packrat_cache_lock = RLock()
-    packrat_cache_stats = [0, 0]
-
-    # this method gets repeatedly called during backtracking with the same arguments -
-    # we can cache these arguments and save ourselves the trouble of re-parsing the contained expression
-    def _parseCache( self, instring, loc, doActions=True, callPreParse=True ):
-        HIT, MISS = 0, 1
-        lookup = (self, instring, loc, callPreParse, doActions)
-        with ParserElement.packrat_cache_lock:
-            cache = ParserElement.packrat_cache
-            value = cache.get(lookup)
-            if value is cache.not_in_cache:
-                ParserElement.packrat_cache_stats[MISS] += 1
-                try:
-                    value = self._parseNoCache(instring, loc, doActions, callPreParse)
-                except ParseBaseException as pe:
-                    # cache a copy of the exception, without the traceback
-                    cache.set(lookup, pe.__class__(*pe.args))
-                    raise
-                else:
-                    cache.set(lookup, (value[0], value[1].copy()))
-                    return value
-            else:
-                ParserElement.packrat_cache_stats[HIT] += 1
-                if isinstance(value, Exception):
-                    raise value
-                return (value[0], value[1].copy())
-
-    _parse = _parseNoCache
-
-    @staticmethod
-    def resetCache():
-        ParserElement.packrat_cache.clear()
-        ParserElement.packrat_cache_stats[:] = [0] * len(ParserElement.packrat_cache_stats)
-
-    _packratEnabled = False
-    @staticmethod
-    def enablePackrat(cache_size_limit=128):
-        """Enables "packrat" parsing, which adds memoizing to the parsing logic.
-           Repeated parse attempts at the same string location (which happens
-           often in many complex grammars) can immediately return a cached value,
-           instead of re-executing parsing/validating code.  Memoizing is done of
-           both valid results and parsing exceptions.
-           
-           Parameters:
-            - cache_size_limit - (default=C{128}) - if an integer value is provided
-              will limit the size of the packrat cache; if None is passed, then
-              the cache size will be unbounded; if 0 is passed, the cache will
-              be effectively disabled.
-            
-           This speedup may break existing programs that use parse actions that
-           have side-effects.  For this reason, packrat parsing is disabled when
-           you first import pyparsing.  To activate the packrat feature, your
-           program must call the class method C{ParserElement.enablePackrat()}.  If
-           your program uses C{psyco} to "compile as you go", you must call
-           C{enablePackrat} before calling C{psyco.full()}.  If you do not do this,
-           Python will crash.  For best results, call C{enablePackrat()} immediately
-           after importing pyparsing.
-           
-           Example::
-               import pyparsing
-               pyparsing.ParserElement.enablePackrat()
-        """
-        if not ParserElement._packratEnabled:
-            ParserElement._packratEnabled = True
-            if cache_size_limit is None:
-                ParserElement.packrat_cache = ParserElement._UnboundedCache()
-            else:
-                ParserElement.packrat_cache = ParserElement._FifoCache(cache_size_limit)
-            ParserElement._parse = ParserElement._parseCache
-
-    def parseString( self, instring, parseAll=False ):
-        """
-        Execute the parse expression with the given string.
-        This is the main interface to the client code, once the complete
-        expression has been built.
-
-        If you want the grammar to require that the entire input string be
-        successfully parsed, then set C{parseAll} to True (equivalent to ending
-        the grammar with C{L{StringEnd()}}).
-
-        Note: C{parseString} implicitly calls C{expandtabs()} on the input string,
-        in order to report proper column numbers in parse actions.
-        If the input string contains tabs and
-        the grammar uses parse actions that use the C{loc} argument to index into the
-        string being parsed, you can ensure you have a consistent view of the input
-        string by:
-         - calling C{parseWithTabs} on your grammar before calling C{parseString}
-           (see L{I{parseWithTabs}<parseWithTabs>})
-         - define your parse action using the full C{(s,loc,toks)} signature, and
-           reference the input string using the parse action's C{s} argument
-         - explicitly expand the tabs in your input string before calling
-           C{parseString}
-        
-        Example::
-            Word('a').parseString('aaaaabaaa')  # -> ['aaaaa']
-            Word('a').parseString('aaaaabaaa', parseAll=True)  # -> Exception: Expected end of text
-        """
-        ParserElement.resetCache()
-        if not self.streamlined:
-            self.streamline()
-            #~ self.saveAsList = True
-        for e in self.ignoreExprs:
-            e.streamline()
-        if not self.keepTabs:
-            instring = instring.expandtabs()
-        try:
-            loc, tokens = self._parse( instring, 0 )
-            if parseAll:
-                loc = self.preParse( instring, loc )
-                se = Empty() + StringEnd()
-                se._parse( instring, loc )
-        except ParseBaseException as exc:
-            if ParserElement.verbose_stacktrace:
-                raise
-            else:
-                # catch and re-raise exception from here, clears out pyparsing internal stack trace
-                raise exc
-        else:
-            return tokens
-
-    def scanString( self, instring, maxMatches=_MAX_INT, overlap=False ):
-        """
-        Scan the input string for expression matches.  Each match will return the
-        matching tokens, start location, and end location.  May be called with optional
-        C{maxMatches} argument, to clip scanning after 'n' matches are found.  If
-        C{overlap} is specified, then overlapping matches will be reported.
-
-        Note that the start and end locations are reported relative to the string
-        being parsed.  See L{I{parseString}<parseString>} for more information on parsing
-        strings with embedded tabs.
-
-        Example::
-            source = "sldjf123lsdjjkf345sldkjf879lkjsfd987"
-            print(source)
-            for tokens,start,end in Word(alphas).scanString(source):
-                print(' '*start + '^'*(end-start))
-                print(' '*start + tokens[0])
-        
-        prints::
-        
-            sldjf123lsdjjkf345sldkjf879lkjsfd987
-            ^^^^^
-            sldjf
-                    ^^^^^^^
-                    lsdjjkf
-                              ^^^^^^
-                              sldkjf
-                                       ^^^^^^
-                                       lkjsfd
-        """
-        if not self.streamlined:
-            self.streamline()
-        for e in self.ignoreExprs:
-            e.streamline()
-
-        if not self.keepTabs:
-            instring = _ustr(instring).expandtabs()
-        instrlen = len(instring)
-        loc = 0
-        preparseFn = self.preParse
-        parseFn = self._parse
-        ParserElement.resetCache()
-        matches = 0
-        try:
-            while loc <= instrlen and matches < maxMatches:
-                try:
-                    preloc = preparseFn( instring, loc )
-                    nextLoc,tokens = parseFn( instring, preloc, callPreParse=False )
-                except ParseException:
-                    loc = preloc+1
-                else:
-                    if nextLoc > loc:
-                        matches += 1
-                        yield tokens, preloc, nextLoc
-                        if overlap:
-                            nextloc = preparseFn( instring, loc )
-                            if nextloc > loc:
-                                loc = nextLoc
-                            else:
-                                loc += 1
-                        else:
-                            loc = nextLoc
-                    else:
-                        loc = preloc+1
-        except ParseBaseException as exc:
-            if ParserElement.verbose_stacktrace:
-                raise
-            else:
-                # catch and re-raise exception from here, clears out pyparsing internal stack trace
-                raise exc
-
-    def transformString( self, instring ):
-        """
-        Extension to C{L{scanString}}, to modify matching text with modified tokens that may
-        be returned from a parse action.  To use C{transformString}, define a grammar and
-        attach a parse action to it that modifies the returned token list.
-        Invoking C{transformString()} on a target string will then scan for matches,
-        and replace the matched text patterns according to the logic in the parse
-        action.  C{transformString()} returns the resulting transformed string.
-        
-        Example::
-            wd = Word(alphas)
-            wd.setParseAction(lambda toks: toks[0].title())
-            
-            print(wd.transformString("now is the winter of our discontent made glorious summer by this sun of york."))
-        Prints::
-            Now Is The Winter Of Our Discontent Made Glorious Summer By This Sun Of York.
-        """
-        out = []
-        lastE = 0
-        # force preservation of <TAB>s, to minimize unwanted transformation of string, and to
-        # keep string locs straight between transformString and scanString
-        self.keepTabs = True
-        try:
-            for t,s,e in self.scanString( instring ):
-                out.append( instring[lastE:s] )
-                if t:
-                    if isinstance(t,ParseResults):
-                        out += t.asList()
-                    elif isinstance(t,list):
-                        out += t
-                    else:
-                        out.append(t)
-                lastE = e
-            out.append(instring[lastE:])
-            out = [o for o in out if o]
-            return "".join(map(_ustr,_flatten(out)))
-        except ParseBaseException as exc:
-            if ParserElement.verbose_stacktrace:
-                raise
-            else:
-                # catch and re-raise exception from here, clears out pyparsing internal stack trace
-                raise exc
-
-    def searchString( self, instring, maxMatches=_MAX_INT ):
-        """
-        Another extension to C{L{scanString}}, simplifying the access to the tokens found
-        to match the given parse expression.  May be called with optional
-        C{maxMatches} argument, to clip searching after 'n' matches are found.
-        
-        Example::
-            # a capitalized word starts with an uppercase letter, followed by zero or more lowercase letters
-            cap_word = Word(alphas.upper(), alphas.lower())
-            
-            print(cap_word.searchString("More than Iron, more than Lead, more than Gold I need Electricity"))
-
-            # the sum() builtin can be used to merge results into a single ParseResults object
-            print(sum(cap_word.searchString("More than Iron, more than Lead, more than Gold I need Electricity")))
-        prints::
-            [['More'], ['Iron'], ['Lead'], ['Gold'], ['I'], ['Electricity']]
-            ['More', 'Iron', 'Lead', 'Gold', 'I', 'Electricity']
-        """
-        try:
-            return ParseResults([ t for t,s,e in self.scanString( instring, maxMatches ) ])
-        except ParseBaseException as exc:
-            if ParserElement.verbose_stacktrace:
-                raise
-            else:
-                # catch and re-raise exception from here, clears out pyparsing internal stack trace
-                raise exc
-
-    def split(self, instring, maxsplit=_MAX_INT, includeSeparators=False):
-        """
-        Generator method to split a string using the given expression as a separator.
-        May be called with optional C{maxsplit} argument, to limit the number of splits;
-        and the optional C{includeSeparators} argument (default=C{False}), if the separating
-        matching text should be included in the split results.
-        
-        Example::        
-            punc = oneOf(list(".,;:/-!?"))
-            print(list(punc.split("This, this?, this sentence, is badly punctuated!")))
-        prints::
-            ['This', ' this', '', ' this sentence', ' is badly punctuated', '']
-        """
-        splits = 0
-        last = 0
-        for t,s,e in self.scanString(instring, maxMatches=maxsplit):
-            yield instring[last:s]
-            if includeSeparators:
-                yield t[0]
-            last = e
-        yield instring[last:]
-
-    def __add__(self, other ):
-        """
-        Implementation of + operator - returns C{L{And}}. Adding strings to a ParserElement
-        converts them to L{Literal}s by default.
-        
-        Example::
-            greet = Word(alphas) + "," + Word(alphas) + "!"
-            hello = "Hello, World!"
-            print (hello, "->", greet.parseString(hello))
-        Prints::
-            Hello, World! -> ['Hello', ',', 'World', '!']
-        """
-        if isinstance( other, basestring ):
-            other = ParserElement._literalStringClass( other )
-        if not isinstance( other, ParserElement ):
-            warnings.warn("Cannot combine element of type %s with ParserElement" % type(other),
-                    SyntaxWarning, stacklevel=2)
-            return None
-        return And( [ self, other ] )
-
-    def __radd__(self, other ):
-        """
-        Implementation of + operator when left operand is not a C{L{ParserElement}}
-        """
-        if isinstance( other, basestring ):
-            other = ParserElement._literalStringClass( other )
-        if not isinstance( other, ParserElement ):
-            warnings.warn("Cannot combine element of type %s with ParserElement" % type(other),
-                    SyntaxWarning, stacklevel=2)
-            return None
-        return other + self
-
-    def __sub__(self, other):
-        """
-        Implementation of - operator, returns C{L{And}} with error stop
-        """
-        if isinstance( other, basestring ):
-            other = ParserElement._literalStringClass( other )
-        if not isinstance( other, ParserElement ):
-            warnings.warn("Cannot combine element of type %s with ParserElement" % type(other),
-                    SyntaxWarning, stacklevel=2)
-            return None
-        return self + And._ErrorStop() + other
-
-    def __rsub__(self, other ):
-        """
-        Implementation of - operator when left operand is not a C{L{ParserElement}}
-        """
-        if isinstance( other, basestring ):
-            other = ParserElement._literalStringClass( other )
-        if not isinstance( other, ParserElement ):
-            warnings.warn("Cannot combine element of type %s with ParserElement" % type(other),
-                    SyntaxWarning, stacklevel=2)
-            return None
-        return other - self
-
-    def __mul__(self,other):
-        """
-        Implementation of * operator, allows use of C{expr * 3} in place of
-        C{expr + expr + expr}.  Expressions may also me multiplied by a 2-integer
-        tuple, similar to C{{min,max}} multipliers in regular expressions.  Tuples
-        may also include C{None} as in:
-         - C{expr*(n,None)} or C{expr*(n,)} is equivalent
-              to C{expr*n + L{ZeroOrMore}(expr)}
-              (read as "at least n instances of C{expr}")
-         - C{expr*(None,n)} is equivalent to C{expr*(0,n)}
-              (read as "0 to n instances of C{expr}")
-         - C{expr*(None,None)} is equivalent to C{L{ZeroOrMore}(expr)}
-         - C{expr*(1,None)} is equivalent to C{L{OneOrMore}(expr)}
-
-        Note that C{expr*(None,n)} does not raise an exception if
-        more than n exprs exist in the input stream; that is,
-        C{expr*(None,n)} does not enforce a maximum number of expr
-        occurrences.  If this behavior is desired, then write
-        C{expr*(None,n) + ~expr}
-        """
-        if isinstance(other,int):
-            minElements, optElements = other,0
-        elif isinstance(other,tuple):
-            other = (other + (None, None))[:2]
-            if other[0] is None:
-                other = (0, other[1])
-            if isinstance(other[0],int) and other[1] is None:
-                if other[0] == 0:
-                    return ZeroOrMore(self)
-                if other[0] == 1:
-                    return OneOrMore(self)
-                else:
-                    return self*other[0] + ZeroOrMore(self)
-            elif isinstance(other[0],int) and isinstance(other[1],int):
-                minElements, optElements = other
-                optElements -= minElements
-            else:
-                raise TypeError("cannot multiply 'ParserElement' and ('%s','%s') objects", type(other[0]),type(other[1]))
-        else:
-            raise TypeError("cannot multiply 'ParserElement' and '%s' objects", type(other))
-
-        if minElements < 0:
-            raise ValueError("cannot multiply ParserElement by negative value")
-        if optElements < 0:
-            raise ValueError("second tuple value must be greater or equal to first tuple value")
-        if minElements == optElements == 0:
-            raise ValueError("cannot multiply ParserElement by 0 or (0,0)")
-
-        if (optElements):
-            def makeOptionalList(n):
-                if n>1:
-                    return Optional(self + makeOptionalList(n-1))
-                else:
-                    return Optional(self)
-            if minElements:
-                if minElements == 1:
-                    ret = self + makeOptionalList(optElements)
-                else:
-                    ret = And([self]*minElements) + makeOptionalList(optElements)
-            else:
-                ret = makeOptionalList(optElements)
-        else:
-            if minElements == 1:
-                ret = self
-            else:
-                ret = And([self]*minElements)
-        return ret
-
-    def __rmul__(self, other):
-        return self.__mul__(other)
-
-    def __or__(self, other ):
-        """
-        Implementation of | operator - returns C{L{MatchFirst}}
-        """
-        if isinstance( other, basestring ):
-            other = ParserElement._literalStringClass( other )
-        if not isinstance( other, ParserElement ):
-            warnings.warn("Cannot combine element of type %s with ParserElement" % type(other),
-                    SyntaxWarning, stacklevel=2)
-            return None
-        return MatchFirst( [ self, other ] )
-
-    def __ror__(self, other ):
-        """
-        Implementation of | operator when left operand is not a C{L{ParserElement}}
-        """
-        if isinstance( other, basestring ):
-            other = ParserElement._literalStringClass( other )
-        if not isinstance( other, ParserElement ):
-            warnings.warn("Cannot combine element of type %s with ParserElement" % type(other),
-                    SyntaxWarning, stacklevel=2)
-            return None
-        return other | self
-
-    def __xor__(self, other ):
-        """
-        Implementation of ^ operator - returns C{L{Or}}
-        """
-        if isinstance( other, basestring ):
-            other = ParserElement._literalStringClass( other )
-        if not isinstance( other, ParserElement ):
-            warnings.warn("Cannot combine element of type %s with ParserElement" % type(other),
-                    SyntaxWarning, stacklevel=2)
-            return None
-        return Or( [ self, other ] )
-
-    def __rxor__(self, other ):
-        """
-        Implementation of ^ operator when left operand is not a C{L{ParserElement}}
-        """
-        if isinstance( other, basestring ):
-            other = ParserElement._literalStringClass( other )
-        if not isinstance( other, ParserElement ):
-            warnings.warn("Cannot combine element of type %s with ParserElement" % type(other),
-                    SyntaxWarning, stacklevel=2)
-            return None
-        return other ^ self
-
-    def __and__(self, other ):
-        """
-        Implementation of & operator - returns C{L{Each}}
-        """
-        if isinstance( other, basestring ):
-            other = ParserElement._literalStringClass( other )
-        if not isinstance( other, ParserElement ):
-            warnings.warn("Cannot combine element of type %s with ParserElement" % type(other),
-                    SyntaxWarning, stacklevel=2)
-            return None
-        return Each( [ self, other ] )
-
-    def __rand__(self, other ):
-        """
-        Implementation of & operator when left operand is not a C{L{ParserElement}}
-        """
-        if isinstance( other, basestring ):
-            other = ParserElement._literalStringClass( other )
-        if not isinstance( other, ParserElement ):
-            warnings.warn("Cannot combine element of type %s with ParserElement" % type(other),
-                    SyntaxWarning, stacklevel=2)
-            return None
-        return other & self
-
-    def __invert__( self ):
-        """
-        Implementation of ~ operator - returns C{L{NotAny}}
-        """
-        return NotAny( self )
-
-    def __call__(self, name=None):
-        """
-        Shortcut for C{L{setResultsName}}, with C{listAllMatches=False}.
-        
-        If C{name} is given with a trailing C{'*'} character, then C{listAllMatches} will be
-        passed as C{True}.
-           
-        If C{name} is omitted, same as calling C{L{copy}}.
-
-        Example::
-            # these are equivalent
-            userdata = Word(alphas).setResultsName("name") + Word(nums+"-").setResultsName("socsecno")
-            userdata = Word(alphas)("name") + Word(nums+"-")("socsecno")             
-        """
-        if name is not None:
-            return self.setResultsName(name)
-        else:
-            return self.copy()
-
-    def suppress( self ):
-        """
-        Suppresses the output of this C{ParserElement}; useful to keep punctuation from
-        cluttering up returned output.
-        """
-        return Suppress( self )
-
-    def leaveWhitespace( self ):
-        """
-        Disables the skipping of whitespace before matching the characters in the
-        C{ParserElement}'s defined pattern.  This is normally only used internally by
-        the pyparsing module, but may be needed in some whitespace-sensitive grammars.
-        """
-        self.skipWhitespace = False
-        return self
-
-    def setWhitespaceChars( self, chars ):
-        """
-        Overrides the default whitespace chars
-        """
-        self.skipWhitespace = True
-        self.whiteChars = chars
-        self.copyDefaultWhiteChars = False
-        return self
-
-    def parseWithTabs( self ):
-        """
-        Overrides default behavior to expand C{<TAB>}s to spaces before parsing the input string.
-        Must be called before C{parseString} when the input grammar contains elements that
-        match C{<TAB>} characters.
-        """
-        self.keepTabs = True
-        return self
-
-    def ignore( self, other ):
-        """
-        Define expression to be ignored (e.g., comments) while doing pattern
-        matching; may be called repeatedly, to define multiple comment or other
-        ignorable patterns.
-        
-        Example::
-            patt = OneOrMore(Word(alphas))
-            patt.parseString('ablaj /* comment */ lskjd') # -> ['ablaj']
-            
-            patt.ignore(cStyleComment)
-            patt.parseString('ablaj /* comment */ lskjd') # -> ['ablaj', 'lskjd']
-        """
-        if isinstance(other, basestring):
-            other = Suppress(other)
-
-        if isinstance( other, Suppress ):
-            if other not in self.ignoreExprs:
-                self.ignoreExprs.append(other)
-        else:
-            self.ignoreExprs.append( Suppress( other.copy() ) )
-        return self
-
-    def setDebugActions( self, startAction, successAction, exceptionAction ):
-        """
-        Enable display of debugging messages while doing pattern matching.
-        """
-        self.debugActions = (startAction or _defaultStartDebugAction,
-                             successAction or _defaultSuccessDebugAction,
-                             exceptionAction or _defaultExceptionDebugAction)
-        self.debug = True
-        return self
-
-    def setDebug( self, flag=True ):
-        """
-        Enable display of debugging messages while doing pattern matching.
-        Set C{flag} to True to enable, False to disable.
-
-        Example::
-            wd = Word(alphas).setName("alphaword")
-            integer = Word(nums).setName("numword")
-            term = wd | integer
-            
-            # turn on debugging for wd
-            wd.setDebug()
-
-            OneOrMore(term).parseString("abc 123 xyz 890")
-        
-        prints::
-            Match alphaword at loc 0(1,1)
-            Matched alphaword -> ['abc']
-            Match alphaword at loc 3(1,4)
-            Exception raised:Expected alphaword (at char 4), (line:1, col:5)
-            Match alphaword at loc 7(1,8)
-            Matched alphaword -> ['xyz']
-            Match alphaword at loc 11(1,12)
-            Exception raised:Expected alphaword (at char 12), (line:1, col:13)
-            Match alphaword at loc 15(1,16)
-            Exception raised:Expected alphaword (at char 15), (line:1, col:16)
-
-        The output shown is that produced by the default debug actions - custom debug actions can be
-        specified using L{setDebugActions}. Prior to attempting
-        to match the C{wd} expression, the debugging message C{"Match <exprname> at loc <n>(<line>,<col>)"}
-        is shown. Then if the parse succeeds, a C{"Matched"} message is shown, or an C{"Exception raised"}
-        message is shown. Also note the use of L{setName} to assign a human-readable name to the expression,
-        which makes debugging and exception messages easier to understand - for instance, the default
-        name created for the C{Word} expression without calling C{setName} is C{"W:(ABCD...)"}.
-        """
-        if flag:
-            self.setDebugActions( _defaultStartDebugAction, _defaultSuccessDebugAction, _defaultExceptionDebugAction )
-        else:
-            self.debug = False
-        return self
-
-    def __str__( self ):
-        return self.name
-
-    def __repr__( self ):
-        return _ustr(self)
-
-    def streamline( self ):
-        self.streamlined = True
-        self.strRepr = None
-        return self
-
-    def checkRecursion( self, parseElementList ):
-        pass
-
-    def validate( self, validateTrace=[] ):
-        """
-        Check defined expressions for valid structure, check for infinite recursive definitions.
-        """
-        self.checkRecursion( [] )
-
-    def parseFile( self, file_or_filename, parseAll=False ):
-        """
-        Execute the parse expression on the given file or filename.
-        If a filename is specified (instead of a file object),
-        the entire file is opened, read, and closed before parsing.
-        """
-        try:
-            file_contents = file_or_filename.read()
-        except AttributeError:
-            with open(file_or_filename, "r") as f:
-                file_contents = f.read()
-        try:
-            return self.parseString(file_contents, parseAll)
-        except ParseBaseException as exc:
-            if ParserElement.verbose_stacktrace:
-                raise
-            else:
-                # catch and re-raise exception from here, clears out pyparsing internal stack trace
-                raise exc
-
-    def __eq__(self,other):
-        if isinstance(other, ParserElement):
-            return self is other or vars(self) == vars(other)
-        elif isinstance(other, basestring):
-            return self.matches(other)
-        else:
-            return super(ParserElement,self)==other
-
-    def __ne__(self,other):
-        return not (self == other)
-
-    def __hash__(self):
-        return hash(id(self))
-
-    def __req__(self,other):
-        return self == other
-
-    def __rne__(self,other):
-        return not (self == other)
-
-    def matches(self, testString, parseAll=True):
-        """
-        Method for quick testing of a parser against a test string. Good for simple 
-        inline microtests of sub expressions while building up larger parser.
-           
-        Parameters:
-         - testString - to test against this expression for a match
-         - parseAll - (default=C{True}) - flag to pass to C{L{parseString}} when running tests
-            
-        Example::
-            expr = Word(nums)
-            assert expr.matches("100")
-        """
-        try:
-            self.parseString(_ustr(testString), parseAll=parseAll)
-            return True
-        except ParseBaseException:
-            return False
-                
-    def runTests(self, tests, parseAll=True, comment='#', fullDump=True, printResults=True, failureTests=False):
-        """
-        Execute the parse expression on a series of test strings, showing each
-        test, the parsed results or where the parse failed. Quick and easy way to
-        run a parse expression against a list of sample strings.
-           
-        Parameters:
-         - tests - a list of separate test strings, or a multiline string of test strings
-         - parseAll - (default=C{True}) - flag to pass to C{L{parseString}} when running tests           
-         - comment - (default=C{'#'}) - expression for indicating embedded comments in the test 
-              string; pass None to disable comment filtering
-         - fullDump - (default=C{True}) - dump results as list followed by results names in nested outline;
-              if False, only dump nested list
-         - printResults - (default=C{True}) prints test output to stdout
-         - failureTests - (default=C{False}) indicates if these tests are expected to fail parsing
-
-        Returns: a (success, results) tuple, where success indicates that all tests succeeded
-        (or failed if C{failureTests} is True), and the results contain a list of lines of each 
-        test's output
-        
-        Example::
-            number_expr = pyparsing_common.number.copy()
-
-            result = number_expr.runTests('''
-                # unsigned integer
-                100
-                # negative integer
-                -100
-                # float with scientific notation
-                6.02e23
-                # integer with scientific notation
-                1e-12
-                ''')
-            print("Success" if result[0] else "Failed!")
-
-            result = number_expr.runTests('''
-                # stray character
-                100Z
-                # missing leading digit before '.'
-                -.100
-                # too many '.'
-                3.14.159
-                ''', failureTests=True)
-            print("Success" if result[0] else "Failed!")
-        prints::
-            # unsigned integer
-            100
-            [100]
-
-            # negative integer
-            -100
-            [-100]
-
-            # float with scientific notation
-            6.02e23
-            [6.02e+23]
-
-            # integer with scientific notation
-            1e-12
-            [1e-12]
-
-            Success
-            
-            # stray character
-            100Z
-               ^
-            FAIL: Expected end of text (at char 3), (line:1, col:4)
-
-            # missing leading digit before '.'
-            -.100
-            ^
-            FAIL: Expected {real number with scientific notation | real number | signed integer} (at char 0), (line:1, col:1)
-
-            # too many '.'
-            3.14.159
-                ^
-            FAIL: Expected end of text (at char 4), (line:1, col:5)
-
-            Success
-
-        Each test string must be on a single line. If you want to test a string that spans multiple
-        lines, create a test like this::
-
-            expr.runTest(r"this is a test\\n of strings that spans \\n 3 lines")
-        
-        (Note that this is a raw string literal, you must include the leading 'r'.)
-        """
-        if isinstance(tests, basestring):
-            tests = list(map(str.strip, tests.rstrip().splitlines()))
-        if isinstance(comment, basestring):
-            comment = Literal(comment)
-        allResults = []
-        comments = []
-        success = True
-        for t in tests:
-            if comment is not None and comment.matches(t, False) or comments and not t:
-                comments.append(t)
-                continue
-            if not t:
-                continue
-            out = ['\n'.join(comments), t]
-            comments = []
-            try:
-                t = t.replace(r'\n','\n')
-                result = self.parseString(t, parseAll=parseAll)
-                out.append(result.dump(full=fullDump))
-                success = success and not failureTests
-            except ParseBaseException as pe:
-                fatal = "(FATAL)" if isinstance(pe, ParseFatalException) else ""
-                if '\n' in t:
-                    out.append(line(pe.loc, t))
-                    out.append(' '*(col(pe.loc,t)-1) + '^' + fatal)
-                else:
-                    out.append(' '*pe.loc + '^' + fatal)
-                out.append("FAIL: " + str(pe))
-                success = success and failureTests
-                result = pe
-            except Exception as exc:
-                out.append("FAIL-EXCEPTION: " + str(exc))
-                success = success and failureTests
-                result = exc
-
-            if printResults:
-                if fullDump:
-                    out.append('')
-                print('\n'.join(out))
-
-            allResults.append((t, result))
-        
-        return success, allResults
-
-        
-class Token(ParserElement):
-    """
-    Abstract C{ParserElement} subclass, for defining atomic matching patterns.
-    """
-    def __init__( self ):
-        super(Token,self).__init__( savelist=False )
-
-
-class Empty(Token):
-    """
-    An empty token, will always match.
-    """
-    def __init__( self ):
-        super(Empty,self).__init__()
-        self.name = "Empty"
-        self.mayReturnEmpty = True
-        self.mayIndexError = False
-
-
-class NoMatch(Token):
-    """
-    A token that will never match.
-    """
-    def __init__( self ):
-        super(NoMatch,self).__init__()
-        self.name = "NoMatch"
-        self.mayReturnEmpty = True
-        self.mayIndexError = False
-        self.errmsg = "Unmatchable token"
-
-    def parseImpl( self, instring, loc, doActions=True ):
-        raise ParseException(instring, loc, self.errmsg, self)
-
-
-class Literal(Token):
-    """
-    Token to exactly match a specified string.
-    
-    Example::
-        Literal('blah').parseString('blah')  # -> ['blah']
-        Literal('blah').parseString('blahfooblah')  # -> ['blah']
-        Literal('blah').parseString('bla')  # -> Exception: Expected "blah"
-    
-    For case-insensitive matching, use L{CaselessLiteral}.
-    
-    For keyword matching (force word break before and after the matched string),
-    use L{Keyword} or L{CaselessKeyword}.
-    """
-    def __init__( self, matchString ):
-        super(Literal,self).__init__()
-        self.match = matchString
-        self.matchLen = len(matchString)
-        try:
-            self.firstMatchChar = matchString[0]
-        except IndexError:
-            warnings.warn("null string passed to Literal; use Empty() instead",
-                            SyntaxWarning, stacklevel=2)
-            self.__class__ = Empty
-        self.name = '"%s"' % _ustr(self.match)
-        self.errmsg = "Expected " + self.name
-        self.mayReturnEmpty = False
-        self.mayIndexError = False
-
-    # Performance tuning: this routine gets called a *lot*
-    # if this is a single character match string  and the first character matches,
-    # short-circuit as quickly as possible, and avoid calling startswith
-    #~ @profile
-    def parseImpl( self, instring, loc, doActions=True ):
-        if (instring[loc] == self.firstMatchChar and
-            (self.matchLen==1 or instring.startswith(self.match,loc)) ):
-            return loc+self.matchLen, self.match
-        raise ParseException(instring, loc, self.errmsg, self)
-_L = Literal
-ParserElement._literalStringClass = Literal
-
-class Keyword(Token):
-    """
-    Token to exactly match a specified string as a keyword, that is, it must be
-    immediately followed by a non-keyword character.  Compare with C{L{Literal}}:
-     - C{Literal("if")} will match the leading C{'if'} in C{'ifAndOnlyIf'}.
-     - C{Keyword("if")} will not; it will only match the leading C{'if'} in C{'if x=1'}, or C{'if(y==2)'}
-    Accepts two optional constructor arguments in addition to the keyword string:
-     - C{identChars} is a string of characters that would be valid identifier characters,
-          defaulting to all alphanumerics + "_" and "$"
-     - C{caseless} allows case-insensitive matching, default is C{False}.
-       
-    Example::
-        Keyword("start").parseString("start")  # -> ['start']
-        Keyword("start").parseString("starting")  # -> Exception
-
-    For case-insensitive matching, use L{CaselessKeyword}.
-    """
-    DEFAULT_KEYWORD_CHARS = alphanums+"_$"
-
-    def __init__( self, matchString, identChars=None, caseless=False ):
-        super(Keyword,self).__init__()
-        if identChars is None:
-            identChars = Keyword.DEFAULT_KEYWORD_CHARS
-        self.match = matchString
-        self.matchLen = len(matchString)
-        try:
-            self.firstMatchChar = matchString[0]
-        except IndexError:
-            warnings.warn("null string passed to Keyword; use Empty() instead",
-                            SyntaxWarning, stacklevel=2)
-        self.name = '"%s"' % self.match
-        self.errmsg = "Expected " + self.name
-        self.mayReturnEmpty = False
-        self.mayIndexError = False
-        self.caseless = caseless
-        if caseless:
-            self.caselessmatch = matchString.upper()
-            identChars = identChars.upper()
-        self.identChars = set(identChars)
-
-    def parseImpl( self, instring, loc, doActions=True ):
-        if self.caseless:
-            if ( (instring[ loc:loc+self.matchLen ].upper() == self.caselessmatch) and
-                 (loc >= len(instring)-self.matchLen or instring[loc+self.matchLen].upper() not in self.identChars) and
-                 (loc == 0 or instring[loc-1].upper() not in self.identChars) ):
-                return loc+self.matchLen, self.match
-        else:
-            if (instring[loc] == self.firstMatchChar and
-                (self.matchLen==1 or instring.startswith(self.match,loc)) and
-                (loc >= len(instring)-self.matchLen or instring[loc+self.matchLen] not in self.identChars) and
-                (loc == 0 or instring[loc-1] not in self.identChars) ):
-                return loc+self.matchLen, self.match
-        raise ParseException(instring, loc, self.errmsg, self)
-
-    def copy(self):
-        c = super(Keyword,self).copy()
-        c.identChars = Keyword.DEFAULT_KEYWORD_CHARS
-        return c
-
-    @staticmethod
-    def setDefaultKeywordChars( chars ):
-        """Overrides the default Keyword chars
-        """
-        Keyword.DEFAULT_KEYWORD_CHARS = chars
-
-class CaselessLiteral(Literal):
-    """
-    Token to match a specified string, ignoring case of letters.
-    Note: the matched results will always be in the case of the given
-    match string, NOT the case of the input text.
-
-    Example::
-        OneOrMore(CaselessLiteral("CMD")).parseString("cmd CMD Cmd10") # -> ['CMD', 'CMD', 'CMD']
-        
-    (Contrast with example for L{CaselessKeyword}.)
-    """
-    def __init__( self, matchString ):
-        super(CaselessLiteral,self).__init__( matchString.upper() )
-        # Preserve the defining literal.
-        self.returnString = matchString
-        self.name = "'%s'" % self.returnString
-        self.errmsg = "Expected " + self.name
-
-    def parseImpl( self, instring, loc, doActions=True ):
-        if instring[ loc:loc+self.matchLen ].upper() == self.match:
-            return loc+self.matchLen, self.returnString
-        raise ParseException(instring, loc, self.errmsg, self)
-
-class CaselessKeyword(Keyword):
-    """
-    Caseless version of L{Keyword}.
-
-    Example::
-        OneOrMore(CaselessKeyword("CMD")).parseString("cmd CMD Cmd10") # -> ['CMD', 'CMD']
-        
-    (Contrast with example for L{CaselessLiteral}.)
-    """
-    def __init__( self, matchString, identChars=None ):
-        super(CaselessKeyword,self).__init__( matchString, identChars, caseless=True )
-
-    def parseImpl( self, instring, loc, doActions=True ):
-        if ( (instring[ loc:loc+self.matchLen ].upper() == self.caselessmatch) and
-             (loc >= len(instring)-self.matchLen or instring[loc+self.matchLen].upper() not in self.identChars) ):
-            return loc+self.matchLen, self.match
-        raise ParseException(instring, loc, self.errmsg, self)
-
-class CloseMatch(Token):
-    """
-    A variation on L{Literal} which matches "close" matches, that is, 
-    strings with at most 'n' mismatching characters. C{CloseMatch} takes parameters:
-     - C{match_string} - string to be matched
-     - C{maxMismatches} - (C{default=1}) maximum number of mismatches allowed to count as a match
-    
-    The results from a successful parse will contain the matched text from the input string and the following named results:
-     - C{mismatches} - a list of the positions within the match_string where mismatches were found
-     - C{original} - the original match_string used to compare against the input string
-    
-    If C{mismatches} is an empty list, then the match was an exact match.
-    
-    Example::
-        patt = CloseMatch("ATCATCGAATGGA")
-        patt.parseString("ATCATCGAAXGGA") # -> (['ATCATCGAAXGGA'], {'mismatches': [[9]], 'original': ['ATCATCGAATGGA']})
-        patt.parseString("ATCAXCGAAXGGA") # -> Exception: Expected 'ATCATCGAATGGA' (with up to 1 mismatches) (at char 0), (line:1, col:1)
-
-        # exact match
-        patt.parseString("ATCATCGAATGGA") # -> (['ATCATCGAATGGA'], {'mismatches': [[]], 'original': ['ATCATCGAATGGA']})
-
-        # close match allowing up to 2 mismatches
-        patt = CloseMatch("ATCATCGAATGGA", maxMismatches=2)
-        patt.parseString("ATCAXCGAAXGGA") # -> (['ATCAXCGAAXGGA'], {'mismatches': [[4, 9]], 'original': ['ATCATCGAATGGA']})
-    """
-    def __init__(self, match_string, maxMismatches=1):
-        super(CloseMatch,self).__init__()
-        self.name = match_string
-        self.match_string = match_string
-        self.maxMismatches = maxMismatches
-        self.errmsg = "Expected %r (with up to %d mismatches)" % (self.match_string, self.maxMismatches)
-        self.mayIndexError = False
-        self.mayReturnEmpty = False
-
-    def parseImpl( self, instring, loc, doActions=True ):
-        start = loc
-        instrlen = len(instring)
-        maxloc = start + len(self.match_string)
-
-        if maxloc <= instrlen:
-            match_string = self.match_string
-            match_stringloc = 0
-            mismatches = []
-            maxMismatches = self.maxMismatches
-
-            for match_stringloc,s_m in enumerate(zip(instring[loc:maxloc], self.match_string)):
-                src,mat = s_m
-                if src != mat:
-                    mismatches.append(match_stringloc)
-                    if len(mismatches) > maxMismatches:
-                        break
-            else:
-                loc = match_stringloc + 1
-                results = ParseResults([instring[start:loc]])
-                results['original'] = self.match_string
-                results['mismatches'] = mismatches
-                return loc, results
-
-        raise ParseException(instring, loc, self.errmsg, self)
-
-
-class Word(Token):
-    """
-    Token for matching words composed of allowed character sets.
-    Defined with string containing all allowed initial characters,
-    an optional string containing allowed body characters (if omitted,
-    defaults to the initial character set), and an optional minimum,
-    maximum, and/or exact length.  The default value for C{min} is 1 (a
-    minimum value < 1 is not valid); the default values for C{max} and C{exact}
-    are 0, meaning no maximum or exact length restriction. An optional
-    C{excludeChars} parameter can list characters that might be found in 
-    the input C{bodyChars} string; useful to define a word of all printables
-    except for one or two characters, for instance.
-    
-    L{srange} is useful for defining custom character set strings for defining 
-    C{Word} expressions, using range notation from regular expression character sets.
-    
-    A common mistake is to use C{Word} to match a specific literal string, as in 
-    C{Word("Address")}. Remember that C{Word} uses the string argument to define
-    I{sets} of matchable characters. This expression would match "Add", "AAA",
-    "dAred", or any other word made up of the characters 'A', 'd', 'r', 'e', and 's'.
-    To match an exact literal string, use L{Literal} or L{Keyword}.
-
-    pyparsing includes helper strings for building Words:
-     - L{alphas}
-     - L{nums}
-     - L{alphanums}
-     - L{hexnums}
-     - L{alphas8bit} (alphabetic characters in ASCII range 128-255 - accented, tilded, umlauted, etc.)
-     - L{punc8bit} (non-alphabetic characters in ASCII range 128-255 - currency, symbols, superscripts, diacriticals, etc.)
-     - L{printables} (any non-whitespace character)
-
-    Example::
-        # a word composed of digits
-        integer = Word(nums) # equivalent to Word("0123456789") or Word(srange("0-9"))
-        
-        # a word with a leading capital, and zero or more lowercase
-        capital_word = Word(alphas.upper(), alphas.lower())
-
-        # hostnames are alphanumeric, with leading alpha, and '-'
-        hostname = Word(alphas, alphanums+'-')
-        
-        # roman numeral (not a strict parser, accepts invalid mix of characters)
-        roman = Word("IVXLCDM")
-        
-        # any string of non-whitespace characters, except for ','
-        csv_value = Word(printables, excludeChars=",")
-    """
-    def __init__( self, initChars, bodyChars=None, min=1, max=0, exact=0, asKeyword=False, excludeChars=None ):
-        super(Word,self).__init__()
-        if excludeChars:
-            initChars = ''.join(c for c in initChars if c not in excludeChars)
-            if bodyChars:
-                bodyChars = ''.join(c for c in bodyChars if c not in excludeChars)
-        self.initCharsOrig = initChars
-        self.initChars = set(initChars)
-        if bodyChars :
-            self.bodyCharsOrig = bodyChars
-            self.bodyChars = set(bodyChars)
-        else:
-            self.bodyCharsOrig = initChars
-            self.bodyChars = set(initChars)
-
-        self.maxSpecified = max > 0
-
-        if min < 1:
-            raise ValueError("cannot specify a minimum length < 1; use Optional(Word()) if zero-length word is permitted")
-
-        self.minLen = min
-
-        if max > 0:
-            self.maxLen = max
-        else:
-            self.maxLen = _MAX_INT
-
-        if exact > 0:
-            self.maxLen = exact
-            self.minLen = exact
-
-        self.name = _ustr(self)
-        self.errmsg = "Expected " + self.name
-        self.mayIndexError = False
-        self.asKeyword = asKeyword
-
-        if ' ' not in self.initCharsOrig+self.bodyCharsOrig and (min==1 and max==0 and exact==0):
-            if self.bodyCharsOrig == self.initCharsOrig:
-                self.reString = "[%s]+" % _escapeRegexRangeChars(self.initCharsOrig)
-            elif len(self.initCharsOrig) == 1:
-                self.reString = "%s[%s]*" % \
-                                      (re.escape(self.initCharsOrig),
-                                      _escapeRegexRangeChars(self.bodyCharsOrig),)
-            else:
-                self.reString = "[%s][%s]*" % \
-                                      (_escapeRegexRangeChars(self.initCharsOrig),
-                                      _escapeRegexRangeChars(self.bodyCharsOrig),)
-            if self.asKeyword:
-                self.reString = r"\b"+self.reString+r"\b"
-            try:
-                self.re = re.compile( self.reString )
-            except Exception:
-                self.re = None
-
-    def parseImpl( self, instring, loc, doActions=True ):
-        if self.re:
-            result = self.re.match(instring,loc)
-            if not result:
-                raise ParseException(instring, loc, self.errmsg, self)
-
-            loc = result.end()
-            return loc, result.group()
-
-        if not(instring[ loc ] in self.initChars):
-            raise ParseException(instring, loc, self.errmsg, self)
-
-        start = loc
-        loc += 1
-        instrlen = len(instring)
-        bodychars = self.bodyChars
-        maxloc = start + self.maxLen
-        maxloc = min( maxloc, instrlen )
-        while loc < maxloc and instring[loc] in bodychars:
-            loc += 1
-
-        throwException = False
-        if loc - start < self.minLen:
-            throwException = True
-        if self.maxSpecified and loc < instrlen and instring[loc] in bodychars:
-            throwException = True
-        if self.asKeyword:
-            if (start>0 and instring[start-1] in bodychars) or (loc<instrlen and instring[loc] in bodychars):
-                throwException = True
-
-        if throwException:
-            raise ParseException(instring, loc, self.errmsg, self)
-
-        return loc, instring[start:loc]
-
-    def __str__( self ):
-        try:
-            return super(Word,self).__str__()
-        except Exception:
-            pass
-
-
-        if self.strRepr is None:
-
-            def charsAsStr(s):
-                if len(s)>4:
-                    return s[:4]+"..."
-                else:
-                    return s
-
-            if ( self.initCharsOrig != self.bodyCharsOrig ):
-                self.strRepr = "W:(%s,%s)" % ( charsAsStr(self.initCharsOrig), charsAsStr(self.bodyCharsOrig) )
-            else:
-                self.strRepr = "W:(%s)" % charsAsStr(self.initCharsOrig)
-
-        return self.strRepr
-
-
-class Regex(Token):
-    r"""
-    Token for matching strings that match a given regular expression.
-    Defined with string specifying the regular expression in a form recognized by the inbuilt Python re module.
-    If the given regex contains named groups (defined using C{(?P<name>...)}), these will be preserved as 
-    named parse results.
-
-    Example::
-        realnum = Regex(r"[+-]?\d+\.\d*")
-        date = Regex(r'(?P<year>\d{4})-(?P<month>\d\d?)-(?P<day>\d\d?)')
-        # ref: https://stackoverflow.com/questions/267399/how-do-you-match-only-valid-roman-numerals-with-a-regular-expression
-        roman = Regex(r"M{0,4}(CM|CD|D?C{0,3})(XC|XL|L?X{0,3})(IX|IV|V?I{0,3})")
-    """
-    compiledREtype = type(re.compile("[A-Z]"))
-    def __init__( self, pattern, flags=0):
-        """The parameters C{pattern} and C{flags} are passed to the C{re.compile()} function as-is. See the Python C{re} module for an explanation of the acceptable patterns and flags."""
-        super(Regex,self).__init__()
-
-        if isinstance(pattern, basestring):
-            if not pattern:
-                warnings.warn("null string passed to Regex; use Empty() instead",
-                        SyntaxWarning, stacklevel=2)
-
-            self.pattern = pattern
-            self.flags = flags
-
-            try:
-                self.re = re.compile(self.pattern, self.flags)
-                self.reString = self.pattern
-            except sre_constants.error:
-                warnings.warn("invalid pattern (%s) passed to Regex" % pattern,
-                    SyntaxWarning, stacklevel=2)
-                raise
-
-        elif isinstance(pattern, Regex.compiledREtype):
-            self.re = pattern
-            self.pattern = \
-            self.reString = str(pattern)
-            self.flags = flags
-            
-        else:
-            raise ValueError("Regex may only be constructed with a string or a compiled RE object")
-
-        self.name = _ustr(self)
-        self.errmsg = "Expected " + self.name
-        self.mayIndexError = False
-        self.mayReturnEmpty = True
-
-    def parseImpl( self, instring, loc, doActions=True ):
-        result = self.re.match(instring,loc)
-        if not result:
-            raise ParseException(instring, loc, self.errmsg, self)
-
-        loc = result.end()
-        d = result.groupdict()
-        ret = ParseResults(result.group())
-        if d:
-            for k in d:
-                ret[k] = d[k]
-        return loc,ret
-
-    def __str__( self ):
-        try:
-            return super(Regex,self).__str__()
-        except Exception:
-            pass
-
-        if self.strRepr is None:
-            self.strRepr = "Re:(%s)" % repr(self.pattern)
-
-        return self.strRepr
-
-
-class QuotedString(Token):
-    r"""
-    Token for matching strings that are delimited by quoting characters.
-    
-    Defined with the following parameters:
-        - quoteChar - string of one or more characters defining the quote delimiting string
-        - escChar - character to escape quotes, typically backslash (default=C{None})
-        - escQuote - special quote sequence to escape an embedded quote string (such as SQL's "" to escape an embedded ") (default=C{None})
-        - multiline - boolean indicating whether quotes can span multiple lines (default=C{False})
-        - unquoteResults - boolean indicating whether the matched text should be unquoted (default=C{True})
-        - endQuoteChar - string of one or more characters defining the end of the quote delimited string (default=C{None} => same as quoteChar)
-        - convertWhitespaceEscapes - convert escaped whitespace (C{'\t'}, C{'\n'}, etc.) to actual whitespace (default=C{True})
-
-    Example::
-        qs = QuotedString('"')
-        print(qs.searchString('lsjdf "This is the quote" sldjf'))
-        complex_qs = QuotedString('{{', endQuoteChar='}}')
-        print(complex_qs.searchString('lsjdf {{This is the "quote"}} sldjf'))
-        sql_qs = QuotedString('"', escQuote='""')
-        print(sql_qs.searchString('lsjdf "This is the quote with ""embedded"" quotes" sldjf'))
-    prints::
-        [['This is the quote']]
-        [['This is the "quote"']]
-        [['This is the quote with "embedded" quotes']]
-    """
-    def __init__( self, quoteChar, escChar=None, escQuote=None, multiline=False, unquoteResults=True, endQuoteChar=None, convertWhitespaceEscapes=True):
-        super(QuotedString,self).__init__()
-
-        # remove white space from quote chars - won't work anyway
-        quoteChar = quoteChar.strip()
-        if not quoteChar:
-            warnings.warn("quoteChar cannot be the empty string",SyntaxWarning,stacklevel=2)
-            raise SyntaxError()
-
-        if endQuoteChar is None:
-            endQuoteChar = quoteChar
-        else:
-            endQuoteChar = endQuoteChar.strip()
-            if not endQuoteChar:
-                warnings.warn("endQuoteChar cannot be the empty string",SyntaxWarning,stacklevel=2)
-                raise SyntaxError()
-
-        self.quoteChar = quoteChar
-        self.quoteCharLen = len(quoteChar)
-        self.firstQuoteChar = quoteChar[0]
-        self.endQuoteChar = endQuoteChar
-        self.endQuoteCharLen = len(endQuoteChar)
-        self.escChar = escChar
-        self.escQuote = escQuote
-        self.unquoteResults = unquoteResults
-        self.convertWhitespaceEscapes = convertWhitespaceEscapes
-
-        if multiline:
-            self.flags = re.MULTILINE | re.DOTALL
-            self.pattern = r'%s(?:[^%s%s]' % \
-                ( re.escape(self.quoteChar),
-                  _escapeRegexRangeChars(self.endQuoteChar[0]),
-                  (escChar is not None and _escapeRegexRangeChars(escChar) or '') )
-        else:
-            self.flags = 0
-            self.pattern = r'%s(?:[^%s\n\r%s]' % \
-                ( re.escape(self.quoteChar),
-                  _escapeRegexRangeChars(self.endQuoteChar[0]),
-                  (escChar is not None and _escapeRegexRangeChars(escChar) or '') )
-        if len(self.endQuoteChar) > 1:
-            self.pattern += (
-                '|(?:' + ')|(?:'.join("%s[^%s]" % (re.escape(self.endQuoteChar[:i]),
-                                               _escapeRegexRangeChars(self.endQuoteChar[i]))
-                                    for i in range(len(self.endQuoteChar)-1,0,-1)) + ')'
-                )
-        if escQuote:
-            self.pattern += (r'|(?:%s)' % re.escape(escQuote))
-        if escChar:
-            self.pattern += (r'|(?:%s.)' % re.escape(escChar))
-            self.escCharReplacePattern = re.escape(self.escChar)+"(.)"
-        self.pattern += (r')*%s' % re.escape(self.endQuoteChar))
-
-        try:
-            self.re = re.compile(self.pattern, self.flags)
-            self.reString = self.pattern
-        except sre_constants.error:
-            warnings.warn("invalid pattern (%s) passed to Regex" % self.pattern,
-                SyntaxWarning, stacklevel=2)
-            raise
-
-        self.name = _ustr(self)
-        self.errmsg = "Expected " + self.name
-        self.mayIndexError = False
-        self.mayReturnEmpty = True
-
-    def parseImpl( self, instring, loc, doActions=True ):
-        result = instring[loc] == self.firstQuoteChar and self.re.match(instring,loc) or None
-        if not result:
-            raise ParseException(instring, loc, self.errmsg, self)
-
-        loc = result.end()
-        ret = result.group()
-
-        if self.unquoteResults:
-
-            # strip off quotes
-            ret = ret[self.quoteCharLen:-self.endQuoteCharLen]
-
-            if isinstance(ret,basestring):
-                # replace escaped whitespace
-                if '\\' in ret and self.convertWhitespaceEscapes:
-                    ws_map = {
-                        r'\t' : '\t',
-                        r'\n' : '\n',
-                        r'\f' : '\f',
-                        r'\r' : '\r',
-                    }
-                    for wslit,wschar in ws_map.items():
-                        ret = ret.replace(wslit, wschar)
-
-                # replace escaped characters
-                if self.escChar:
-                    ret = re.sub(self.escCharReplacePattern, r"\g<1>", ret)
-
-                # replace escaped quotes
-                if self.escQuote:
-                    ret = ret.replace(self.escQuote, self.endQuoteChar)
-
-        return loc, ret
-
-    def __str__( self ):
-        try:
-            return super(QuotedString,self).__str__()
-        except Exception:
-            pass
-
-        if self.strRepr is None:
-            self.strRepr = "quoted string, starting with %s ending with %s" % (self.quoteChar, self.endQuoteChar)
-
-        return self.strRepr
-
-
-class CharsNotIn(Token):
-    """
-    Token for matching words composed of characters I{not} in a given set (will
-    include whitespace in matched characters if not listed in the provided exclusion set - see example).
-    Defined with string containing all disallowed characters, and an optional
-    minimum, maximum, and/or exact length.  The default value for C{min} is 1 (a
-    minimum value < 1 is not valid); the default values for C{max} and C{exact}
-    are 0, meaning no maximum or exact length restriction.
-
-    Example::
-        # define a comma-separated-value as anything that is not a ','
-        csv_value = CharsNotIn(',')
-        print(delimitedList(csv_value).parseString("dkls,lsdkjf,s12 34,@!#,213"))
-    prints::
-        ['dkls', 'lsdkjf', 's12 34', '@!#', '213']
-    """
-    def __init__( self, notChars, min=1, max=0, exact=0 ):
-        super(CharsNotIn,self).__init__()
-        self.skipWhitespace = False
-        self.notChars = notChars
-
-        if min < 1:
-            raise ValueError("cannot specify a minimum length < 1; use Optional(CharsNotIn()) if zero-length char group is permitted")
-
-        self.minLen = min
-
-        if max > 0:
-            self.maxLen = max
-        else:
-            self.maxLen = _MAX_INT
-
-        if exact > 0:
-            self.maxLen = exact
-            self.minLen = exact
-
-        self.name = _ustr(self)
-        self.errmsg = "Expected " + self.name
-        self.mayReturnEmpty = ( self.minLen == 0 )
-        self.mayIndexError = False
-
-    def parseImpl( self, instring, loc, doActions=True ):
-        if instring[loc] in self.notChars:
-            raise ParseException(instring, loc, self.errmsg, self)
-
-        start = loc
-        loc += 1
-        notchars = self.notChars
-        maxlen = min( start+self.maxLen, len(instring) )
-        while loc < maxlen and \
-              (instring[loc] not in notchars):
-            loc += 1
-
-        if loc - start < self.minLen:
-            raise ParseException(instring, loc, self.errmsg, self)
-
-        return loc, instring[start:loc]
-
-    def __str__( self ):
-        try:
-            return super(CharsNotIn, self).__str__()
-        except Exception:
-            pass
-
-        if self.strRepr is None:
-            if len(self.notChars) > 4:
-                self.strRepr = "!W:(%s...)" % self.notChars[:4]
-            else:
-                self.strRepr = "!W:(%s)" % self.notChars
-
-        return self.strRepr
-
-class White(Token):
-    """
-    Special matching class for matching whitespace.  Normally, whitespace is ignored
-    by pyparsing grammars.  This class is included when some whitespace structures
-    are significant.  Define with a string containing the whitespace characters to be
-    matched; default is C{" \\t\\r\\n"}.  Also takes optional C{min}, C{max}, and C{exact} arguments,
-    as defined for the C{L{Word}} class.
-    """
-    whiteStrs = {
-        " " : "<SPC>",
-        "\t": "<TAB>",
-        "\n": "<LF>",
-        "\r": "<CR>",
-        "\f": "<FF>",
-        }
-    def __init__(self, ws=" \t\r\n", min=1, max=0, exact=0):
-        super(White,self).__init__()
-        self.matchWhite = ws
-        self.setWhitespaceChars( "".join(c for c in self.whiteChars if c not in self.matchWhite) )
-        #~ self.leaveWhitespace()
-        self.name = ("".join(White.whiteStrs[c] for c in self.matchWhite))
-        self.mayReturnEmpty = True
-        self.errmsg = "Expected " + self.name
-
-        self.minLen = min
-
-        if max > 0:
-            self.maxLen = max
-        else:
-            self.maxLen = _MAX_INT
-
-        if exact > 0:
-            self.maxLen = exact
-            self.minLen = exact
-
-    def parseImpl( self, instring, loc, doActions=True ):
-        if not(instring[ loc ] in self.matchWhite):
-            raise ParseException(instring, loc, self.errmsg, self)
-        start = loc
-        loc += 1
-        maxloc = start + self.maxLen
-        maxloc = min( maxloc, len(instring) )
-        while loc < maxloc and instring[loc] in self.matchWhite:
-            loc += 1
-
-        if loc - start < self.minLen:
-            raise ParseException(instring, loc, self.errmsg, self)
-
-        return loc, instring[start:loc]
-
-
-class _PositionToken(Token):
-    def __init__( self ):
-        super(_PositionToken,self).__init__()
-        self.name=self.__class__.__name__
-        self.mayReturnEmpty = True
-        self.mayIndexError = False
-
-class GoToColumn(_PositionToken):
-    """
-    Token to advance to a specific column of input text; useful for tabular report scraping.
-    """
-    def __init__( self, colno ):
-        super(GoToColumn,self).__init__()
-        self.col = colno
-
-    def preParse( self, instring, loc ):
-        if col(loc,instring) != self.col:
-            instrlen = len(instring)
-            if self.ignoreExprs:
-                loc = self._skipIgnorables( instring, loc )
-            while loc < instrlen and instring[loc].isspace() and col( loc, instring ) != self.col :
-                loc += 1
-        return loc
-
-    def parseImpl( self, instring, loc, doActions=True ):
-        thiscol = col( loc, instring )
-        if thiscol > self.col:
-            raise ParseException( instring, loc, "Text not in expected column", self )
-        newloc = loc + self.col - thiscol
-        ret = instring[ loc: newloc ]
-        return newloc, ret
-
-
-class LineStart(_PositionToken):
-    """
-    Matches if current position is at the beginning of a line within the parse string
-    
-    Example::
-    
-        test = '''\
-        AAA this line
-        AAA and this line
-          AAA but not this one
-        B AAA and definitely not this one
-        '''
-
-        for t in (LineStart() + 'AAA' + restOfLine).searchString(test):
-            print(t)
-    
-    Prints::
-        ['AAA', ' this line']
-        ['AAA', ' and this line']    
-
-    """
-    def __init__( self ):
-        super(LineStart,self).__init__()
-        self.errmsg = "Expected start of line"
-
-    def parseImpl( self, instring, loc, doActions=True ):
-        if col(loc, instring) == 1:
-            return loc, []
-        raise ParseException(instring, loc, self.errmsg, self)
-
-class LineEnd(_PositionToken):
-    """
-    Matches if current position is at the end of a line within the parse string
-    """
-    def __init__( self ):
-        super(LineEnd,self).__init__()
-        self.setWhitespaceChars( ParserElement.DEFAULT_WHITE_CHARS.replace("\n","") )
-        self.errmsg = "Expected end of line"
-
-    def parseImpl( self, instring, loc, doActions=True ):
-        if loc<len(instring):
-            if instring[loc] == "\n":
-                return loc+1, "\n"
-            else:
-                raise ParseException(instring, loc, self.errmsg, self)
-        elif loc == len(instring):
-            return loc+1, []
-        else:
-            raise ParseException(instring, loc, self.errmsg, self)
-
-class StringStart(_PositionToken):
-    """
-    Matches if current position is at the beginning of the parse string
-    """
-    def __init__( self ):
-        super(StringStart,self).__init__()
-        self.errmsg = "Expected start of text"
-
-    def parseImpl( self, instring, loc, doActions=True ):
-        if loc != 0:
-            # see if entire string up to here is just whitespace and ignoreables
-            if loc != self.preParse( instring, 0 ):
-                raise ParseException(instring, loc, self.errmsg, self)
-        return loc, []
-
-class StringEnd(_PositionToken):
-    """
-    Matches if current position is at the end of the parse string
-    """
-    def __init__( self ):
-        super(StringEnd,self).__init__()
-        self.errmsg = "Expected end of text"
-
-    def parseImpl( self, instring, loc, doActions=True ):
-        if loc < len(instring):
-            raise ParseException(instring, loc, self.errmsg, self)
-        elif loc == len(instring):
-            return loc+1, []
-        elif loc > len(instring):
-            return loc, []
-        else:
-            raise ParseException(instring, loc, self.errmsg, self)
-
-class WordStart(_PositionToken):
-    """
-    Matches if the current position is at the beginning of a Word, and
-    is not preceded by any character in a given set of C{wordChars}
-    (default=C{printables}). To emulate the C{\b} behavior of regular expressions,
-    use C{WordStart(alphanums)}. C{WordStart} will also match at the beginning of
-    the string being parsed, or at the beginning of a line.
-    """
-    def __init__(self, wordChars = printables):
-        super(WordStart,self).__init__()
-        self.wordChars = set(wordChars)
-        self.errmsg = "Not at the start of a word"
-
-    def parseImpl(self, instring, loc, doActions=True ):
-        if loc != 0:
-            if (instring[loc-1] in self.wordChars or
-                instring[loc] not in self.wordChars):
-                raise ParseException(instring, loc, self.errmsg, self)
-        return loc, []
-
-class WordEnd(_PositionToken):
-    """
-    Matches if the current position is at the end of a Word, and
-    is not followed by any character in a given set of C{wordChars}
-    (default=C{printables}). To emulate the C{\b} behavior of regular expressions,
-    use C{WordEnd(alphanums)}. C{WordEnd} will also match at the end of
-    the string being parsed, or at the end of a line.
-    """
-    def __init__(self, wordChars = printables):
-        super(WordEnd,self).__init__()
-        self.wordChars = set(wordChars)
-        self.skipWhitespace = False
-        self.errmsg = "Not at the end of a word"
-
-    def parseImpl(self, instring, loc, doActions=True ):
-        instrlen = len(instring)
-        if instrlen>0 and loc<instrlen:
-            if (instring[loc] in self.wordChars or
-                instring[loc-1] not in self.wordChars):
-                raise ParseException(instring, loc, self.errmsg, self)
-        return loc, []
-
-
-class ParseExpression(ParserElement):
-    """
-    Abstract subclass of ParserElement, for combining and post-processing parsed tokens.
-    """
-    def __init__( self, exprs, savelist = False ):
-        super(ParseExpression,self).__init__(savelist)
-        if isinstance( exprs, _generatorType ):
-            exprs = list(exprs)
-
-        if isinstance( exprs, basestring ):
-            self.exprs = [ ParserElement._literalStringClass( exprs ) ]
-        elif isinstance( exprs, collections.Iterable ):
-            exprs = list(exprs)
-            # if sequence of strings provided, wrap with Literal
-            if all(isinstance(expr, basestring) for expr in exprs):
-                exprs = map(ParserElement._literalStringClass, exprs)
-            self.exprs = list(exprs)
-        else:
-            try:
-                self.exprs = list( exprs )
-            except TypeError:
-                self.exprs = [ exprs ]
-        self.callPreparse = False
-
-    def __getitem__( self, i ):
-        return self.exprs[i]
-
-    def append( self, other ):
-        self.exprs.append( other )
-        self.strRepr = None
-        return self
-
-    def leaveWhitespace( self ):
-        """Extends C{leaveWhitespace} defined in base class, and also invokes C{leaveWhitespace} on
-           all contained expressions."""
-        self.skipWhitespace = False
-        self.exprs = [ e.copy() for e in self.exprs ]
-        for e in self.exprs:
-            e.leaveWhitespace()
-        return self
-
-    def ignore( self, other ):
-        if isinstance( other, Suppress ):
-            if other not in self.ignoreExprs:
-                super( ParseExpression, self).ignore( other )
-                for e in self.exprs:
-                    e.ignore( self.ignoreExprs[-1] )
-        else:
-            super( ParseExpression, self).ignore( other )
-            for e in self.exprs:
-                e.ignore( self.ignoreExprs[-1] )
-        return self
-
-    def __str__( self ):
-        try:
-            return super(ParseExpression,self).__str__()
-        except Exception:
-            pass
-
-        if self.strRepr is None:
-            self.strRepr = "%s:(%s)" % ( self.__class__.__name__, _ustr(self.exprs) )
-        return self.strRepr
-
-    def streamline( self ):
-        super(ParseExpression,self).streamline()
-
-        for e in self.exprs:
-            e.streamline()
-
-        # collapse nested And's of the form And( And( And( a,b), c), d) to And( a,b,c,d )
-        # but only if there are no parse actions or resultsNames on the nested And's
-        # (likewise for Or's and MatchFirst's)
-        if ( len(self.exprs) == 2 ):
-            other = self.exprs[0]
-            if ( isinstance( other, self.__class__ ) and
-                  not(other.parseAction) and
-                  other.resultsName is None and
-                  not other.debug ):
-                self.exprs = other.exprs[:] + [ self.exprs[1] ]
-                self.strRepr = None
-                self.mayReturnEmpty |= other.mayReturnEmpty
-                self.mayIndexError  |= other.mayIndexError
-
-            other = self.exprs[-1]
-            if ( isinstance( other, self.__class__ ) and
-                  not(other.parseAction) and
-                  other.resultsName is None and
-                  not other.debug ):
-                self.exprs = self.exprs[:-1] + other.exprs[:]
-                self.strRepr = None
-                self.mayReturnEmpty |= other.mayReturnEmpty
-                self.mayIndexError  |= other.mayIndexError
-
-        self.errmsg = "Expected " + _ustr(self)
-        
-        return self
-
-    def setResultsName( self, name, listAllMatches=False ):
-        ret = super(ParseExpression,self).setResultsName(name,listAllMatches)
-        return ret
-
-    def validate( self, validateTrace=[] ):
-        tmp = validateTrace[:]+[self]
-        for e in self.exprs:
-            e.validate(tmp)
-        self.checkRecursion( [] )
-        
-    def copy(self):
-        ret = super(ParseExpression,self).copy()
-        ret.exprs = [e.copy() for e in self.exprs]
-        return ret
-
-class And(ParseExpression):
-    """
-    Requires all given C{ParseExpression}s to be found in the given order.
-    Expressions may be separated by whitespace.
-    May be constructed using the C{'+'} operator.
-    May also be constructed using the C{'-'} operator, which will suppress backtracking.
-
-    Example::
-        integer = Word(nums)
-        name_expr = OneOrMore(Word(alphas))
-
-        expr = And([integer("id"),name_expr("name"),integer("age")])
-        # more easily written as:
-        expr = integer("id") + name_expr("name") + integer("age")
-    """
-
-    class _ErrorStop(Empty):
-        def __init__(self, *args, **kwargs):
-            super(And._ErrorStop,self).__init__(*args, **kwargs)
-            self.name = '-'
-            self.leaveWhitespace()
-
-    def __init__( self, exprs, savelist = True ):
-        super(And,self).__init__(exprs, savelist)
-        self.mayReturnEmpty = all(e.mayReturnEmpty for e in self.exprs)
-        self.setWhitespaceChars( self.exprs[0].whiteChars )
-        self.skipWhitespace = self.exprs[0].skipWhitespace
-        self.callPreparse = True
-
-    def parseImpl( self, instring, loc, doActions=True ):
-        # pass False as last arg to _parse for first element, since we already
-        # pre-parsed the string as part of our And pre-parsing
-        loc, resultlist = self.exprs[0]._parse( instring, loc, doActions, callPreParse=False )
-        errorStop = False
-        for e in self.exprs[1:]:
-            if isinstance(e, And._ErrorStop):
-                errorStop = True
-                continue
-            if errorStop:
-                try:
-                    loc, exprtokens = e._parse( instring, loc, doActions )
-                except ParseSyntaxException:
-                    raise
-                except ParseBaseException as pe:
-                    pe.__traceback__ = None
-                    raise ParseSyntaxException._from_exception(pe)
-                except IndexError:
-                    raise ParseSyntaxException(instring, len(instring), self.errmsg, self)
-            else:
-                loc, exprtokens = e._parse( instring, loc, doActions )
-            if exprtokens or exprtokens.haskeys():
-                resultlist += exprtokens
-        return loc, resultlist
-
-    def __iadd__(self, other ):
-        if isinstance( other, basestring ):
-            other = ParserElement._literalStringClass( other )
-        return self.append( other ) #And( [ self, other ] )
-
-    def checkRecursion( self, parseElementList ):
-        subRecCheckList = parseElementList[:] + [ self ]
-        for e in self.exprs:
-            e.checkRecursion( subRecCheckList )
-            if not e.mayReturnEmpty:
-                break
-
-    def __str__( self ):
-        if hasattr(self,"name"):
-            return self.name
-
-        if self.strRepr is None:
-            self.strRepr = "{" + " ".join(_ustr(e) for e in self.exprs) + "}"
-
-        return self.strRepr
-
-
-class Or(ParseExpression):
-    """
-    Requires that at least one C{ParseExpression} is found.
-    If two expressions match, the expression that matches the longest string will be used.
-    May be constructed using the C{'^'} operator.
-
-    Example::
-        # construct Or using '^' operator
-        
-        number = Word(nums) ^ Combine(Word(nums) + '.' + Word(nums))
-        print(number.searchString("123 3.1416 789"))
-    prints::
-        [['123'], ['3.1416'], ['789']]
-    """
-    def __init__( self, exprs, savelist = False ):
-        super(Or,self).__init__(exprs, savelist)
-        if self.exprs:
-            self.mayReturnEmpty = any(e.mayReturnEmpty for e in self.exprs)
-        else:
-            self.mayReturnEmpty = True
-
-    def parseImpl( self, instring, loc, doActions=True ):
-        maxExcLoc = -1
-        maxException = None
-        matches = []
-        for e in self.exprs:
-            try:
-                loc2 = e.tryParse( instring, loc )
-            except ParseException as err:
-                err.__traceback__ = None
-                if err.loc > maxExcLoc:
-                    maxException = err
-                    maxExcLoc = err.loc
-            except IndexError:
-                if len(instring) > maxExcLoc:
-                    maxException = ParseException(instring,len(instring),e.errmsg,self)
-                    maxExcLoc = len(instring)
-            else:
-                # save match among all matches, to retry longest to shortest
-                matches.append((loc2, e))
-
-        if matches:
-            matches.sort(key=lambda x: -x[0])
-            for _,e in matches:
-                try:
-                    return e._parse( instring, loc, doActions )
-                except ParseException as err:
-                    err.__traceback__ = None
-                    if err.loc > maxExcLoc:
-                        maxException = err
-                        maxExcLoc = err.loc
-
-        if maxException is not None:
-            maxException.msg = self.errmsg
-            raise maxException
-        else:
-            raise ParseException(instring, loc, "no defined alternatives to match", self)
-
-
-    def __ixor__(self, other ):
-        if isinstance( other, basestring ):
-            other = ParserElement._literalStringClass( other )
-        return self.append( other ) #Or( [ self, other ] )
-
-    def __str__( self ):
-        if hasattr(self,"name"):
-            return self.name
-
-        if self.strRepr is None:
-            self.strRepr = "{" + " ^ ".join(_ustr(e) for e in self.exprs) + "}"
-
-        return self.strRepr
-
-    def checkRecursion( self, parseElementList ):
-        subRecCheckList = parseElementList[:] + [ self ]
-        for e in self.exprs:
-            e.checkRecursion( subRecCheckList )
-
-
-class MatchFirst(ParseExpression):
-    """
-    Requires that at least one C{ParseExpression} is found.
-    If two expressions match, the first one listed is the one that will match.
-    May be constructed using the C{'|'} operator.
-
-    Example::
-        # construct MatchFirst using '|' operator
-        
-        # watch the order of expressions to match
-        number = Word(nums) | Combine(Word(nums) + '.' + Word(nums))
-        print(number.searchString("123 3.1416 789")) #  Fail! -> [['123'], ['3'], ['1416'], ['789']]
-
-        # put more selective expression first
-        number = Combine(Word(nums) + '.' + Word(nums)) | Word(nums)
-        print(number.searchString("123 3.1416 789")) #  Better -> [['123'], ['3.1416'], ['789']]
-    """
-    def __init__( self, exprs, savelist = False ):
-        super(MatchFirst,self).__init__(exprs, savelist)
-        if self.exprs:
-            self.mayReturnEmpty = any(e.mayReturnEmpty for e in self.exprs)
-        else:
-            self.mayReturnEmpty = True
-
-    def parseImpl( self, instring, loc, doActions=True ):
-        maxExcLoc = -1
-        maxException = None
-        for e in self.exprs:
-            try:
-                ret = e._parse( instring, loc, doActions )
-                return ret
-            except ParseException as err:
-                if err.loc > maxExcLoc:
-                    maxException = err
-                    maxExcLoc = err.loc
-            except IndexError:
-                if len(instring) > maxExcLoc:
-                    maxException = ParseException(instring,len(instring),e.errmsg,self)
-                    maxExcLoc = len(instring)
-
-        # only got here if no expression matched, raise exception for match that made it the furthest
-        else:
-            if maxException is not None:
-                maxException.msg = self.errmsg
-                raise maxException
-            else:
-                raise ParseException(instring, loc, "no defined alternatives to match", self)
-
-    def __ior__(self, other ):
-        if isinstance( other, basestring ):
-            other = ParserElement._literalStringClass( other )
-        return self.append( other ) #MatchFirst( [ self, other ] )
-
-    def __str__( self ):
-        if hasattr(self,"name"):
-            return self.name
-
-        if self.strRepr is None:
-            self.strRepr = "{" + " | ".join(_ustr(e) for e in self.exprs) + "}"
-
-        return self.strRepr
-
-    def checkRecursion( self, parseElementList ):
-        subRecCheckList = parseElementList[:] + [ self ]
-        for e in self.exprs:
-            e.checkRecursion( subRecCheckList )
-
-
-class Each(ParseExpression):
-    """
-    Requires all given C{ParseExpression}s to be found, but in any order.
-    Expressions may be separated by whitespace.
-    May be constructed using the C{'&'} operator.
-
-    Example::
-        color = oneOf("RED ORANGE YELLOW GREEN BLUE PURPLE BLACK WHITE BROWN")
-        shape_type = oneOf("SQUARE CIRCLE TRIANGLE STAR HEXAGON OCTAGON")
-        integer = Word(nums)
-        shape_attr = "shape:" + shape_type("shape")
-        posn_attr = "posn:" + Group(integer("x") + ',' + integer("y"))("posn")
-        color_attr = "color:" + color("color")
-        size_attr = "size:" + integer("size")
-
-        # use Each (using operator '&') to accept attributes in any order 
-        # (shape and posn are required, color and size are optional)
-        shape_spec = shape_attr & posn_attr & Optional(color_attr) & Optional(size_attr)
-
-        shape_spec.runTests('''
-            shape: SQUARE color: BLACK posn: 100, 120
-            shape: CIRCLE size: 50 color: BLUE posn: 50,80
-            color:GREEN size:20 shape:TRIANGLE posn:20,40
-            '''
-            )
-    prints::
-        shape: SQUARE color: BLACK posn: 100, 120
-        ['shape:', 'SQUARE', 'color:', 'BLACK', 'posn:', ['100', ',', '120']]
-        - color: BLACK
-        - posn: ['100', ',', '120']
-          - x: 100
-          - y: 120
-        - shape: SQUARE
-
-
-        shape: CIRCLE size: 50 color: BLUE posn: 50,80
-        ['shape:', 'CIRCLE', 'size:', '50', 'color:', 'BLUE', 'posn:', ['50', ',', '80']]
-        - color: BLUE
-        - posn: ['50', ',', '80']
-          - x: 50
-          - y: 80
-        - shape: CIRCLE
-        - size: 50
-
-
-        color: GREEN size: 20 shape: TRIANGLE posn: 20,40
-        ['color:', 'GREEN', 'size:', '20', 'shape:', 'TRIANGLE', 'posn:', ['20', ',', '40']]
-        - color: GREEN
-        - posn: ['20', ',', '40']
-          - x: 20
-          - y: 40
-        - shape: TRIANGLE
-        - size: 20
-    """
-    def __init__( self, exprs, savelist = True ):
-        super(Each,self).__init__(exprs, savelist)
-        self.mayReturnEmpty = all(e.mayReturnEmpty for e in self.exprs)
-        self.skipWhitespace = True
-        self.initExprGroups = True
-
-    def parseImpl( self, instring, loc, doActions=True ):
-        if self.initExprGroups:
-            self.opt1map = dict((id(e.expr),e) for e in self.exprs if isinstance(e,Optional))
-            opt1 = [ e.expr for e in self.exprs if isinstance(e,Optional) ]
-            opt2 = [ e for e in self.exprs if e.mayReturnEmpty and not isinstance(e,Optional)]
-            self.optionals = opt1 + opt2
-            self.multioptionals = [ e.expr for e in self.exprs if isinstance(e,ZeroOrMore) ]
-            self.multirequired = [ e.expr for e in self.exprs if isinstance(e,OneOrMore) ]
-            self.required = [ e for e in self.exprs if not isinstance(e,(Optional,ZeroOrMore,OneOrMore)) ]
-            self.required += self.multirequired
-            self.initExprGroups = False
-        tmpLoc = loc
-        tmpReqd = self.required[:]
-        tmpOpt  = self.optionals[:]
-        matchOrder = []
-
-        keepMatching = True
-        while keepMatching:
-            tmpExprs = tmpReqd + tmpOpt + self.multioptionals + self.multirequired
-            failed = []
-            for e in tmpExprs:
-                try:
-                    tmpLoc = e.tryParse( instring, tmpLoc )
-                except ParseException:
-                    failed.append(e)
-                else:
-                    matchOrder.append(self.opt1map.get(id(e),e))
-                    if e in tmpReqd:
-                        tmpReqd.remove(e)
-                    elif e in tmpOpt:
-                        tmpOpt.remove(e)
-            if len(failed) == len(tmpExprs):
-                keepMatching = False
-
-        if tmpReqd:
-            missing = ", ".join(_ustr(e) for e in tmpReqd)
-            raise ParseException(instring,loc,"Missing one or more required elements (%s)" % missing )
-
-        # add any unmatched Optionals, in case they have default values defined
-        matchOrder += [e for e in self.exprs if isinstance(e,Optional) and e.expr in tmpOpt]
-
-        resultlist = []
-        for e in matchOrder:
-            loc,results = e._parse(instring,loc,doActions)
-            resultlist.append(results)
-
-        finalResults = sum(resultlist, ParseResults([]))
-        return loc, finalResults
-
-    def __str__( self ):
-        if hasattr(self,"name"):
-            return self.name
-
-        if self.strRepr is None:
-            self.strRepr = "{" + " & ".join(_ustr(e) for e in self.exprs) + "}"
-
-        return self.strRepr
-
-    def checkRecursion( self, parseElementList ):
-        subRecCheckList = parseElementList[:] + [ self ]
-        for e in self.exprs:
-            e.checkRecursion( subRecCheckList )
-
-
-class ParseElementEnhance(ParserElement):
-    """
-    Abstract subclass of C{ParserElement}, for combining and post-processing parsed tokens.
-    """
-    def __init__( self, expr, savelist=False ):
-        super(ParseElementEnhance,self).__init__(savelist)
-        if isinstance( expr, basestring ):
-            if issubclass(ParserElement._literalStringClass, Token):
-                expr = ParserElement._literalStringClass(expr)
-            else:
-                expr = ParserElement._literalStringClass(Literal(expr))
-        self.expr = expr
-        self.strRepr = None
-        if expr is not None:
-            self.mayIndexError = expr.mayIndexError
-            self.mayReturnEmpty = expr.mayReturnEmpty
-            self.setWhitespaceChars( expr.whiteChars )
-            self.skipWhitespace = expr.skipWhitespace
-            self.saveAsList = expr.saveAsList
-            self.callPreparse = expr.callPreparse
-            self.ignoreExprs.extend(expr.ignoreExprs)
-
-    def parseImpl( self, instring, loc, doActions=True ):
-        if self.expr is not None:
-            return self.expr._parse( instring, loc, doActions, callPreParse=False )
-        else:
-            raise ParseException("",loc,self.errmsg,self)
-
-    def leaveWhitespace( self ):
-        self.skipWhitespace = False
-        self.expr = self.expr.copy()
-        if self.expr is not None:
-            self.expr.leaveWhitespace()
-        return self
-
-    def ignore( self, other ):
-        if isinstance( other, Suppress ):
-            if other not in self.ignoreExprs:
-                super( ParseElementEnhance, self).ignore( other )
-                if self.expr is not None:
-                    self.expr.ignore( self.ignoreExprs[-1] )
-        else:
-            super( ParseElementEnhance, self).ignore( other )
-            if self.expr is not None:
-                self.expr.ignore( self.ignoreExprs[-1] )
-        return self
-
-    def streamline( self ):
-        super(ParseElementEnhance,self).streamline()
-        if self.expr is not None:
-            self.expr.streamline()
-        return self
-
-    def checkRecursion( self, parseElementList ):
-        if self in parseElementList:
-            raise RecursiveGrammarException( parseElementList+[self] )
-        subRecCheckList = parseElementList[:] + [ self ]
-        if self.expr is not None:
-            self.expr.checkRecursion( subRecCheckList )
-
-    def validate( self, validateTrace=[] ):
-        tmp = validateTrace[:]+[self]
-        if self.expr is not None:
-            self.expr.validate(tmp)
-        self.checkRecursion( [] )
-
-    def __str__( self ):
-        try:
-            return super(ParseElementEnhance,self).__str__()
-        except Exception:
-            pass
-
-        if self.strRepr is None and self.expr is not None:
-            self.strRepr = "%s:(%s)" % ( self.__class__.__name__, _ustr(self.expr) )
-        return self.strRepr
-
-
-class FollowedBy(ParseElementEnhance):
-    """
-    Lookahead matching of the given parse expression.  C{FollowedBy}
-    does I{not} advance the parsing position within the input string, it only
-    verifies that the specified parse expression matches at the current
-    position.  C{FollowedBy} always returns a null token list.
-
-    Example::
-        # use FollowedBy to match a label only if it is followed by a ':'
-        data_word = Word(alphas)
-        label = data_word + FollowedBy(':')
-        attr_expr = Group(label + Suppress(':') + OneOrMore(data_word, stopOn=label).setParseAction(' '.join))
-        
-        OneOrMore(attr_expr).parseString("shape: SQUARE color: BLACK posn: upper left").pprint()
-    prints::
-        [['shape', 'SQUARE'], ['color', 'BLACK'], ['posn', 'upper left']]
-    """
-    def __init__( self, expr ):
-        super(FollowedBy,self).__init__(expr)
-        self.mayReturnEmpty = True
-
-    def parseImpl( self, instring, loc, doActions=True ):
-        self.expr.tryParse( instring, loc )
-        return loc, []
-
-
-class NotAny(ParseElementEnhance):
-    """
-    Lookahead to disallow matching with the given parse expression.  C{NotAny}
-    does I{not} advance the parsing position within the input string, it only
-    verifies that the specified parse expression does I{not} match at the current
-    position.  Also, C{NotAny} does I{not} skip over leading whitespace. C{NotAny}
-    always returns a null token list.  May be constructed using the '~' operator.
-
-    Example::
-        
-    """
-    def __init__( self, expr ):
-        super(NotAny,self).__init__(expr)
-        #~ self.leaveWhitespace()
-        self.skipWhitespace = False  # do NOT use self.leaveWhitespace(), don't want to propagate to exprs
-        self.mayReturnEmpty = True
-        self.errmsg = "Found unwanted token, "+_ustr(self.expr)
-
-    def parseImpl( self, instring, loc, doActions=True ):
-        if self.expr.canParseNext(instring, loc):
-            raise ParseException(instring, loc, self.errmsg, self)
-        return loc, []
-
-    def __str__( self ):
-        if hasattr(self,"name"):
-            return self.name
-
-        if self.strRepr is None:
-            self.strRepr = "~{" + _ustr(self.expr) + "}"
-
-        return self.strRepr
-
-class _MultipleMatch(ParseElementEnhance):
-    def __init__( self, expr, stopOn=None):
-        super(_MultipleMatch, self).__init__(expr)
-        self.saveAsList = True
-        ender = stopOn
-        if isinstance(ender, basestring):
-            ender = ParserElement._literalStringClass(ender)
-        self.not_ender = ~ender if ender is not None else None
-
-    def parseImpl( self, instring, loc, doActions=True ):
-        self_expr_parse = self.expr._parse
-        self_skip_ignorables = self._skipIgnorables
-        check_ender = self.not_ender is not None
-        if check_ender:
-            try_not_ender = self.not_ender.tryParse
-        
-        # must be at least one (but first see if we are the stopOn sentinel;
-        # if so, fail)
-        if check_ender:
-            try_not_ender(instring, loc)
-        loc, tokens = self_expr_parse( instring, loc, doActions, callPreParse=False )
-        try:
-            hasIgnoreExprs = bool(self.ignoreExprs)
-            while 1:
-                if check_ender:
-                    try_not_ender(instring, loc)
-                if hasIgnoreExprs:
-                    preloc = self_skip_ignorables( instring, loc )
-                else:
-                    preloc = loc
-                loc, tmptokens = self_expr_parse( instring, preloc, doActions )
-                if tmptokens or tmptokens.haskeys():
-                    tokens += tmptokens
-        except (ParseException,IndexError):
-            pass
-
-        return loc, tokens
-        
-class OneOrMore(_MultipleMatch):
-    """
-    Repetition of one or more of the given expression.
-    
-    Parameters:
-     - expr - expression that must match one or more times
-     - stopOn - (default=C{None}) - expression for a terminating sentinel
-          (only required if the sentinel would ordinarily match the repetition 
-          expression)          
-
-    Example::
-        data_word = Word(alphas)
-        label = data_word + FollowedBy(':')
-        attr_expr = Group(label + Suppress(':') + OneOrMore(data_word).setParseAction(' '.join))
-
-        text = "shape: SQUARE posn: upper left color: BLACK"
-        OneOrMore(attr_expr).parseString(text).pprint()  # Fail! read 'color' as data instead of next label -> [['shape', 'SQUARE color']]
-
-        # use stopOn attribute for OneOrMore to avoid reading label string as part of the data
-        attr_expr = Group(label + Suppress(':') + OneOrMore(data_word, stopOn=label).setParseAction(' '.join))
-        OneOrMore(attr_expr).parseString(text).pprint() # Better -> [['shape', 'SQUARE'], ['posn', 'upper left'], ['color', 'BLACK']]
-        
-        # could also be written as
-        (attr_expr * (1,)).parseString(text).pprint()
-    """
-
-    def __str__( self ):
-        if hasattr(self,"name"):
-            return self.name
-
-        if self.strRepr is None:
-            self.strRepr = "{" + _ustr(self.expr) + "}..."
-
-        return self.strRepr
-
-class ZeroOrMore(_MultipleMatch):
-    """
-    Optional repetition of zero or more of the given expression.
-    
-    Parameters:
-     - expr - expression that must match zero or more times
-     - stopOn - (default=C{None}) - expression for a terminating sentinel
-          (only required if the sentinel would ordinarily match the repetition 
-          expression)          
-
-    Example: similar to L{OneOrMore}
-    """
-    def __init__( self, expr, stopOn=None):
-        super(ZeroOrMore,self).__init__(expr, stopOn=stopOn)
-        self.mayReturnEmpty = True
-        
-    def parseImpl( self, instring, loc, doActions=True ):
-        try:
-            return super(ZeroOrMore, self).parseImpl(instring, loc, doActions)
-        except (ParseException,IndexError):
-            return loc, []
-
-    def __str__( self ):
-        if hasattr(self,"name"):
-            return self.name
-
-        if self.strRepr is None:
-            self.strRepr = "[" + _ustr(self.expr) + "]..."
-
-        return self.strRepr
-
-class _NullToken(object):
-    def __bool__(self):
-        return False
-    __nonzero__ = __bool__
-    def __str__(self):
-        return ""
-
-_optionalNotMatched = _NullToken()
-class Optional(ParseElementEnhance):
-    """
-    Optional matching of the given expression.
-
-    Parameters:
-     - expr - expression that must match zero or more times
-     - default (optional) - value to be returned if the optional expression is not found.
-
-    Example::
-        # US postal code can be a 5-digit zip, plus optional 4-digit qualifier
-        zip = Combine(Word(nums, exact=5) + Optional('-' + Word(nums, exact=4)))
-        zip.runTests('''
-            # traditional ZIP code
-            12345
-            
-            # ZIP+4 form
-            12101-0001
-            
-            # invalid ZIP
-            98765-
-            ''')
-    prints::
-        # traditional ZIP code
-        12345
-        ['12345']
-
-        # ZIP+4 form
-        12101-0001
-        ['12101-0001']
-
-        # invalid ZIP
-        98765-
-             ^
-        FAIL: Expected end of text (at char 5), (line:1, col:6)
-    """
-    def __init__( self, expr, default=_optionalNotMatched ):
-        super(Optional,self).__init__( expr, savelist=False )
-        self.saveAsList = self.expr.saveAsList
-        self.defaultValue = default
-        self.mayReturnEmpty = True
-
-    def parseImpl( self, instring, loc, doActions=True ):
-        try:
-            loc, tokens = self.expr._parse( instring, loc, doActions, callPreParse=False )
-        except (ParseException,IndexError):
-            if self.defaultValue is not _optionalNotMatched:
-                if self.expr.resultsName:
-                    tokens = ParseResults([ self.defaultValue ])
-                    tokens[self.expr.resultsName] = self.defaultValue
-                else:
-                    tokens = [ self.defaultValue ]
-            else:
-                tokens = []
-        return loc, tokens
-
-    def __str__( self ):
-        if hasattr(self,"name"):
-            return self.name
-
-        if self.strRepr is None:
-            self.strRepr = "[" + _ustr(self.expr) + "]"
-
-        return self.strRepr
-
-class SkipTo(ParseElementEnhance):
-    """
-    Token for skipping over all undefined text until the matched expression is found.
-
-    Parameters:
-     - expr - target expression marking the end of the data to be skipped
-     - include - (default=C{False}) if True, the target expression is also parsed 
-          (the skipped text and target expression are returned as a 2-element list).
-     - ignore - (default=C{None}) used to define grammars (typically quoted strings and 
-          comments) that might contain false matches to the target expression
-     - failOn - (default=C{None}) define expressions that are not allowed to be 
-          included in the skipped test; if found before the target expression is found, 
-          the SkipTo is not a match
-
-    Example::
-        report = '''
-            Outstanding Issues Report - 1 Jan 2000
-
-               # | Severity | Description                               |  Days Open
-            -----+----------+-------------------------------------------+-----------
-             101 | Critical | Intermittent system crash                 |          6
-              94 | Cosmetic | Spelling error on Login ('log|n')         |         14
-              79 | Minor    | System slow when running too many reports |         47
-            '''
-        integer = Word(nums)
-        SEP = Suppress('|')
-        # use SkipTo to simply match everything up until the next SEP
-        # - ignore quoted strings, so that a '|' character inside a quoted string does not match
-        # - parse action will call token.strip() for each matched token, i.e., the description body
-        string_data = SkipTo(SEP, ignore=quotedString)
-        string_data.setParseAction(tokenMap(str.strip))
-        ticket_expr = (integer("issue_num") + SEP 
-                      + string_data("sev") + SEP 
-                      + string_data("desc") + SEP 
-                      + integer("days_open"))
-        
-        for tkt in ticket_expr.searchString(report):
-            print tkt.dump()
-    prints::
-        ['101', 'Critical', 'Intermittent system crash', '6']
-        - days_open: 6
-        - desc: Intermittent system crash
-        - issue_num: 101
-        - sev: Critical
-        ['94', 'Cosmetic', "Spelling error on Login ('log|n')", '14']
-        - days_open: 14
-        - desc: Spelling error on Login ('log|n')
-        - issue_num: 94
-        - sev: Cosmetic
-        ['79', 'Minor', 'System slow when running too many reports', '47']
-        - days_open: 47
-        - desc: System slow when running too many reports
-        - issue_num: 79
-        - sev: Minor
-    """
-    def __init__( self, other, include=False, ignore=None, failOn=None ):
-        super( SkipTo, self ).__init__( other )
-        self.ignoreExpr = ignore
-        self.mayReturnEmpty = True
-        self.mayIndexError = False
-        self.includeMatch = include
-        self.asList = False
-        if isinstance(failOn, basestring):
-            self.failOn = ParserElement._literalStringClass(failOn)
-        else:
-            self.failOn = failOn
-        self.errmsg = "No match found for "+_ustr(self.expr)
-
-    def parseImpl( self, instring, loc, doActions=True ):
-        startloc = loc
-        instrlen = len(instring)
-        expr = self.expr
-        expr_parse = self.expr._parse
-        self_failOn_canParseNext = self.failOn.canParseNext if self.failOn is not None else None
-        self_ignoreExpr_tryParse = self.ignoreExpr.tryParse if self.ignoreExpr is not None else None
-        
-        tmploc = loc
-        while tmploc <= instrlen:
-            if self_failOn_canParseNext is not None:
-                # break if failOn expression matches
-                if self_failOn_canParseNext(instring, tmploc):
-                    break
-                    
-            if self_ignoreExpr_tryParse is not None:
-                # advance past ignore expressions
-                while 1:
-                    try:
-                        tmploc = self_ignoreExpr_tryParse(instring, tmploc)
-                    except ParseBaseException:
-                        break
-            
-            try:
-                expr_parse(instring, tmploc, doActions=False, callPreParse=False)
-            except (ParseException, IndexError):
-                # no match, advance loc in string
-                tmploc += 1
-            else:
-                # matched skipto expr, done
-                break
-
-        else:
-            # ran off the end of the input string without matching skipto expr, fail
-            raise ParseException(instring, loc, self.errmsg, self)
-
-        # build up return values
-        loc = tmploc
-        skiptext = instring[startloc:loc]
-        skipresult = ParseResults(skiptext)
-        
-        if self.includeMatch:
-            loc, mat = expr_parse(instring,loc,doActions,callPreParse=False)
-            skipresult += mat
-
-        return loc, skipresult
-
-class Forward(ParseElementEnhance):
-    """
-    Forward declaration of an expression to be defined later -
-    used for recursive grammars, such as algebraic infix notation.
-    When the expression is known, it is assigned to the C{Forward} variable using the '<<' operator.
-
-    Note: take care when assigning to C{Forward} not to overlook precedence of operators.
-    Specifically, '|' has a lower precedence than '<<', so that::
-        fwdExpr << a | b | c
-    will actually be evaluated as::
-        (fwdExpr << a) | b | c
-    thereby leaving b and c out as parseable alternatives.  It is recommended that you
-    explicitly group the values inserted into the C{Forward}::
-        fwdExpr << (a | b | c)
-    Converting to use the '<<=' operator instead will avoid this problem.
-
-    See L{ParseResults.pprint} for an example of a recursive parser created using
-    C{Forward}.
-    """
-    def __init__( self, other=None ):
-        super(Forward,self).__init__( other, savelist=False )
-
-    def __lshift__( self, other ):
-        if isinstance( other, basestring ):
-            other = ParserElement._literalStringClass(other)
-        self.expr = other
-        self.strRepr = None
-        self.mayIndexError = self.expr.mayIndexError
-        self.mayReturnEmpty = self.expr.mayReturnEmpty
-        self.setWhitespaceChars( self.expr.whiteChars )
-        self.skipWhitespace = self.expr.skipWhitespace
-        self.saveAsList = self.expr.saveAsList
-        self.ignoreExprs.extend(self.expr.ignoreExprs)
-        return self
-        
-    def __ilshift__(self, other):
-        return self << other
-    
-    def leaveWhitespace( self ):
-        self.skipWhitespace = False
-        return self
-
-    def streamline( self ):
-        if not self.streamlined:
-            self.streamlined = True
-            if self.expr is not None:
-                self.expr.streamline()
-        return self
-
-    def validate( self, validateTrace=[] ):
-        if self not in validateTrace:
-            tmp = validateTrace[:]+[self]
-            if self.expr is not None:
-                self.expr.validate(tmp)
-        self.checkRecursion([])
-
-    def __str__( self ):
-        if hasattr(self,"name"):
-            return self.name
-        return self.__class__.__name__ + ": ..."
-
-        # stubbed out for now - creates awful memory and perf issues
-        self._revertClass = self.__class__
-        self.__class__ = _ForwardNoRecurse
-        try:
-            if self.expr is not None:
-                retString = _ustr(self.expr)
-            else:
-                retString = "None"
-        finally:
-            self.__class__ = self._revertClass
-        return self.__class__.__name__ + ": " + retString
-
-    def copy(self):
-        if self.expr is not None:
-            return super(Forward,self).copy()
-        else:
-            ret = Forward()
-            ret <<= self
-            return ret
-
-class _ForwardNoRecurse(Forward):
-    def __str__( self ):
-        return "..."
-
-class TokenConverter(ParseElementEnhance):
-    """
-    Abstract subclass of C{ParseExpression}, for converting parsed results.
-    """
-    def __init__( self, expr, savelist=False ):
-        super(TokenConverter,self).__init__( expr )#, savelist )
-        self.saveAsList = False
-
-class Combine(TokenConverter):
-    """
-    Converter to concatenate all matching tokens to a single string.
-    By default, the matching patterns must also be contiguous in the input string;
-    this can be disabled by specifying C{'adjacent=False'} in the constructor.
-
-    Example::
-        real = Word(nums) + '.' + Word(nums)
-        print(real.parseString('3.1416')) # -> ['3', '.', '1416']
-        # will also erroneously match the following
-        print(real.parseString('3. 1416')) # -> ['3', '.', '1416']
-
-        real = Combine(Word(nums) + '.' + Word(nums))
-        print(real.parseString('3.1416')) # -> ['3.1416']
-        # no match when there are internal spaces
-        print(real.parseString('3. 1416')) # -> Exception: Expected W:(0123...)
-    """
-    def __init__( self, expr, joinString="", adjacent=True ):
-        super(Combine,self).__init__( expr )
-        # suppress whitespace-stripping in contained parse expressions, but re-enable it on the Combine itself
-        if adjacent:
-            self.leaveWhitespace()
-        self.adjacent = adjacent
-        self.skipWhitespace = True
-        self.joinString = joinString
-        self.callPreparse = True
-
-    def ignore( self, other ):
-        if self.adjacent:
-            ParserElement.ignore(self, other)
-        else:
-            super( Combine, self).ignore( other )
-        return self
-
-    def postParse( self, instring, loc, tokenlist ):
-        retToks = tokenlist.copy()
-        del retToks[:]
-        retToks += ParseResults([ "".join(tokenlist._asStringList(self.joinString)) ], modal=self.modalResults)
-
-        if self.resultsName and retToks.haskeys():
-            return [ retToks ]
-        else:
-            return retToks
-
-class Group(TokenConverter):
-    """
-    Converter to return the matched tokens as a list - useful for returning tokens of C{L{ZeroOrMore}} and C{L{OneOrMore}} expressions.
-
-    Example::
-        ident = Word(alphas)
-        num = Word(nums)
-        term = ident | num
-        func = ident + Optional(delimitedList(term))
-        print(func.parseString("fn a,b,100"))  # -> ['fn', 'a', 'b', '100']
-
-        func = ident + Group(Optional(delimitedList(term)))
-        print(func.parseString("fn a,b,100"))  # -> ['fn', ['a', 'b', '100']]
-    """
-    def __init__( self, expr ):
-        super(Group,self).__init__( expr )
-        self.saveAsList = True
-
-    def postParse( self, instring, loc, tokenlist ):
-        return [ tokenlist ]
-
-class Dict(TokenConverter):
-    """
-    Converter to return a repetitive expression as a list, but also as a dictionary.
-    Each element can also be referenced using the first token in the expression as its key.
-    Useful for tabular report scraping when the first column can be used as a item key.
-
-    Example::
-        data_word = Word(alphas)
-        label = data_word + FollowedBy(':')
-        attr_expr = Group(label + Suppress(':') + OneOrMore(data_word).setParseAction(' '.join))
-
-        text = "shape: SQUARE posn: upper left color: light blue texture: burlap"
-        attr_expr = (label + Suppress(':') + OneOrMore(data_word, stopOn=label).setParseAction(' '.join))
-        
-        # print attributes as plain groups
-        print(OneOrMore(attr_expr).parseString(text).dump())
-        
-        # instead of OneOrMore(expr), parse using Dict(OneOrMore(Group(expr))) - Dict will auto-assign names
-        result = Dict(OneOrMore(Group(attr_expr))).parseString(text)
-        print(result.dump())
-        
-        # access named fields as dict entries, or output as dict
-        print(result['shape'])        
-        print(result.asDict())
-    prints::
-        ['shape', 'SQUARE', 'posn', 'upper left', 'color', 'light blue', 'texture', 'burlap']
-
-        [['shape', 'SQUARE'], ['posn', 'upper left'], ['color', 'light blue'], ['texture', 'burlap']]
-        - color: light blue
-        - posn: upper left
-        - shape: SQUARE
-        - texture: burlap
-        SQUARE
-        {'color': 'light blue', 'posn': 'upper left', 'texture': 'burlap', 'shape': 'SQUARE'}
-    See more examples at L{ParseResults} of accessing fields by results name.
-    """
-    def __init__( self, expr ):
-        super(Dict,self).__init__( expr )
-        self.saveAsList = True
-
-    def postParse( self, instring, loc, tokenlist ):
-        for i,tok in enumerate(tokenlist):
-            if len(tok) == 0:
-                continue
-            ikey = tok[0]
-            if isinstance(ikey,int):
-                ikey = _ustr(tok[0]).strip()
-            if len(tok)==1:
-                tokenlist[ikey] = _ParseResultsWithOffset("",i)
-            elif len(tok)==2 and not isinstance(tok[1],ParseResults):
-                tokenlist[ikey] = _ParseResultsWithOffset(tok[1],i)
-            else:
-                dictvalue = tok.copy() #ParseResults(i)
-                del dictvalue[0]
-                if len(dictvalue)!= 1 or (isinstance(dictvalue,ParseResults) and dictvalue.haskeys()):
-                    tokenlist[ikey] = _ParseResultsWithOffset(dictvalue,i)
-                else:
-                    tokenlist[ikey] = _ParseResultsWithOffset(dictvalue[0],i)
-
-        if self.resultsName:
-            return [ tokenlist ]
-        else:
-            return tokenlist
-
-
-class Suppress(TokenConverter):
-    """
-    Converter for ignoring the results of a parsed expression.
-
-    Example::
-        source = "a, b, c,d"
-        wd = Word(alphas)
-        wd_list1 = wd + ZeroOrMore(',' + wd)
-        print(wd_list1.parseString(source))
-
-        # often, delimiters that are useful during parsing are just in the
-        # way afterward - use Suppress to keep them out of the parsed output
-        wd_list2 = wd + ZeroOrMore(Suppress(',') + wd)
-        print(wd_list2.parseString(source))
-    prints::
-        ['a', ',', 'b', ',', 'c', ',', 'd']
-        ['a', 'b', 'c', 'd']
-    (See also L{delimitedList}.)
-    """
-    def postParse( self, instring, loc, tokenlist ):
-        return []
-
-    def suppress( self ):
-        return self
-
-
-class OnlyOnce(object):
-    """
-    Wrapper for parse actions, to ensure they are only called once.
-    """
-    def __init__(self, methodCall):
-        self.callable = _trim_arity(methodCall)
-        self.called = False
-    def __call__(self,s,l,t):
-        if not self.called:
-            results = self.callable(s,l,t)
-            self.called = True
-            return results
-        raise ParseException(s,l,"")
-    def reset(self):
-        self.called = False
-
-def traceParseAction(f):
-    """
-    Decorator for debugging parse actions. 
-    
-    When the parse action is called, this decorator will print C{">> entering I{method-name}(line:I{current_source_line}, I{parse_location}, I{matched_tokens})".}
-    When the parse action completes, the decorator will print C{"<<"} followed by the returned value, or any exception that the parse action raised.
-
-    Example::
-        wd = Word(alphas)
-
-        @traceParseAction
-        def remove_duplicate_chars(tokens):
-            return ''.join(sorted(set(''.join(tokens)))
-
-        wds = OneOrMore(wd).setParseAction(remove_duplicate_chars)
-        print(wds.parseString("slkdjs sld sldd sdlf sdljf"))
-    prints::
-        >>entering remove_duplicate_chars(line: 'slkdjs sld sldd sdlf sdljf', 0, (['slkdjs', 'sld', 'sldd', 'sdlf', 'sdljf'], {}))
-        <<leaving remove_duplicate_chars (ret: 'dfjkls')
-        ['dfjkls']
-    """
-    f = _trim_arity(f)
-    def z(*paArgs):
-        thisFunc = f.__name__
-        s,l,t = paArgs[-3:]
-        if len(paArgs)>3:
-            thisFunc = paArgs[0].__class__.__name__ + '.' + thisFunc
-        sys.stderr.write( ">>entering %s(line: '%s', %d, %r)\n" % (thisFunc,line(l,s),l,t) )
-        try:
-            ret = f(*paArgs)
-        except Exception as exc:
-            sys.stderr.write( "<<leaving %s (exception: %s)\n" % (thisFunc,exc) )
-            raise
-        sys.stderr.write( "<<leaving %s (ret: %r)\n" % (thisFunc,ret) )
-        return ret
-    try:
-        z.__name__ = f.__name__
-    except AttributeError:
-        pass
-    return z
-
-#
-# global helpers
-#
-def delimitedList( expr, delim=",", combine=False ):
-    """
-    Helper to define a delimited list of expressions - the delimiter defaults to ','.
-    By default, the list elements and delimiters can have intervening whitespace, and
-    comments, but this can be overridden by passing C{combine=True} in the constructor.
-    If C{combine} is set to C{True}, the matching tokens are returned as a single token
-    string, with the delimiters included; otherwise, the matching tokens are returned
-    as a list of tokens, with the delimiters suppressed.
-
-    Example::
-        delimitedList(Word(alphas)).parseString("aa,bb,cc") # -> ['aa', 'bb', 'cc']
-        delimitedList(Word(hexnums), delim=':', combine=True).parseString("AA:BB:CC:DD:EE") # -> ['AA:BB:CC:DD:EE']
-    """
-    dlName = _ustr(expr)+" ["+_ustr(delim)+" "+_ustr(expr)+"]..."
-    if combine:
-        return Combine( expr + ZeroOrMore( delim + expr ) ).setName(dlName)
-    else:
-        return ( expr + ZeroOrMore( Suppress( delim ) + expr ) ).setName(dlName)
-
-def countedArray( expr, intExpr=None ):
-    """
-    Helper to define a counted list of expressions.
-    This helper defines a pattern of the form::
-        integer expr expr expr...
-    where the leading integer tells how many expr expressions follow.
-    The matched tokens returns the array of expr tokens as a list - the leading count token is suppressed.
-    
-    If C{intExpr} is specified, it should be a pyparsing expression that produces an integer value.
-
-    Example::
-        countedArray(Word(alphas)).parseString('2 ab cd ef')  # -> ['ab', 'cd']
-
-        # in this parser, the leading integer value is given in binary,
-        # '10' indicating that 2 values are in the array
-        binaryConstant = Word('01').setParseAction(lambda t: int(t[0], 2))
-        countedArray(Word(alphas), intExpr=binaryConstant).parseString('10 ab cd ef')  # -> ['ab', 'cd']
-    """
-    arrayExpr = Forward()
-    def countFieldParseAction(s,l,t):
-        n = t[0]
-        arrayExpr << (n and Group(And([expr]*n)) or Group(empty))
-        return []
-    if intExpr is None:
-        intExpr = Word(nums).setParseAction(lambda t:int(t[0]))
-    else:
-        intExpr = intExpr.copy()
-    intExpr.setName("arrayLen")
-    intExpr.addParseAction(countFieldParseAction, callDuringTry=True)
-    return ( intExpr + arrayExpr ).setName('(len) ' + _ustr(expr) + '...')
-
-def _flatten(L):
-    ret = []
-    for i in L:
-        if isinstance(i,list):
-            ret.extend(_flatten(i))
-        else:
-            ret.append(i)
-    return ret
-
-def matchPreviousLiteral(expr):
-    """
-    Helper to define an expression that is indirectly defined from
-    the tokens matched in a previous expression, that is, it looks
-    for a 'repeat' of a previous expression.  For example::
-        first = Word(nums)
-        second = matchPreviousLiteral(first)
-        matchExpr = first + ":" + second
-    will match C{"1:1"}, but not C{"1:2"}.  Because this matches a
-    previous literal, will also match the leading C{"1:1"} in C{"1:10"}.
-    If this is not desired, use C{matchPreviousExpr}.
-    Do I{not} use with packrat parsing enabled.
-    """
-    rep = Forward()
-    def copyTokenToRepeater(s,l,t):
-        if t:
-            if len(t) == 1:
-                rep << t[0]
-            else:
-                # flatten t tokens
-                tflat = _flatten(t.asList())
-                rep << And(Literal(tt) for tt in tflat)
-        else:
-            rep << Empty()
-    expr.addParseAction(copyTokenToRepeater, callDuringTry=True)
-    rep.setName('(prev) ' + _ustr(expr))
-    return rep
-
-def matchPreviousExpr(expr):
-    """
-    Helper to define an expression that is indirectly defined from
-    the tokens matched in a previous expression, that is, it looks
-    for a 'repeat' of a previous expression.  For example::
-        first = Word(nums)
-        second = matchPreviousExpr(first)
-        matchExpr = first + ":" + second
-    will match C{"1:1"}, but not C{"1:2"}.  Because this matches by
-    expressions, will I{not} match the leading C{"1:1"} in C{"1:10"};
-    the expressions are evaluated first, and then compared, so
-    C{"1"} is compared with C{"10"}.
-    Do I{not} use with packrat parsing enabled.
-    """
-    rep = Forward()
-    e2 = expr.copy()
-    rep <<= e2
-    def copyTokenToRepeater(s,l,t):
-        matchTokens = _flatten(t.asList())
-        def mustMatchTheseTokens(s,l,t):
-            theseTokens = _flatten(t.asList())
-            if  theseTokens != matchTokens:
-                raise ParseException("",0,"")
-        rep.setParseAction( mustMatchTheseTokens, callDuringTry=True )
-    expr.addParseAction(copyTokenToRepeater, callDuringTry=True)
-    rep.setName('(prev) ' + _ustr(expr))
-    return rep
-
-def _escapeRegexRangeChars(s):
-    #~  escape these chars: ^-]
-    for c in r"\^-]":
-        s = s.replace(c,_bslash+c)
-    s = s.replace("\n",r"\n")
-    s = s.replace("\t",r"\t")
-    return _ustr(s)
-
-def oneOf( strs, caseless=False, useRegex=True ):
-    """
-    Helper to quickly define a set of alternative Literals, and makes sure to do
-    longest-first testing when there is a conflict, regardless of the input order,
-    but returns a C{L{MatchFirst}} for best performance.
-
-    Parameters:
-     - strs - a string of space-delimited literals, or a collection of string literals
-     - caseless - (default=C{False}) - treat all literals as caseless
-     - useRegex - (default=C{True}) - as an optimization, will generate a Regex
-          object; otherwise, will generate a C{MatchFirst} object (if C{caseless=True}, or
-          if creating a C{Regex} raises an exception)
-
-    Example::
-        comp_oper = oneOf("< = > <= >= !=")
-        var = Word(alphas)
-        number = Word(nums)
-        term = var | number
-        comparison_expr = term + comp_oper + term
-        print(comparison_expr.searchString("B = 12  AA=23 B<=AA AA>12"))
-    prints::
-        [['B', '=', '12'], ['AA', '=', '23'], ['B', '<=', 'AA'], ['AA', '>', '12']]
-    """
-    if caseless:
-        isequal = ( lambda a,b: a.upper() == b.upper() )
-        masks = ( lambda a,b: b.upper().startswith(a.upper()) )
-        parseElementClass = CaselessLiteral
-    else:
-        isequal = ( lambda a,b: a == b )
-        masks = ( lambda a,b: b.startswith(a) )
-        parseElementClass = Literal
-
-    symbols = []
-    if isinstance(strs,basestring):
-        symbols = strs.split()
-    elif isinstance(strs, collections.Iterable):
-        symbols = list(strs)
-    else:
-        warnings.warn("Invalid argument to oneOf, expected string or iterable",
-                SyntaxWarning, stacklevel=2)
-    if not symbols:
-        return NoMatch()
-
-    i = 0
-    while i < len(symbols)-1:
-        cur = symbols[i]
-        for j,other in enumerate(symbols[i+1:]):
-            if ( isequal(other, cur) ):
-                del symbols[i+j+1]
-                break
-            elif ( masks(cur, other) ):
-                del symbols[i+j+1]
-                symbols.insert(i,other)
-                cur = other
-                break
-        else:
-            i += 1
-
-    if not caseless and useRegex:
-        #~ print (strs,"->", "|".join( [ _escapeRegexChars(sym) for sym in symbols] ))
-        try:
-            if len(symbols)==len("".join(symbols)):
-                return Regex( "[%s]" % "".join(_escapeRegexRangeChars(sym) for sym in symbols) ).setName(' | '.join(symbols))
-            else:
-                return Regex( "|".join(re.escape(sym) for sym in symbols) ).setName(' | '.join(symbols))
-        except Exception:
-            warnings.warn("Exception creating Regex for oneOf, building MatchFirst",
-                    SyntaxWarning, stacklevel=2)
-
-
-    # last resort, just use MatchFirst
-    return MatchFirst(parseElementClass(sym) for sym in symbols).setName(' | '.join(symbols))
-
-def dictOf( key, value ):
-    """
-    Helper to easily and clearly define a dictionary by specifying the respective patterns
-    for the key and value.  Takes care of defining the C{L{Dict}}, C{L{ZeroOrMore}}, and C{L{Group}} tokens
-    in the proper order.  The key pattern can include delimiting markers or punctuation,
-    as long as they are suppressed, thereby leaving the significant key text.  The value
-    pattern can include named results, so that the C{Dict} results can include named token
-    fields.
-
-    Example::
-        text = "shape: SQUARE posn: upper left color: light blue texture: burlap"
-        attr_expr = (label + Suppress(':') + OneOrMore(data_word, stopOn=label).setParseAction(' '.join))
-        print(OneOrMore(attr_expr).parseString(text).dump())
-        
-        attr_label = label
-        attr_value = Suppress(':') + OneOrMore(data_word, stopOn=label).setParseAction(' '.join)
-
-        # similar to Dict, but simpler call format
-        result = dictOf(attr_label, attr_value).parseString(text)
-        print(result.dump())
-        print(result['shape'])
-        print(result.shape)  # object attribute access works too
-        print(result.asDict())
-    prints::
-        [['shape', 'SQUARE'], ['posn', 'upper left'], ['color', 'light blue'], ['texture', 'burlap']]
-        - color: light blue
-        - posn: upper left
-        - shape: SQUARE
-        - texture: burlap
-        SQUARE
-        SQUARE
-        {'color': 'light blue', 'shape': 'SQUARE', 'posn': 'upper left', 'texture': 'burlap'}
-    """
-    return Dict( ZeroOrMore( Group ( key + value ) ) )
-
-def originalTextFor(expr, asString=True):
-    """
-    Helper to return the original, untokenized text for a given expression.  Useful to
-    restore the parsed fields of an HTML start tag into the raw tag text itself, or to
-    revert separate tokens with intervening whitespace back to the original matching
-    input text. By default, returns astring containing the original parsed text.  
-       
-    If the optional C{asString} argument is passed as C{False}, then the return value is a 
-    C{L{ParseResults}} containing any results names that were originally matched, and a 
-    single token containing the original matched text from the input string.  So if 
-    the expression passed to C{L{originalTextFor}} contains expressions with defined
-    results names, you must set C{asString} to C{False} if you want to preserve those
-    results name values.
-
-    Example::
-        src = "this is test <b> bold <i>text</i> </b> normal text "
-        for tag in ("b","i"):
-            opener,closer = makeHTMLTags(tag)
-            patt = originalTextFor(opener + SkipTo(closer) + closer)
-            print(patt.searchString(src)[0])
-    prints::
-        ['<b> bold <i>text</i> </b>']
-        ['<i>text</i>']
-    """
-    locMarker = Empty().setParseAction(lambda s,loc,t: loc)
-    endlocMarker = locMarker.copy()
-    endlocMarker.callPreparse = False
-    matchExpr = locMarker("_original_start") + expr + endlocMarker("_original_end")
-    if asString:
-        extractText = lambda s,l,t: s[t._original_start:t._original_end]
-    else:
-        def extractText(s,l,t):
-            t[:] = [s[t.pop('_original_start'):t.pop('_original_end')]]
-    matchExpr.setParseAction(extractText)
-    matchExpr.ignoreExprs = expr.ignoreExprs
-    return matchExpr
-
-def ungroup(expr): 
-    """
-    Helper to undo pyparsing's default grouping of And expressions, even
-    if all but one are non-empty.
-    """
-    return TokenConverter(expr).setParseAction(lambda t:t[0])
-
-def locatedExpr(expr):
-    """
-    Helper to decorate a returned token with its starting and ending locations in the input string.
-    This helper adds the following results names:
-     - locn_start = location where matched expression begins
-     - locn_end = location where matched expression ends
-     - value = the actual parsed results
-
-    Be careful if the input text contains C{<TAB>} characters, you may want to call
-    C{L{ParserElement.parseWithTabs}}
-
-    Example::
-        wd = Word(alphas)
-        for match in locatedExpr(wd).searchString("ljsdf123lksdjjf123lkkjj1222"):
-            print(match)
-    prints::
-        [[0, 'ljsdf', 5]]
-        [[8, 'lksdjjf', 15]]
-        [[18, 'lkkjj', 23]]
-    """
-    locator = Empty().setParseAction(lambda s,l,t: l)
-    return Group(locator("locn_start") + expr("value") + locator.copy().leaveWhitespace()("locn_end"))
-
-
-# convenience constants for positional expressions
-empty       = Empty().setName("empty")
-lineStart   = LineStart().setName("lineStart")
-lineEnd     = LineEnd().setName("lineEnd")
-stringStart = StringStart().setName("stringStart")
-stringEnd   = StringEnd().setName("stringEnd")
-
-_escapedPunc = Word( _bslash, r"\[]-*.$+^?()~ ", exact=2 ).setParseAction(lambda s,l,t:t[0][1])
-_escapedHexChar = Regex(r"\\0?[xX][0-9a-fA-F]+").setParseAction(lambda s,l,t:unichr(int(t[0].lstrip(r'\0x'),16)))
-_escapedOctChar = Regex(r"\\0[0-7]+").setParseAction(lambda s,l,t:unichr(int(t[0][1:],8)))
-_singleChar = _escapedPunc | _escapedHexChar | _escapedOctChar | Word(printables, excludeChars=r'\]', exact=1) | Regex(r"\w", re.UNICODE)
-_charRange = Group(_singleChar + Suppress("-") + _singleChar)
-_reBracketExpr = Literal("[") + Optional("^").setResultsName("negate") + Group( OneOrMore( _charRange | _singleChar ) ).setResultsName("body") + "]"
-
-def srange(s):
-    r"""
-    Helper to easily define string ranges for use in Word construction.  Borrows
-    syntax from regexp '[]' string range definitions::
-        srange("[0-9]")   -> "0123456789"
-        srange("[a-z]")   -> "abcdefghijklmnopqrstuvwxyz"
-        srange("[a-z$_]") -> "abcdefghijklmnopqrstuvwxyz$_"
-    The input string must be enclosed in []'s, and the returned string is the expanded
-    character set joined into a single string.
-    The values enclosed in the []'s may be:
-     - a single character
-     - an escaped character with a leading backslash (such as C{\-} or C{\]})
-     - an escaped hex character with a leading C{'\x'} (C{\x21}, which is a C{'!'} character) 
-         (C{\0x##} is also supported for backwards compatibility) 
-     - an escaped octal character with a leading C{'\0'} (C{\041}, which is a C{'!'} character)
-     - a range of any of the above, separated by a dash (C{'a-z'}, etc.)
-     - any combination of the above (C{'aeiouy'}, C{'a-zA-Z0-9_$'}, etc.)
-    """
-    _expanded = lambda p: p if not isinstance(p,ParseResults) else ''.join(unichr(c) for c in range(ord(p[0]),ord(p[1])+1))
-    try:
-        return "".join(_expanded(part) for part in _reBracketExpr.parseString(s).body)
-    except Exception:
-        return ""
-
-def matchOnlyAtCol(n):
-    """
-    Helper method for defining parse actions that require matching at a specific
-    column in the input text.
-    """
-    def verifyCol(strg,locn,toks):
-        if col(locn,strg) != n:
-            raise ParseException(strg,locn,"matched token not at column %d" % n)
-    return verifyCol
-
-def replaceWith(replStr):
-    """
-    Helper method for common parse actions that simply return a literal value.  Especially
-    useful when used with C{L{transformString<ParserElement.transformString>}()}.
-
-    Example::
-        num = Word(nums).setParseAction(lambda toks: int(toks[0]))
-        na = oneOf("N/A NA").setParseAction(replaceWith(math.nan))
-        term = na | num
-        
-        OneOrMore(term).parseString("324 234 N/A 234") # -> [324, 234, nan, 234]
-    """
-    return lambda s,l,t: [replStr]
-
-def removeQuotes(s,l,t):
-    """
-    Helper parse action for removing quotation marks from parsed quoted strings.
-
-    Example::
-        # by default, quotation marks are included in parsed results
-        quotedString.parseString("'Now is the Winter of our Discontent'") # -> ["'Now is the Winter of our Discontent'"]
-
-        # use removeQuotes to strip quotation marks from parsed results
-        quotedString.setParseAction(removeQuotes)
-        quotedString.parseString("'Now is the Winter of our Discontent'") # -> ["Now is the Winter of our Discontent"]
-    """
-    return t[0][1:-1]
-
-def tokenMap(func, *args):
-    """
-    Helper to define a parse action by mapping a function to all elements of a ParseResults list.If any additional 
-    args are passed, they are forwarded to the given function as additional arguments after
-    the token, as in C{hex_integer = Word(hexnums).setParseAction(tokenMap(int, 16))}, which will convert the
-    parsed data to an integer using base 16.
-
-    Example (compare the last to example in L{ParserElement.transformString}::
-        hex_ints = OneOrMore(Word(hexnums)).setParseAction(tokenMap(int, 16))
-        hex_ints.runTests('''
-            00 11 22 aa FF 0a 0d 1a
-            ''')
-        
-        upperword = Word(alphas).setParseAction(tokenMap(str.upper))
-        OneOrMore(upperword).runTests('''
-            my kingdom for a horse
-            ''')
-
-        wd = Word(alphas).setParseAction(tokenMap(str.title))
-        OneOrMore(wd).setParseAction(' '.join).runTests('''
-            now is the winter of our discontent made glorious summer by this sun of york
-            ''')
-    prints::
-        00 11 22 aa FF 0a 0d 1a
-        [0, 17, 34, 170, 255, 10, 13, 26]
-
-        my kingdom for a horse
-        ['MY', 'KINGDOM', 'FOR', 'A', 'HORSE']
-
-        now is the winter of our discontent made glorious summer by this sun of york
-        ['Now Is The Winter Of Our Discontent Made Glorious Summer By This Sun Of York']
-    """
-    def pa(s,l,t):
-        return [func(tokn, *args) for tokn in t]
-
-    try:
-        func_name = getattr(func, '__name__', 
-                            getattr(func, '__class__').__name__)
-    except Exception:
-        func_name = str(func)
-    pa.__name__ = func_name
-
-    return pa
-
-upcaseTokens = tokenMap(lambda t: _ustr(t).upper())
-"""(Deprecated) Helper parse action to convert tokens to upper case. Deprecated in favor of L{pyparsing_common.upcaseTokens}"""
-
-downcaseTokens = tokenMap(lambda t: _ustr(t).lower())
-"""(Deprecated) Helper parse action to convert tokens to lower case. Deprecated in favor of L{pyparsing_common.downcaseTokens}"""
-    
-def _makeTags(tagStr, xml):
-    """Internal helper to construct opening and closing tag expressions, given a tag name"""
-    if isinstance(tagStr,basestring):
-        resname = tagStr
-        tagStr = Keyword(tagStr, caseless=not xml)
-    else:
-        resname = tagStr.name
-
-    tagAttrName = Word(alphas,alphanums+"_-:")
-    if (xml):
-        tagAttrValue = dblQuotedString.copy().setParseAction( removeQuotes )
-        openTag = Suppress("<") + tagStr("tag") + \
-                Dict(ZeroOrMore(Group( tagAttrName + Suppress("=") + tagAttrValue ))) + \
-                Optional("/",default=[False]).setResultsName("empty").setParseAction(lambda s,l,t:t[0]=='/') + Suppress(">")
-    else:
-        printablesLessRAbrack = "".join(c for c in printables if c not in ">")
-        tagAttrValue = quotedString.copy().setParseAction( removeQuotes ) | Word(printablesLessRAbrack)
-        openTag = Suppress("<") + tagStr("tag") + \
-                Dict(ZeroOrMore(Group( tagAttrName.setParseAction(downcaseTokens) + \
-                Optional( Suppress("=") + tagAttrValue ) ))) + \
-                Optional("/",default=[False]).setResultsName("empty").setParseAction(lambda s,l,t:t[0]=='/') + Suppress(">")
-    closeTag = Combine(_L("</") + tagStr + ">")
-
-    openTag = openTag.setResultsName("start"+"".join(resname.replace(":"," ").title().split())).setName("<%s>" % resname)
-    closeTag = closeTag.setResultsName("end"+"".join(resname.replace(":"," ").title().split())).setName("</%s>" % resname)
-    openTag.tag = resname
-    closeTag.tag = resname
-    return openTag, closeTag
-
-def makeHTMLTags(tagStr):
-    """
-    Helper to construct opening and closing tag expressions for HTML, given a tag name. Matches
-    tags in either upper or lower case, attributes with namespaces and with quoted or unquoted values.
-
-    Example::
-        text = '<td>More info at the <a href="http://pyparsing.wikispaces.com">pyparsing</a> wiki page</td>'
-        # makeHTMLTags returns pyparsing expressions for the opening and closing tags as a 2-tuple
-        a,a_end = makeHTMLTags("A")
-        link_expr = a + SkipTo(a_end)("link_text") + a_end
-        
-        for link in link_expr.searchString(text):
-            # attributes in the <A> tag (like "href" shown here) are also accessible as named results
-            print(link.link_text, '->', link.href)
-    prints::
-        pyparsing -> http://pyparsing.wikispaces.com
-    """
-    return _makeTags( tagStr, False )
-
-def makeXMLTags(tagStr):
-    """
-    Helper to construct opening and closing tag expressions for XML, given a tag name. Matches
-    tags only in the given upper/lower case.
-
-    Example: similar to L{makeHTMLTags}
-    """
-    return _makeTags( tagStr, True )
-
-def withAttribute(*args,**attrDict):
-    """
-    Helper to create a validating parse action to be used with start tags created
-    with C{L{makeXMLTags}} or C{L{makeHTMLTags}}. Use C{withAttribute} to qualify a starting tag
-    with a required attribute value, to avoid false matches on common tags such as
-    C{<TD>} or C{<DIV>}.
-
-    Call C{withAttribute} with a series of attribute names and values. Specify the list
-    of filter attributes names and values as:
-     - keyword arguments, as in C{(align="right")}, or
-     - as an explicit dict with C{**} operator, when an attribute name is also a Python
-          reserved word, as in C{**{"class":"Customer", "align":"right"}}
-     - a list of name-value tuples, as in ( ("ns1:class", "Customer"), ("ns2:align","right") )
-    For attribute names with a namespace prefix, you must use the second form.  Attribute
-    names are matched insensitive to upper/lower case.
-       
-    If just testing for C{class} (with or without a namespace), use C{L{withClass}}.
-
-    To verify that the attribute exists, but without specifying a value, pass
-    C{withAttribute.ANY_VALUE} as the value.
-
-    Example::
-        html = '''
-            <div>
-            Some text
-            <div type="grid">1 4 0 1 0</div>
-            <div type="graph">1,3 2,3 1,1</div>
-            <div>this has no type</div>
-            </div>
-                
-        '''
-        div,div_end = makeHTMLTags("div")
-
-        # only match div tag having a type attribute with value "grid"
-        div_grid = div().setParseAction(withAttribute(type="grid"))
-        grid_expr = div_grid + SkipTo(div | div_end)("body")
-        for grid_header in grid_expr.searchString(html):
-            print(grid_header.body)
-        
-        # construct a match with any div tag having a type attribute, regardless of the value
-        div_any_type = div().setParseAction(withAttribute(type=withAttribute.ANY_VALUE))
-        div_expr = div_any_type + SkipTo(div | div_end)("body")
-        for div_header in div_expr.searchString(html):
-            print(div_header.body)
-    prints::
-        1 4 0 1 0
-
-        1 4 0 1 0
-        1,3 2,3 1,1
-    """
-    if args:
-        attrs = args[:]
-    else:
-        attrs = attrDict.items()
-    attrs = [(k,v) for k,v in attrs]
-    def pa(s,l,tokens):
-        for attrName,attrValue in attrs:
-            if attrName not in tokens:
-                raise ParseException(s,l,"no matching attribute " + attrName)
-            if attrValue != withAttribute.ANY_VALUE and tokens[attrName] != attrValue:
-                raise ParseException(s,l,"attribute '%s' has value '%s', must be '%s'" %
-                                            (attrName, tokens[attrName], attrValue))
-    return pa
-withAttribute.ANY_VALUE = object()
-
-def withClass(classname, namespace=''):
-    """
-    Simplified version of C{L{withAttribute}} when matching on a div class - made
-    difficult because C{class} is a reserved word in Python.
-
-    Example::
-        html = '''
-            <div>
-            Some text
-            <div class="grid">1 4 0 1 0</div>
-            <div class="graph">1,3 2,3 1,1</div>
-            <div>this &lt;div&gt; has no class</div>
-            </div>
-                
-        '''
-        div,div_end = makeHTMLTags("div")
-        div_grid = div().setParseAction(withClass("grid"))
-        
-        grid_expr = div_grid + SkipTo(div | div_end)("body")
-        for grid_header in grid_expr.searchString(html):
-            print(grid_header.body)
-        
-        div_any_type = div().setParseAction(withClass(withAttribute.ANY_VALUE))
-        div_expr = div_any_type + SkipTo(div | div_end)("body")
-        for div_header in div_expr.searchString(html):
-            print(div_header.body)
-    prints::
-        1 4 0 1 0
-
-        1 4 0 1 0
-        1,3 2,3 1,1
-    """
-    classattr = "%s:class" % namespace if namespace else "class"
-    return withAttribute(**{classattr : classname})        
-
-opAssoc = _Constants()
-opAssoc.LEFT = object()
-opAssoc.RIGHT = object()
-
-def infixNotation( baseExpr, opList, lpar=Suppress('('), rpar=Suppress(')') ):
-    """
-    Helper method for constructing grammars of expressions made up of
-    operators working in a precedence hierarchy.  Operators may be unary or
-    binary, left- or right-associative.  Parse actions can also be attached
-    to operator expressions. The generated parser will also recognize the use 
-    of parentheses to override operator precedences (see example below).
-    
-    Note: if you define a deep operator list, you may see performance issues
-    when using infixNotation. See L{ParserElement.enablePackrat} for a
-    mechanism to potentially improve your parser performance.
-
-    Parameters:
-     - baseExpr - expression representing the most basic element for the nested
-     - opList - list of tuples, one for each operator precedence level in the
-      expression grammar; each tuple is of the form
-      (opExpr, numTerms, rightLeftAssoc, parseAction), where:
-       - opExpr is the pyparsing expression for the operator;
-          may also be a string, which will be converted to a Literal;
-          if numTerms is 3, opExpr is a tuple of two expressions, for the
-          two operators separating the 3 terms
-       - numTerms is the number of terms for this operator (must
-          be 1, 2, or 3)
-       - rightLeftAssoc is the indicator whether the operator is
-          right or left associative, using the pyparsing-defined
-          constants C{opAssoc.RIGHT} and C{opAssoc.LEFT}.
-       - parseAction is the parse action to be associated with
-          expressions matching this operator expression (the
-          parse action tuple member may be omitted); if the parse action
-          is passed a tuple or list of functions, this is equivalent to
-          calling C{setParseAction(*fn)} (L{ParserElement.setParseAction})
-     - lpar - expression for matching left-parentheses (default=C{Suppress('(')})
-     - rpar - expression for matching right-parentheses (default=C{Suppress(')')})
-
-    Example::
-        # simple example of four-function arithmetic with ints and variable names
-        integer = pyparsing_common.signed_integer
-        varname = pyparsing_common.identifier 
-        
-        arith_expr = infixNotation(integer | varname,
-            [
-            ('-', 1, opAssoc.RIGHT),
-            (oneOf('* /'), 2, opAssoc.LEFT),
-            (oneOf('+ -'), 2, opAssoc.LEFT),
-            ])
-        
-        arith_expr.runTests('''
-            5+3*6
-            (5+3)*6
-            -2--11
-            ''', fullDump=False)
-    prints::
-        5+3*6
-        [[5, '+', [3, '*', 6]]]
-
-        (5+3)*6
-        [[[5, '+', 3], '*', 6]]
-
-        -2--11
-        [[['-', 2], '-', ['-', 11]]]
-    """
-    ret = Forward()
-    lastExpr = baseExpr | ( lpar + ret + rpar )
-    for i,operDef in enumerate(opList):
-        opExpr,arity,rightLeftAssoc,pa = (operDef + (None,))[:4]
-        termName = "%s term" % opExpr if arity < 3 else "%s%s term" % opExpr
-        if arity == 3:
-            if opExpr is None or len(opExpr) != 2:
-                raise ValueError("if numterms=3, opExpr must be a tuple or list of two expressions")
-            opExpr1, opExpr2 = opExpr
-        thisExpr = Forward().setName(termName)
-        if rightLeftAssoc == opAssoc.LEFT:
-            if arity == 1:
-                matchExpr = FollowedBy(lastExpr + opExpr) + Group( lastExpr + OneOrMore( opExpr ) )
-            elif arity == 2:
-                if opExpr is not None:
-                    matchExpr = FollowedBy(lastExpr + opExpr + lastExpr) + Group( lastExpr + OneOrMore( opExpr + lastExpr ) )
-                else:
-                    matchExpr = FollowedBy(lastExpr+lastExpr) + Group( lastExpr + OneOrMore(lastExpr) )
-            elif arity == 3:
-                matchExpr = FollowedBy(lastExpr + opExpr1 + lastExpr + opExpr2 + lastExpr) + \
-                            Group( lastExpr + opExpr1 + lastExpr + opExpr2 + lastExpr )
-            else:
-                raise ValueError("operator must be unary (1), binary (2), or ternary (3)")
-        elif rightLeftAssoc == opAssoc.RIGHT:
-            if arity == 1:
-                # try to avoid LR with this extra test
-                if not isinstance(opExpr, Optional):
-                    opExpr = Optional(opExpr)
-                matchExpr = FollowedBy(opExpr.expr + thisExpr) + Group( opExpr + thisExpr )
-            elif arity == 2:
-                if opExpr is not None:
-                    matchExpr = FollowedBy(lastExpr + opExpr + thisExpr) + Group( lastExpr + OneOrMore( opExpr + thisExpr ) )
-                else:
-                    matchExpr = FollowedBy(lastExpr + thisExpr) + Group( lastExpr + OneOrMore( thisExpr ) )
-            elif arity == 3:
-                matchExpr = FollowedBy(lastExpr + opExpr1 + thisExpr + opExpr2 + thisExpr) + \
-                            Group( lastExpr + opExpr1 + thisExpr + opExpr2 + thisExpr )
-            else:
-                raise ValueError("operator must be unary (1), binary (2), or ternary (3)")
-        else:
-            raise ValueError("operator must indicate right or left associativity")
-        if pa:
-            if isinstance(pa, (tuple, list)):
-                matchExpr.setParseAction(*pa)
-            else:
-                matchExpr.setParseAction(pa)
-        thisExpr <<= ( matchExpr.setName(termName) | lastExpr )
-        lastExpr = thisExpr
-    ret <<= lastExpr
-    return ret
-
-operatorPrecedence = infixNotation
-"""(Deprecated) Former name of C{L{infixNotation}}, will be dropped in a future release."""
-
-dblQuotedString = Combine(Regex(r'"(?:[^"\n\r\\]|(?:"")|(?:\\(?:[^x]|x[0-9a-fA-F]+)))*')+'"').setName("string enclosed in double quotes")
-sglQuotedString = Combine(Regex(r"'(?:[^'\n\r\\]|(?:'')|(?:\\(?:[^x]|x[0-9a-fA-F]+)))*")+"'").setName("string enclosed in single quotes")
-quotedString = Combine(Regex(r'"(?:[^"\n\r\\]|(?:"")|(?:\\(?:[^x]|x[0-9a-fA-F]+)))*')+'"'|
-                       Regex(r"'(?:[^'\n\r\\]|(?:'')|(?:\\(?:[^x]|x[0-9a-fA-F]+)))*")+"'").setName("quotedString using single or double quotes")
-unicodeString = Combine(_L('u') + quotedString.copy()).setName("unicode string literal")
-
-def nestedExpr(opener="(", closer=")", content=None, ignoreExpr=quotedString.copy()):
-    """
-    Helper method for defining nested lists enclosed in opening and closing
-    delimiters ("(" and ")" are the default).
-
-    Parameters:
-     - opener - opening character for a nested list (default=C{"("}); can also be a pyparsing expression
-     - closer - closing character for a nested list (default=C{")"}); can also be a pyparsing expression
-     - content - expression for items within the nested lists (default=C{None})
-     - ignoreExpr - expression for ignoring opening and closing delimiters (default=C{quotedString})
-
-    If an expression is not provided for the content argument, the nested
-    expression will capture all whitespace-delimited content between delimiters
-    as a list of separate values.
-
-    Use the C{ignoreExpr} argument to define expressions that may contain
-    opening or closing characters that should not be treated as opening
-    or closing characters for nesting, such as quotedString or a comment
-    expression.  Specify multiple expressions using an C{L{Or}} or C{L{MatchFirst}}.
-    The default is L{quotedString}, but if no expressions are to be ignored,
-    then pass C{None} for this argument.
-
-    Example::
-        data_type = oneOf("void int short long char float double")
-        decl_data_type = Combine(data_type + Optional(Word('*')))
-        ident = Word(alphas+'_', alphanums+'_')
-        number = pyparsing_common.number
-        arg = Group(decl_data_type + ident)
-        LPAR,RPAR = map(Suppress, "()")
-
-        code_body = nestedExpr('{', '}', ignoreExpr=(quotedString | cStyleComment))
-
-        c_function = (decl_data_type("type") 
-                      + ident("name")
-                      + LPAR + Optional(delimitedList(arg), [])("args") + RPAR 
-                      + code_body("body"))
-        c_function.ignore(cStyleComment)
-        
-        source_code = '''
-            int is_odd(int x) { 
-                return (x%2); 
-            }
-                
-            int dec_to_hex(char hchar) { 
-                if (hchar >= '0' && hchar <= '9') { 
-                    return (ord(hchar)-ord('0')); 
-                } else { 
-                    return (10+ord(hchar)-ord('A'));
-                } 
-            }
-        '''
-        for func in c_function.searchString(source_code):
-            print("%(name)s (%(type)s) args: %(args)s" % func)
-
-    prints::
-        is_odd (int) args: [['int', 'x']]
-        dec_to_hex (int) args: [['char', 'hchar']]
-    """
-    if opener == closer:
-        raise ValueError("opening and closing strings cannot be the same")
-    if content is None:
-        if isinstance(opener,basestring) and isinstance(closer,basestring):
-            if len(opener) == 1 and len(closer)==1:
-                if ignoreExpr is not None:
-                    content = (Combine(OneOrMore(~ignoreExpr +
-                                    CharsNotIn(opener+closer+ParserElement.DEFAULT_WHITE_CHARS,exact=1))
-                                ).setParseAction(lambda t:t[0].strip()))
-                else:
-                    content = (empty.copy()+CharsNotIn(opener+closer+ParserElement.DEFAULT_WHITE_CHARS
-                                ).setParseAction(lambda t:t[0].strip()))
-            else:
-                if ignoreExpr is not None:
-                    content = (Combine(OneOrMore(~ignoreExpr + 
-                                    ~Literal(opener) + ~Literal(closer) +
-                                    CharsNotIn(ParserElement.DEFAULT_WHITE_CHARS,exact=1))
-                                ).setParseAction(lambda t:t[0].strip()))
-                else:
-                    content = (Combine(OneOrMore(~Literal(opener) + ~Literal(closer) +
-                                    CharsNotIn(ParserElement.DEFAULT_WHITE_CHARS,exact=1))
-                                ).setParseAction(lambda t:t[0].strip()))
-        else:
-            raise ValueError("opening and closing arguments must be strings if no content expression is given")
-    ret = Forward()
-    if ignoreExpr is not None:
-        ret <<= Group( Suppress(opener) + ZeroOrMore( ignoreExpr | ret | content ) + Suppress(closer) )
-    else:
-        ret <<= Group( Suppress(opener) + ZeroOrMore( ret | content )  + Suppress(closer) )
-    ret.setName('nested %s%s expression' % (opener,closer))
-    return ret
-
-def indentedBlock(blockStatementExpr, indentStack, indent=True):
-    """
-    Helper method for defining space-delimited indentation blocks, such as
-    those used to define block statements in Python source code.
-
-    Parameters:
-     - blockStatementExpr - expression defining syntax of statement that
-            is repeated within the indented block
-     - indentStack - list created by caller to manage indentation stack
-            (multiple statementWithIndentedBlock expressions within a single grammar
-            should share a common indentStack)
-     - indent - boolean indicating whether block must be indented beyond the
-            the current level; set to False for block of left-most statements
-            (default=C{True})
-
-    A valid block must contain at least one C{blockStatement}.
-
-    Example::
-        data = '''
-        def A(z):
-          A1
-          B = 100
-          G = A2
-          A2
-          A3
-        B
-        def BB(a,b,c):
-          BB1
-          def BBA():
-            bba1
-            bba2
-            bba3
-        C
-        D
-        def spam(x,y):
-             def eggs(z):
-                 pass
-        '''
-
-
-        indentStack = [1]
-        stmt = Forward()
-
-        identifier = Word(alphas, alphanums)
-        funcDecl = ("def" + identifier + Group( "(" + Optional( delimitedList(identifier) ) + ")" ) + ":")
-        func_body = indentedBlock(stmt, indentStack)
-        funcDef = Group( funcDecl + func_body )
-
-        rvalue = Forward()
-        funcCall = Group(identifier + "(" + Optional(delimitedList(rvalue)) + ")")
-        rvalue << (funcCall | identifier | Word(nums))
-        assignment = Group(identifier + "=" + rvalue)
-        stmt << ( funcDef | assignment | identifier )
-
-        module_body = OneOrMore(stmt)
-
-        parseTree = module_body.parseString(data)
-        parseTree.pprint()
-    prints::
-        [['def',
-          'A',
-          ['(', 'z', ')'],
-          ':',
-          [['A1'], [['B', '=', '100']], [['G', '=', 'A2']], ['A2'], ['A3']]],
-         'B',
-         ['def',
-          'BB',
-          ['(', 'a', 'b', 'c', ')'],
-          ':',
-          [['BB1'], [['def', 'BBA', ['(', ')'], ':', [['bba1'], ['bba2'], ['bba3']]]]]],
-         'C',
-         'D',
-         ['def',
-          'spam',
-          ['(', 'x', 'y', ')'],
-          ':',
-          [[['def', 'eggs', ['(', 'z', ')'], ':', [['pass']]]]]]] 
-    """
-    def checkPeerIndent(s,l,t):
-        if l >= len(s): return
-        curCol = col(l,s)
-        if curCol != indentStack[-1]:
-            if curCol > indentStack[-1]:
-                raise ParseFatalException(s,l,"illegal nesting")
-            raise ParseException(s,l,"not a peer entry")
-
-    def checkSubIndent(s,l,t):
-        curCol = col(l,s)
-        if curCol > indentStack[-1]:
-            indentStack.append( curCol )
-        else:
-            raise ParseException(s,l,"not a subentry")
-
-    def checkUnindent(s,l,t):
-        if l >= len(s): return
-        curCol = col(l,s)
-        if not(indentStack and curCol < indentStack[-1] and curCol <= indentStack[-2]):
-            raise ParseException(s,l,"not an unindent")
-        indentStack.pop()
-
-    NL = OneOrMore(LineEnd().setWhitespaceChars("\t ").suppress())
-    INDENT = (Empty() + Empty().setParseAction(checkSubIndent)).setName('INDENT')
-    PEER   = Empty().setParseAction(checkPeerIndent).setName('')
-    UNDENT = Empty().setParseAction(checkUnindent).setName('UNINDENT')
-    if indent:
-        smExpr = Group( Optional(NL) +
-            #~ FollowedBy(blockStatementExpr) +
-            INDENT + (OneOrMore( PEER + Group(blockStatementExpr) + Optional(NL) )) + UNDENT)
-    else:
-        smExpr = Group( Optional(NL) +
-            (OneOrMore( PEER + Group(blockStatementExpr) + Optional(NL) )) )
-    blockStatementExpr.ignore(_bslash + LineEnd())
-    return smExpr.setName('indented block')
-
-alphas8bit = srange(r"[\0xc0-\0xd6\0xd8-\0xf6\0xf8-\0xff]")
-punc8bit = srange(r"[\0xa1-\0xbf\0xd7\0xf7]")
-
-anyOpenTag,anyCloseTag = makeHTMLTags(Word(alphas,alphanums+"_:").setName('any tag'))
-_htmlEntityMap = dict(zip("gt lt amp nbsp quot apos".split(),'><& "\''))
-commonHTMLEntity = Regex('&(?P<entity>' + '|'.join(_htmlEntityMap.keys()) +");").setName("common HTML entity")
-def replaceHTMLEntity(t):
-    """Helper parser action to replace common HTML entities with their special characters"""
-    return _htmlEntityMap.get(t.entity)
-
-# it's easy to get these comment structures wrong - they're very common, so may as well make them available
-cStyleComment = Combine(Regex(r"/\*(?:[^*]|\*(?!/))*") + '*/').setName("C style comment")
-"Comment of the form C{/* ... */}"
-
-htmlComment = Regex(r"<!--[\s\S]*?-->").setName("HTML comment")
-"Comment of the form C{<!-- ... -->}"
-
-restOfLine = Regex(r".*").leaveWhitespace().setName("rest of line")
-dblSlashComment = Regex(r"//(?:\\\n|[^\n])*").setName("// comment")
-"Comment of the form C{// ... (to end of line)}"
-
-cppStyleComment = Combine(Regex(r"/\*(?:[^*]|\*(?!/))*") + '*/'| dblSlashComment).setName("C++ style comment")
-"Comment of either form C{L{cStyleComment}} or C{L{dblSlashComment}}"
-
-javaStyleComment = cppStyleComment
-"Same as C{L{cppStyleComment}}"
-
-pythonStyleComment = Regex(r"#.*").setName("Python style comment")
-"Comment of the form C{# ... (to end of line)}"
-
-_commasepitem = Combine(OneOrMore(Word(printables, excludeChars=',') +
-                                  Optional( Word(" \t") +
-                                            ~Literal(",") + ~LineEnd() ) ) ).streamline().setName("commaItem")
-commaSeparatedList = delimitedList( Optional( quotedString.copy() | _commasepitem, default="") ).setName("commaSeparatedList")
-"""(Deprecated) Predefined expression of 1 or more printable words or quoted strings, separated by commas.
-   This expression is deprecated in favor of L{pyparsing_common.comma_separated_list}."""
-
-# some other useful expressions - using lower-case class name since we are really using this as a namespace
-class pyparsing_common:
-    """
-    Here are some common low-level expressions that may be useful in jump-starting parser development:
-     - numeric forms (L{integers<integer>}, L{reals<real>}, L{scientific notation<sci_real>})
-     - common L{programming identifiers<identifier>}
-     - network addresses (L{MAC<mac_address>}, L{IPv4<ipv4_address>}, L{IPv6<ipv6_address>})
-     - ISO8601 L{dates<iso8601_date>} and L{datetime<iso8601_datetime>}
-     - L{UUID<uuid>}
-     - L{comma-separated list<comma_separated_list>}
-    Parse actions:
-     - C{L{convertToInteger}}
-     - C{L{convertToFloat}}
-     - C{L{convertToDate}}
-     - C{L{convertToDatetime}}
-     - C{L{stripHTMLTags}}
-     - C{L{upcaseTokens}}
-     - C{L{downcaseTokens}}
-
-    Example::
-        pyparsing_common.number.runTests('''
-            # any int or real number, returned as the appropriate type
-            100
-            -100
-            +100
-            3.14159
-            6.02e23
-            1e-12
-            ''')
-
-        pyparsing_common.fnumber.runTests('''
-            # any int or real number, returned as float
-            100
-            -100
-            +100
-            3.14159
-            6.02e23
-            1e-12
-            ''')
-
-        pyparsing_common.hex_integer.runTests('''
-            # hex numbers
-            100
-            FF
-            ''')
-
-        pyparsing_common.fraction.runTests('''
-            # fractions
-            1/2
-            -3/4
-            ''')
-
-        pyparsing_common.mixed_integer.runTests('''
-            # mixed fractions
-            1
-            1/2
-            -3/4
-            1-3/4
-            ''')
-
-        import uuid
-        pyparsing_common.uuid.setParseAction(tokenMap(uuid.UUID))
-        pyparsing_common.uuid.runTests('''
-            # uuid
-            12345678-1234-5678-1234-567812345678
-            ''')
-    prints::
-        # any int or real number, returned as the appropriate type
-        100
-        [100]
-
-        -100
-        [-100]
-
-        +100
-        [100]
-
-        3.14159
-        [3.14159]
-
-        6.02e23
-        [6.02e+23]
-
-        1e-12
-        [1e-12]
-
-        # any int or real number, returned as float
-        100
-        [100.0]
-
-        -100
-        [-100.0]
-
-        +100
-        [100.0]
-
-        3.14159
-        [3.14159]
-
-        6.02e23
-        [6.02e+23]
-
-        1e-12
-        [1e-12]
-
-        # hex numbers
-        100
-        [256]
-
-        FF
-        [255]
-
-        # fractions
-        1/2
-        [0.5]
-
-        -3/4
-        [-0.75]
-
-        # mixed fractions
-        1
-        [1]
-
-        1/2
-        [0.5]
-
-        -3/4
-        [-0.75]
-
-        1-3/4
-        [1.75]
-
-        # uuid
-        12345678-1234-5678-1234-567812345678
-        [UUID('12345678-1234-5678-1234-567812345678')]
-    """
-
-    convertToInteger = tokenMap(int)
-    """
-    Parse action for converting parsed integers to Python int
-    """
-
-    convertToFloat = tokenMap(float)
-    """
-    Parse action for converting parsed numbers to Python float
-    """
-
-    integer = Word(nums).setName("integer").setParseAction(convertToInteger)
-    """expression that parses an unsigned integer, returns an int"""
-
-    hex_integer = Word(hexnums).setName("hex integer").setParseAction(tokenMap(int,16))
-    """expression that parses a hexadecimal integer, returns an int"""
-
-    signed_integer = Regex(r'[+-]?\d+').setName("signed integer").setParseAction(convertToInteger)
-    """expression that parses an integer with optional leading sign, returns an int"""
-
-    fraction = (signed_integer().setParseAction(convertToFloat) + '/' + signed_integer().setParseAction(convertToFloat)).setName("fraction")
-    """fractional expression of an integer divided by an integer, returns a float"""
-    fraction.addParseAction(lambda t: t[0]/t[-1])
-
-    mixed_integer = (fraction | signed_integer + Optional(Optional('-').suppress() + fraction)).setName("fraction or mixed integer-fraction")
-    """mixed integer of the form 'integer - fraction', with optional leading integer, returns float"""
-    mixed_integer.addParseAction(sum)
-
-    real = Regex(r'[+-]?\d+\.\d*').setName("real number").setParseAction(convertToFloat)
-    """expression that parses a floating point number and returns a float"""
-
-    sci_real = Regex(r'[+-]?\d+([eE][+-]?\d+|\.\d*([eE][+-]?\d+)?)').setName("real number with scientific notation").setParseAction(convertToFloat)
-    """expression that parses a floating point number with optional scientific notation and returns a float"""
-
-    # streamlining this expression makes the docs nicer-looking
-    number = (sci_real | real | signed_integer).streamline()
-    """any numeric expression, returns the corresponding Python type"""
-
-    fnumber = Regex(r'[+-]?\d+\.?\d*([eE][+-]?\d+)?').setName("fnumber").setParseAction(convertToFloat)
-    """any int or real number, returned as float"""
-    
-    identifier = Word(alphas+'_', alphanums+'_').setName("identifier")
-    """typical code identifier (leading alpha or '_', followed by 0 or more alphas, nums, or '_')"""
-    
-    ipv4_address = Regex(r'(25[0-5]|2[0-4][0-9]|1?[0-9]{1,2})(\.(25[0-5]|2[0-4][0-9]|1?[0-9]{1,2})){3}').setName("IPv4 address")
-    "IPv4 address (C{0.0.0.0 - 255.255.255.255})"
-
-    _ipv6_part = Regex(r'[0-9a-fA-F]{1,4}').setName("hex_integer")
-    _full_ipv6_address = (_ipv6_part + (':' + _ipv6_part)*7).setName("full IPv6 address")
-    _short_ipv6_address = (Optional(_ipv6_part + (':' + _ipv6_part)*(0,6)) + "::" + Optional(_ipv6_part + (':' + _ipv6_part)*(0,6))).setName("short IPv6 address")
-    _short_ipv6_address.addCondition(lambda t: sum(1 for tt in t if pyparsing_common._ipv6_part.matches(tt)) < 8)
-    _mixed_ipv6_address = ("::ffff:" + ipv4_address).setName("mixed IPv6 address")
-    ipv6_address = Combine((_full_ipv6_address | _mixed_ipv6_address | _short_ipv6_address).setName("IPv6 address")).setName("IPv6 address")
-    "IPv6 address (long, short, or mixed form)"
-    
-    mac_address = Regex(r'[0-9a-fA-F]{2}([:.-])[0-9a-fA-F]{2}(?:\1[0-9a-fA-F]{2}){4}').setName("MAC address")
-    "MAC address xx:xx:xx:xx:xx (may also have '-' or '.' delimiters)"
-
-    @staticmethod
-    def convertToDate(fmt="%Y-%m-%d"):
-        """
-        Helper to create a parse action for converting parsed date string to Python datetime.date
-
-        Params -
-         - fmt - format to be passed to datetime.strptime (default=C{"%Y-%m-%d"})
-
-        Example::
-            date_expr = pyparsing_common.iso8601_date.copy()
-            date_expr.setParseAction(pyparsing_common.convertToDate())
-            print(date_expr.parseString("1999-12-31"))
-        prints::
-            [datetime.date(1999, 12, 31)]
-        """
-        def cvt_fn(s,l,t):
-            try:
-                return datetime.strptime(t[0], fmt).date()
-            except ValueError as ve:
-                raise ParseException(s, l, str(ve))
-        return cvt_fn
-
-    @staticmethod
-    def convertToDatetime(fmt="%Y-%m-%dT%H:%M:%S.%f"):
-        """
-        Helper to create a parse action for converting parsed datetime string to Python datetime.datetime
-
-        Params -
-         - fmt - format to be passed to datetime.strptime (default=C{"%Y-%m-%dT%H:%M:%S.%f"})
-
-        Example::
-            dt_expr = pyparsing_common.iso8601_datetime.copy()
-            dt_expr.setParseAction(pyparsing_common.convertToDatetime())
-            print(dt_expr.parseString("1999-12-31T23:59:59.999"))
-        prints::
-            [datetime.datetime(1999, 12, 31, 23, 59, 59, 999000)]
-        """
-        def cvt_fn(s,l,t):
-            try:
-                return datetime.strptime(t[0], fmt)
-            except ValueError as ve:
-                raise ParseException(s, l, str(ve))
-        return cvt_fn
-
-    iso8601_date = Regex(r'(?P<year>\d{4})(?:-(?P<month>\d\d)(?:-(?P<day>\d\d))?)?').setName("ISO8601 date")
-    "ISO8601 date (C{yyyy-mm-dd})"
-
-    iso8601_datetime = Regex(r'(?P<year>\d{4})-(?P<month>\d\d)-(?P<day>\d\d)[T ](?P<hour>\d\d):(?P<minute>\d\d)(:(?P<second>\d\d(\.\d*)?)?)?(?P<tz>Z|[+-]\d\d:?\d\d)?').setName("ISO8601 datetime")
-    "ISO8601 datetime (C{yyyy-mm-ddThh:mm:ss.s(Z|+-00:00)}) - trailing seconds, milliseconds, and timezone optional; accepts separating C{'T'} or C{' '}"
-
-    uuid = Regex(r'[0-9a-fA-F]{8}(-[0-9a-fA-F]{4}){3}-[0-9a-fA-F]{12}').setName("UUID")
-    "UUID (C{xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx})"
-
-    _html_stripper = anyOpenTag.suppress() | anyCloseTag.suppress()
-    @staticmethod
-    def stripHTMLTags(s, l, tokens):
-        """
-        Parse action to remove HTML tags from web page HTML source
-
-        Example::
-            # strip HTML links from normal text 
-            text = '<td>More info at the <a href="http://pyparsing.wikispaces.com">pyparsing</a> wiki page</td>'
-            td,td_end = makeHTMLTags("TD")
-            table_text = td + SkipTo(td_end).setParseAction(pyparsing_common.stripHTMLTags)("body") + td_end
-            
-            print(table_text.parseString(text).body) # -> 'More info at the pyparsing wiki page'
-        """
-        return pyparsing_common._html_stripper.transformString(tokens[0])
-
-    _commasepitem = Combine(OneOrMore(~Literal(",") + ~LineEnd() + Word(printables, excludeChars=',') 
-                                        + Optional( White(" \t") ) ) ).streamline().setName("commaItem")
-    comma_separated_list = delimitedList( Optional( quotedString.copy() | _commasepitem, default="") ).setName("comma separated list")
-    """Predefined expression of 1 or more printable words or quoted strings, separated by commas."""
-
-    upcaseTokens = staticmethod(tokenMap(lambda t: _ustr(t).upper()))
-    """Parse action to convert tokens to upper case."""
-
-    downcaseTokens = staticmethod(tokenMap(lambda t: _ustr(t).lower()))
-    """Parse action to convert tokens to lower case."""
-
-
-if __name__ == "__main__":
-
-    selectToken    = CaselessLiteral("select")
-    fromToken      = CaselessLiteral("from")
-
-    ident          = Word(alphas, alphanums + "_$")
-
-    columnName     = delimitedList(ident, ".", combine=True).setParseAction(upcaseTokens)
-    columnNameList = Group(delimitedList(columnName)).setName("columns")
-    columnSpec     = ('*' | columnNameList)
-
-    tableName      = delimitedList(ident, ".", combine=True).setParseAction(upcaseTokens)
-    tableNameList  = Group(delimitedList(tableName)).setName("tables")
-    
-    simpleSQL      = selectToken("command") + columnSpec("columns") + fromToken + tableNameList("tables")
-
-    # demo runTests method, including embedded comments in test string
-    simpleSQL.runTests("""
-        # '*' as column list and dotted table name
-        select * from SYS.XYZZY
-
-        # caseless match on "SELECT", and casts back to "select"
-        SELECT * from XYZZY, ABC
-
-        # list of column names, and mixed case SELECT keyword
-        Select AA,BB,CC from Sys.dual
-
-        # multiple tables
-        Select A, B, C from Sys.dual, Table2
-
-        # invalid SELECT keyword - should fail
-        Xelect A, B, C from Sys.dual
-
-        # incomplete command - should fail
-        Select
-
-        # invalid column name - should fail
-        Select ^^^ frox Sys.dual
-
-        """)
-
-    pyparsing_common.number.runTests("""
-        100
-        -100
-        +100
-        3.14159
-        6.02e23
-        1e-12
-        """)
-
-    # any int or real number, returned as float
-    pyparsing_common.fnumber.runTests("""
-        100
-        -100
-        +100
-        3.14159
-        6.02e23
-        1e-12
-        """)
-
-    pyparsing_common.hex_integer.runTests("""
-        100
-        FF
-        """)
-
-    import uuid
-    pyparsing_common.uuid.setParseAction(tokenMap(uuid.UUID))
-    pyparsing_common.uuid.runTests("""
-        12345678-1234-5678-1234-567812345678
-        """)
diff --git a/doc/tutorial/machine_learning_map/svg2imagemap.py b/doc/tutorial/machine_learning_map/svg2imagemap.py
deleted file mode 100644
index 80f06c8fb9738..0000000000000
--- a/doc/tutorial/machine_learning_map/svg2imagemap.py
+++ /dev/null
@@ -1,111 +0,0 @@
-#!/usr/local/bin/python
-
-"""
-This script converts a subset of SVG into an HTML imagemap
-
-Note *subset*.  It only handles <path> elements, for which it only pays
-attention to the M and L commands.  Further, it only notices the "translate"
-transform.
-
-It was written to generate the examples in the documentation for maphilight,
-and thus is very squarely aimed at handling several SVG maps from wikipedia.
-It *assumes* that all the <path>s it will need are inside a <g>.  Any <path>
-outside of a <g> will be ignored.
-
-It takes several possible arguments, in the form:
-$ svn2imagemap.py FILENAME [x y [group1 group2 ... groupN]]
-
-FILENAME must be the name of an SVG file.  All other arguments are optional.
-
-x and y, if present, are the dimensions of the image you'll be creating from
-the SVG.  If not present, it assumes the values of the width and height
-attributes in the SVG file.
-
-group1 through groupN are group ids.  If only want particular groups used,
-enter their ids here and all others will be ignored.
-"""
-import os
-import re
-import sys
-import xml.dom.minidom
-
-import parse_path
-
-if len(sys.argv) == 1:
-    sys.exit("svn2imagemap.py FILENAME [x y [group1 group2 ... groupN]]")
-if not os.path.exists(sys.argv[1]):
-    sys.exit("Input file does not exist")
-x, y, groups = None, None, None
-if len(sys.argv) >= 3:
-    x = float(sys.argv[2])
-    y = float(sys.argv[3])
-    if len(sys.argv) > 3:
-        groups = sys.argv[4:]
-
-svg_file = xml.dom.minidom.parse(sys.argv[1])
-svg = svg_file.getElementsByTagName("svg")[0]
-
-raw_width = float(svg.getAttribute("width"))
-raw_height = float(svg.getAttribute("height"))
-width_ratio = x and (x / raw_width) or 1
-height_ratio = y and (y / raw_height) or 1
-
-if groups:
-    elements = [
-        g
-        for g in svg.getElementsByTagName("g")
-        if (g.hasAttribute("id") and g.getAttribute("id") in groups)
-    ]
-    elements.extend(
-        [
-            p
-            for p in svg.getElementsByTagName("path")
-            if (p.hasAttribute("id") and p.getAttribute("id") in groups)
-        ]
-    )
-else:
-    elements = svg.getElementsByTagName("g")
-
-parsed_groups = {}
-for e in elements:
-    paths = []
-    if e.nodeName == "g":
-        for path in e.getElementsByTagName("path"):
-            points = parse_path.get_points(path.getAttribute("d"))
-            for pointset in points:
-                paths.append([path.getAttribute("id"), pointset])
-    else:
-        points = parse_path.get_points(e.getAttribute("d"))
-        for pointset in points:
-            paths.append([e.getAttribute("id"), pointset])
-    if e.hasAttribute("transform"):
-        print(e.getAttribute("id"), e.getAttribute("transform"))
-        for transform in re.findall(
-            r"(\w+)\((-?\d+.?\d*),(-?\d+.?\d*)\)", e.getAttribute("transform")
-        ):
-            if transform[0] == "translate":
-                x_shift = float(transform[1])
-                y_shift = float(transform[2])
-                for path in paths:
-                    path[1] = [(p[0] + x_shift, p[1] + y_shift) for p in path[1]]
-
-    parsed_groups[e.getAttribute("id")] = paths
-
-out = []
-for g in parsed_groups:
-    for path in parsed_groups[g]:
-        out.append(
-            '<area href="#" title="%s" shape="poly" coords="%s"></area>'
-            % (
-                path[0],
-                ", ".join(
-                    [
-                        "%d,%d" % (p[0] * width_ratio, p[1] * height_ratio)
-                        for p in path[1]
-                    ]
-                ),
-            )
-        )
-
-with open(sys.argv[1].replace(".svg", ".html"), "w") as f:
-    f.write("\n".join(out))
diff --git a/doc/tutorial/statistical_inference/index.rst b/doc/tutorial/statistical_inference/index.rst
index 1ea527054fc38..358bf16512254 100644
--- a/doc/tutorial/statistical_inference/index.rst
+++ b/doc/tutorial/statistical_inference/index.rst
@@ -4,17 +4,17 @@
 A tutorial on statistical-learning for scientific data processing
 ==========================================================================
 
-.. topic:: Statistical learning 
+.. topic:: Statistical learning
 
     `Machine learning <https://en.wikipedia.org/wiki/Machine_learning>`_ is
     a technique with a growing importance, as the
     size of the datasets experimental sciences are facing is rapidly
     growing. Problems it tackles range from building a prediction function
     linking different observations, to classifying observations, or
-    learning the structure in an unlabeled dataset. 
-    
+    learning the structure in an unlabeled dataset.
+
     This tutorial will explore *statistical learning*, the use of
-    machine learning techniques with the goal of `statistical inference 
+    machine learning techniques with the goal of `statistical inference
     <https://en.wikipedia.org/wiki/Statistical_inference>`_:
     drawing conclusions on the data at hand.
 
@@ -24,8 +24,6 @@ A tutorial on statistical-learning for scientific data processing
     <https://scipy.org/>`_, `matplotlib
     <https://matplotlib.org/>`_).
 
-.. include:: ../../includes/big_toc_css.rst
-
 .. toctree::
    :maxdepth: 2
 
diff --git a/doc/tutorial/statistical_inference/model_selection.rst b/doc/tutorial/statistical_inference/model_selection.rst
index 87423ef1c3925..7d7d5f69f18c4 100644
--- a/doc/tutorial/statistical_inference/model_selection.rst
+++ b/doc/tutorial/statistical_inference/model_selection.rst
@@ -196,41 +196,38 @@ scoring method.
         >>> scores = list()
         >>> scores_std = list()
 
-    |details-start|
-    **Solution**
-    |details-split|
-
-    .. plot::
-        :context: close-figs
-        :align: center
-
-        import numpy as np
-        from sklearn import datasets, svm
-        from sklearn.model_selection import cross_val_score
-        X, y = datasets.load_digits(return_X_y=True)
-        svc = svm.SVC(kernel="linear")
-        C_s = np.logspace(-10, 0, 10)
-        scores = list()
-        scores_std = list()
-        for C in C_s:
-            svc.C = C
-            this_scores = cross_val_score(svc, X, y, n_jobs=1)
-            scores.append(np.mean(this_scores))
-            scores_std.append(np.std(this_scores))
-
-        import matplotlib.pyplot as plt
-
-        plt.figure()
-        plt.semilogx(C_s, scores)
-        plt.semilogx(C_s, np.array(scores) + np.array(scores_std), "b--")
-        plt.semilogx(C_s, np.array(scores) - np.array(scores_std), "b--")
-        locs, labels = plt.yticks()
-        plt.yticks(locs, list(map(lambda x: "%g" % x, locs)))
-        plt.ylabel("CV score")
-        plt.xlabel("Parameter C")
-        plt.ylim(0, 1.1)
-        plt.show()
-    |details-end|
+    .. dropdown:: Solution
+
+        .. plot::
+            :context: close-figs
+            :align: center
+
+            import numpy as np
+            from sklearn import datasets, svm
+            from sklearn.model_selection import cross_val_score
+            X, y = datasets.load_digits(return_X_y=True)
+            svc = svm.SVC(kernel="linear")
+            C_s = np.logspace(-10, 0, 10)
+            scores = list()
+            scores_std = list()
+            for C in C_s:
+                svc.C = C
+                this_scores = cross_val_score(svc, X, y, n_jobs=1)
+                scores.append(np.mean(this_scores))
+                scores_std.append(np.std(this_scores))
+
+            import matplotlib.pyplot as plt
+
+            plt.figure()
+            plt.semilogx(C_s, scores)
+            plt.semilogx(C_s, np.array(scores) + np.array(scores_std), "b--")
+            plt.semilogx(C_s, np.array(scores) - np.array(scores_std), "b--")
+            locs, labels = plt.yticks()
+            plt.yticks(locs, list(map(lambda x: "%g" % x, locs)))
+            plt.ylabel("CV score")
+            plt.xlabel("Parameter C")
+            plt.ylim(0, 1.1)
+            plt.show()
 
 Grid-search and cross-validated estimators
 ============================================
diff --git a/doc/tutorial/statistical_inference/supervised_learning.rst b/doc/tutorial/statistical_inference/supervised_learning.rst
index 45fc4cf5b9bc0..41adf60c44fc7 100644
--- a/doc/tutorial/statistical_inference/supervised_learning.rst
+++ b/doc/tutorial/statistical_inference/supervised_learning.rst
@@ -426,9 +426,9 @@ the separating line (less regularization).
 
    **Regularized SVM (default)**
 
-.. topic:: Example:
+.. rubric:: Examples
 
- - :ref:`sphx_glr_auto_examples_svm_plot_iris_svc.py`
+- :ref:`sphx_glr_auto_examples_svm_plot_iris_svc.py`
 
 
 SVMs can be used in regression --:class:`SVR` (Support Vector Regression)--, or in
@@ -503,13 +503,6 @@ Sigmoid kernel
    :target: ../../auto_examples/svm/plot_svm_kernels.html
 
 
-
-.. topic:: **Interactive example**
-
-   See the :ref:`SVM GUI <sphx_glr_auto_examples_applications_svm_gui.py>` to download
-   ``svm_gui.py``; add data points of both classes with right and left button,
-   fit the model and change parameters and data.
-
 .. topic:: **Exercise**
    :class: green
 
diff --git a/doc/unsupervised_learning.rst b/doc/unsupervised_learning.rst
index 9c1de0c134623..57e8b429fd67e 100644
--- a/doc/unsupervised_learning.rst
+++ b/doc/unsupervised_learning.rst
@@ -1,9 +1,3 @@
-.. Places parent toc into the sidebar
-
-:parenttoc: True
-
-.. include:: includes/big_toc_css.rst
-
 .. _unsupervised-learning:
 
 Unsupervised learning
diff --git a/doc/user_guide.rst b/doc/user_guide.rst
index cd2f331004aa9..a32c63db9de01 100644
--- a/doc/user_guide.rst
+++ b/doc/user_guide.rst
@@ -1,21 +1,9 @@
-.. Places parent toc into the sidebar
-
-:parenttoc: True
-
-.. title:: User guide: contents
-
 .. _user_guide:
 
 ==========
 User Guide
 ==========
 
-.. include:: includes/big_toc_css.rst
-
-.. nice layout in the toc
-
-.. include:: tune_toc.rst
-
 .. toctree::
    :numbered:
    :maxdepth: 3
diff --git a/doc/visualizations.rst b/doc/visualizations.rst
index 9a44f6feb1b48..7252a2900eb26 100644
--- a/doc/visualizations.rst
+++ b/doc/visualizations.rst
@@ -1,9 +1,3 @@
-.. Places parent toc into the sidebar
-
-:parenttoc: True
-
-.. include:: includes/big_toc_css.rst
-
 .. _visualizations:
 
 ==============
@@ -63,12 +57,12 @@ the previously computed roc curve again by using the `plot` method of the
 Notice that we pass `alpha=0.8` to the plot functions to adjust the alpha
 values of the curves.
 
-.. topic:: Examples:
+.. rubric:: Examples
 
-    * :ref:`sphx_glr_auto_examples_miscellaneous_plot_roc_curve_visualization_api.py`
-    * :ref:`sphx_glr_auto_examples_miscellaneous_plot_partial_dependence_visualization_api.py`
-    * :ref:`sphx_glr_auto_examples_miscellaneous_plot_display_object_visualization.py`
-    * :ref:`sphx_glr_auto_examples_calibration_plot_compare_calibration.py`
+* :ref:`sphx_glr_auto_examples_miscellaneous_plot_roc_curve_visualization_api.py`
+* :ref:`sphx_glr_auto_examples_miscellaneous_plot_partial_dependence_visualization_api.py`
+* :ref:`sphx_glr_auto_examples_miscellaneous_plot_display_object_visualization.py`
+* :ref:`sphx_glr_auto_examples_calibration_plot_compare_calibration.py`
 
 Available Plotting Utilities
 ============================
diff --git a/doc/whats_new.rst b/doc/whats_new.rst
index ecf657936186d..e659e6453f9a0 100644
--- a/doc/whats_new.rst
+++ b/doc/whats_new.rst
@@ -15,6 +15,7 @@ Changelogs and release notes for all scikit-learn releases are linked in this pa
 .. toctree::
    :maxdepth: 2
 
+   whats_new/v1.6.rst
    whats_new/v1.5.rst
    whats_new/v1.4.rst
    whats_new/v1.3.rst
diff --git a/doc/whats_new/_contributors.rst b/doc/whats_new/_contributors.rst
index 21559e8112c0a..83f6ca5448b24 100644
--- a/doc/whats_new/_contributors.rst
+++ b/doc/whats_new/_contributors.rst
@@ -12,12 +12,12 @@
 .. role:: raw-latex(raw)
    :format: latex
 
-.. |MajorFeature| replace:: :raw-html:`<span class="badge badge-success">Major Feature</span>` :raw-latex:`{\small\sc [Major Feature]}`
-.. |Feature| replace:: :raw-html:`<span class="badge badge-success">Feature</span>` :raw-latex:`{\small\sc [Feature]}`
-.. |Efficiency| replace:: :raw-html:`<span class="badge badge-info">Efficiency</span>` :raw-latex:`{\small\sc [Efficiency]}`
-.. |Enhancement| replace:: :raw-html:`<span class="badge badge-info">Enhancement</span>` :raw-latex:`{\small\sc [Enhancement]}`
-.. |Fix| replace:: :raw-html:`<span class="badge badge-danger">Fix</span>` :raw-latex:`{\small\sc [Fix]}`
-.. |API| replace:: :raw-html:`<span class="badge badge-warning">API Change</span>` :raw-latex:`{\small\sc [API Change]}`
+.. |MajorFeature| replace:: :raw-html:`<span class="badge text-bg-success">Major Feature</span>` :raw-latex:`{\small\sc [Major Feature]}`
+.. |Feature| replace:: :raw-html:`<span class="badge text-bg-success">Feature</span>` :raw-latex:`{\small\sc [Feature]}`
+.. |Efficiency| replace:: :raw-html:`<span class="badge text-bg-info">Efficiency</span>` :raw-latex:`{\small\sc [Efficiency]}`
+.. |Enhancement| replace:: :raw-html:`<span class="badge text-bg-info">Enhancement</span>` :raw-latex:`{\small\sc [Enhancement]}`
+.. |Fix| replace:: :raw-html:`<span class="badge text-bg-danger">Fix</span>` :raw-latex:`{\small\sc [Fix]}`
+.. |API| replace:: :raw-html:`<span class="badge text-bg-warning">API Change</span>` :raw-latex:`{\small\sc [API Change]}`
 
 
 .. _Olivier Grisel: https://twitter.com/ogrisel
diff --git a/doc/whats_new/older_versions.rst b/doc/whats_new/older_versions.rst
index f4e1d1c0cdf10..dc440fcc800fe 100644
--- a/doc/whats_new/older_versions.rst
+++ b/doc/whats_new/older_versions.rst
@@ -1153,7 +1153,6 @@ Changelog
   datasets was created. These include:
   :ref:`sphx_glr_auto_examples_applications_plot_face_recognition.py`,
   :ref:`sphx_glr_auto_examples_applications_plot_species_distribution_modeling.py`,
-  :ref:`sphx_glr_auto_examples_applications_svm_gui.py`,
   :ref:`sphx_glr_auto_examples_applications_wikipedia_principal_eigenvector.py` and
   others.
 
diff --git a/doc/whats_new/v1.5.rst b/doc/whats_new/v1.5.rst
index f313d0c675ceb..60b8dadc97373 100644
--- a/doc/whats_new/v1.5.rst
+++ b/doc/whats_new/v1.5.rst
@@ -8,19 +8,61 @@
 Version 1.5
 ===========
 
-..
-  -- UNCOMMENT WHEN 1.5.0 IS RELEASED --
-  For a short description of the main highlights of the release, please refer to
-  :ref:`sphx_glr_auto_examples_release_highlights_plot_release_highlights_1_5_0.py`.
+For a short description of the main highlights of the release, please refer to
+:ref:`sphx_glr_auto_examples_release_highlights_plot_release_highlights_1_5_0.py`.
 
 .. include:: changelog_legend.inc
 
+.. _changes_1_5_1:
+
+Version 1.5.1
+=============
+
+**TODO**
+
+Changelog
+---------
+
+:mod:`sklearn.metrics`
+......................
+
+- |Fix| Fix a regression in :func:`metrics.r2_score`. Passing torch CPU tensors
+  with array API dispatched disabled would complain about non-CPU devices
+  instead of implicitly converting those inputs as regular NumPy arrays.
+  :pr:`29119` by :user:`Olivier Grisel`.
+
+:mod:`sklearn.model_selection`
+..............................
+
+- |Fix| Fix a regression in :class:`model_selection.GridSearchCV` for parameter
+  grids that have heterogeneous parameter values.
+  :pr:`29078` by :user:`Loïc Estève <lesteve>`.
+
+
 .. _changes_1_5:
 
 Version 1.5.0
 =============
 
-**In Development**
+**May 2024**
+
+Security
+--------
+
+- |Fix| :class:`feature_extraction.text.CountVectorizer` and
+  :class:`feature_extraction.text.TfidfVectorizer` no longer store discarded
+  tokens from the training set in their `stop_words_` attribute. This attribute
+  would hold too frequent (above `max_df`) but also too rare tokens (below
+  `min_df`). This fixes a potential security issue (data leak) if the discarded
+  rare tokens hold sensitive information from the training set without the
+  model developer's knowledge.
+
+  Note: users of those classes are encouraged to either retrain their pipelines
+  with the new scikit-learn version or to manually clear the `stop_words_`
+  attribute from previously trained instances of those transformers. This
+  attribute was designed only for model inspection purposes and has no impact
+  on the behavior of the transformers.
+  :pr:`28823` by :user:`Olivier Grisel <ogrisel>`.
 
 Changed models
 --------------
@@ -38,6 +80,21 @@ Changed models
   signs across all `PCA` solvers, including the new
   `svd_solver="covariance_eigh"` option introduced in this release.
 
+Changes impacting many modules
+------------------------------
+
+- |Fix| Raise `ValueError` with an informative error message when passing 1D
+  sparse arrays to methods that expect 2D sparse inputs.
+  :pr:`28988` by :user:`Olivier Grisel <ogrisel>`.
+
+- |API| The name of the input of the `inverse_transform` method of estimators has been
+  standardized to `X`. As a consequence, `Xt` is deprecated and will be removed in
+  version 1.7 in the following estimators: :class:`cluster.FeatureAgglomeration`,
+  :class:`decomposition.MiniBatchNMF`, :class:`decomposition.NMF`,
+  :class:`model_selection.GridSearchCV`, :class:`model_selection.RandomizedSearchCV`,
+  :class:`pipeline.Pipeline` and :class:`preprocessing.KBinsDiscretizer`.
+  :pr:`28756` by :user:`Will Dean <wd60622>`.
+
 Support for Array API
 ---------------------
 
@@ -49,20 +106,29 @@ See :ref:`array_api` for more details.
 **Functions:**
 
 - :func:`sklearn.metrics.r2_score` now supports Array API compliant inputs.
-  :pr:`27904` by :user:`Eric Lindgren <elindgren>`, `Franck Charras <fcharras>`,
-  `Olivier Grisel <ogrisel>` and `Tim Head <betatim>`.
+  :pr:`27904` by :user:`Eric Lindgren <elindgren>`, :user:`Franck Charras <fcharras>`,
+  :user:`Olivier Grisel <ogrisel>` and :user:`Tim Head <betatim>`.
 
 **Classes:**
 
+- :class:`linear_model.Ridge` now supports the Array API for the `svd` solver.
+  See :ref:`array_api` for more details.
+  :pr:`27800` by :user:`Franck Charras <fcharras>`, :user:`Olivier Grisel <ogrisel>`
+  and :user:`Tim Head <betatim>`.
+
 Support for building with Meson
 -------------------------------
 
-Meson is now supported as a build backend, see :ref:`Building from source
-<install_bleeding_edge>` for more details.
+From scikit-learn 1.5 onwards, Meson is the main supported way to build
+scikit-learn, see :ref:`Building from source <install_bleeding_edge>` for more
+details.
 
-:pr:`28040` by :user:`Loïc Estève <lesteve>`
+Unless we discover a major blocker, setuptools support will be dropped in
+scikit-learn 1.6. The 1.5.x releases will support building scikit-learn with
+setuptools.
 
-TODO Fill more details before the 1.5 release, when the Meson story has settled down.
+Meson support for building scikit-learn was added in :pr:`28040` by
+:user:`Loïc Estève <lesteve>`
 
 Metadata Routing
 ----------------
@@ -78,7 +144,8 @@ more details.
   now support metadata routing. The fit methods now
   accept ``**fit_params`` which are passed to the underlying estimators
   via their `fit` methods.
-  :pr:`28432` by :user:`Adam Li <adam2392>` and :user:`Benjamin Bossan <BenjaminBossan>`.
+  :pr:`28432` by :user:`Adam Li <adam2392>` and
+  :user:`Benjamin Bossan <BenjaminBossan>`.
 
 - |Feature| :class:`linear_model.RidgeCV` and
   :class:`linear_model.RidgeClassifierCV` now support metadata routing in
@@ -102,8 +169,8 @@ more details.
 
 - |Feature| :class:`pipeline.FeatureUnion` now supports metadata routing in its
   ``fit`` and ``fit_transform`` methods and route metadata to the underlying
-  transformers' ``fit`` and ``fit_transform``. :pr:`28205` by :user:`Stefanie
-  Senger <StefanieSenger>`.
+  transformers' ``fit`` and ``fit_transform``.
+  :pr:`28205` by :user:`Stefanie Senger <StefanieSenger>`.
 
 - |Fix| Fix an issue when resolving default routing requests set via class
   attributes.
@@ -113,6 +180,10 @@ more details.
   methods, which can happen if one tries to decorate them.
   :pr:`28651` by `Adrin Jalali`_.
 
+- |FIX| Prevent a `RecursionError` when estimators with the default `scoring`
+  param (`None`) route metadata.
+  :pr:`28712` by :user:`Stefanie Senger <StefanieSenger>`.
+
 Changelog
 ---------
 
@@ -131,12 +202,15 @@ Changelog
 ..........................
 
 - |Fix| Fixed a regression in :class:`calibration.CalibratedClassifierCV` where
-  an error was wrongly raised with string targets. 
+  an error was wrongly raised with string targets.
   :pr:`28843` by :user:`Jérémie du Boisberranger <jeremiedbb>`.
 
 :mod:`sklearn.cluster`
 ......................
 
+- |Fix| The :class:`cluster.MeanShift` class now properly converges for constant data.
+  :pr:`28951` by :user:`Akihiro Kuno <akikuno>`.
+
 - |FIX| Create copy of precomputed sparse matrix within the `fit` method of
   :class:`~cluster.OPTICS` to avoid in-place modification of the sparse matrix.
   :pr:`28491` by :user:`Thanh Lam Dang <lamdang2k>`.
@@ -156,16 +230,31 @@ Changelog
   being explicitly set as well.
   :pr:`28483` by :user:`Stefanie Senger <StefanieSenger>`.
 
+- |Enhancement| :class:`compose.ColumnTransformer` can now expose the "remainder"
+  columns in the fitted `transformers_` attribute as column names or boolean
+  masks, rather than column indices.
+  :pr:`27657` by :user:`Jérôme Dockès <jeromedockes>`.
+
+- |Fix| Fixed an bug in :class:`compose.ColumnTransformer` with `n_jobs > 1`, where the
+  intermediate selected columns were passed to the transformers as read-only arrays.
+  :pr:`28822` by :user:`Jérémie du Boisberranger <jeremiedbb>`.
+
 :mod:`sklearn.cross_decomposition`
 ..................................
 
-- |API| Deprecates `Y` in favor of `y` in the methods fit, transform and inverse_transform of:
+- |Fix| The `coef_` fitted attribute of :class:`cross_decomposition.PLSRegression`
+  now takes into account both the scale of `X` and `Y` when `scale=True`. Note that
+  the previous predicted values were not affected by this bug.
+  :pr:`28612` by :user:`Guillaume Lemaitre <glemaitre>`.
+
+- |API| Deprecates `Y` in favor of `y` in the methods fit, transform and
+  inverse_transform of:
   :class:`cross_decomposition.PLSRegression`.
   :class:`cross_decomposition.PLSCanonical`,
   :class:`cross_decomposition.CCA`,
   and :class:`cross_decomposition.PLSSVD`.
   `Y` will be removed in version 1.7.
-  :pr:`28604` by :user:`David Leon <davidleon123>`
+  :pr:`28604` by :user:`David Leon <davidleon123>`.
 
 :mod:`sklearn.datasets`
 .......................
@@ -182,7 +271,8 @@ Changelog
   :func:`datasets.fetch_rcv1`,
   and :func:`datasets.fetch_species_distributions`.
   By default, the functions will retry up to 3 times in case of network failures.
-  :pr:`28160` by :user:`Zhehao Liu <MaxwellLZH>` and :user:`Filip Karlo Došilović <fkdosilovic>`.
+  :pr:`28160` by :user:`Zhehao Liu <MaxwellLZH>` and
+  :user:`Filip Karlo Došilović <fkdosilovic>`.
 
 :mod:`sklearn.decomposition`
 ............................
@@ -233,6 +323,11 @@ Changelog
   pre-sorting the data before finding the thresholds for binning.
   :pr:`28102` by :user:`Christian Lorentzen <lorentzenchr>`.
 
+- |Fix| Fixes a bug in :class:`ensemble.HistGradientBoostingClassifier` and
+  :class:`ensemble.HistGradientBoostingRegressor` when `monotonic_cst` is specified
+  for non-categorical features.
+  :pr:`28925` by :user:`Xiao Yuan <yuanx749>`.
+
 :mod:`sklearn.feature_extraction`
 .................................
 
@@ -282,13 +377,8 @@ Changelog
 
 - |Fix| :class:`linear_model.ElasticNet`, :class:`linear_model.ElasticNetCV`,
   :class:`linear_model.Lasso` and :class:`linear_model.LassoCV` now explicitly don't
-  accept large sparse data formats. :pr:`27576` by :user:`Stefanie Senger
-  <StefanieSenger>`.
-
-- |API| :class:`linear_model.RidgeCV` and :class:`linear_model.RidgeClassifierCV`
-  will now allow `alpha=0` when `cv != None`, which is consistent with
-  :class:`linear_model.Ridge` and :class:`linear_model.RidgeClassifier`.
-  :pr:`28425` by :user:`Lucy Liu <lucyleeow>`.
+  accept large sparse data formats.
+  :pr:`27576` by :user:`Stefanie Senger <StefanieSenger>`.
 
 - |Fix| :class:`linear_model.RidgeCV` and :class:`RidgeClassifierCV` correctly pass
   `sample_weight` to the underlying scorer when `cv` is None.
@@ -298,6 +388,11 @@ Changelog
   will now always be `None` when `tol` is set, as `n_nonzero_coefs` is ignored in
   this case. :pr:`28557` by :user:`Lucy Liu <lucyleeow>`.
 
+- |API| :class:`linear_model.RidgeCV` and :class:`linear_model.RidgeClassifierCV`
+  will now allow `alpha=0` when `cv != None`, which is consistent with
+  :class:`linear_model.Ridge` and :class:`linear_model.RidgeClassifier`.
+  :pr:`28425` by :user:`Lucy Liu <lucyleeow>`.
+
 - |API| Passing `average=0` to disable averaging is deprecated in
   :class:`linear_model.PassiveAggressiveClassifier`,
   :class:`linear_model.PassiveAggressiveRegressor`,
@@ -305,6 +400,19 @@ Changelog
   :class:`linear_model.SGDOneClassSVM`. Pass `average=False` instead.
   :pr:`28582` by :user:`Jérémie du Boisberranger <jeremiedbb>`.
 
+- |API| Parameter `multi_class` was deprecated in
+  :class:`linear_model.LogisticRegression` and
+  :class:`linear_model.LogisticRegressionCV`. `multi_class` will be removed in 1.7,
+  and internally, for 3 and more classes, it will always use multinomial.
+  If you still want to use the one-vs-rest scheme, you can use
+  `OneVsRestClassifier(LogisticRegression(..))`.
+  :pr:`28703` by :user:`Christian Lorentzen <lorentzenchr>`.
+
+- |API| `store_cv_values` and `cv_values_` are deprecated in favor of
+  `store_cv_results` and `cv_results_` in `~linear_model.RidgeCV` and
+  `~linear_model.RidgeClassifierCV`.
+  :pr:`28915` by :user:`Lucy Liu <lucyleeow>`.
+
 :mod:`sklearn.manifold`
 .......................
 
@@ -321,8 +429,15 @@ Changelog
   :pr:`27456` by :user:`Venkatachalam N <venkyyuvy>`, :user:`Kshitij Mathur <Kshitij68>`
   and :user:`Julian Libiseller-Egger <julibeg>`.
 
+- |Feature| :func:`sklearn.metrics.check_scoring` now returns a multi-metric scorer
+  when `scoring` as a `dict`, `set`, `tuple`, or `list`. :pr:`28360` by `Thomas Fan`_.
+
+- |Feature| :func:`metrics.d2_log_loss_score` has been added which
+  calculates the D^2 score for the log loss.
+  :pr:`28351` by :user:`Omar Salman <OmarManzoor>`.
+
 - |Efficiency| Improve efficiency of functions :func:`~metrics.brier_score_loss`,
-  :func:`~metrics.calibration_curve`, :func:`~metrics.det_curve`,
+  :func:`~calibration.calibration_curve`, :func:`~metrics.det_curve`,
   :func:`~metrics.precision_recall_curve`,
   :func:`~metrics.roc_curve` when `pos_label` argument is specified.
   Also improve efficiency of methods `from_estimator`
@@ -331,9 +446,6 @@ Changelog
   :class:`~calibration.CalibrationDisplay`.
   :pr:`28051` by :user:`Pierre de Fréminville <pidefrem>`.
 
-- |Feature| :func:`sklearn.metrics.check_scoring` now returns a multi-metric scorer
-  when `scoring` as a `dict`, `set`, `tuple`, or `list`. :pr:`28360` by `Thomas Fan`_.
-
 - |Fix|:class:`metrics.classification_report` now shows only accuracy and not
   micro-average when input is a subset of labels.
   :pr:`28399` by :user:`Vineet Joshi <vjoshi253>`.
@@ -342,8 +454,8 @@ Changelog
   computation. This is likely to affect neighbor-based algorithms.
   :pr:`28692` by :user:`Loïc Estève <lesteve>`.
 
-- |API| :func:`metrics.precision_recall_curve` deprecated the keyword argument `probas_pred`
-  in favor of `y_score`. `probas_pred` will be removed in version 1.7.
+- |API| :func:`metrics.precision_recall_curve` deprecated the keyword argument
+  `probas_pred` in favor of `y_score`. `probas_pred` will be removed in version 1.7.
   :pr:`28092` by :user:`Adam Li <adam2392>`.
 
 - |API| :func:`metrics.brier_score_loss` deprecated the keyword argument `y_prob`
@@ -351,7 +463,7 @@ Changelog
   :pr:`28092` by :user:`Adam Li <adam2392>`.
 
 - |API| For classifiers and classification metrics, labels encoded as bytes
-  is deprecated and will raise an error in v1.6.
+  is deprecated and will raise an error in v1.7.
   :pr:`18555` by :user:`Kaushik Amar Das <cozek>`.
 
 :mod:`sklearn.mixture`
@@ -365,26 +477,33 @@ Changelog
 :mod:`sklearn.model_selection`
 ..............................
 
+- |MajorFeature| :class:`model_selection.TunedThresholdClassifierCV` finds
+  the decision threshold of a binary classifier that maximizes a
+  classification metric through cross-validation.
+  :class:`model_selection.FixedThresholdClassifier` is an alternative when one wants
+  to use a fixed decision threshold without any tuning scheme.
+  :pr:`26120` by :user:`Guillaume Lemaitre <glemaitre>`.
+
 - |Enhancement| :term:`CV splitters <CV splitter>` that ignores the group parameter now
   raises a warning when groups are passed in to :term:`split`. :pr:`28210` by
   `Thomas Fan`_.
 
+- |Enhancement| The HTML diagram representation of
+  :class:`~model_selection.GridSearchCV`,
+  :class:`~model_selection.RandomizedSearchCV`,
+  :class:`~model_selection.HalvingGridSearchCV`, and
+  :class:`~model_selection.HalvingRandomSearchCV` will show the best estimator when
+  `refit=True`. :pr:`28722` by :user:`Yao Xiao <Charlie-XIAO>` and `Thomas Fan`_.
+
 - |Fix| the ``cv_results_`` attribute (of :class:`model_selection.GridSearchCV`) now
   returns masked arrays of the appropriate NumPy dtype, as opposed to always returning
   dtype ``object``. :pr:`28352` by :user:`Marco Gorelli<MarcoGorelli>`.
 
-- |Fix| :func:`sklearn.model_selection.train_test_score` works with Array API inputs.
+- |Fix| :func:`model_selection.train_test_split` works with Array API inputs.
   Previously indexing was not handled correctly leading to exceptions when using strict
   implementations of the Array API like CuPY.
   :pr:`28407` by :user:`Tim Head <betatim>`.
 
-- |Enhancement| The HTML diagram representation of
-  :class:`~model_selection.GridSearchCV`,
-  :class:`~model_selection.RandomizedSearchCV`,
-  :class:`~model_selection.HalvingGridSearchCV`, and
-  :class:`~model_selection.HalvingRandomSearchCV` will show the best estimator when
-  `refit=True`. :pr:`28722` by :user:`Yao Xiao <Charlie-XIAO>` and `Thomas Fan`_.
-
 :mod:`sklearn.multioutput`
 ..........................
 
@@ -427,6 +546,10 @@ Changelog
 :mod:`sklearn.utils`
 ....................
 
+- |Fix| :func:`~utils._safe_indexing` now works correctly for polars DataFrame when
+  `axis=0` and supports indexing polars Series.
+  :pr:`28521` by :user:`Yao Xiao <Charlie-XIAO>`.
+
 - |API| :data:`utils.IS_PYPY` is deprecated and will be removed in version 1.7.
   :pr:`28768` by :user:`Jérémie du Boisberranger <jeremiedbb>`.
 
@@ -438,18 +561,44 @@ Changelog
   `joblib.register_parallel_backend` instead.
   :pr:`28847` by :user:`Jérémie du Boisberranger <jeremiedbb>`.
 
-- |API| Raise informative warning message in :func:`type_of_target` when
-  represented as bytes. For classifiers and classification metrics, labels encoded
-  as bytes is deprecated and will raise an error in v1.6.
+- |API| Raise informative warning message in :func:`~utils.multiclass.type_of_target`
+  when represented as bytes. For classifiers and classification metrics, labels encoded
+  as bytes is deprecated and will raise an error in v1.7.
   :pr:`18555` by :user:`Kaushik Amar Das <cozek>`.
 
-- |Fix| :func:`~utils._safe_indexing` now works correctly for polars DataFrame when
-  `axis=0` and supports indexing polars Series.
-  :pr:`28521` by :user:`Yao Xiao <Charlie-XIAO>`.
+- |API| :func:`utils.estimator_checks.check_estimator_sparse_data` was split into two
+  functions: :func:`utils.estimator_checks.check_estimator_sparse_matrix` and
+  :func:`utils.estimator_checks.check_estimator_sparse_array`.
+  :pr:`27576` by :user:`Stefanie Senger <StefanieSenger>`.
 
 .. rubric:: Code and documentation contributors
 
 Thanks to everyone who has contributed to the maintenance and improvement of
 the project since version 1.4, including:
 
-TODO: update at the time of the release.
+101AlexMartin, Abdulaziz Aloqeely, Adam J. Stewart, Adam Li, Adarsh Wase, Adrin
+Jalali, Advik Sinha, Akash Srivastava, Akihiro Kuno, Alan Guedes, Alexis
+IMBERT, Ana Paula Gomes, Anderson Nelson, Andrei Dzis, Arnaud Capitaine, Arturo
+Amor, Aswathavicky, Bharat Raghunathan, Brendan Lu, Bruno, Cemlyn, Christian
+Lorentzen, Christian Veenhuis, Cindy Liang, Claudio Salvatore Arcidiacono,
+Connor Boyle, Conrad Stevens, crispinlogan, davidleon123, DerWeh, Dipan Banik,
+Duarte São José, DUONG, Eddie Bergman, Edoardo Abati, Egehan Gunduz, Emad
+Izadifar, Erich Schubert, Filip Karlo Došilović, Franck Charras, Gael
+Varoquaux, Gönül Aycı, Guillaume Lemaitre, Gyeongjae Choi, Harmanan Kohli,
+Hong Xiang Yue, Ian Faust, itsaphel, Ivan Wiryadi, Jack Bowyer, Javier Marin
+Tur, Jérémie du Boisberranger, Jérôme Dockès, Jiawei Zhang, Joel Nothman,
+Johanna Bayer, John Cant, John Hopfensperger, jpcars, jpienaar-tuks, Julian
+Libiseller-Egger, Julien Jerphanion, KanchiMoe, Kaushik Amar Das, keyber,
+Koustav Ghosh, kraktus, Krsto Proroković, ldwy4, LeoGrin, lihaitao, Linus
+Sommer, Loic Esteve, Lucy Liu, Lukas Geiger, manasimj, Manuel Labbé, Manuel
+Morales, Marco Edward Gorelli, Maren Westermann, Marija Vlajic, Mark Elliot,
+Mateusz Sokół, Mavs, Michael Higgins, Michael Mayer, miguelcsilva, Miki
+Watanabe, Mohammed Hamdy, myenugula, Nathan Goldbaum, Naziya Mahimkar, Neto,
+Olivier Grisel, Omar Salman, Patrick Wang, Pierre de Fréminville, Priyash
+Shah, Puneeth K, Rahil Parikh, raisadz, Raj Pulapakura, Ralf Gommers, Ralph
+Urlus, Randolf Scholz, Reshama Shaikh, Richard Barnes, Rodrigo Romero, Saad
+Mahmood, Salim Dohri, Sandip Dutta, SarahRemus, scikit-learn-bot, Shaharyar
+Choudhry, Shubham, sperret6, Stefanie Senger, Suha Siddiqui, Thanh Lam DANG,
+thebabush, Thomas J. Fan, Thomas Lazarus, Thomas Li, Tialo, Tim Head, Tuhin
+Sharma, VarunChaduvula, Vineet Joshi, virchan, Waël Boukhobza, Weyb, Will
+Dean, Xavier Beltran, Xiao Yuan, Xuefeng Xu, Yao Xiao
diff --git a/doc/whats_new/v1.6.rst b/doc/whats_new/v1.6.rst
new file mode 100644
index 0000000000000..bc27f894ff9a1
--- /dev/null
+++ b/doc/whats_new/v1.6.rst
@@ -0,0 +1,116 @@
+.. include:: _contributors.rst
+
+.. currentmodule:: sklearn
+
+.. _release_notes_1_6:
+
+===========
+Version 1.6
+===========
+
+..
+  -- UNCOMMENT WHEN 1.6.0 IS RELEASED --
+  For a short description of the main highlights of the release, please refer to
+  :ref:`sphx_glr_auto_examples_release_highlights_plot_release_highlights_1_6_0.py`.
+
+.. include:: changelog_legend.inc
+
+.. _changes_1_6:
+
+Version 1.6.0
+=============
+
+**In Development**
+
+Support for Array API
+---------------------
+
+Additional estimators and functions have been updated to include support for all
+`Array API <https://data-apis.org/array-api/latest/>`_ compliant inputs.
+
+See :ref:`array_api` for more details.
+
+**Functions:**
+
+- :func:`sklearn.metrics.mean_tweedie_deviance` now supports Array API compatible
+  inputs.
+  :pr:`28106` by :user:`Thomas Li <lithomas1>`;
+- :func:`sklearn.metrics.mean_absolute_error` :pr:`27736` by :user:`Edoardo Abati <EdAbati>`;
+- :func:`sklearn.metrics.pairwise.cosine_similarity` :pr:`29014` by :user:`Edoardo Abati <EdAbati>`.
+
+
+**Classes:**
+
+- :class:`preprocessing.LabelEncoder` now supports Array API compatible inputs.
+  :pr:`27381` by :user:`Omar Salman <OmarManzoor>`.
+
+Metadata Routing
+----------------
+
+The following models now support metadata routing in one or more of their
+methods. Refer to the :ref:`Metadata Routing User Guide <metadata_routing>` for
+more details.
+
+- |Feature| :func:`model_selection.learning_curve` now supports metadata routing for the
+  `fit` method of its estimator and for its underlying CV splitter and scorer.
+  :pr:`28975` by :user:`Stefanie Senger <StefanieSenger>`.
+
+- |Feature| :class:`ensemble.StackingClassifier` and
+  :class:`ensemble.StackingRegressor` now support metadata routing and pass
+  ``**fit_params`` to the underlying estimators via their `fit` methods.
+  :pr:`28701` by :user:`Stefanie Senger <StefanieSenger>`.
+
+Changelog
+---------
+
+..
+    Entries should be grouped by module (in alphabetic order) and prefixed with
+    one of the labels: |MajorFeature|, |Feature|, |Efficiency|, |Enhancement|,
+    |Fix| or |API| (see whats_new.rst for descriptions).
+    Entries should be ordered by those labels (e.g. |Fix| after |Efficiency|).
+    Changes not specific to a module should be listed under *Multiple Modules*
+    or *Miscellaneous*.
+    Entries should end with:
+    :pr:`123456` by :user:`Joe Bloggs <joeongithub>`.
+    where 123455 is the *pull request* number, not the issue number.
+
+:mod:`sklearn.base`
+...................
+
+- |Enhancement| Added a function :func:`base.is_clusterer` which determines
+  whether a given estimator is of category clusterer.
+  :pr:`28936` by :user:`Christian Veenhuis <ChVeen>`.
+
+:mod:`sklearn.linear_model`
+...........................
+
+- |API| Deprecates `copy_X` in :class:`linear_model.TheilSenRegressor` as the parameter
+  has no effect. `copy_X` will be removed in 1.8.
+  :pr:`29105` by :user:`Adam Li <adam2392>`.
+
+:mod:`sklearn.metrics`
+......................
+
+- |Enhancement| :func:`sklearn.metrics.check_scoring` now accepts `raise_exc` to specify
+  whether to raise an exception if a subset of the scorers in multimetric scoring fails
+  or to return an error code. :pr:`28992` by :user:`Stefanie Senger <StefanieSenger>`.
+
+:mod:`sklearn.model_selection`
+..............................
+
+- |Enhancement| Add the parameter `prefit` to
+  :class:`model_selection.FixedThresholdClassifier` allowing the use of a pre-fitted
+  estimator without re-fitting it.
+  :pr:`29067` by :user:`Guillaume Lemaitre <glemaitre>`.
+
+:mod:`sklearn.neighbors`
+........................
+
+- |Fix| :class:`neighbors.LocalOutlierFactor` raises a warning in the `fit` method
+  when duplicate values in the training data lead to inaccurate outlier detection.
+  :pr:`28773` by :user:`Henrique Caroço <HenriqueProj>`.
+
+Thanks to everyone who has contributed to the maintenance and improvement of
+the project since version 1.5, including:
+
+TODO: update at the time of the release.
diff --git a/examples/README.txt b/examples/README.txt
index 958de667a5c69..57a4e7e60eb32 100644
--- a/examples/README.txt
+++ b/examples/README.txt
@@ -2,3 +2,8 @@
 
 Examples
 ========
+
+This is the gallery of examples that showcase how scikit-learn can be used. Some
+examples demonstrate the use of the :ref:`API <api_ref>` in general and some
+demonstrate specific applications in tutorial form. Also check out our
+:ref:`user guide <user_guide>` for more detailed illustrations.
diff --git a/examples/applications/plot_digits_denoising.py b/examples/applications/plot_digits_denoising.py
index 10d94aa0212d6..9d4b0cae79d06 100644
--- a/examples/applications/plot_digits_denoising.py
+++ b/examples/applications/plot_digits_denoising.py
@@ -12,12 +12,12 @@
 
 We will use USPS digits dataset to reproduce presented in Sect. 4 of [1]_.
 
-.. topic:: References
+.. rubric:: References
 
-   .. [1] `Bakır, Gökhan H., Jason Weston, and Bernhard Schölkopf.
-      "Learning to find pre-images."
-      Advances in neural information processing systems 16 (2004): 449-456.
-      <https://papers.nips.cc/paper/2003/file/ac1ad983e08ad3304a97e147f522747e-Paper.pdf>`_
+.. [1] `Bakır, Gökhan H., Jason Weston, and Bernhard Schölkopf.
+    "Learning to find pre-images."
+    Advances in neural information processing systems 16 (2004): 449-456.
+    <https://papers.nips.cc/paper/2003/file/ac1ad983e08ad3304a97e147f522747e-Paper.pdf>`_
 
 """
 
diff --git a/examples/classification/plot_classification_probability.py b/examples/classification/plot_classification_probability.py
index 4e8f0763d3b47..42c8643b9107a 100644
--- a/examples/classification/plot_classification_probability.py
+++ b/examples/classification/plot_classification_probability.py
@@ -5,8 +5,8 @@
 
 Plot the classification probability for different classifiers. We use a 3 class
 dataset, and we classify it with a Support Vector classifier, L1 and L2
-penalized logistic regression with either a One-Vs-Rest or multinomial setting,
-and Gaussian process classification.
+penalized logistic regression (multinomial multiclass), a One-Vs-Rest version with
+logistic regression, and Gaussian process classification.
 
 Linear SVC is not a probabilistic classifier by default but it has a built-in
 calibration option enabled in this example (`probability=True`).
@@ -30,6 +30,7 @@
 from sklearn.inspection import DecisionBoundaryDisplay
 from sklearn.linear_model import LogisticRegression
 from sklearn.metrics import accuracy_score
+from sklearn.multiclass import OneVsRestClassifier
 from sklearn.svm import SVC
 
 iris = datasets.load_iris()
@@ -43,14 +44,12 @@
 
 # Create different classifiers.
 classifiers = {
-    "L1 logistic": LogisticRegression(
-        C=C, penalty="l1", solver="saga", multi_class="multinomial", max_iter=10000
-    ),
+    "L1 logistic": LogisticRegression(C=C, penalty="l1", solver="saga", max_iter=10000),
     "L2 logistic (Multinomial)": LogisticRegression(
-        C=C, penalty="l2", solver="saga", multi_class="multinomial", max_iter=10000
+        C=C, penalty="l2", solver="saga", max_iter=10000
     ),
-    "L2 logistic (OvR)": LogisticRegression(
-        C=C, penalty="l2", solver="saga", multi_class="ovr", max_iter=10000
+    "L2 logistic (OvR)": OneVsRestClassifier(
+        LogisticRegression(C=C, penalty="l2", solver="saga", max_iter=10000)
     ),
     "Linear SVC": SVC(kernel="linear", C=C, probability=True, random_state=0),
     "GPC": GaussianProcessClassifier(kernel),
diff --git a/examples/covariance/plot_mahalanobis_distances.py b/examples/covariance/plot_mahalanobis_distances.py
index b82c861133de7..537d3fa0d9d8a 100644
--- a/examples/covariance/plot_mahalanobis_distances.py
+++ b/examples/covariance/plot_mahalanobis_distances.py
@@ -50,15 +50,15 @@
 
     See also :ref:`sphx_glr_auto_examples_covariance_plot_robust_vs_empirical_covariance.py`
 
-.. topic:: References:
-
-    .. [1] P. J. Rousseeuw. `Least median of squares regression
-        <http://web.ipac.caltech.edu/staff/fmasci/home/astro_refs/LeastMedianOfSquares.pdf>`_. J. Am
-        Stat Ass, 79:871, 1984.
-    .. [2] Wilson, E. B., & Hilferty, M. M. (1931). `The distribution of chi-square.
-        <https://water.usgs.gov/osw/bulletin17b/Wilson_Hilferty_1931.pdf>`_
-        Proceedings of the National Academy of Sciences of the United States
-        of America, 17, 684-688.
+.. rubric:: References
+
+.. [1] P. J. Rousseeuw. `Least median of squares regression
+    <http://web.ipac.caltech.edu/staff/fmasci/home/astro_refs/LeastMedianOfSquares.pdf>`_. J. Am
+    Stat Ass, 79:871, 1984.
+.. [2] Wilson, E. B., & Hilferty, M. M. (1931). `The distribution of chi-square.
+    <https://water.usgs.gov/osw/bulletin17b/Wilson_Hilferty_1931.pdf>`_
+    Proceedings of the National Academy of Sciences of the United States
+    of America, 17, 684-688.
 
 """  # noqa: E501
 
diff --git a/examples/ensemble/plot_adaboost_multiclass.py b/examples/ensemble/plot_adaboost_multiclass.py
index 35b0d1bb86470..8c14d7196999d 100644
--- a/examples/ensemble/plot_adaboost_multiclass.py
+++ b/examples/ensemble/plot_adaboost_multiclass.py
@@ -17,11 +17,11 @@
 be selected. This ensures that subsequent iterations of the algorithm focus on
 the difficult-to-classify samples.
 
-.. topic:: References:
+.. rubric:: References
 
-    .. [1] :doi:`J. Zhu, H. Zou, S. Rosset, T. Hastie, "Multi-class adaboost."
-           Statistics and its Interface 2.3 (2009): 349-360.
-           <10.4310/SII.2009.v2.n3.a8>`
+.. [1] :doi:`J. Zhu, H. Zou, S. Rosset, T. Hastie, "Multi-class adaboost."
+    Statistics and its Interface 2.3 (2009): 349-360.
+    <10.4310/SII.2009.v2.n3.a8>`
 
 """
 
@@ -231,16 +231,16 @@ def misclassification_error(y_true, y_pred):
 # decision. Indeed, this exactly is the formulation of updating the base
 # estimators' weights after each iteration in AdaBoost.
 #
-# |details-start| Mathematical details |details-split|
+# .. dropdown:: Mathematical details
 #
-# The weight associated with a weak learner trained at the stage :math:`m` is
-# inversely associated with its misclassification error such that:
+#    The weight associated with a weak learner trained at the stage :math:`m` is
+#    inversely associated with its misclassification error such that:
 #
-# .. math:: \alpha^{(m)} = \log \frac{1 - err^{(m)}}{err^{(m)}} + \log (K - 1),
+#    .. math:: \alpha^{(m)} = \log \frac{1 - err^{(m)}}{err^{(m)}} + \log (K - 1),
 #
-# where :math:`\alpha^{(m)}` and :math:`err^{(m)}` are the weight and the error
-# of the :math:`m` th weak learner, respectively, and :math:`K` is the number of
-# classes in our classification problem. |details-end|
+#    where :math:`\alpha^{(m)}` and :math:`err^{(m)}` are the weight and the error
+#    of the :math:`m` th weak learner, respectively, and :math:`K` is the number of
+#    classes in our classification problem.
 #
 # Another interesting observation boils down to the fact that the first weak
 # learners of the model make fewer errors than later weak learners of the
diff --git a/examples/ensemble/plot_hgbt_regression.py b/examples/ensemble/plot_hgbt_regression.py
index 3bf3762bae406..a704fefbe9a31 100644
--- a/examples/ensemble/plot_hgbt_regression.py
+++ b/examples/ensemble/plot_hgbt_regression.py
@@ -13,10 +13,10 @@
 
 The top usability features of HGBT models are:
 
-1. Several available loss function for mean and quantile regression tasks, see
+1. Several available loss functions for mean and quantile regression tasks, see
    :ref:`Quantile loss <quantile_support_hgbdt>`.
-2. :ref:`categorical_support_gbdt` (see
-   :ref:`sphx_glr_auto_examples_ensemble_plot_gradient_boosting_categorical.py`).
+2. :ref:`categorical_support_gbdt`, see
+   :ref:`sphx_glr_auto_examples_ensemble_plot_gradient_boosting_categorical.py`.
 3. Early stopping.
 4. :ref:`nan_support_hgbt`, which avoids the need for an imputer.
 5. :ref:`monotonic_cst_gbdt`.
@@ -38,17 +38,18 @@
 # set every five minutes. Electricity transfers to/from the neighboring state of
 # Victoria were done to alleviate fluctuations.
 #
-# The dataset (originally named ELEC2) contains 45,312 instances dated from 7
+# The dataset, originally named ELEC2, contains 45,312 instances dated from 7
 # May 1996 to 5 December 1998. Each sample of the dataset refers to a period of
 # 30 minutes, i.e. there are 48 instances for each time period of one day. Each
 # sample on the dataset has 7 columns:
-#   - date: between 7 May 1996 to 5 December 1998. Normalized between 0 and 1;
-#   - day: day of week (1-7);
-#   - period: half hour intervals over 24 hours. Normalized between 0 and 1;
-#   - nswprice/nswdemand: electricity price/demand of New South Wales;
-#   - vicprice/vicdemand: electricity price/demand of Victoria.
 #
-# It is originally a classification task, but here we use it for the regression
+# - date: between 7 May 1996 to 5 December 1998. Normalized between 0 and 1;
+# - day: day of week (1-7);
+# - period: half hour intervals over 24 hours. Normalized between 0 and 1;
+# - nswprice/nswdemand: electricity price/demand of New South Wales;
+# - vicprice/vicdemand: electricity price/demand of Victoria.
+#
+# Originally, it is a classification task, but here we use it for the regression
 # task to predict the scheduled electricity transfer between states.
 
 from sklearn.datasets import fetch_openml
@@ -86,7 +87,7 @@
 _ = ax.legend(handles, ["Sun", "Mon", "Tue", "Wed", "Thu", "Fri", "Sat"])
 
 # %%
-# Notice energy transfer increases systematically during weekends.
+# Notice that energy transfer increases systematically during weekends.
 #
 # Effect of number of trees and early stopping
 # ============================================
@@ -95,7 +96,7 @@
 # daily electricity transfer using the whole dataset. Then we visualize its
 # predictions depending on the `max_iter` parameter. Here we don't try to
 # evaluate the performance of the model and its capacity to generalize but
-# rather its capacity to learn from the training data.
+# rather its capability to learn from the training data.
 
 from sklearn.ensemble import HistGradientBoostingRegressor
 from sklearn.model_selection import train_test_split
@@ -144,22 +145,24 @@
 # With just a few iterations, HGBT models can achieve convergence (see
 # :ref:`sphx_glr_auto_examples_ensemble_plot_forest_hist_grad_boosting_comparison.py`),
 # meaning that adding more trees does not improve the model anymore. In the
-# figure above, 5 iterations are not enough to be able to predict. With 50
+# figure above, 5 iterations are not enough to get good predictions. With 50
 # iterations, we are already able to do a good job.
 #
-# Instead of relying on `max_iter` alone to determine when to stop, the HGBT
-# implementation in scikit-learn supports early stopping. With it, the model
+# Setting `max_iter` too high might degrade the prediction quality and cost a lot of
+# avoidable computing resources. Therefore, the HGBT implementation in scikit-learn
+# provides an automatic **early stopping** strategy. With it, the model
 # uses a fraction of the training data as internal validation set
 # (`validation_fraction`) and stops training if the validation score does not
 # improve (or degrades) after `n_iter_no_change` iterations up to a certain
-# `tol`.
+# tolerance (`tol`).
 #
 # Notice that there is a trade-off between `learning_rate` and `max_iter`:
 # Generally, smaller learning rates are preferable but require more iterations
 # to converge to the minimum loss, while larger learning rates converge faster
 # (less iterations/trees needed) but at the cost of a larger minimum loss.
 #
-# Indeed, a good practice is to tune the learning rate along with any other
+# Because of this high correlation between the learning rate the number of iterations,
+# a good practice is to tune the learning rate along with all (important) other
 # hyperparameters, fit the HBGT on the training set with a large enough value
 # for `max_iter` and determine the best `max_iter` via early stopping and some
 # explicit `validation_fraction`.
@@ -204,9 +207,9 @@
 # HGBT models have native support of missing values. During training, the tree
 # grower decides where samples with missing values should go (left or right
 # child) at each split, based on the potential gain. When predicting, these
-# samples are sent to either child accordingly. If a feature had no missing
-# values during training, samples with missing values for that feature are sent
-# to the child with the most samples.
+# samples are sent to the learnt child accordingly. If a feature had no missing
+# values during training, then for prediction, samples with missing values for that
+# feature are sent to the child with the most samples (as seen during fit).
 #
 # The present example shows how HGBT regressions deal with values missing
 # completely at random (MCAR), i.e. the missingness does not depend on the
@@ -313,10 +316,10 @@ def generate_missing_values(X, missing_fraction):
 # model. One can still improve the quality of such estimations by:
 #
 # - collecting more data-points;
-# - better tuning of the model hyperparameters (see
-#   :ref:`sphx_glr_auto_examples_ensemble_plot_gradient_boosting_quantile.py`);
-# - engineering more predictive features from the same data (see
-#   :ref:`sphx_glr_auto_examples_applications_plot_cyclical_feature_engineering.py`).
+# - better tuning of the model hyperparameters, see
+#   :ref:`sphx_glr_auto_examples_ensemble_plot_gradient_boosting_quantile.py`;
+# - engineering more predictive features from the same data, see
+#   :ref:`sphx_glr_auto_examples_applications_plot_cyclical_feature_engineering.py`.
 #
 # Monotonic constraints
 # =====================
@@ -342,7 +345,8 @@ def generate_missing_values(X, missing_fraction):
 # - 0: no constraint
 # - -1: monotonic decrease
 #
-# Else, one can pass an array-like encoding the above convention by position.
+# Alternatively, one can pass an array-like object encoding the above convention by
+# position.
 
 from sklearn.inspection import PartialDependenceDisplay
 
@@ -394,8 +398,9 @@ def generate_missing_values(X, missing_fraction):
 _ = plt.legend()
 
 # %%
-# Observe that `nswdemand` seems already monotonic without constraint. This is a
-# good example to show that the model is "overconstraining".
+# Observe that `nswdemand` and `vicdemand` seem already monotonic without constraint.
+# This is a good example to show that the model with monotonicity constraints is
+# "overconstraining".
 #
 # Additionally, we can verify that the predictive quality of the model is not
 # significantly degraded by introducing the monotonic constraints. For such
diff --git a/examples/feature_selection/plot_rfe_digits.py b/examples/feature_selection/plot_rfe_digits.py
index 553f38f9c674f..198a3d6f3af90 100644
--- a/examples/feature_selection/plot_rfe_digits.py
+++ b/examples/feature_selection/plot_rfe_digits.py
@@ -3,8 +3,14 @@
 Recursive feature elimination
 =============================
 
-A recursive feature elimination example showing the relevance of pixels in
-a digit classification task.
+This example demonstrates how Recursive Feature Elimination
+(:class:`~sklearn.feature_selection.RFE`) can be used to determine the
+importance of individual pixels for classifying handwritten digits.
+:class:`~sklearn.feature_selection.RFE` recursively removes the least
+significant features, assigning ranks based on their importance, where higher
+`ranking_` values denote lower importance. The ranking is visualized using both
+shades of blue and pixel annotations for clarity. As expected, pixels positioned
+at the center of the image tend to be more predictive than those near the edges.
 
 .. note::
 
@@ -16,21 +22,33 @@
 
 from sklearn.datasets import load_digits
 from sklearn.feature_selection import RFE
-from sklearn.svm import SVC
+from sklearn.linear_model import LogisticRegression
+from sklearn.pipeline import Pipeline
+from sklearn.preprocessing import MinMaxScaler
 
 # Load the digits dataset
 digits = load_digits()
 X = digits.images.reshape((len(digits.images), -1))
 y = digits.target
 
-# Create the RFE object and rank each pixel
-svc = SVC(kernel="linear", C=1)
-rfe = RFE(estimator=svc, n_features_to_select=1, step=1)
-rfe.fit(X, y)
-ranking = rfe.ranking_.reshape(digits.images[0].shape)
+pipe = Pipeline(
+    [
+        ("scaler", MinMaxScaler()),
+        ("rfe", RFE(estimator=LogisticRegression(), n_features_to_select=1, step=1)),
+    ]
+)
+
+pipe.fit(X, y)
+ranking = pipe.named_steps["rfe"].ranking_.reshape(digits.images[0].shape)
 
 # Plot pixel ranking
 plt.matshow(ranking, cmap=plt.cm.Blues)
+
+# Add annotations for pixel numbers
+for i in range(ranking.shape[0]):
+    for j in range(ranking.shape[1]):
+        plt.text(j, i, str(ranking[i, j]), ha="center", va="center", color="black")
+
 plt.colorbar()
-plt.title("Ranking of pixels with RFE")
+plt.title("Ranking of pixels with RFE\n(Logistic Regression)")
 plt.show()
diff --git a/examples/gaussian_process/plot_gpr_co2.py b/examples/gaussian_process/plot_gpr_co2.py
index 33b0ab7271549..bf64092b0482e 100644
--- a/examples/gaussian_process/plot_gpr_co2.py
+++ b/examples/gaussian_process/plot_gpr_co2.py
@@ -4,7 +4,7 @@
 ====================================================================================
 
 This example is based on Section 5.4.3 of "Gaussian Processes for Machine
-Learning" [RW2006]_. It illustrates an example of complex kernel engineering
+Learning" [1]_. It illustrates an example of complex kernel engineering
 and hyperparameter optimization using gradient ascent on the
 log-marginal-likelihood. The data consists of the monthly average atmospheric
 CO2 concentrations (in parts per million by volume (ppm)) collected at the
@@ -12,12 +12,11 @@
 model the CO2 concentration as a function of the time :math:`t` and extrapolate
 for years after 2001.
 
-.. topic: References
+.. rubric:: References
 
-    .. [RW2006] `Rasmussen, Carl Edward.
-       "Gaussian processes in machine learning."
-       Summer school on machine learning. Springer, Berlin, Heidelberg, 2003
-       <http://www.gaussianprocess.org/gpml/chapters/RW.pdf>`_.
+.. [1] `Rasmussen, Carl Edward. "Gaussian processes in machine learning."
+    Summer school on machine learning. Springer, Berlin, Heidelberg, 2003
+    <http://www.gaussianprocess.org/gpml/chapters/RW.pdf>`_.
 """
 
 print(__doc__)
@@ -33,24 +32,25 @@
 # We will derive a dataset from the Mauna Loa Observatory that collected air
 # samples. We are interested in estimating the concentration of CO2 and
 # extrapolate it for further year. First, we load the original dataset available
-# in OpenML.
+# in OpenML as a pandas dataframe. This will be replaced with Polars
+# once `fetch_openml` adds a native support for it.
 from sklearn.datasets import fetch_openml
 
 co2 = fetch_openml(data_id=41187, as_frame=True)
 co2.frame.head()
 
 # %%
-# First, we process the original dataframe to create a date index and select
-# only the CO2 column.
-import pandas as pd
+# First, we process the original dataframe to create a date column and select
+# it along with the CO2 column.
+import polars as pl
 
-co2_data = co2.frame
-co2_data["date"] = pd.to_datetime(co2_data[["year", "month", "day"]])
-co2_data = co2_data[["date", "co2"]].set_index("date")
+co2_data = pl.DataFrame(co2.frame[["year", "month", "day", "co2"]]).select(
+    pl.date("year", "month", "day"), "co2"
+)
 co2_data.head()
 
 # %%
-co2_data.index.min(), co2_data.index.max()
+co2_data["date"].min(), co2_data["date"].max()
 
 # %%
 # We see that we get CO2 concentration for some days from March, 1958 to
@@ -58,7 +58,8 @@
 # understanding.
 import matplotlib.pyplot as plt
 
-co2_data.plot()
+plt.plot(co2_data["date"], co2_data["co2"])
+plt.xlabel("date")
 plt.ylabel("CO$_2$ concentration (ppm)")
 _ = plt.title("Raw air samples measurements from the Mauna Loa Observatory")
 
@@ -67,15 +68,14 @@
 # for which no measurements were collected. Such a processing will have an
 # smoothing effect on the data.
 
-try:
-    co2_data_resampled_monthly = co2_data.resample("ME")
-except ValueError:
-    # pandas < 2.2 uses M instead of ME
-    co2_data_resampled_monthly = co2_data.resample("M")
-
-
-co2_data = co2_data_resampled_monthly.mean().dropna(axis="index", how="any")
-co2_data.plot()
+co2_data = (
+    co2_data.sort(by="date")
+    .group_by_dynamic("date", every="1mo")
+    .agg(pl.col("co2").mean())
+    .drop_nulls()
+)
+plt.plot(co2_data["date"], co2_data["co2"])
+plt.xlabel("date")
 plt.ylabel("Monthly average of CO$_2$ concentration (ppm)")
 _ = plt.title(
     "Monthly average of air samples measurements\nfrom the Mauna Loa Observatory"
@@ -88,7 +88,9 @@
 #
 # As a first step, we will divide the data and the target to estimate. The data
 # being a date, we will convert it into a numeric.
-X = (co2_data.index.year + co2_data.index.month / 12).to_numpy().reshape(-1, 1)
+X = co2_data.select(
+    pl.col("date").dt.year() + pl.col("date").dt.month() / 12
+).to_numpy()
 y = co2_data["co2"].to_numpy()
 
 # %%
diff --git a/examples/inspection/plot_permutation_importance.py b/examples/inspection/plot_permutation_importance.py
index 8cf63dd80fd4d..66b40fcd25bc8 100644
--- a/examples/inspection/plot_permutation_importance.py
+++ b/examples/inspection/plot_permutation_importance.py
@@ -18,10 +18,10 @@
 This example shows how to use Permutation Importances as an alternative that
 can mitigate those limitations.
 
-.. topic:: References:
+.. rubric:: References
 
-   * :doi:`L. Breiman, "Random Forests", Machine Learning, 45(1), 5-32,
-     2001. <10.1023/A:1010933404324>`
+* :doi:`L. Breiman, "Random Forests", Machine Learning, 45(1), 5-32,
+  2001. <10.1023/A:1010933404324>`
 
 """
 
diff --git a/examples/linear_model/plot_lasso_lars_ic.py b/examples/linear_model/plot_lasso_lars_ic.py
index 8f1e7034a108a..ab53a4f7612e3 100644
--- a/examples/linear_model/plot_lasso_lars_ic.py
+++ b/examples/linear_model/plot_lasso_lars_ic.py
@@ -16,12 +16,12 @@
     of the two approaches, you can refer to the following example:
     :ref:`sphx_glr_auto_examples_linear_model_plot_lasso_model_selection.py`.
 
-.. topic:: References
+.. rubric:: References
 
-    .. [ZHT2007] :arxiv:`Zou, Hui, Trevor Hastie, and Robert Tibshirani.
-       "On the degrees of freedom of the lasso."
-       The Annals of Statistics 35.5 (2007): 2173-2192.
-       <0712.0881>`
+.. [ZHT2007] :arxiv:`Zou, Hui, Trevor Hastie, and Robert Tibshirani.
+    "On the degrees of freedom of the lasso."
+    The Annals of Statistics 35.5 (2007): 2173-2192.
+    <0712.0881>`
 """
 
 # Author: Alexandre Gramfort
diff --git a/examples/linear_model/plot_logistic_multinomial.py b/examples/linear_model/plot_logistic_multinomial.py
index 791a788b2238b..c21cfd2523b2f 100644
--- a/examples/linear_model/plot_logistic_multinomial.py
+++ b/examples/linear_model/plot_logistic_multinomial.py
@@ -18,6 +18,7 @@
 from sklearn.datasets import make_blobs
 from sklearn.inspection import DecisionBoundaryDisplay
 from sklearn.linear_model import LogisticRegression
+from sklearn.multiclass import OneVsRestClassifier
 
 # make 3-class dataset for classification
 centers = [[-5, 0], [0, 1.5], [5, -1]]
@@ -26,9 +27,10 @@
 X = np.dot(X, transformation)
 
 for multi_class in ("multinomial", "ovr"):
-    clf = LogisticRegression(
-        solver="sag", max_iter=100, random_state=42, multi_class=multi_class
-    ).fit(X, y)
+    clf = LogisticRegression(solver="sag", max_iter=100, random_state=42)
+    if multi_class == "ovr":
+        clf = OneVsRestClassifier(clf)
+    clf.fit(X, y)
 
     # print the training scores
     print("training score : %.3f (%s)" % (clf.score(X, y), multi_class))
@@ -44,15 +46,17 @@
     colors = "bry"
     for i, color in zip(clf.classes_, colors):
         idx = np.where(y == i)
-        plt.scatter(
-            X[idx, 0], X[idx, 1], c=color, cmap=plt.cm.Paired, edgecolor="black", s=20
-        )
+        plt.scatter(X[idx, 0], X[idx, 1], c=color, edgecolor="black", s=20)
 
     # Plot the three one-against-all classifiers
     xmin, xmax = plt.xlim()
     ymin, ymax = plt.ylim()
-    coef = clf.coef_
-    intercept = clf.intercept_
+    if multi_class == "ovr":
+        coef = np.concatenate([est.coef_ for est in clf.estimators_])
+        intercept = np.concatenate([est.intercept_ for est in clf.estimators_])
+    else:
+        coef = clf.coef_
+        intercept = clf.intercept_
 
     def plot_hyperplane(c, color):
         def line(x0):
diff --git a/examples/linear_model/plot_poisson_regression_non_normal_loss.py b/examples/linear_model/plot_poisson_regression_non_normal_loss.py
index 2a80c3db0ff40..180ee3b70671c 100644
--- a/examples/linear_model/plot_poisson_regression_non_normal_loss.py
+++ b/examples/linear_model/plot_poisson_regression_non_normal_loss.py
@@ -1,3 +1,5 @@
+# Authors: The scikit-learn developers
+# SPDX-License-Identifier: BSD-3-Clause
 """
 ======================================
 Poisson regression and non-normal loss
@@ -36,11 +38,6 @@
 
 """
 
-# Authors: Christian Lorentzen <lorentzen.ch@gmail.com>
-#          Roman Yurchak <rth.yurchak@gmail.com>
-#          Olivier Grisel <olivier.grisel@ensta.org>
-# License: BSD 3 clause
-
 import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
diff --git a/examples/linear_model/plot_sgd_iris.py b/examples/linear_model/plot_sgd_iris.py
index 5d9b923f9b444..838f612d4659c 100644
--- a/examples/linear_model/plot_sgd_iris.py
+++ b/examples/linear_model/plot_sgd_iris.py
@@ -58,7 +58,6 @@
         X[idx, 1],
         c=color,
         label=iris.target_names[i],
-        cmap=plt.cm.Paired,
         edgecolor="black",
         s=20,
     )
diff --git a/examples/linear_model/plot_sparse_logistic_regression_20newsgroups.py b/examples/linear_model/plot_sparse_logistic_regression_20newsgroups.py
index f62208aab154a..404250a855e0a 100644
--- a/examples/linear_model/plot_sparse_logistic_regression_20newsgroups.py
+++ b/examples/linear_model/plot_sparse_logistic_regression_20newsgroups.py
@@ -32,6 +32,7 @@
 from sklearn.exceptions import ConvergenceWarning
 from sklearn.linear_model import LogisticRegression
 from sklearn.model_selection import train_test_split
+from sklearn.multiclass import OneVsRestClassifier
 
 warnings.filterwarnings("ignore", category=ConvergenceWarning, module="sklearn")
 t0 = timeit.default_timer()
@@ -76,20 +77,25 @@
             "[model=%s, solver=%s] Number of epochs: %s"
             % (model_params["name"], solver, this_max_iter)
         )
-        lr = LogisticRegression(
+        clf = LogisticRegression(
             solver=solver,
-            multi_class=model,
             penalty="l1",
             max_iter=this_max_iter,
             random_state=42,
         )
+        if model == "ovr":
+            clf = OneVsRestClassifier(clf)
         t1 = timeit.default_timer()
-        lr.fit(X_train, y_train)
+        clf.fit(X_train, y_train)
         train_time = timeit.default_timer() - t1
 
-        y_pred = lr.predict(X_test)
+        y_pred = clf.predict(X_test)
         accuracy = np.sum(y_pred == y_test) / y_test.shape[0]
-        density = np.mean(lr.coef_ != 0, axis=1) * 100
+        if model == "ovr":
+            coef = np.concatenate([est.coef_ for est in clf.estimators_])
+        else:
+            coef = clf.coef_
+        density = np.mean(coef != 0, axis=1) * 100
         accuracies.append(accuracy)
         densities.append(density)
         times.append(train_time)
diff --git a/examples/linear_model/plot_tweedie_regression_insurance_claims.py b/examples/linear_model/plot_tweedie_regression_insurance_claims.py
index 96e32ee031190..31a91fb37c766 100644
--- a/examples/linear_model/plot_tweedie_regression_insurance_claims.py
+++ b/examples/linear_model/plot_tweedie_regression_insurance_claims.py
@@ -1,3 +1,5 @@
+# Authors: The scikit-learn developers
+# SPDX-License-Identifier: BSD-3-Clause
 """
 ======================================
 Tweedie regression on insurance claims
@@ -37,11 +39,6 @@
     <https://doi.org/10.2139/ssrn.3164764>`_
 """
 
-# Authors: Christian Lorentzen <lorentzen.ch@gmail.com>
-#          Roman Yurchak <rth.yurchak@gmail.com>
-#          Olivier Grisel <olivier.grisel@ensta.org>
-# License: BSD 3 clause
-
 # %%
 
 from functools import partial
diff --git a/examples/miscellaneous/plot_metadata_routing.py b/examples/miscellaneous/plot_metadata_routing.py
index b9683d114e257..e96b54436cf30 100644
--- a/examples/miscellaneous/plot_metadata_routing.py
+++ b/examples/miscellaneous/plot_metadata_routing.py
@@ -6,17 +6,23 @@
 .. currentmodule:: sklearn
 
 This document shows how you can use the :ref:`metadata routing mechanism
-<metadata_routing>` in scikit-learn to route metadata through meta-estimators
-to the estimators consuming them. To better understand the rest of the
-document, we need to introduce two concepts: routers and consumers. A router is
-an object, in most cases a meta-estimator, which forwards given data and
-metadata to other objects and estimators. A consumer, on the other hand, is an
-object which accepts and uses a certain given metadata. For instance, an
-estimator taking into account ``sample_weight`` in its :term:`fit` method is a
-consumer of ``sample_weight``. It is possible for an object to be both a router
-and a consumer. For instance, a meta-estimator may take into account
-``sample_weight`` in certain calculations, but it may also route it to the
-underlying estimator.
+<metadata_routing>` in scikit-learn to route metadata to the estimators,
+scorers, and CV splitters consuming them.
+
+To better understand the following document, we need to introduce two concepts:
+routers and consumers. A router is an object which forwards some given data and
+metadata to other objects. In most cases, a router is a :term:`meta-estimator`,
+i.e. an estimator which takes another estimator as a parameter. A function such
+as :func:`sklearn.model_selection.cross_validate` which takes an estimator as a
+parameter and forwards data and metadata, is also a router.
+
+A consumer, on the other hand, is an object which accepts and uses some given
+metadata. For instance, an estimator taking into account ``sample_weight`` in
+its :term:`fit` method is a consumer of ``sample_weight``.
+
+It is possible for an object to be both a router and a consumer. For instance,
+a meta-estimator may take into account ``sample_weight`` in certain
+calculations, but it may also route it to the underlying estimator.
 
 First a few imports and some random data for the rest of the script.
 """
@@ -56,13 +62,12 @@
 my_other_weights = rng.rand(n_samples)
 
 # %%
-# This feature is only available if explicitly enabled:
+# Metadata routing is only available if explicitly enabled:
 set_config(enable_metadata_routing=True)
 
-# %%
-# This utility function is a dummy to check if a metadata is passed.
-
 
+# %%
+# This utility function is a dummy to check if a metadata is passed:
 def check_metadata(obj, **kwargs):
     for key, value in kwargs.items():
         if value is not None:
@@ -74,14 +79,14 @@ def check_metadata(obj, **kwargs):
 
 
 # %%
-# A utility function to nicely print the routing information of an object
+# A utility function to nicely print the routing information of an object:
 def print_routing(obj):
     pprint(obj.get_metadata_routing()._serialize())
 
 
 # %%
-# Estimators
-# ----------
+# Consuming Estimator
+# -------------------
 # Here we demonstrate how an estimator can expose the required API to support
 # metadata routing as a consumer. Imagine a simple classifier accepting
 # ``sample_weight`` as a metadata on its ``fit`` and ``groups`` in its
@@ -117,11 +122,11 @@ def predict(self, X, groups=None):
 
 # %%
 # The above output means that ``sample_weight`` and ``groups`` are not
-# requested, but if a router is given those metadata, it should raise an error,
-# since the user has not explicitly set whether they are required or not. The
-# same is true for ``sample_weight`` in the ``score`` method, which is
-# inherited from :class:`~base.ClassifierMixin`. In order to explicitly set
-# request values for those metadata, we can use these methods:
+# requested by `ExampleClassifier`, and if a router is given those metadata, it
+# should raise an error, since the user has not explicitly set whether they are
+# required or not. The same is true for ``sample_weight`` in the ``score``
+# method, which is inherited from :class:`~base.ClassifierMixin`. In order to
+# explicitly set request values for those metadata, we can use these methods:
 
 est = (
     ExampleClassifier()
@@ -133,7 +138,7 @@ def predict(self, X, groups=None):
 
 # %%
 # .. note ::
-#     Please note that as long as the above estimator is not used in another
+#     Please note that as long as the above estimator is not used in a
 #     meta-estimator, the user does not need to set any requests for the
 #     metadata and the set values are ignored, since a consumer does not
 #     validate or route given metadata. A simple usage of the above estimator
@@ -144,8 +149,11 @@ def predict(self, X, groups=None):
 est.predict(X[:3, :], groups=my_groups)
 
 # %%
-# Now let's have a meta-estimator, which doesn't do much other than routing the
-# metadata.
+# Routing Meta-Estimator
+# ----------------------
+# Now, we show how to design a meta-estimator to be a router. As a simplified
+# example, here is a meta-estimator, which doesn't do much other than routing
+# the metadata.
 
 
 class MetaClassifier(MetaEstimatorMixin, ClassifierMixin, BaseEstimator):
@@ -155,35 +163,43 @@ def __init__(self, estimator):
     def get_metadata_routing(self):
         # This method defines the routing for this meta-estimator.
         # In order to do so, a `MetadataRouter` instance is created, and the
-        # right routing is added to it. More explanations follow.
+        # routing is added to it. More explanations follow below.
         router = MetadataRouter(owner=self.__class__.__name__).add(
-            estimator=self.estimator, method_mapping="one-to-one"
+            estimator=self.estimator,
+            method_mapping=MethodMapping()
+            .add(caller="fit", callee="fit")
+            .add(caller="predict", callee="predict")
+            .add(caller="score", callee="score"),
         )
         return router
 
     def fit(self, X, y, **fit_params):
-        # meta-estimators are responsible for validating the given metadata.
-        # `get_routing_for_object` is a safe way to construct a
-        # `MetadataRouter` or a `MetadataRequest` from the given object.
+        # `get_routing_for_object` returns a copy of the `MetadataRouter`
+        # constructed by the above `get_metadata_routing` method, that is
+        # internally called.
         request_router = get_routing_for_object(self)
+        # Meta-estimators are responsible for validating the given metadata.
+        # `method` refers to the parent's method, i.e. `fit` in this example.
         request_router.validate_metadata(params=fit_params, method="fit")
-        # we can use provided utility methods to map the given metadata to what
-        # is required by the underlying estimator. Here `method` refers to the
-        # parent's method, i.e. `fit` in this example.
+        # `MetadataRouter.route_params` maps the given metadata to the metadata
+        # required by the underlying estimator based on the routing information
+        # defined by the MetadataRouter. The output of type `Bunch` has a key
+        # for each consuming object and those hold keys for their consuming
+        # methods, which then contain key for the metadata which should be
+        # routed to them.
         routed_params = request_router.route_params(params=fit_params, caller="fit")
 
-        # the output has a key for each object's method which is used here,
-        # i.e. parent's `fit` method, containing the metadata which should be
-        # routed to them, based on the information provided in
-        # `get_metadata_routing`.
+        # A sub-estimator is fitted and its classes are attributed to the
+        # meta-estimator.
         self.estimator_ = clone(self.estimator).fit(X, y, **routed_params.estimator.fit)
         self.classes_ = self.estimator_.classes_
         return self
 
     def predict(self, X, **predict_params):
         check_is_fitted(self)
-        # same as in `fit`, we validate the given metadata
+        # As in `fit`, we get a copy of the object's MetadataRouter,
         request_router = get_routing_for_object(self)
+        # then we validate the given metadata,
         request_router.validate_metadata(params=predict_params, method="predict")
         # and then prepare the input to the underlying `predict` method.
         routed_params = request_router.route_params(
@@ -195,77 +211,82 @@ def predict(self, X, **predict_params):
 # %%
 # Let's break down different parts of the above code.
 #
-# First, the :meth:`~utils.metadata_routing.get_routing_for_object` takes an
-# estimator (``self``) and returns a
-# :class:`~utils.metadata_routing.MetadataRouter` or a
-# :class:`~utils.metadata_routing.MetadataRequest` based on the output of the
-# estimator's ``get_metadata_routing`` method.
+# First, the :meth:`~utils.metadata_routing.get_routing_for_object` takes our
+# meta-estimator (``self``) and returns a
+# :class:`~utils.metadata_routing.MetadataRouter` or, a
+# :class:`~utils.metadata_routing.MetadataRequest` if the object is a consumer,
+# based on the output of the estimator's ``get_metadata_routing`` method.
 #
 # Then in each method, we use the ``route_params`` method to construct a
 # dictionary of the form ``{"object_name": {"method_name": {"metadata":
 # value}}}`` to pass to the underlying estimator's method. The ``object_name``
 # (``estimator`` in the above ``routed_params.estimator.fit`` example) is the
 # same as the one added in the ``get_metadata_routing``. ``validate_metadata``
-# makes sure all given metadata are requested to avoid silent bugs. Now, we
-# illustrate the different behaviors and notably the type of errors raised:
+# makes sure all given metadata are requested to avoid silent bugs.
+#
+# Next, we illustrate the different behaviors and notably the type of errors
+# raised.
 
-est = MetaClassifier(estimator=ExampleClassifier().set_fit_request(sample_weight=True))
-est.fit(X, y, sample_weight=my_weights)
+meta_est = MetaClassifier(
+    estimator=ExampleClassifier().set_fit_request(sample_weight=True)
+)
+meta_est.fit(X, y, sample_weight=my_weights)
 
 # %%
-# Note that the above example checks that ``sample_weight`` is correctly passed
-# to ``ExampleClassifier``, or else it would print that ``sample_weight`` is
-# ``None``:
+# Note that the above example is calling our utility function
+# `check_metadata()` via the `ExampleClassifier`. It checks that
+# ``sample_weight`` is correctly passed to it. If it is not, like in the
+# following example, it would print that ``sample_weight`` is ``None``:
 
-est.fit(X, y)
+meta_est.fit(X, y)
 
 # %%
 # If we pass an unknown metadata, an error is raised:
 try:
-    est.fit(X, y, test=my_weights)
+    meta_est.fit(X, y, test=my_weights)
 except TypeError as e:
     print(e)
 
 # %%
 # And if we pass a metadata which is not explicitly requested:
 try:
-    est.fit(X, y, sample_weight=my_weights).predict(X, groups=my_groups)
+    meta_est.fit(X, y, sample_weight=my_weights).predict(X, groups=my_groups)
 except ValueError as e:
     print(e)
 
 # %%
 # Also, if we explicitly set it as not requested, but it is provided:
-est = MetaClassifier(
+meta_est = MetaClassifier(
     estimator=ExampleClassifier()
     .set_fit_request(sample_weight=True)
     .set_predict_request(groups=False)
 )
 try:
-    est.fit(X, y, sample_weight=my_weights).predict(X[:3, :], groups=my_groups)
+    meta_est.fit(X, y, sample_weight=my_weights).predict(X[:3, :], groups=my_groups)
 except TypeError as e:
     print(e)
 
 # %%
-# Another concept to introduce is **aliased metadata**. This is when an estimator
-# requests a metadata with a different name than the default value. For
-# instance, in a setting where there are two estimators in a pipeline, one
-# could request ``sample_weight1`` and the other ``sample_weight2``. Note that
-# this doesn't change what the estimator expects, it only tells the
-# meta-estimator how to map the provided metadata to what's required. Here's an
-# example, where we pass ``aliased_sample_weight`` to the meta-estimator, but
-# the meta-estimator understands that ``aliased_sample_weight`` is an alias for
-# ``sample_weight``, and passes it as ``sample_weight`` to the underlying
-# estimator:
-est = MetaClassifier(
+# Another concept to introduce is **aliased metadata**. This is when an
+# estimator requests a metadata with a different variable name than the default
+# variable name. For instance, in a setting where there are two estimators in a
+# pipeline, one could request ``sample_weight1`` and the other
+# ``sample_weight2``. Note that this doesn't change what the estimator expects,
+# it only tells the meta-estimator how to map the provided metadata to what is
+# required. Here's an example, where we pass ``aliased_sample_weight`` to the
+# meta-estimator, but the meta-estimator understands that
+# ``aliased_sample_weight`` is an alias for ``sample_weight``, and passes it as
+# ``sample_weight`` to the underlying estimator:
+meta_est = MetaClassifier(
     estimator=ExampleClassifier().set_fit_request(sample_weight="aliased_sample_weight")
 )
-est.fit(X, y, aliased_sample_weight=my_weights)
+meta_est.fit(X, y, aliased_sample_weight=my_weights)
 
 # %%
-# And passing ``sample_weight`` here will fail since it is requested with an
+# Passing ``sample_weight`` here will fail since it is requested with an
 # alias and ``sample_weight`` with that name is not requested:
 try:
-    est.fit(X, y, sample_weight=my_weights)
+    meta_est.fit(X, y, sample_weight=my_weights)
 except TypeError as e:
     print(e)
 
@@ -280,41 +301,46 @@ def predict(self, X, **predict_params):
 # corresponding method routings, i.e. which method of a sub-estimator is used
 # in which method of a meta-estimator:
 
-print_routing(est)
+print_routing(meta_est)
 
 # %%
 # As you can see, the only metadata requested for method ``fit`` is
 # ``"sample_weight"`` with ``"aliased_sample_weight"`` as the alias. The
 # ``~utils.metadata_routing.MetadataRouter`` class enables us to easily create
 # the routing object which would create the output we need for our
-# ``get_metadata_routing``. In the above implementation,
-# ``mapping="one-to-one"`` means there is a one to one mapping between
-# sub-estimator's methods and meta-estimator's ones, i.e. ``fit`` used in
-# ``fit`` and so on. In order to understand how aliases work in
-# meta-estimators, imagine our meta-estimator inside another one:
+# ``get_metadata_routing``.
+#
+# In order to understand how aliases work in meta-estimators, imagine our
+# meta-estimator inside another one:
 
-meta_est = MetaClassifier(estimator=est).fit(X, y, aliased_sample_weight=my_weights)
+meta_meta_est = MetaClassifier(estimator=meta_est).fit(
+    X, y, aliased_sample_weight=my_weights
+)
 
 # %%
-# In the above example, this is how each ``fit`` method will call the
-# sub-estimator's ``fit``::
+# In the above example, this is how the ``fit`` method of `meta_meta_est`
+# will call their sub-estimator's ``fit`` methods::
+#
+#     # user feeds `my_weights` as `aliased_sample_weight` into `meta_meta_est`:
+#     meta_meta_est.fit(X, y, aliased_sample_weight=my_weights):
+#         ...
 #
-#     meta_est.fit(X, y, aliased_sample_weight=my_weights):
-#         ...  # this estimator (est), expects aliased_sample_weight as seen above
+#         # the first sub-estimator (`meta_est`) expects `aliased_sample_weight`
 #         self.estimator_.fit(X, y, aliased_sample_weight=aliased_sample_weight):
-#             ...  # now est passes aliased_sample_weight's value as sample_weight,
-#                  # which is expected by the sub-estimator
-#             self.estimator_.fit(X, y, sample_weight=aliased_sample_weight)
-#    ...
+#             ...
+#
+#             # the second sub-estimator (`est`) expects `sample_weight`
+#             self.estimator_.fit(X, y, sample_weight=aliased_sample_weight):
+#                 ...
 
 # %%
-# Router and Consumer
-# -------------------
-# To show how a slightly more complex case would work, consider a case
-# where a meta-estimator uses some metadata, but it also routes them to an
-# underlying estimator. In this case, this meta-estimator is a consumer and a
-# router at the same time. This is how we can implement one, and it is very
-# similar to what we had before, with a few tweaks.
+# Consuming and routing Meta-Estimator
+# ------------------------------------
+# For a slightly more complex example, consider a meta-estimator that routes
+# metadata to an underlying estimator as before, but it also uses some metadata
+# in its own methods. This meta-estimator is a consumer and a router at the
+# same time. Implementing one is very similar to what we had before, but with a
+# few tweaks.
 
 
 class RouterConsumerClassifier(MetaEstimatorMixin, ClassifierMixin, BaseEstimator):
@@ -324,47 +350,59 @@ def __init__(self, estimator):
     def get_metadata_routing(self):
         router = (
             MetadataRouter(owner=self.__class__.__name__)
+            # defining metadata routing request values for usage in the meta-estimator
             .add_self_request(self)
-            .add(estimator=self.estimator, method_mapping="one-to-one")
+            # defining metadata routing request values for usage in the sub-estimator
+            .add(
+                estimator=self.estimator,
+                method_mapping=MethodMapping()
+                .add(caller="fit", callee="fit")
+                .add(caller="predict", callee="predict")
+                .add(caller="score", callee="score"),
+            )
         )
         return router
 
+    # Since `sample_weight` is used and consumed here, it should be defined as
+    # an explicit argument in the method's signature. All other metadata which
+    # are only routed, will be passed as `**fit_params`:
     def fit(self, X, y, sample_weight, **fit_params):
         if self.estimator is None:
             raise ValueError("estimator cannot be None!")
 
         check_metadata(self, sample_weight=sample_weight)
 
+        # We add `sample_weight` to the `fit_params` dictionary.
         if sample_weight is not None:
             fit_params["sample_weight"] = sample_weight
 
-        # meta-estimators are responsible for validating the given metadata
         request_router = get_routing_for_object(self)
         request_router.validate_metadata(params=fit_params, method="fit")
-        # we can use provided utility methods to map the given metadata to what
-        # is required by the underlying estimator
-        params = request_router.route_params(params=fit_params, caller="fit")
-        self.estimator_ = clone(self.estimator).fit(X, y, **params.estimator.fit)
+        routed_params = request_router.route_params(params=fit_params, caller="fit")
+        self.estimator_ = clone(self.estimator).fit(X, y, **routed_params.estimator.fit)
         self.classes_ = self.estimator_.classes_
         return self
 
     def predict(self, X, **predict_params):
         check_is_fitted(self)
-        # same as in ``fit``, we validate the given metadata
+        # As in `fit`, we get a copy of the object's MetadataRouter,
         request_router = get_routing_for_object(self)
+        # we validate the given metadata,
         request_router.validate_metadata(params=predict_params, method="predict")
         # and then prepare the input to the underlying ``predict`` method.
-        params = request_router.route_params(params=predict_params, caller="predict")
-        return self.estimator_.predict(X, **params.estimator.predict)
+        routed_params = request_router.route_params(
+            params=predict_params, caller="predict"
+        )
+        return self.estimator_.predict(X, **routed_params.estimator.predict)
 
 
 # %%
-# The key parts where the above estimator differs from our previous
+# The key parts where the above meta-estimator differs from our previous
 # meta-estimator is accepting ``sample_weight`` explicitly in ``fit`` and
-# including it in ``fit_params``. Making ``sample_weight`` an explicit argument
-# makes sure ``set_fit_request(sample_weight=...)`` is present for this class.
-# In a sense, this means the estimator is both a consumer, as well as a router
-# of ``sample_weight``.
+# including it in ``fit_params``. Since ``sample_weight`` is an explicit
+# argument, we can be sure that ``set_fit_request(sample_weight=...)`` is
+# present for this method. The meta-estimator is both a consumer, as well as a
+# router of ``sample_weight``.
 #
 # In ``get_metadata_routing``, we add ``self`` to the routing using
 # ``add_self_request`` to indicate this estimator is consuming
@@ -374,61 +412,68 @@ def predict(self, X, **predict_params):
 
 # %%
 # - No metadata requested
-est = RouterConsumerClassifier(estimator=ExampleClassifier())
-print_routing(est)
+meta_est = RouterConsumerClassifier(estimator=ExampleClassifier())
+print_routing(meta_est)
 
 
 # %%
-# - ``sample_weight`` requested by underlying estimator
-est = RouterConsumerClassifier(
+# - ``sample_weight`` requested by sub-estimator
+meta_est = RouterConsumerClassifier(
     estimator=ExampleClassifier().set_fit_request(sample_weight=True)
 )
-print_routing(est)
+print_routing(meta_est)
 
 # %%
 # - ``sample_weight`` requested by meta-estimator
-est = RouterConsumerClassifier(estimator=ExampleClassifier()).set_fit_request(
+meta_est = RouterConsumerClassifier(estimator=ExampleClassifier()).set_fit_request(
     sample_weight=True
 )
-print_routing(est)
+print_routing(meta_est)
 
 # %%
 # Note the difference in the requested metadata representations above.
 #
-# - We can also alias the metadata to pass different values to them:
+# - We can also alias the metadata to pass different values to the fit methods
+#   of the meta- and the sub-estimator:
 
-est = RouterConsumerClassifier(
+meta_est = RouterConsumerClassifier(
     estimator=ExampleClassifier().set_fit_request(sample_weight="clf_sample_weight"),
 ).set_fit_request(sample_weight="meta_clf_sample_weight")
-print_routing(est)
+print_routing(meta_est)
 
 # %%
 # However, ``fit`` of the meta-estimator only needs the alias for the
-# sub-estimator, since it doesn't validate and route its own required metadata:
-est.fit(X, y, sample_weight=my_weights, clf_sample_weight=my_other_weights)
+# sub-estimator and addresses their own sample weight as `sample_weight`, since
+# it doesn't validate and route its own required metadata:
+meta_est.fit(X, y, sample_weight=my_weights, clf_sample_weight=my_other_weights)
 
 # %%
-# - Alias only on the sub-estimator. This is useful if we don't want the
-#   meta-estimator to use the metadata, and we only want the metadata to be used
-#   by the sub-estimator.
-est = RouterConsumerClassifier(
+# - Alias only on the sub-estimator:
+#
+# This is useful when we don't want the meta-estimator to use the metadata, but
+# the sub-estimator should.
+meta_est = RouterConsumerClassifier(
     estimator=ExampleClassifier().set_fit_request(sample_weight="aliased_sample_weight")
-).set_fit_request(sample_weight=True)
-print_routing(est)
-
+)
+print_routing(meta_est)
+# %%
+# The meta-estimator cannot use `aliased_sample_weight`, because it expects
+# it passed as `sample_weight`. This would apply even if
+# `set_fit_request(sample_weight=True)` was set on it.
 
 # %%
 # Simple Pipeline
 # ---------------
-# A slightly more complicated use-case is a meta-estimator which does something
-# similar to the :class:`~pipeline.Pipeline`. Here is a meta-estimator, which
-# accepts a transformer and a classifier, and applies the transformer before
-# running the classifier.
+# A slightly more complicated use-case is a meta-estimator resembling a
+# :class:`~pipeline.Pipeline`. Here is a meta-estimator, which accepts a
+# transformer and a classifier. When calling its `fit` method, it applies the
+# transformer's `fit` and `transform` before running the classifier on the
+# transformed data. Upon `predict`, it applies the transformer's `transform`
+# before predicting with the classifier's `predict` method on the transformed
+# new data.
 
 
 class SimplePipeline(ClassifierMixin, BaseEstimator):
-    _required_parameters = ["estimator"]
-
     def __init__(self, transformer, classifier):
         self.transformer = transformer
         self.classifier = classifier
@@ -436,48 +481,66 @@ def __init__(self, transformer, classifier):
     def get_metadata_routing(self):
         router = (
             MetadataRouter(owner=self.__class__.__name__)
+            # We add the routing for the transformer.
             .add(
                 transformer=self.transformer,
                 method_mapping=MethodMapping()
-                .add(callee="fit", caller="fit")
-                .add(callee="transform", caller="fit")
-                .add(callee="transform", caller="predict"),
+                # The metadata is routed such that it retraces how
+                # `SimplePipeline` internally calls the transformer's `fit` and
+                # `transform` methods in its own methods (`fit` and `predict`).
+                .add(caller="fit", callee="fit")
+                .add(caller="fit", callee="transform")
+                .add(caller="predict", callee="transform"),
+            )
+            # We add the routing for the classifier.
+            .add(
+                classifier=self.classifier,
+                method_mapping=MethodMapping()
+                .add(caller="fit", callee="fit")
+                .add(caller="predict", callee="predict"),
             )
-            .add(classifier=self.classifier, method_mapping="one-to-one")
         )
         return router
 
     def fit(self, X, y, **fit_params):
-        params = process_routing(self, "fit", **fit_params)
+        routed_params = process_routing(self, "fit", **fit_params)
 
-        self.transformer_ = clone(self.transformer).fit(X, y, **params.transformer.fit)
-        X_transformed = self.transformer_.transform(X, **params.transformer.transform)
+        self.transformer_ = clone(self.transformer).fit(
+            X, y, **routed_params.transformer.fit
+        )
+        X_transformed = self.transformer_.transform(
+            X, **routed_params.transformer.transform
+        )
 
         self.classifier_ = clone(self.classifier).fit(
-            X_transformed, y, **params.classifier.fit
+            X_transformed, y, **routed_params.classifier.fit
         )
         return self
 
     def predict(self, X, **predict_params):
-        params = process_routing(self, "predict", **predict_params)
+        routed_params = process_routing(self, "predict", **predict_params)
 
-        X_transformed = self.transformer_.transform(X, **params.transformer.transform)
-        return self.classifier_.predict(X_transformed, **params.classifier.predict)
+        X_transformed = self.transformer_.transform(
+            X, **routed_params.transformer.transform
+        )
+        return self.classifier_.predict(
+            X_transformed, **routed_params.classifier.predict
+        )
 
 
 # %%
-# Note the usage of :class:`~utils.metadata_routing.MethodMapping` to declare
-# which methods of the child estimator (callee) are used in which methods of
-# the meta estimator (caller). As you can see, we use the transformer's
-# ``transform`` and ``fit`` methods in ``fit``, and its ``transform`` method in
-# ``predict``, and that's what you see implemented in the routing structure of
-# the pipeline class.
+# Note the usage of :class:`~utils.metadata_routing.MethodMapping` to
+# declare which methods of the child estimator (callee) are used in which
+# methods of the meta estimator (caller). As you can see, `SimplePipeline` uses
+# the transformer's ``transform`` and ``fit`` methods in ``fit``, and its
+# ``transform`` method in ``predict``, and that's what you see implemented in
+# the routing structure of the pipeline class.
 #
 # Another difference in the above example with the previous ones is the usage
 # of :func:`~utils.metadata_routing.process_routing`, which processes the input
-# parameters, does the required validation, and returns the `params` which we
-# had created in previous examples. This reduces the boilerplate code a
-# developer needs to write in each meta-estimator's method. Developers are
+# parameters, does the required validation, and returns the `routed_params`
+# which we had created in previous examples. This reduces the boilerplate code
+# a developer needs to write in each meta-estimator's method. Developers are
 # strongly recommended to use this function unless there is a good reason
 # against it.
 #
@@ -505,14 +568,14 @@ def fit_transform(self, X, y, sample_weight=None, groups=None):
 # ``transform``.
 #
 # Now we can test our pipeline, and see if metadata is correctly passed around.
-# This example uses our simple pipeline, and our transformer, and our
-# consumer+router estimator which uses our simple classifier.
+# This example uses our `SimplePipeline`, our `ExampleTransformer`, and our
+# `RouterConsumerClassifier` which uses our `ExampleClassifier`.
 
-est = SimplePipeline(
+pipe = SimplePipeline(
     transformer=ExampleTransformer()
-    # we transformer's fit to receive sample_weight
+    # we set transformer's fit to receive sample_weight
     .set_fit_request(sample_weight=True)
-    # we want transformer's transform to receive groups
+    # we set transformer's transform to receive groups
     .set_transform_request(groups=True),
     classifier=RouterConsumerClassifier(
         estimator=ExampleClassifier()
@@ -520,12 +583,11 @@ def fit_transform(self, X, y, sample_weight=None, groups=None):
         .set_fit_request(sample_weight=True)
         # but not groups in predict
         .set_predict_request(groups=False),
-    ).set_fit_request(
-        # and we want the meta-estimator to receive sample_weight as well
-        sample_weight=True
-    ),
+    )
+    # and we want the meta-estimator to receive sample_weight as well
+    .set_fit_request(sample_weight=True),
 )
-est.fit(X, y, sample_weight=my_weights, groups=my_groups).predict(
+pipe.fit(X, y, sample_weight=my_weights, groups=my_groups).predict(
     X[:3], groups=my_groups
 )
 
@@ -544,18 +606,20 @@ def __init__(self, estimator):
         self.estimator = estimator
 
     def fit(self, X, y, **fit_params):
-        params = process_routing(self, "fit", **fit_params)
-        self.estimator_ = clone(self.estimator).fit(X, y, **params.estimator.fit)
+        routed_params = process_routing(self, "fit", **fit_params)
+        self.estimator_ = clone(self.estimator).fit(X, y, **routed_params.estimator.fit)
 
     def get_metadata_routing(self):
         router = MetadataRouter(owner=self.__class__.__name__).add(
-            estimator=self.estimator, method_mapping="one-to-one"
+            estimator=self.estimator,
+            method_mapping=MethodMapping().add(caller="fit", callee="fit"),
         )
         return router
 
 
 # %%
-# As explained above, this is now a valid usage:
+# As explained above, this is a valid usage if `my_weights` aren't supposed
+# to be passed as `sample_weight` to `MetaRegressor`:
 
 reg = MetaRegressor(estimator=LinearRegression().set_fit_request(sample_weight=True))
 reg.fit(X, y, sample_weight=my_weights)
@@ -567,29 +631,36 @@ def get_metadata_routing(self):
 
 
 class WeightedMetaRegressor(MetaEstimatorMixin, RegressorMixin, BaseEstimator):
+    # show warning to remind user to explicitly set the value with
+    # `.set_{method}_request(sample_weight={boolean})`
     __metadata_request__fit = {"sample_weight": metadata_routing.WARN}
 
     def __init__(self, estimator):
         self.estimator = estimator
 
     def fit(self, X, y, sample_weight=None, **fit_params):
-        params = process_routing(self, "fit", sample_weight=sample_weight, **fit_params)
+        routed_params = process_routing(
+            self, "fit", sample_weight=sample_weight, **fit_params
+        )
         check_metadata(self, sample_weight=sample_weight)
-        self.estimator_ = clone(self.estimator).fit(X, y, **params.estimator.fit)
+        self.estimator_ = clone(self.estimator).fit(X, y, **routed_params.estimator.fit)
 
     def get_metadata_routing(self):
         router = (
             MetadataRouter(owner=self.__class__.__name__)
             .add_self_request(self)
-            .add(estimator=self.estimator, method_mapping="one-to-one")
+            .add(
+                estimator=self.estimator,
+                method_mapping=MethodMapping().add(caller="fit", callee="fit"),
+            )
         )
         return router
 
 
 # %%
-# The above implementation is almost no different than ``MetaRegressor``, and
+# The above implementation is almost the same as ``MetaRegressor``, and
 # because of the default request value defined in ``__metadata_request__fit``
-# there is a warning raised.
+# there is a warning raised when fitted.
 
 with warnings.catch_warnings(record=True) as record:
     WeightedMetaRegressor(
@@ -600,7 +671,7 @@ def get_metadata_routing(self):
 
 
 # %%
-# When an estimator supports a metadata which wasn't supported before, the
+# When an estimator consumes a metadata which it didn't consume before, the
 # following pattern can be used to warn the users about it.
 
 
@@ -621,7 +692,7 @@ def predict(self, X):
     print(w.message)
 
 # %%
-# In the end, we disable the configuration flag for metadata routing:
+# At the end we disable the configuration flag for metadata routing:
 
 set_config(enable_metadata_routing=False)
 
@@ -634,12 +705,12 @@ def predict(self, X):
 # :class:`~utils.metadata_routing.MetadataRouter`. It is strongly not advised,
 # but possible to vendor the tools related to metadata-routing if you strictly
 # want to have a scikit-learn compatible estimator, without depending on the
-# scikit-learn package. If the following conditions are met, you do NOT need to
-# modify your code at all:
+# scikit-learn package. If all of the following conditions are met, you do NOT
+# need to modify your code at all:
 #
 # - your estimator inherits from :class:`~base.BaseEstimator`
 # - the parameters consumed by your estimator's methods, e.g. ``fit``, are
 #   explicitly defined in the method's signature, as opposed to being
 #   ``*args`` or ``*kwargs``.
-# - you do not route any metadata to the underlying objects, i.e. you're not a
-#   *router*.
+# - your estimator does not route any metadata to the underlying objects, i.e.
+#   it's not a *router*.
diff --git a/examples/model_selection/plot_cost_sensitive_learning.py b/examples/model_selection/plot_cost_sensitive_learning.py
new file mode 100644
index 0000000000000..12d636d556c66
--- /dev/null
+++ b/examples/model_selection/plot_cost_sensitive_learning.py
@@ -0,0 +1,702 @@
+"""
+==============================================================
+Post-tuning the decision threshold for cost-sensitive learning
+==============================================================
+
+Once a classifier is trained, the output of the :term:`predict` method outputs class
+label predictions corresponding to a thresholding of either the
+:term:`decision_function` or the :term:`predict_proba` output. For a binary classifier,
+the default threshold is defined as a posterior probability estimate of 0.5 or a
+decision score of 0.0.
+
+However, this default strategy is most likely not optimal for the task at hand.
+Here, we use the "Statlog" German credit dataset [1]_ to illustrate a use case.
+In this dataset, the task is to predict whether a person has a "good" or "bad" credit.
+In addition, a cost-matrix is provided that specifies the cost of
+misclassification. Specifically, misclassifying a "bad" credit as "good" is five
+times more costly on average than misclassifying a "good" credit as "bad".
+
+We use the :class:`~sklearn.model_selection.TunedThresholdClassifierCV` to select the
+cut-off point of the decision function that minimizes the provided business
+cost.
+
+In the second part of the example, we further extend this approach by
+considering the problem of fraud detection in credit card transactions: in this
+case, the business metric depends on the amount of each individual transaction.
+
+.. rubric :: References
+
+.. [1] "Statlog (German Credit Data) Data Set", UCI Machine Learning Repository,
+    `Link <https://archive.ics.uci.edu/ml/datasets/Statlog+%28German+Credit+Data%29>`_.
+
+.. [2] `Charles Elkan, "The Foundations of Cost-Sensitive Learning",
+    International joint conference on artificial intelligence.
+    Vol. 17. No. 1. Lawrence Erlbaum Associates Ltd, 2001.
+    <https://cseweb.ucsd.edu/~elkan/rescale.pdf>`_
+"""
+
+# %%
+# Cost-sensitive learning with constant gains and costs
+# -----------------------------------------------------
+#
+# In this first section, we illustrate the use of the
+# :class:`~sklearn.model_selection.TunedThresholdClassifierCV` in a setting of
+# cost-sensitive learning when the gains and costs associated to each entry of the
+# confusion matrix are constant. We use the problematic presented in [2]_ using the
+# "Statlog" German credit dataset [1]_.
+#
+# "Statlog" German credit dataset
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+#
+# We fetch the German credit dataset from OpenML.
+import sklearn
+from sklearn.datasets import fetch_openml
+
+sklearn.set_config(transform_output="pandas")
+
+german_credit = fetch_openml(data_id=31, as_frame=True, parser="pandas")
+X, y = german_credit.data, german_credit.target
+
+# %%
+# We check the feature types available in `X`.
+X.info()
+
+# %%
+# Many features are categorical and usually string-encoded. We need to encode
+# these categories when we develop our predictive model. Let's check the targets.
+y.value_counts()
+
+# %%
+# Another observation is that the dataset is imbalanced. We would need to be careful
+# when evaluating our predictive model and use a family of metrics that are adapted
+# to this setting.
+#
+# In addition, we observe that the target is string-encoded. Some metrics
+# (e.g. precision and recall) require to provide the label of interest also called
+# the "positive label". Here, we define that our goal is to predict whether or not
+# a sample is a "bad" credit.
+pos_label, neg_label = "bad", "good"
+
+# %%
+# To carry our analysis, we split our dataset using a single stratified split.
+from sklearn.model_selection import train_test_split
+
+X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, random_state=0)
+
+# %%
+# We are ready to design our predictive model and the associated evaluation strategy.
+#
+# Evaluation metrics
+# ^^^^^^^^^^^^^^^^^^
+#
+# In this section, we define a set of metrics that we use later. To see
+# the effect of tuning the cut-off point, we evaluate the predictive model using
+# the Receiver Operating Characteristic (ROC) curve and the Precision-Recall curve.
+# The values reported on these plots are therefore the true positive rate (TPR),
+# also known as the recall or the sensitivity, and the false positive rate (FPR),
+# also known as the specificity, for the ROC curve and the precision and recall for
+# the Precision-Recall curve.
+#
+# From these four metrics, scikit-learn does not provide a scorer for the FPR. We
+# therefore need to define a small custom function to compute it.
+from sklearn.metrics import confusion_matrix
+
+
+def fpr_score(y, y_pred, neg_label, pos_label):
+    cm = confusion_matrix(y, y_pred, labels=[neg_label, pos_label])
+    tn, fp, _, _ = cm.ravel()
+    tnr = tn / (tn + fp)
+    return 1 - tnr
+
+
+# %%
+# As previously stated, the "positive label" is not defined as the value "1" and calling
+# some of the metrics with this non-standard value raise an error. We need to
+# provide the indication of the "positive label" to the metrics.
+#
+# We therefore need to define a scikit-learn scorer using
+# :func:`~sklearn.metrics.make_scorer` where the information is passed. We store all
+# the custom scorers in a dictionary. To use them, we need to pass the fitted model,
+# the data and the target on which we want to evaluate the predictive model.
+from sklearn.metrics import make_scorer, precision_score, recall_score
+
+tpr_score = recall_score  # TPR and recall are the same metric
+scoring = {
+    "precision": make_scorer(precision_score, pos_label=pos_label),
+    "recall": make_scorer(recall_score, pos_label=pos_label),
+    "fpr": make_scorer(fpr_score, neg_label=neg_label, pos_label=pos_label),
+    "tpr": make_scorer(tpr_score, pos_label=pos_label),
+}
+
+# %%
+# In addition, the original research [1]_ defines a custom business metric. We
+# call a "business metric" any metric function that aims at quantifying how the
+# predictions (correct or wrong) might impact the business value of deploying a
+# given machine learning model in a specific application context. For our
+# credit prediction task, the authors provide a custom cost-matrix which
+# encodes that classifying a a "bad" credit as "good" is 5 times more costly on
+# average than the opposite: it is less costly for the financing institution to
+# not grant a credit to a potential customer that will not default (and
+# therefore miss a good customer that would have otherwise both reimbursed the
+# credit and payed interests) than to grant a credit to a customer that will
+# default.
+#
+# We define a python function that weight the confusion matrix and return the
+# overall cost.
+import numpy as np
+
+
+def credit_gain_score(y, y_pred, neg_label, pos_label):
+    cm = confusion_matrix(y, y_pred, labels=[neg_label, pos_label])
+    # The rows of the confusion matrix hold the counts of observed classes
+    # while the columns hold counts of predicted classes. Recall that here we
+    # consider "bad" as the positive class (second row and column).
+    # Scikit-learn model selection tools expect that we follow a convention
+    # that "higher" means "better", hence the following gain matrix assigns
+    # negative gains (costs) to the two kinds of prediction errors:
+    # - a gain of -1 for each false positive ("good" credit labeled as "bad"),
+    # - a gain of -5 for each false negative ("bad" credit labeled as "good"),
+    # The true positives and true negatives are assigned null gains in this
+    # metric.
+    #
+    # Note that theoretically, given that our model is calibrated and our data
+    # set representative and large enough, we do not need to tune the
+    # threshold, but can safely set it to the cost ration 1/5, as stated by Eq.
+    # (2) in Elkan paper [2]_.
+    gain_matrix = np.array(
+        [
+            [0, -1],  # -1 gain for false positives
+            [-5, 0],  # -5 gain for false negatives
+        ]
+    )
+    return np.sum(cm * gain_matrix)
+
+
+scoring["cost_gain"] = make_scorer(
+    credit_gain_score, neg_label=neg_label, pos_label=pos_label
+)
+# %%
+# Vanilla predictive model
+# ^^^^^^^^^^^^^^^^^^^^^^^^
+#
+# We use :class:`~sklearn.ensemble.HistGradientBoostingClassifier` as a predictive model
+# that natively handles categorical features and missing values.
+from sklearn.ensemble import HistGradientBoostingClassifier
+
+model = HistGradientBoostingClassifier(
+    categorical_features="from_dtype", random_state=0
+).fit(X_train, y_train)
+model
+
+# %%
+# We evaluate the performance of our predictive model using the ROC and Precision-Recall
+# curves.
+import matplotlib.pyplot as plt
+
+from sklearn.metrics import PrecisionRecallDisplay, RocCurveDisplay
+
+fig, axs = plt.subplots(nrows=1, ncols=2, figsize=(14, 6))
+
+PrecisionRecallDisplay.from_estimator(
+    model, X_test, y_test, pos_label=pos_label, ax=axs[0], name="GBDT"
+)
+axs[0].plot(
+    scoring["recall"](model, X_test, y_test),
+    scoring["precision"](model, X_test, y_test),
+    marker="o",
+    markersize=10,
+    color="tab:blue",
+    label="Default cut-off point at a probability of 0.5",
+)
+axs[0].set_title("Precision-Recall curve")
+axs[0].legend()
+
+RocCurveDisplay.from_estimator(
+    model,
+    X_test,
+    y_test,
+    pos_label=pos_label,
+    ax=axs[1],
+    name="GBDT",
+    plot_chance_level=True,
+)
+axs[1].plot(
+    scoring["fpr"](model, X_test, y_test),
+    scoring["tpr"](model, X_test, y_test),
+    marker="o",
+    markersize=10,
+    color="tab:blue",
+    label="Default cut-off point at a probability of 0.5",
+)
+axs[1].set_title("ROC curve")
+axs[1].legend()
+_ = fig.suptitle("Evaluation of the vanilla GBDT model")
+
+# %%
+# We recall that these curves give insights on the statistical performance of the
+# predictive model for different cut-off points. For the Precision-Recall curve, the
+# reported metrics are the precision and recall and for the ROC curve, the reported
+# metrics are the TPR (same as recall) and FPR.
+#
+# Here, the different cut-off points correspond to different levels of posterior
+# probability estimates ranging between 0 and 1. By default, `model.predict` uses a
+# cut-off point at a probability estimate of 0.5. The metrics for such a cut-off point
+# are reported with the blue dot on the curves: it corresponds to the statistical
+# performance of the model when using `model.predict`.
+#
+# However, we recall that the original aim was to minimize the cost (or maximize the
+# gain) as defined by the business metric. We can compute the value of the business
+# metric:
+print(f"Business defined metric: {scoring['cost_gain'](model, X_test, y_test)}")
+
+# %%
+# At this stage we don't know if any other cut-off can lead to a greater gain. To find
+# the optimal one, we need to compute the cost-gain using the business metric for all
+# possible cut-off points and choose the best. This strategy can be quite tedious to
+# implement by hand, but the
+# :class:`~sklearn.model_selection.TunedThresholdClassifierCV` class is here to help us.
+# It automatically computes the cost-gain for all possible cut-off points and optimizes
+# for the `scoring`.
+#
+# .. _cost_sensitive_learning_example:
+#
+# Tuning the cut-off point
+# ^^^^^^^^^^^^^^^^^^^^^^^^
+#
+# We use :class:`~sklearn.model_selection.TunedThresholdClassifierCV` to tune the
+# cut-off point. We need to provide the business metric to optimize as well as the
+# positive label. Internally, the optimum cut-off point is chosen such that it maximizes
+# the business metric via cross-validation. By default a 5-fold stratified
+# cross-validation is used.
+from sklearn.model_selection import TunedThresholdClassifierCV
+
+tuned_model = TunedThresholdClassifierCV(
+    estimator=model,
+    scoring=scoring["cost_gain"],
+    store_cv_results=True,  # necessary to inspect all results
+)
+tuned_model.fit(X_train, y_train)
+print(f"{tuned_model.best_threshold_=:0.2f}")
+
+# %%
+# We plot the ROC and Precision-Recall curves for the vanilla model and the tuned model.
+# Also we plot the cut-off points that would be used by each model. Because, we are
+# reusing the same code later, we define a function that generates the plots.
+
+
+def plot_roc_pr_curves(vanilla_model, tuned_model, *, title):
+    fig, axs = plt.subplots(nrows=1, ncols=3, figsize=(21, 6))
+
+    linestyles = ("dashed", "dotted")
+    markerstyles = ("o", ">")
+    colors = ("tab:blue", "tab:orange")
+    names = ("Vanilla GBDT", "Tuned GBDT")
+    for idx, (est, linestyle, marker, color, name) in enumerate(
+        zip((vanilla_model, tuned_model), linestyles, markerstyles, colors, names)
+    ):
+        decision_threshold = getattr(est, "best_threshold_", 0.5)
+        PrecisionRecallDisplay.from_estimator(
+            est,
+            X_test,
+            y_test,
+            pos_label=pos_label,
+            linestyle=linestyle,
+            color=color,
+            ax=axs[0],
+            name=name,
+        )
+        axs[0].plot(
+            scoring["recall"](est, X_test, y_test),
+            scoring["precision"](est, X_test, y_test),
+            marker,
+            markersize=10,
+            color=color,
+            label=f"Cut-off point at probability of {decision_threshold:.2f}",
+        )
+        RocCurveDisplay.from_estimator(
+            est,
+            X_test,
+            y_test,
+            pos_label=pos_label,
+            linestyle=linestyle,
+            color=color,
+            ax=axs[1],
+            name=name,
+            plot_chance_level=idx == 1,
+        )
+        axs[1].plot(
+            scoring["fpr"](est, X_test, y_test),
+            scoring["tpr"](est, X_test, y_test),
+            marker,
+            markersize=10,
+            color=color,
+            label=f"Cut-off point at probability of {decision_threshold:.2f}",
+        )
+
+    axs[0].set_title("Precision-Recall curve")
+    axs[0].legend()
+    axs[1].set_title("ROC curve")
+    axs[1].legend()
+
+    axs[2].plot(
+        tuned_model.cv_results_["thresholds"],
+        tuned_model.cv_results_["scores"],
+        color="tab:orange",
+    )
+    axs[2].plot(
+        tuned_model.best_threshold_,
+        tuned_model.best_score_,
+        "o",
+        markersize=10,
+        color="tab:orange",
+        label="Optimal cut-off point for the business metric",
+    )
+    axs[2].legend()
+    axs[2].set_xlabel("Decision threshold (probability)")
+    axs[2].set_ylabel("Objective score (using cost-matrix)")
+    axs[2].set_title("Objective score as a function of the decision threshold")
+    fig.suptitle(title)
+
+
+# %%
+title = "Comparison of the cut-off point for the vanilla and tuned GBDT model"
+plot_roc_pr_curves(model, tuned_model, title=title)
+
+# %%
+# The first remark is that both classifiers have exactly the same ROC and
+# Precision-Recall curves. It is expected because by default, the classifier is fitted
+# on the same training data. In a later section, we discuss more in detail the
+# available options regarding model refitting and cross-validation.
+#
+# The second remark is that the cut-off points of the vanilla and tuned model are
+# different. To understand why the tuned model has chosen this cut-off point, we can
+# look at the right-hand side plot that plots the objective score that is our exactly
+# the same as our business metric. We see that the optimum threshold corresponds to the
+# maximum of the objective score. This maximum is reached for a decision threshold
+# much lower than 0.5: the tuned model enjoys a much higher recall at the cost of
+# of significantly lower precision: the tuned model is much more eager to
+# predict the "bad" class label to larger fraction of individuals.
+#
+# We can now check if choosing this cut-off point leads to a better score on the testing
+# set:
+print(f"Business defined metric: {scoring['cost_gain'](tuned_model, X_test, y_test)}")
+
+# %%
+# We observe that tuning the decision threshold almost improves our business gains
+# by factor of 2.
+#
+# .. _TunedThresholdClassifierCV_no_cv:
+#
+# Consideration regarding model refitting and cross-validation
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+#
+# In the above experiment, we used the default setting of the
+# :class:`~sklearn.model_selection.TunedThresholdClassifierCV`. In particular, the
+# cut-off point is tuned using a 5-fold stratified cross-validation. Also, the
+# underlying predictive model is refitted on the entire training data once the cut-off
+# point is chosen.
+#
+# These two strategies can be changed by providing the `refit` and `cv` parameters.
+# For instance, one could provide a fitted `estimator` and set `cv="prefit"`, in which
+# case the cut-off point is found on the entire dataset provided at fitting time.
+# Also, the underlying classifier is not be refitted by setting `refit=False`. Here, we
+# can try to do such experiment.
+model.fit(X_train, y_train)
+tuned_model.set_params(cv="prefit", refit=False).fit(X_train, y_train)
+print(f"{tuned_model.best_threshold_=:0.2f}")
+
+
+# %%
+# Then, we evaluate our model with the same approach as before:
+title = "Tuned GBDT model without refitting and using the entire dataset"
+plot_roc_pr_curves(model, tuned_model, title=title)
+
+# %%
+# We observe the that the optimum cut-off point is different from the one found
+# in the previous experiment. If we look at the right-hand side plot, we
+# observe that the business gain has large plateau of near-optimal 0 gain for a
+# large span of decision thresholds. This behavior is symptomatic of an
+# overfitting. Because we disable cross-validation, we tuned the cut-off point
+# on the same set as the model was trained on, and this is the reason for the
+# observed overfitting.
+#
+# This option should therefore be used with caution. One needs to make sure that the
+# data provided at fitting time to the
+# :class:`~sklearn.model_selection.TunedThresholdClassifierCV` is not the same as the
+# data used to train the underlying classifier. This could happen sometimes when the
+# idea is just to tune the predictive model on a completely new validation set without a
+# costly complete refit.
+#
+# When cross-validation is too costly, a potential alternative is to use a
+# single train-test split by providing a floating number in range `[0, 1]` to the `cv`
+# parameter. It splits the data into a training and testing set. Let's explore this
+# option:
+tuned_model.set_params(cv=0.75).fit(X_train, y_train)
+
+# %%
+title = "Tuned GBDT model without refitting and using the entire dataset"
+plot_roc_pr_curves(model, tuned_model, title=title)
+
+# %%
+# Regarding the cut-off point, we observe that the optimum is similar to the multiple
+# repeated cross-validation case. However, be aware that a single split does not account
+# for the variability of the fit/predict process and thus we are unable to know if there
+# is any variance in the cut-off point. The repeated cross-validation averages out
+# this effect.
+#
+# Another observation concerns the ROC and Precision-Recall curves of the tuned model.
+# As expected, these curves differ from those of the vanilla model, given that we
+# trained the underlying classifier on a subset of the data provided during fitting and
+# reserved a validation set for tuning the cut-off point.
+#
+# Cost-sensitive learning when gains and costs are not constant
+# -------------------------------------------------------------
+#
+# As stated in [2]_, gains and costs are generally not constant in real-world problems.
+# In this section, we use a similar example as in [2]_ for the problem of
+# detecting fraud in credit card transaction records.
+#
+# The credit card dataset
+# ^^^^^^^^^^^^^^^^^^^^^^^
+credit_card = fetch_openml(data_id=1597, as_frame=True, parser="pandas")
+credit_card.frame.info()
+
+# %%
+# The dataset contains information about credit card records from which some are
+# fraudulent and others are legitimate. The goal is therefore to predict whether or
+# not a credit card record is fraudulent.
+columns_to_drop = ["Class"]
+data = credit_card.frame.drop(columns=columns_to_drop)
+target = credit_card.frame["Class"].astype(int)
+
+# %%
+# First, we check the class distribution of the datasets.
+target.value_counts(normalize=True)
+
+# %%
+# The dataset is highly imbalanced with fraudulent transaction representing only 0.17%
+# of the data. Since we are interested in training a machine learning model, we should
+# also make sure that we have enough samples in the minority class to train the model.
+target.value_counts()
+
+# %%
+# We observe that we have around 500 samples that is on the low end of the number of
+# samples required to train a machine learning model. In addition of the target
+# distribution, we check the distribution of the amount of the
+# fraudulent transactions.
+fraud = target == 1
+amount_fraud = data["Amount"][fraud]
+_, ax = plt.subplots()
+ax.hist(amount_fraud, bins=100)
+ax.set_title("Amount of fraud transaction")
+_ = ax.set_xlabel("Amount (€)")
+
+# %%
+# Addressing the problem with a business metric
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+#
+# Now, we create the business metric that depends on the amount of each transaction. We
+# define the cost matrix similarly to [2]_. Accepting a legitimate transaction provides
+# a gain of 2% of the amount of the transaction. However, accepting a fraudulent
+# transaction result in a loss of the amount of the transaction. As stated in [2]_, the
+# gain and loss related to refusals (of fraudulent and legitimate transactions) are not
+# trivial to define. Here, we define that a refusal of a legitimate transaction is
+# estimated to a loss of 5€ while the refusal of a fraudulent transaction is estimated
+# to a gain of 50€ and the amount of the transaction. Therefore, we define the
+# following function to compute the total benefit of a given decision:
+
+
+def business_metric(y_true, y_pred, amount):
+    mask_true_positive = (y_true == 1) & (y_pred == 1)
+    mask_true_negative = (y_true == 0) & (y_pred == 0)
+    mask_false_positive = (y_true == 0) & (y_pred == 1)
+    mask_false_negative = (y_true == 1) & (y_pred == 0)
+    fraudulent_refuse = (mask_true_positive.sum() * 50) + amount[
+        mask_true_positive
+    ].sum()
+    fraudulent_accept = -amount[mask_false_negative].sum()
+    legitimate_refuse = mask_false_positive.sum() * -5
+    legitimate_accept = (amount[mask_true_negative] * 0.02).sum()
+    return fraudulent_refuse + fraudulent_accept + legitimate_refuse + legitimate_accept
+
+
+# %%
+# From this business metric, we create a scikit-learn scorer that given a fitted
+# classifier and a test set compute the business metric. In this regard, we use
+# the :func:`~sklearn.metrics.make_scorer` factory. The variable `amount` is an
+# additional metadata to be passed to the scorer and we need to use
+# :ref:`metadata routing <metadata_routing>` to take into account this information.
+sklearn.set_config(enable_metadata_routing=True)
+business_scorer = make_scorer(business_metric).set_score_request(amount=True)
+
+# %%
+# So at this stage, we observe that the amount of the transaction is used twice: once
+# as a feature to train our predictive model and once as a metadata to compute the
+# the business metric and thus the statistical performance of our model. When used as a
+# feature, we are only required to have a column in `data` that contains the amount of
+# each transaction. To use this information as metadata, we need to have an external
+# variable that we can pass to the scorer or the model that internally routes this
+# metadata to the scorer. So let's create this variable.
+amount = credit_card.frame["Amount"].to_numpy()
+
+# %%
+# We first start to train a dummy classifier to have some baseline results.
+from sklearn.model_selection import train_test_split
+
+data_train, data_test, target_train, target_test, amount_train, amount_test = (
+    train_test_split(
+        data, target, amount, stratify=target, test_size=0.5, random_state=42
+    )
+)
+
+# %%
+from sklearn.dummy import DummyClassifier
+
+easy_going_classifier = DummyClassifier(strategy="constant", constant=0)
+easy_going_classifier.fit(data_train, target_train)
+benefit_cost = business_scorer(
+    easy_going_classifier, data_test, target_test, amount=amount_test
+)
+print(f"Benefit/cost of our easy-going classifier: {benefit_cost:,.2f}€")
+
+# %%
+# A classifier that predict all transactions as legitimate would create a profit of
+# around 220,000.€ We make the same evaluation for a classifier that predicts all
+# transactions as fraudulent.
+intolerant_classifier = DummyClassifier(strategy="constant", constant=1)
+intolerant_classifier.fit(data_train, target_train)
+benefit_cost = business_scorer(
+    intolerant_classifier, data_test, target_test, amount=amount_test
+)
+print(f"Benefit/cost of our intolerant classifier: {benefit_cost:,.2f}€")
+
+# %%
+# Such a classifier create a loss of around 670,000.€ A predictive model should allow
+# us to make a profit larger than 220,000.€ It is interesting to compare this business
+# metric with another "standard" statistical metric such as the balanced accuracy.
+from sklearn.metrics import get_scorer
+
+balanced_accuracy_scorer = get_scorer("balanced_accuracy")
+print(
+    "Balanced accuracy of our easy-going classifier: "
+    f"{balanced_accuracy_scorer(easy_going_classifier, data_test, target_test):.3f}"
+)
+print(
+    "Balanced accuracy of our intolerant classifier: "
+    f"{balanced_accuracy_scorer(intolerant_classifier, data_test, target_test):.3f}"
+)
+
+# %%
+# This is not a surprise that the balanced accuracy is at 0.5 for both classifiers.
+# However, we need to be careful in the rest of the evaluation: we potentially can
+# obtain a model with a decent balanced accuracy that does not make any profit.
+# In this case, the model would be harmful for our business.
+#
+# Let's now create a predictive model using a logistic regression without tuning the
+# decision threshold.
+from sklearn.linear_model import LogisticRegression
+from sklearn.model_selection import GridSearchCV
+from sklearn.pipeline import make_pipeline
+from sklearn.preprocessing import StandardScaler
+
+logistic_regression = make_pipeline(StandardScaler(), LogisticRegression())
+param_grid = {"logisticregression__C": np.logspace(-6, 6, 13)}
+model = GridSearchCV(logistic_regression, param_grid, scoring="neg_log_loss").fit(
+    data_train, target_train
+)
+
+print(
+    "Benefit/cost of our logistic regression: "
+    f"{business_scorer(model, data_test, target_test, amount=amount_test):,.2f}€"
+)
+print(
+    "Balanced accuracy of our logistic regression: "
+    f"{balanced_accuracy_scorer(model, data_test, target_test):.3f}"
+)
+
+# %%
+# By observing the balanced accuracy, we see that our predictive model is learning
+# some associations between the features and the target. The business metric also shows
+# that our model is beating the baseline in terms of profit and it would be already
+# beneficial to use it instead of ignoring the fraud detection problem.
+#
+# Tuning the decision threshold
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+#
+# Now the question is: is our model optimum for the type of decision that we want to do?
+# Up to now, we did not optimize the decision threshold. We use the
+# :class:`~sklearn.model_selection.TunedThresholdClassifierCV` to optimize the decision
+# given our business scorer. To avoid a nested cross-validation, we will use the
+# best estimator found during the previous grid-search.
+tuned_model = TunedThresholdClassifierCV(
+    estimator=model.best_estimator_,
+    scoring=business_scorer,
+    thresholds=100,
+    n_jobs=2,
+)
+
+# %%
+# Since our business scorer requires the amount of each transaction, we need to pass
+# this information in the `fit` method. The
+# :class:`~sklearn.model_selection.TunedThresholdClassifierCV` is in charge of
+# automatically dispatching this metadata to the underlying scorer.
+tuned_model.fit(data_train, target_train, amount=amount_train)
+
+# %%
+print(
+    "Benefit/cost of our logistic regression: "
+    f"{business_scorer(tuned_model, data_test, target_test, amount=amount_test):,.2f}€"
+)
+print(
+    "Balanced accuracy of our logistic regression: "
+    f"{balanced_accuracy_scorer(tuned_model, data_test, target_test):.3f}"
+)
+
+# %%
+# We observe that tuning the decision threshold increases the expected profit of
+# deploying our model as estimated by the business metric.
+# Eventually, the balanced accuracy also increased. Note that it might not always be
+# the case because the statistical metric is not necessarily a surrogate of the
+# business metric. It is therefore important, whenever possible, optimize the decision
+# threshold with respect to the business metric.
+#
+# Finally, the estimate of the business metric itself can be unreliable, in
+# particular when the number of data points in the minority class is so small.
+# Any business impact estimated by cross-validation of a business metric on
+# historical data (offline evaluation) should ideally be confirmed by A/B testing
+# on live data (online evaluation). Note however that A/B testing models is
+# beyond the scope of the scikit-learn library itself.
+#
+# Manually setting the decision threshold instead of tuning it
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+#
+# In the previous example, we used the
+# :class:`~sklearn.model_selection.TunedThresholdClassifierCV` to find the optimal
+# decision threshold. However, in some cases, we might have some prior knowledge about
+# the problem at hand and we might be happy to set the decision threshold manually.
+#
+# The class :class:`~sklearn.model_selection.FixedThresholdClassifier` allows us to
+# manually set the decision threshold. At prediction time, it behave as the previous
+# tuned model but no search is performed during the fitting process.
+#
+# Here, we will reuse the decision threshold found in the previous section to create a
+# new model and check that it gives the same results.
+from sklearn.model_selection import FixedThresholdClassifier
+
+model_fixed_threshold = FixedThresholdClassifier(
+    estimator=model, threshold=tuned_model.best_threshold_
+).fit(data_train, target_train)
+
+# %%
+business_score = business_scorer(
+    model_fixed_threshold, data_test, target_test, amount=amount_test
+)
+print(f"Benefit/cost of our logistic regression: {business_score:,.2f}€")
+print(
+    "Balanced accuracy of our logistic regression: "
+    f"{balanced_accuracy_scorer(model_fixed_threshold, data_test, target_test):.3f}"
+)
+
+# %%
+# We observe that we obtained the exact same results but the fitting process was much
+# faster since we did not perform any search.
diff --git a/examples/model_selection/plot_cv_indices.py b/examples/model_selection/plot_cv_indices.py
index e6c3580c787f0..d456546891069 100644
--- a/examples/model_selection/plot_cv_indices.py
+++ b/examples/model_selection/plot_cv_indices.py
@@ -99,9 +99,10 @@ def visualize_groups(classes, groups, name):
 
 def plot_cv_indices(cv, X, y, group, ax, n_splits, lw=10):
     """Create a sample plot for indices of a cross-validation object."""
-
+    use_groups = "Group" in type(cv).__name__
+    groups = group if use_groups else None
     # Generate the training/testing visualizations for each CV split
-    for ii, (tr, tt) in enumerate(cv.split(X=X, y=y, groups=group)):
+    for ii, (tr, tt) in enumerate(cv.split(X=X, y=y, groups=groups)):
         # Fill in indices with the training/test groups
         indices = np.array([np.nan] * len(X))
         indices[tt] = 1
diff --git a/examples/model_selection/plot_grid_search_stats.py b/examples/model_selection/plot_grid_search_stats.py
index fbeb485d8db44..9335b12055515 100644
--- a/examples/model_selection/plot_grid_search_stats.py
+++ b/examples/model_selection/plot_grid_search_stats.py
@@ -540,27 +540,27 @@ def compute_corrected_ttest(differences, df, n_train, n_test):
 #   correction is needed when using the frequentist approach.
 
 # %%
-# .. topic:: References
-#
-#    .. [1] Dietterich, T. G. (1998). `Approximate statistical tests for
-#           comparing supervised classification learning algorithms
-#           <http://web.cs.iastate.edu/~jtian/cs573/Papers/Dietterich-98.pdf>`_.
-#           Neural computation, 10(7).
-#    .. [2] Nadeau, C., & Bengio, Y. (2000). `Inference for the generalization
-#           error
-#           <https://papers.nips.cc/paper/1661-inference-for-the-generalization-error.pdf>`_.
-#           In Advances in neural information processing systems.
-#    .. [3] Bouckaert, R. R., & Frank, E. (2004). `Evaluating the replicability
-#           of significance tests for comparing learning algorithms
-#           <https://www.cms.waikato.ac.nz/~ml/publications/2004/bouckaert-frank.pdf>`_.
-#           In Pacific-Asia Conference on Knowledge Discovery and Data Mining.
-#    .. [4] Benavoli, A., Corani, G., Demšar, J., & Zaffalon, M. (2017). `Time
-#           for a change: a tutorial for comparing multiple classifiers through
-#           Bayesian analysis
-#           <http://www.jmlr.org/papers/volume18/16-305/16-305.pdf>`_.
-#           The Journal of Machine Learning Research, 18(1). See the Python
-#           library that accompanies this paper `here
-#           <https://github.com/janezd/baycomp>`_.
-#    .. [5] Diebold, F.X. & Mariano R.S. (1995). `Comparing predictive accuracy
-#           <http://www.est.uc3m.es/esp/nueva_docencia/comp_col_get/lade/tecnicas_prediccion/Practicas0708/Comparing%20Predictive%20Accuracy%20(Dielbold).pdf>`_
-#           Journal of Business & economic statistics, 20(1), 134-144.
+# .. rubric:: References
+#
+# .. [1] Dietterich, T. G. (1998). `Approximate statistical tests for
+#        comparing supervised classification learning algorithms
+#        <http://web.cs.iastate.edu/~jtian/cs573/Papers/Dietterich-98.pdf>`_.
+#        Neural computation, 10(7).
+# .. [2] Nadeau, C., & Bengio, Y. (2000). `Inference for the generalization
+#        error
+#        <https://papers.nips.cc/paper/1661-inference-for-the-generalization-error.pdf>`_.
+#        In Advances in neural information processing systems.
+# .. [3] Bouckaert, R. R., & Frank, E. (2004). `Evaluating the replicability
+#        of significance tests for comparing learning algorithms
+#        <https://www.cms.waikato.ac.nz/~ml/publications/2004/bouckaert-frank.pdf>`_.
+#        In Pacific-Asia Conference on Knowledge Discovery and Data Mining.
+# .. [4] Benavoli, A., Corani, G., Demšar, J., & Zaffalon, M. (2017). `Time
+#        for a change: a tutorial for comparing multiple classifiers through
+#        Bayesian analysis
+#        <http://www.jmlr.org/papers/volume18/16-305/16-305.pdf>`_.
+#        The Journal of Machine Learning Research, 18(1). See the Python
+#        library that accompanies this paper `here
+#        <https://github.com/janezd/baycomp>`_.
+# .. [5] Diebold, F.X. & Mariano R.S. (1995). `Comparing predictive accuracy
+#        <http://www.est.uc3m.es/esp/nueva_docencia/comp_col_get/lade/tecnicas_prediccion/Practicas0708/Comparing%20Predictive%20Accuracy%20(Dielbold).pdf>`_
+#        Journal of Business & economic statistics, 20(1), 134-144.
diff --git a/examples/model_selection/plot_nested_cross_validation_iris.py b/examples/model_selection/plot_nested_cross_validation_iris.py
index 7513a078b68ce..38defdadf4165 100644
--- a/examples/model_selection/plot_nested_cross_validation_iris.py
+++ b/examples/model_selection/plot_nested_cross_validation_iris.py
@@ -30,17 +30,17 @@
 performance of non-nested and nested CV strategies by taking the difference
 between their scores.
 
-.. topic:: See Also:
+.. seealso::
 
     - :ref:`cross_validation`
     - :ref:`grid_search`
 
-.. topic:: References:
+.. rubric:: References
 
-    .. [1] `Cawley, G.C.; Talbot, N.L.C. On over-fitting in model selection and
-     subsequent selection bias in performance evaluation.
-     J. Mach. Learn. Res 2010,11, 2079-2107.
-     <http://jmlr.csail.mit.edu/papers/volume11/cawley10a/cawley10a.pdf>`_
+.. [1] `Cawley, G.C.; Talbot, N.L.C. On over-fitting in model selection and
+    subsequent selection bias in performance evaluation.
+    J. Mach. Learn. Res 2010,11, 2079-2107.
+    <http://jmlr.csail.mit.edu/papers/volume11/cawley10a/cawley10a.pdf>`_
 
 """
 
diff --git a/examples/model_selection/plot_permutation_tests_for_classification.py b/examples/model_selection/plot_permutation_tests_for_classification.py
index a02f6d188f006..102dfaf1fb466 100644
--- a/examples/model_selection/plot_permutation_tests_for_classification.py
+++ b/examples/model_selection/plot_permutation_tests_for_classification.py
@@ -128,10 +128,10 @@
 # Finally, note that this test has been shown to produce low p-values even
 # if there is only weak structure in the data [1]_.
 #
-# .. topic:: References:
+# .. rubric:: References
 #
-#   .. [1] Ojala and Garriga. `Permutation Tests for Studying Classifier
-#          Performance
-#          <http://www.jmlr.org/papers/volume11/ojala10a/ojala10a.pdf>`_. The
-#          Journal of Machine Learning Research (2010) vol. 11
+# .. [1] Ojala and Garriga. `Permutation Tests for Studying Classifier
+#        Performance
+#        <http://www.jmlr.org/papers/volume11/ojala10a/ojala10a.pdf>`_. The
+#        Journal of Machine Learning Research (2010) vol. 11
 #
diff --git a/examples/model_selection/plot_tuned_decision_threshold.py b/examples/model_selection/plot_tuned_decision_threshold.py
new file mode 100644
index 0000000000000..7e997ee255e4d
--- /dev/null
+++ b/examples/model_selection/plot_tuned_decision_threshold.py
@@ -0,0 +1,184 @@
+"""
+======================================================
+Post-hoc tuning the cut-off point of decision function
+======================================================
+
+Once a binary classifier is trained, the :term:`predict` method outputs class label
+predictions corresponding to a thresholding of either the :term:`decision_function` or
+the :term:`predict_proba` output. The default threshold is defined as a posterior
+probability estimate of 0.5 or a decision score of 0.0. However, this default strategy
+may not be optimal for the task at hand.
+
+This example shows how to use the
+:class:`~sklearn.model_selection.TunedThresholdClassifierCV` to tune the decision
+threshold, depending on a metric of interest.
+"""
+
+# %%
+# The diabetes dataset
+# --------------------
+#
+# To illustrate the tuning of the decision threshold, we will use the diabetes dataset.
+# This dataset is available on OpenML: https://www.openml.org/d/37. We use the
+# :func:`~sklearn.datasets.fetch_openml` function to fetch this dataset.
+from sklearn.datasets import fetch_openml
+
+diabetes = fetch_openml(data_id=37, as_frame=True, parser="pandas")
+data, target = diabetes.data, diabetes.target
+
+# %%
+# We look at the target to understand the type of problem we are dealing with.
+target.value_counts()
+
+# %%
+# We can see that we are dealing with a binary classification problem. Since the
+# labels are not encoded as 0 and 1, we make it explicit that we consider the class
+# labeled "tested_negative" as the negative class (which is also the most frequent)
+# and the class labeled "tested_positive" the positive as the positive class:
+neg_label, pos_label = target.value_counts().index
+
+# %%
+# We can also observe that this binary problem is slightly imbalanced where we have
+# around twice more samples from the negative class than from the positive class. When
+# it comes to evaluation, we should consider this aspect to interpret the results.
+#
+# Our vanilla classifier
+# ----------------------
+#
+# We define a basic predictive model composed of a scaler followed by a logistic
+# regression classifier.
+from sklearn.linear_model import LogisticRegression
+from sklearn.pipeline import make_pipeline
+from sklearn.preprocessing import StandardScaler
+
+model = make_pipeline(StandardScaler(), LogisticRegression())
+model
+
+# %%
+# We evaluate our model using cross-validation. We use the accuracy and the balanced
+# accuracy to report the performance of our model. The balanced accuracy is a metric
+# that is less sensitive to class imbalance and will allow us to put the accuracy
+# score in perspective.
+#
+# Cross-validation allows us to study the variance of the decision threshold across
+# different splits of the data. However, the dataset is rather small and it would be
+# detrimental to use more than 5 folds to evaluate the dispersion. Therefore, we use
+# a :class:`~sklearn.model_selection.RepeatedStratifiedKFold` where we apply several
+# repetitions of 5-fold cross-validation.
+import pandas as pd
+
+from sklearn.model_selection import RepeatedStratifiedKFold, cross_validate
+
+scoring = ["accuracy", "balanced_accuracy"]
+cv_scores = [
+    "train_accuracy",
+    "test_accuracy",
+    "train_balanced_accuracy",
+    "test_balanced_accuracy",
+]
+cv = RepeatedStratifiedKFold(n_splits=5, n_repeats=10, random_state=42)
+cv_results_vanilla_model = pd.DataFrame(
+    cross_validate(
+        model,
+        data,
+        target,
+        scoring=scoring,
+        cv=cv,
+        return_train_score=True,
+        return_estimator=True,
+    )
+)
+cv_results_vanilla_model[cv_scores].aggregate(["mean", "std"]).T
+
+# %%
+# Our predictive model succeeds to grasp the relationship between the data and the
+# target. The training and testing scores are close to each other, meaning that our
+# predictive model is not overfitting. We can also observe that the balanced accuracy is
+# lower than the accuracy, due to the class imbalance previously mentioned.
+#
+# For this classifier, we let the decision threshold, used convert the probability of
+# the positive class into a class prediction, to its default value: 0.5. However, this
+# threshold might not be optimal. If our interest is to maximize the balanced accuracy,
+# we should select another threshold that would maximize this metric.
+#
+# The :class:`~sklearn.model_selection.TunedThresholdClassifierCV` meta-estimator allows
+# to tune the decision threshold of a classifier given a metric of interest.
+#
+# Tuning the decision threshold
+# -----------------------------
+#
+# We create a :class:`~sklearn.model_selection.TunedThresholdClassifierCV` and
+# configure it to maximize the balanced accuracy. We evaluate the model using the same
+# cross-validation strategy as previously.
+from sklearn.model_selection import TunedThresholdClassifierCV
+
+tuned_model = TunedThresholdClassifierCV(estimator=model, scoring="balanced_accuracy")
+cv_results_tuned_model = pd.DataFrame(
+    cross_validate(
+        tuned_model,
+        data,
+        target,
+        scoring=scoring,
+        cv=cv,
+        return_train_score=True,
+        return_estimator=True,
+    )
+)
+cv_results_tuned_model[cv_scores].aggregate(["mean", "std"]).T
+
+# %%
+# In comparison with the vanilla model, we observe that the balanced accuracy score
+# increased. Of course, it comes at the cost of a lower accuracy score. It means that
+# our model is now more sensitive to the positive class but makes more mistakes on the
+# negative class.
+#
+# However, it is important to note that this tuned predictive model is internally the
+# same model as the vanilla model: they have the same fitted coefficients.
+import matplotlib.pyplot as plt
+
+vanilla_model_coef = pd.DataFrame(
+    [est[-1].coef_.ravel() for est in cv_results_vanilla_model["estimator"]],
+    columns=diabetes.feature_names,
+)
+tuned_model_coef = pd.DataFrame(
+    [est.estimator_[-1].coef_.ravel() for est in cv_results_tuned_model["estimator"]],
+    columns=diabetes.feature_names,
+)
+
+fig, ax = plt.subplots(ncols=2, figsize=(12, 4), sharex=True, sharey=True)
+vanilla_model_coef.boxplot(ax=ax[0])
+ax[0].set_ylabel("Coefficient value")
+ax[0].set_title("Vanilla model")
+tuned_model_coef.boxplot(ax=ax[1])
+ax[1].set_title("Tuned model")
+_ = fig.suptitle("Coefficients of the predictive models")
+
+# %%
+# Only the decision threshold of each model was changed during the cross-validation.
+decision_threshold = pd.Series(
+    [est.best_threshold_ for est in cv_results_tuned_model["estimator"]],
+)
+ax = decision_threshold.plot.kde()
+ax.axvline(
+    decision_threshold.mean(),
+    color="k",
+    linestyle="--",
+    label=f"Mean decision threshold: {decision_threshold.mean():.2f}",
+)
+ax.set_xlabel("Decision threshold")
+ax.legend(loc="upper right")
+_ = ax.set_title(
+    "Distribution of the decision threshold \nacross different cross-validation folds"
+)
+
+# %%
+# In average, a decision threshold around 0.32 maximizes the balanced accuracy, which is
+# different from the default decision threshold of 0.5. Thus tuning the decision
+# threshold is particularly important when the output of the predictive model
+# is used to make decisions. Besides, the metric used to tune the decision threshold
+# should be chosen carefully. Here, we used the balanced accuracy but it might not be
+# the most appropriate metric for the problem at hand. The choice of the "right" metric
+# is usually problem-dependent and might require some domain knowledge. Refer to the
+# example entitled,
+# :ref:`sphx_glr_auto_examples_model_selection_plot_cost_sensitive_learning.py`,
+# for more details.
diff --git a/examples/release_highlights/plot_release_highlights_1_1_0.py b/examples/release_highlights/plot_release_highlights_1_1_0.py
index 44f85a8bbdf8b..16b359a9b03e7 100644
--- a/examples/release_highlights/plot_release_highlights_1_1_0.py
+++ b/examples/release_highlights/plot_release_highlights_1_1_0.py
@@ -24,8 +24,8 @@
 # %%
 # .. _quantile_support_hgbdt:
 #
-# Quantile loss in :class:`ensemble.HistGradientBoostingRegressor`
-# ----------------------------------------------------------------
+# Quantile loss in :class:`~ensemble.HistGradientBoostingRegressor`
+# -----------------------------------------------------------------
 # :class:`~ensemble.HistGradientBoostingRegressor` can model quantiles with
 # `loss="quantile"` and the new parameter `quantile`.
 from sklearn.ensemble import HistGradientBoostingRegressor
diff --git a/examples/release_highlights/plot_release_highlights_1_3_0.py b/examples/release_highlights/plot_release_highlights_1_3_0.py
index 8521ac3554c46..ebb109e524f1d 100644
--- a/examples/release_highlights/plot_release_highlights_1_3_0.py
+++ b/examples/release_highlights/plot_release_highlights_1_3_0.py
@@ -101,8 +101,8 @@
 tree.predict(X)
 
 # %%
-# New display `model_selection.ValidationCurveDisplay`
-# ----------------------------------------------------
+# New display :class:`~model_selection.ValidationCurveDisplay`
+# ------------------------------------------------------------
 # :class:`model_selection.ValidationCurveDisplay` is now available to plot results
 # from :func:`model_selection.validation_curve`.
 from sklearn.datasets import make_classification
@@ -141,8 +141,8 @@
 cross_val_score(gbdt, X, y).mean()
 
 # %%
-# Grouping infrequent categories in :class:`preprocessing.OrdinalEncoder`
-# -----------------------------------------------------------------------
+# Grouping infrequent categories in :class:`~preprocessing.OrdinalEncoder`
+# ------------------------------------------------------------------------
 # Similarly to :class:`preprocessing.OneHotEncoder`, the class
 # :class:`preprocessing.OrdinalEncoder` now supports aggregating infrequent categories
 # into a single output for each feature. The parameters to enable the gathering of
diff --git a/examples/release_highlights/plot_release_highlights_1_5_0.py b/examples/release_highlights/plot_release_highlights_1_5_0.py
new file mode 100644
index 0000000000000..20ca85a8002da
--- /dev/null
+++ b/examples/release_highlights/plot_release_highlights_1_5_0.py
@@ -0,0 +1,230 @@
+# ruff: noqa
+"""
+=======================================
+Release Highlights for scikit-learn 1.5
+=======================================
+
+.. currentmodule:: sklearn
+
+We are pleased to announce the release of scikit-learn 1.5! Many bug fixes
+and improvements were added, as well as some key new features. Below we
+detail the highlights of this release. **For an exhaustive list of
+all the changes**, please refer to the :ref:`release notes <release_notes_1_5>`.
+
+To install the latest version (with pip)::
+
+    pip install --upgrade scikit-learn
+
+or with conda::
+
+    conda install -c conda-forge scikit-learn
+
+"""
+
+# %%
+# FixedThresholdClassifier: Setting the decision threshold of a binary classifier
+# -------------------------------------------------------------------------------
+# All binary classifiers of scikit-learn use a fixed decision threshold of 0.5
+# to convert probability estimates (i.e. output of `predict_proba`) into class
+# predictions. However, 0.5 is almost never the desired threshold for a given
+# problem. :class:`~model_selection.FixedThresholdClassifier` allows wrapping any
+# binary classifier and setting a custom decision threshold.
+from sklearn.datasets import make_classification
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LogisticRegression
+from sklearn.metrics import ConfusionMatrixDisplay
+
+
+X, y = make_classification(n_samples=10_000, weights=[0.9, 0.1], random_state=0)
+X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
+
+classifier_05 = LogisticRegression(C=1e6, random_state=0).fit(X_train, y_train)
+_ = ConfusionMatrixDisplay.from_estimator(classifier_05, X_test, y_test)
+
+# %%
+# Lowering the threshold, i.e. allowing more samples to be classified as the positive
+# class, increases the number of true positives at the cost of more false positives
+# (as is well known from the concavity of the ROC curve).
+from sklearn.model_selection import FixedThresholdClassifier
+
+classifier_01 = FixedThresholdClassifier(classifier_05, threshold=0.1)
+classifier_01.fit(X_train, y_train)
+_ = ConfusionMatrixDisplay.from_estimator(classifier_01, X_test, y_test)
+
+# %%
+# TunedThresholdClassifierCV: Tuning the decision threshold of a binary classifier
+# --------------------------------------------------------------------------------
+# The decision threshold of a binary classifier can be tuned to optimize a
+# given metric, using :class:`~model_selection.TunedThresholdClassifierCV`.
+#
+# It is particularly useful to find the best decision threshold when the model
+# is meant to be deployed in a specific application context where we can assign
+# different gains or costs for true positives, true negatives, false positives,
+# and false negatives.
+#
+# Let's illustrate this by considering an arbitrary case where:
+#
+# - each true positive gains 1 unit of profit, e.g. euro, year of life in good
+#   health, etc.;
+# - true negatives gain or cost nothing;
+# - each false negative costs 2;
+# - each false positive costs 0.1.
+#
+# Our metric quantifies the average profit per sample, which is defined by the
+# following Python function:
+from sklearn.metrics import confusion_matrix
+
+
+def custom_score(y_observed, y_pred):
+    tn, fp, fn, tp = confusion_matrix(y_observed, y_pred, normalize="all").ravel()
+    return tp - 2 * fn - 0.1 * fp
+
+
+print("Untuned decision threshold: 0.5")
+print(f"Custom score: {custom_score(y_test, classifier_05.predict(X_test)):.2f}")
+
+# %%
+# It is interesting to observe that the average gain per prediction is negative
+# which means that this decision system is making a loss on average.
+#
+# Tuning the threshold to optimize this custom metric gives a smaller threshold
+# that allows more samples to be classified as the positive class. As a result,
+# the average gain per prediction improves.
+from sklearn.model_selection import TunedThresholdClassifierCV
+from sklearn.metrics import make_scorer
+
+custom_scorer = make_scorer(
+    custom_score, response_method="predict", greater_is_better=True
+)
+tuned_classifier = TunedThresholdClassifierCV(
+    classifier_05, cv=5, scoring=custom_scorer
+).fit(X, y)
+
+print(f"Tuned decision threshold: {tuned_classifier.best_threshold_:.3f}")
+print(f"Custom score: {custom_score(y_test, tuned_classifier.predict(X_test)):.2f}")
+
+# %%
+# We observe that tuning the decision threshold can turn a machine
+# learning-based system that makes a loss on average into a beneficial one.
+#
+# In practice, defining a meaningful application-specific metric might involve
+# making those costs for bad predictions and gains for good predictions depend on
+# auxiliary metadata specific to each individual data point such as the amount
+# of a transaction in a fraud detection system.
+#
+# To achieve this, :class:`~model_selection.TunedThresholdClassifierCV`
+# leverages metadata routing support (:ref:`Metadata Routing User
+# Guide<metadata_routing>`) allowing to optimize complex business metrics as
+# detailed in :ref:`Post-tuning the decision threshold for cost-sensitive
+# learning
+# <sphx_glr_auto_examples_model_selection_plot_cost_sensitive_learning.py>`.
+
+# %%
+# Performance improvements in PCA
+# -------------------------------
+# :class:`~decomposition.PCA` has a new solver, `"covariance_eigh"`, which is
+# up to an order of magnitude faster and more memory efficient than the other
+# solvers for datasets with many data points and few features.
+from sklearn.datasets import make_low_rank_matrix
+from sklearn.decomposition import PCA
+
+X = make_low_rank_matrix(
+    n_samples=10_000, n_features=100, tail_strength=0.1, random_state=0
+)
+
+pca = PCA(n_components=10, svd_solver="covariance_eigh").fit(X)
+print(f"Explained variance: {pca.explained_variance_ratio_.sum():.2f}")
+
+
+# %%
+# The new solver also accepts sparse input data:
+from scipy.sparse import random
+
+X = random(10_000, 100, format="csr", random_state=0)
+
+pca = PCA(n_components=10, svd_solver="covariance_eigh").fit(X)
+print(f"Explained variance: {pca.explained_variance_ratio_.sum():.2f}")
+
+# %%
+# The `"full"` solver has also been improved to use less memory and allows
+# faster transformation. The default `svd_solver="auto"`` option takes
+# advantage of the new solver and is now able to select an appropriate solver
+# for sparse datasets.
+#
+# Similarly to most other PCA solvers, the new `"covariance_eigh"` solver can leverage
+# GPU computation if the input data is passed as a PyTorch or CuPy array by
+# enabling the experimental support for :ref:`Array API <array_api>`.
+
+# %%
+# ColumnTransformer is subscriptable
+# ----------------------------------
+# The transformers of a :class:`~compose.ColumnTransformer` can now be directly
+# accessed using indexing by name.
+import numpy as np
+from sklearn.compose import ColumnTransformer
+from sklearn.preprocessing import StandardScaler, OneHotEncoder
+
+X = np.array([[0, 1, 2], [3, 4, 5]])
+column_transformer = ColumnTransformer(
+    [("std_scaler", StandardScaler(), [0]), ("one_hot", OneHotEncoder(), [1, 2])]
+)
+
+column_transformer.fit(X)
+
+print(column_transformer["std_scaler"])
+print(column_transformer["one_hot"])
+
+# %%
+# Custom imputation strategies for the SimpleImputer
+# --------------------------------------------------
+# :class:`~impute.SimpleImputer` now supports custom strategies for imputation,
+# using a callable that computes a scalar value from the non missing values of
+# a column vector.
+from sklearn.impute import SimpleImputer
+
+X = np.array(
+    [
+        [-1.1, 1.1, 1.1],
+        [3.9, -1.2, np.nan],
+        [np.nan, 1.3, np.nan],
+        [-0.1, -1.4, -1.4],
+        [-4.9, 1.5, -1.5],
+        [np.nan, 1.6, 1.6],
+    ]
+)
+
+
+def smallest_abs(arr):
+    """Return the smallest absolute value of a 1D array."""
+    return np.min(np.abs(arr))
+
+
+imputer = SimpleImputer(strategy=smallest_abs)
+
+imputer.fit_transform(X)
+
+# %%
+# Pairwise distances with non-numeric arrays
+# ------------------------------------------
+# :func:`~metrics.pairwise_distances` can now compute distances between
+# non-numeric arrays using a callable metric.
+from sklearn.metrics import pairwise_distances
+
+X = ["cat", "dog"]
+Y = ["cat", "fox"]
+
+
+def levenshtein_distance(x, y):
+    """Return the Levenshtein distance between two strings."""
+    if x == "" or y == "":
+        return max(len(x), len(y))
+    if x[0] == y[0]:
+        return levenshtein_distance(x[1:], y[1:])
+    return 1 + min(
+        levenshtein_distance(x[1:], y),
+        levenshtein_distance(x, y[1:]),
+        levenshtein_distance(x[1:], y[1:]),
+    )
+
+
+pairwise_distances(X, Y, metric=levenshtein_distance)
diff --git a/examples/svm/plot_svm_kernels.py b/examples/svm/plot_svm_kernels.py
index d801e2477e682..a63de6765f083 100644
--- a/examples/svm/plot_svm_kernels.py
+++ b/examples/svm/plot_svm_kernels.py
@@ -110,12 +110,15 @@
 from sklearn.inspection import DecisionBoundaryDisplay
 
 
-def plot_training_data_with_decision_boundary(kernel):
+def plot_training_data_with_decision_boundary(
+    kernel, ax=None, long_title=True, support_vectors=True
+):
     # Train the SVC
     clf = svm.SVC(kernel=kernel, gamma=2).fit(X, y)
 
     # Settings for plotting
-    _, ax = plt.subplots(figsize=(4, 3))
+    if ax is None:
+        _, ax = plt.subplots(figsize=(4, 3))
     x_min, x_max, y_min, y_max = -3, 3, -3, 3
     ax.set(xlim=(x_min, x_max), ylim=(y_min, y_max))
 
@@ -136,20 +139,26 @@ def plot_training_data_with_decision_boundary(kernel):
         linestyles=["--", "-", "--"],
     )
 
-    # Plot bigger circles around samples that serve as support vectors
-    ax.scatter(
-        clf.support_vectors_[:, 0],
-        clf.support_vectors_[:, 1],
-        s=250,
-        facecolors="none",
-        edgecolors="k",
-    )
+    if support_vectors:
+        # Plot bigger circles around samples that serve as support vectors
+        ax.scatter(
+            clf.support_vectors_[:, 0],
+            clf.support_vectors_[:, 1],
+            s=150,
+            facecolors="none",
+            edgecolors="k",
+        )
+
     # Plot samples by color and add legend
-    ax.scatter(X[:, 0], X[:, 1], c=y, s=150, edgecolors="k")
+    ax.scatter(X[:, 0], X[:, 1], c=y, s=30, edgecolors="k")
     ax.legend(*scatter.legend_elements(), loc="upper right", title="Classes")
-    ax.set_title(f" Decision boundaries of {kernel} kernel in SVC")
+    if long_title:
+        ax.set_title(f" Decision boundaries of {kernel} kernel in SVC")
+    else:
+        ax.set_title(kernel)
 
-    _ = plt.show()
+    if ax is None:
+        plt.show()
 
 
 # %%
@@ -237,7 +246,6 @@ def plot_training_data_with_decision_boundary(kernel):
 # using the hyperbolic tangent function (:math:`\tanh`). The kernel function
 # scales and possibly shifts the dot product of the two points
 # (:math:`\mathbf{x}_1` and :math:`\mathbf{x}_2`).
-
 plot_training_data_with_decision_boundary("sigmoid")
 
 # %%
@@ -271,3 +279,26 @@ def plot_training_data_with_decision_boundary(kernel):
 # parameters using techniques such as
 # :class:`~sklearn.model_selection.GridSearchCV` is recommended to capture the
 # underlying structures within the data.
+
+# %%
+# XOR dataset
+# -----------
+# A classical example of a dataset which is not linearly separable is the XOR
+# pattern. HEre we demonstrate how different kernels work on such a dataset.
+
+xx, yy = np.meshgrid(np.linspace(-3, 3, 500), np.linspace(-3, 3, 500))
+np.random.seed(0)
+X = np.random.randn(300, 2)
+y = np.logical_xor(X[:, 0] > 0, X[:, 1] > 0)
+
+_, ax = plt.subplots(2, 2, figsize=(8, 8))
+args = dict(long_title=False, support_vectors=False)
+plot_training_data_with_decision_boundary("linear", ax[0, 0], **args)
+plot_training_data_with_decision_boundary("poly", ax[0, 1], **args)
+plot_training_data_with_decision_boundary("rbf", ax[1, 0], **args)
+plot_training_data_with_decision_boundary("sigmoid", ax[1, 1], **args)
+plt.show()
+
+# %%
+# As you can see from the plots above, only the `rbf` kernel can find a
+# reasonable decision boundary for the above dataset.
diff --git a/examples/svm/plot_svm_nonlinear.py b/examples/svm/plot_svm_nonlinear.py
deleted file mode 100644
index 4990e509661a1..0000000000000
--- a/examples/svm/plot_svm_nonlinear.py
+++ /dev/null
@@ -1,45 +0,0 @@
-"""
-==============
-Non-linear SVM
-==============
-
-Perform binary classification using non-linear SVC
-with RBF kernel. The target to predict is a XOR of the
-inputs.
-
-The color map illustrates the decision function learned by the SVC.
-
-"""
-
-import matplotlib.pyplot as plt
-import numpy as np
-
-from sklearn import svm
-
-xx, yy = np.meshgrid(np.linspace(-3, 3, 500), np.linspace(-3, 3, 500))
-np.random.seed(0)
-X = np.random.randn(300, 2)
-Y = np.logical_xor(X[:, 0] > 0, X[:, 1] > 0)
-
-# fit the model
-clf = svm.NuSVC(gamma="auto")
-clf.fit(X, Y)
-
-# plot the decision function for each datapoint on the grid
-Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])
-Z = Z.reshape(xx.shape)
-
-plt.imshow(
-    Z,
-    interpolation="nearest",
-    extent=(xx.min(), xx.max(), yy.min(), yy.max()),
-    aspect="auto",
-    origin="lower",
-    cmap=plt.cm.PuOr_r,
-)
-contours = plt.contour(xx, yy, Z, levels=[0], linewidths=2, linestyles="dashed")
-plt.scatter(X[:, 0], X[:, 1], s=30, c=Y, cmap=plt.cm.Paired, edgecolors="k")
-plt.xticks(())
-plt.yticks(())
-plt.axis([-3, 3, -3, 3])
-plt.show()
diff --git a/examples/tree/plot_iris_dtc.py b/examples/tree/plot_iris_dtc.py
index 4c54a4119ced3..61c3bec3460ed 100644
--- a/examples/tree/plot_iris_dtc.py
+++ b/examples/tree/plot_iris_dtc.py
@@ -66,7 +66,6 @@
             X[idx, 1],
             c=color,
             label=iris.target_names[i],
-            cmap=plt.cm.RdYlBu,
             edgecolor="black",
             s=15,
         )
diff --git a/meson.build b/meson.build
index 3835a5099abb0..b6b3652a82268 100644
--- a/meson.build
+++ b/meson.build
@@ -6,7 +6,7 @@ project(
   meson_version: '>= 1.1.0',
   default_options: [
     'buildtype=debugoptimized',
-    'c_std=c99',
+    'c_std=c11',
     'cpp_std=c++14',
   ],
 )
diff --git a/pyproject.toml b/pyproject.toml
index 828569ecc71ee..27b0a20dc66d7 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [project]
 name = "scikit-learn"
-version = "1.5.dev0"
+version = "1.6.dev0"
 description = "A set of python modules for machine learning and data mining"
 readme = "README.rst"
 maintainers = [
@@ -10,7 +10,7 @@ dependencies = [
   "numpy>=1.19.5",
   "scipy>=1.6.0",
   "joblib>=1.2.0",
-  "threadpoolctl>=2.0.0",
+  "threadpoolctl>=3.1.0",
 ]
 requires-python = ">=3.9"
 license = {text = "new BSD"}
@@ -44,8 +44,8 @@ tracker = "https://github.com/scikit-learn/scikit-learn/issues"
 "release notes" = "https://scikit-learn.org/stable/whats_new"
 
 [project.optional-dependencies]
-build = ["numpy>=1.19.5", "scipy>=1.6.0", "cython>=3.0.10", "meson-python>=0.15.0"]
-install = ["numpy>=1.19.5", "scipy>=1.6.0", "joblib>=1.2.0", "threadpoolctl>=2.0.0"]
+build = ["numpy>=1.19.5", "scipy>=1.6.0", "cython>=3.0.10", "meson-python>=0.16.0"]
+install = ["numpy>=1.19.5", "scipy>=1.6.0", "joblib>=1.2.0", "threadpoolctl>=3.1.0"]
 benchmark = ["matplotlib>=3.3.4", "pandas>=1.1.5", "memory_profiler>=0.57.0"]
 docs = [
     "matplotlib>=3.3.4",
@@ -53,16 +53,20 @@ docs = [
     "pandas>=1.1.5",
     "seaborn>=0.9.0",
     "memory_profiler>=0.57.0",
-    "sphinx>=6.0.0",
+    "sphinx>=7.3.7",
     "sphinx-copybutton>=0.5.2",
-    "sphinx-gallery>=0.15.0",
+    "sphinx-gallery>=0.16.0",
     "numpydoc>=1.2.0",
     "Pillow>=7.1.2",
     "pooch>=1.6.0",
-    "sphinx-prompt>=1.3.0",
-    "sphinxext-opengraph>=0.4.2",
+    "sphinx-prompt>=1.4.0",
+    "sphinxext-opengraph>=0.9.1",
     "plotly>=5.14.0",
-    "polars>=0.19.12"
+    "polars>=0.20.23",
+    "sphinx-design>=0.5.0",
+    "sphinxcontrib-sass>=0.3.4",
+    "pydata-sphinx-theme>=0.15.3",
+    "sphinx-remove-toctrees>=1.0.0.post1",
 ]
 examples = [
     "matplotlib>=3.3.4",
@@ -82,7 +86,7 @@ tests = [
     "black>=24.3.0",
     "mypy>=1.9",
     "pyamg>=4.0.0",
-    "polars>=0.19.12",
+    "polars>=0.20.23",
     "pyarrow>=12.0.0",
     "numpydoc>=1.2.0",
     "pooch>=1.6.0",
@@ -93,7 +97,7 @@ maintenance = ["conda-lock==2.5.6"]
 build-backend = "mesonpy"
 # Minimum requirements for the build system to execute.
 requires = [
-    "meson-python>=0.15.0",
+    "meson-python>=0.16.0",
     "Cython>=3.0.10",
     "numpy>=1.25",
     "scipy>=1.6.0",
@@ -101,7 +105,7 @@ requires = [
 
 [tool.black]
 line-length = 88
-target_version = ['py39', 'py310', 'py311']
+target-version = ['py39', 'py310', 'py311']
 preview = true
 exclude = '''
 /(
diff --git a/setup.cfg b/setup.cfg
index f2052de285ed6..a11ec816abdc9 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -60,5 +60,5 @@ ignore =
 
 
 [codespell]
-skip = ./.git,./.mypy_cache,./doc/themes/scikit-learn-modern/static/js,./sklearn/feature_extraction/_stop_words.py,./doc/_build,./doc/auto_examples,./doc/modules/generated
+skip = ./.git,./.mypy_cache,./sklearn/feature_extraction/_stop_words.py,./doc/_build,./doc/auto_examples,./doc/modules/generated
 ignore-words = build_tools/codespell_ignore_words.txt
diff --git a/sklearn/__init__.py b/sklearn/__init__.py
index 30a3bbcdcbf66..8e554cca71253 100644
--- a/sklearn/__init__.py
+++ b/sklearn/__init__.py
@@ -1,7 +1,4 @@
-"""
-The :mod:`sklearn` module includes functions to configure global settings and
-get information about the working environment.
-"""
+"""Configure global settings and get information about the working environment."""
 
 # Machine learning module for Python
 # ==================================
@@ -42,7 +39,7 @@
 # Dev branch marker is: 'X.Y.dev' or 'X.Y.devN' where N is an integer.
 # 'X.Y.dev0' is the canonical version of 'X.Y.dev'
 #
-__version__ = "1.5.dev0"
+__version__ = "1.6.dev0"
 
 
 # On OSX, we can get a runtime error due to multiple OpenMP libraries loaded
@@ -73,6 +70,15 @@
     # We are not importing the rest of scikit-learn during the build
     # process, as it may not be compiled yet
 else:
+    # Import numpy, scipy to make sure that the BLAS libs are loaded before
+    # creating the ThreadpoolController. They would be imported just after
+    # when importing utils anyway. This makes it explicit and robust to changes
+    # in utils.
+    # (OpenMP is loaded by importing show_versions right after this block)
+    import numpy  # noqa
+    import scipy.linalg  # noqa
+    from threadpoolctl import ThreadpoolController
+
     # `_distributor_init` allows distributors to run custom init code.
     # For instance, for the Windows wheel, this is used to pre-load the
     # vcomp shared library runtime for OpenMP embedded in the sklearn/.libs
@@ -141,6 +147,12 @@
     except ModuleNotFoundError:
         pass
 
+    # Set a global controller that can be used to locally limit the number of
+    # threads without looping through all shared libraries every time.
+    # This instantitation should not happen earlier because it needs all BLAS and
+    # OpenMP libs to be loaded first.
+    _threadpool_controller = ThreadpoolController()
+
 
 def setup_module(module):
     """Fixture for the tests to assure globally controllable seeding of RNGs"""
diff --git a/sklearn/_build_utils/openmp_helpers.py b/sklearn/_build_utils/openmp_helpers.py
index ed9bf0ea3eea0..66e6089e33fef 100644
--- a/sklearn/_build_utils/openmp_helpers.py
+++ b/sklearn/_build_utils/openmp_helpers.py
@@ -34,7 +34,7 @@ def get_openmp_flag():
 
 def check_openmp_support():
     """Check whether OpenMP test code can be compiled and run"""
-    if "PYODIDE_PACKAGE_ABI" in os.environ:
+    if "PYODIDE" in os.environ:
         # Pyodide doesn't support OpenMP
         return False
 
diff --git a/sklearn/_build_utils/pre_build_helpers.py b/sklearn/_build_utils/pre_build_helpers.py
index b73fa8658739f..8de9b562d916b 100644
--- a/sklearn/_build_utils/pre_build_helpers.py
+++ b/sklearn/_build_utils/pre_build_helpers.py
@@ -60,7 +60,7 @@ def compile_test_program(code, extra_preargs=None, extra_postargs=None):
 
 def basic_check_build():
     """Check basic compilation and linking of C code"""
-    if "PYODIDE_PACKAGE_ABI" in os.environ:
+    if "PYODIDE" in os.environ:
         # The following check won't work in pyodide
         return
 
diff --git a/sklearn/_min_dependencies.py b/sklearn/_min_dependencies.py
index a487a048c53c1..9c108791b45bc 100644
--- a/sklearn/_min_dependencies.py
+++ b/sklearn/_min_dependencies.py
@@ -7,7 +7,7 @@
 NUMPY_MIN_VERSION = "1.19.5"
 SCIPY_MIN_VERSION = "1.6.0"
 JOBLIB_MIN_VERSION = "1.2.0"
-THREADPOOLCTL_MIN_VERSION = "2.0.0"
+THREADPOOLCTL_MIN_VERSION = "3.1.0"
 PYTEST_MIN_VERSION = "7.1.2"
 CYTHON_MIN_VERSION = "3.0.10"
 
@@ -21,7 +21,7 @@
     "joblib": (JOBLIB_MIN_VERSION, "install"),
     "threadpoolctl": (THREADPOOLCTL_MIN_VERSION, "install"),
     "cython": (CYTHON_MIN_VERSION, "build"),
-    "meson-python": ("0.15.0", "build"),
+    "meson-python": ("0.16.0", "build"),
     "matplotlib": ("3.3.4", "benchmark, docs, examples, tests"),
     "scikit-image": ("0.17.2", "docs, examples, tests"),
     "pandas": ("1.1.5", "benchmark, docs, examples, tests"),
@@ -33,17 +33,21 @@
     "black": ("24.3.0", "tests"),
     "mypy": ("1.9", "tests"),
     "pyamg": ("4.0.0", "tests"),
-    "polars": ("0.19.12", "docs, tests"),
+    "polars": ("0.20.23", "docs, tests"),
     "pyarrow": ("12.0.0", "tests"),
-    "sphinx": ("6.0.0", "docs"),
+    "sphinx": ("7.3.7", "docs"),
     "sphinx-copybutton": ("0.5.2", "docs"),
-    "sphinx-gallery": ("0.15.0", "docs"),
+    "sphinx-gallery": ("0.16.0", "docs"),
     "numpydoc": ("1.2.0", "docs, tests"),
     "Pillow": ("7.1.2", "docs"),
     "pooch": ("1.6.0", "docs, examples, tests"),
-    "sphinx-prompt": ("1.3.0", "docs"),
-    "sphinxext-opengraph": ("0.4.2", "docs"),
+    "sphinx-prompt": ("1.4.0", "docs"),
+    "sphinxext-opengraph": ("0.9.1", "docs"),
     "plotly": ("5.14.0", "docs, examples"),
+    "sphinxcontrib-sass": ("0.3.4", "docs"),
+    "sphinx-remove-toctrees": ("1.0.0.post1", "docs"),
+    "sphinx-design": ("0.5.0", "docs"),
+    "pydata-sphinx-theme": ("0.15.3", "docs"),
     # XXX: Pin conda-lock to the latest released version (needs manual update
     # from time to time)
     "conda-lock": ("2.5.6", "maintenance"),
diff --git a/sklearn/base.py b/sklearn/base.py
index d0f861bd2278f..d4245ade4e499 100644
--- a/sklearn/base.py
+++ b/sklearn/base.py
@@ -1,4 +1,4 @@
-"""Base classes for all estimators."""
+"""Base classes for all estimators and various utility functions."""
 
 # Author: Gael Varoquaux <gael.varoquaux@normalesup.org>
 # License: BSD 3 clause
@@ -1374,13 +1374,17 @@ def is_classifier(estimator):
     Examples
     --------
     >>> from sklearn.base import is_classifier
+    >>> from sklearn.cluster import KMeans
     >>> from sklearn.svm import SVC, SVR
     >>> classifier = SVC()
     >>> regressor = SVR()
+    >>> kmeans = KMeans()
     >>> is_classifier(classifier)
     True
     >>> is_classifier(regressor)
     False
+    >>> is_classifier(kmeans)
+    False
     """
     return getattr(estimator, "_estimator_type", None) == "classifier"
 
@@ -1401,17 +1405,54 @@ def is_regressor(estimator):
     Examples
     --------
     >>> from sklearn.base import is_regressor
+    >>> from sklearn.cluster import KMeans
     >>> from sklearn.svm import SVC, SVR
     >>> classifier = SVC()
     >>> regressor = SVR()
+    >>> kmeans = KMeans()
     >>> is_regressor(classifier)
     False
     >>> is_regressor(regressor)
     True
+    >>> is_regressor(kmeans)
+    False
     """
     return getattr(estimator, "_estimator_type", None) == "regressor"
 
 
+def is_clusterer(estimator):
+    """Return True if the given estimator is (probably) a clusterer.
+
+    .. versionadded:: 1.6
+
+    Parameters
+    ----------
+    estimator : object
+        Estimator object to test.
+
+    Returns
+    -------
+    out : bool
+        True if estimator is a clusterer and False otherwise.
+
+    Examples
+    --------
+    >>> from sklearn.base import is_clusterer
+    >>> from sklearn.cluster import KMeans
+    >>> from sklearn.svm import SVC, SVR
+    >>> classifier = SVC()
+    >>> regressor = SVR()
+    >>> kmeans = KMeans()
+    >>> is_clusterer(classifier)
+    False
+    >>> is_clusterer(regressor)
+    False
+    >>> is_clusterer(kmeans)
+    True
+    """
+    return getattr(estimator, "_estimator_type", None) == "clusterer"
+
+
 def is_outlier_detector(estimator):
     """Return True if the given estimator is (probably) an outlier detector.
 
diff --git a/sklearn/calibration.py b/sklearn/calibration.py
index 40d3e5363a7e0..4fb0c508dba71 100644
--- a/sklearn/calibration.py
+++ b/sklearn/calibration.py
@@ -1,4 +1,4 @@
-"""Calibration of predicted probabilities."""
+"""Methods for calibrating predicted probabilities."""
 
 # Author: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
 #         Balazs Kegl <balazs.kegl@gmail.com>
@@ -523,11 +523,11 @@ def get_metadata_routing(self):
             .add_self_request(self)
             .add(
                 estimator=self._get_estimator(),
-                method_mapping=MethodMapping().add(callee="fit", caller="fit"),
+                method_mapping=MethodMapping().add(caller="fit", callee="fit"),
             )
             .add(
                 splitter=self.cv,
-                method_mapping=MethodMapping().add(callee="split", caller="fit"),
+                method_mapping=MethodMapping().add(caller="fit", callee="split"),
             )
         )
         return router
diff --git a/sklearn/cluster/__init__.py b/sklearn/cluster/__init__.py
index f5d3104d816bf..5d497d2976ad1 100644
--- a/sklearn/cluster/__init__.py
+++ b/sklearn/cluster/__init__.py
@@ -1,7 +1,4 @@
-"""
-The :mod:`sklearn.cluster` module gathers popular unsupervised clustering
-algorithms.
-"""
+"""Popular unsupervised clustering algorithms."""
 
 from ._affinity_propagation import AffinityPropagation, affinity_propagation
 from ._agglomerative import (
diff --git a/sklearn/cluster/_agglomerative.py b/sklearn/cluster/_agglomerative.py
index fcecacc9ca57c..d8cf1002dfcfe 100644
--- a/sklearn/cluster/_agglomerative.py
+++ b/sklearn/cluster/_agglomerative.py
@@ -818,6 +818,10 @@ class AgglomerativeClustering(ClusterMixin, BaseEstimator):
         `kneighbors_graph`. Default is ``None``, i.e, the
         hierarchical clustering algorithm is unstructured.
 
+        For an example of connectivity matrix using
+        :class:`~sklearn.neighbors.kneighbors_graph`, see
+        :ref:`sphx_glr_auto_examples_cluster_plot_agglomerative_clustering.py`.
+
     compute_full_tree : 'auto' or bool, default='auto'
         Stop early the construction of the tree at ``n_clusters``. This is
         useful to decrease computation time if the number of clusters is not
@@ -846,6 +850,9 @@ class AgglomerativeClustering(ClusterMixin, BaseEstimator):
         .. versionadded:: 0.20
             Added the 'single' option
 
+        For examples comparing different `linkage` criteria, see
+        :ref:`sphx_glr_auto_examples_cluster_plot_linkage_comparison.py`.
+
     distance_threshold : float, default=None
         The linkage distance threshold at or above which clusters will not be
         merged. If not ``None``, ``n_clusters`` must be ``None`` and
@@ -860,6 +867,9 @@ class AgglomerativeClustering(ClusterMixin, BaseEstimator):
 
         .. versionadded:: 0.24
 
+        For an example of dendrogram visualization, see
+        :ref:`sphx_glr_auto_examples_cluster_plot_agglomerative_dendrogram.py`.
+
     Attributes
     ----------
     n_clusters_ : int
diff --git a/sklearn/cluster/_birch.py b/sklearn/cluster/_birch.py
index d62fb880ba8b2..b7d8ff06622a7 100644
--- a/sklearn/cluster/_birch.py
+++ b/sklearn/cluster/_birch.py
@@ -637,17 +637,6 @@ def partial_fit(self, X=None, y=None):
         else:
             return self._fit(X, partial=True)
 
-    def _check_fit(self, X):
-        check_is_fitted(self)
-
-        if (
-            hasattr(self, "subcluster_centers_")
-            and X.shape[1] != self.subcluster_centers_.shape[1]
-        ):
-            raise ValueError(
-                "Training data and predicted data do not have same number of features."
-            )
-
     def predict(self, X):
         """
         Predict data using the ``centroids_`` of subclusters.
diff --git a/sklearn/cluster/_feature_agglomeration.py b/sklearn/cluster/_feature_agglomeration.py
index 218db48ad2331..c91952061a6f6 100644
--- a/sklearn/cluster/_feature_agglomeration.py
+++ b/sklearn/cluster/_feature_agglomeration.py
@@ -6,13 +6,13 @@
 # Author: V. Michel, A. Gramfort
 # License: BSD 3 clause
 
-import warnings
 
 import numpy as np
 from scipy.sparse import issparse
 
 from ..base import TransformerMixin
 from ..utils import metadata_routing
+from ..utils.deprecation import _deprecate_Xt_in_inverse_transform
 from ..utils.validation import check_is_fitted
 
 ###############################################################################
@@ -25,9 +25,9 @@ class AgglomerationTransform(TransformerMixin):
     """
 
     # This prevents ``set_split_inverse_transform`` to be generated for the
-    # non-standard ``Xred`` arg on ``inverse_transform``.
-    # TODO(1.5): remove when Xred is removed for inverse_transform.
-    __metadata_request__inverse_transform = {"Xred": metadata_routing.UNUSED}
+    # non-standard ``Xt`` arg on ``inverse_transform``.
+    # TODO(1.7): remove when Xt is removed for inverse_transform.
+    __metadata_request__inverse_transform = {"Xt": metadata_routing.UNUSED}
 
     def transform(self, X):
         """
@@ -63,19 +63,20 @@ def transform(self, X):
             nX = np.array(nX).T
         return nX
 
-    def inverse_transform(self, Xt=None, Xred=None):
+    def inverse_transform(self, X=None, *, Xt=None):
         """
         Inverse the transformation and return a vector of size `n_features`.
 
         Parameters
         ----------
-        Xt : array-like of shape (n_samples, n_clusters) or (n_clusters,)
+        X : array-like of shape (n_samples, n_clusters) or (n_clusters,)
             The values to be assigned to each cluster of samples.
 
-        Xred : deprecated
-            Use `Xt` instead.
+        Xt : array-like of shape (n_samples, n_clusters) or (n_clusters,)
+            The values to be assigned to each cluster of samples.
 
-            .. deprecated:: 1.3
+            .. deprecated:: 1.5
+                `Xt` was deprecated in 1.5 and will be removed in 1.7. Use `X` instead.
 
         Returns
         -------
@@ -83,23 +84,9 @@ def inverse_transform(self, Xt=None, Xred=None):
             A vector of size `n_samples` with the values of `Xred` assigned to
             each of the cluster of samples.
         """
-        if Xt is None and Xred is None:
-            raise TypeError("Missing required positional argument: Xt")
-
-        if Xred is not None and Xt is not None:
-            raise ValueError("Please provide only `Xt`, and not `Xred`.")
-
-        if Xred is not None:
-            warnings.warn(
-                (
-                    "Input argument `Xred` was renamed to `Xt` in v1.3 and will be"
-                    " removed in v1.5."
-                ),
-                FutureWarning,
-            )
-            Xt = Xred
+        X = _deprecate_Xt_in_inverse_transform(X, Xt)
 
         check_is_fitted(self)
 
         unil, inverse = np.unique(self.labels_, return_inverse=True)
-        return Xt[..., inverse]
+        return X[..., inverse]
diff --git a/sklearn/cluster/_kmeans.py b/sklearn/cluster/_kmeans.py
index 16df12838d91f..2ab6f1e95563b 100644
--- a/sklearn/cluster/_kmeans.py
+++ b/sklearn/cluster/_kmeans.py
@@ -18,6 +18,7 @@
 import numpy as np
 import scipy.sparse as sp
 
+from .. import _threadpool_controller
 from ..base import (
     BaseEstimator,
     ClassNamePrefixFeaturesOutMixin,
@@ -31,7 +32,6 @@
 from ..utils._openmp_helpers import _openmp_effective_n_threads
 from ..utils._param_validation import Interval, StrOptions, validate_params
 from ..utils.extmath import row_norms, stable_cumsum
-from ..utils.fixes import threadpool_info, threadpool_limits
 from ..utils.sparsefuncs import mean_variance_axis
 from ..utils.sparsefuncs_fast import assign_rows_csr
 from ..utils.validation import (
@@ -622,6 +622,9 @@ def _kmeans_single_elkan(
     return labels, inertia, centers, i + 1
 
 
+# Threadpoolctl context to limit the number of threads in second level of
+# nested parallelism (i.e. BLAS) to avoid oversubscription.
+@_threadpool_controller.wrap(limits=1, user_api="blas")
 def _kmeans_single_lloyd(
     X,
     sample_weight,
@@ -697,59 +700,56 @@ def _kmeans_single_lloyd(
 
     strict_convergence = False
 
-    # Threadpoolctl context to limit the number of threads in second level of
-    # nested parallelism (i.e. BLAS) to avoid oversubscription.
-    with threadpool_limits(limits=1, user_api="blas"):
-        for i in range(max_iter):
-            lloyd_iter(
-                X,
-                sample_weight,
-                centers,
-                centers_new,
-                weight_in_clusters,
-                labels,
-                center_shift,
-                n_threads,
-            )
+    for i in range(max_iter):
+        lloyd_iter(
+            X,
+            sample_weight,
+            centers,
+            centers_new,
+            weight_in_clusters,
+            labels,
+            center_shift,
+            n_threads,
+        )
 
-            if verbose:
-                inertia = _inertia(X, sample_weight, centers, labels, n_threads)
-                print(f"Iteration {i}, inertia {inertia}.")
+        if verbose:
+            inertia = _inertia(X, sample_weight, centers, labels, n_threads)
+            print(f"Iteration {i}, inertia {inertia}.")
 
-            centers, centers_new = centers_new, centers
+        centers, centers_new = centers_new, centers
 
-            if np.array_equal(labels, labels_old):
-                # First check the labels for strict convergence.
+        if np.array_equal(labels, labels_old):
+            # First check the labels for strict convergence.
+            if verbose:
+                print(f"Converged at iteration {i}: strict convergence.")
+            strict_convergence = True
+            break
+        else:
+            # No strict convergence, check for tol based convergence.
+            center_shift_tot = (center_shift**2).sum()
+            if center_shift_tot <= tol:
                 if verbose:
-                    print(f"Converged at iteration {i}: strict convergence.")
-                strict_convergence = True
+                    print(
+                        f"Converged at iteration {i}: center shift "
+                        f"{center_shift_tot} within tolerance {tol}."
+                    )
                 break
-            else:
-                # No strict convergence, check for tol based convergence.
-                center_shift_tot = (center_shift**2).sum()
-                if center_shift_tot <= tol:
-                    if verbose:
-                        print(
-                            f"Converged at iteration {i}: center shift "
-                            f"{center_shift_tot} within tolerance {tol}."
-                        )
-                    break
 
-            labels_old[:] = labels
+        labels_old[:] = labels
 
-        if not strict_convergence:
-            # rerun E-step so that predicted labels match cluster centers
-            lloyd_iter(
-                X,
-                sample_weight,
-                centers,
-                centers,
-                weight_in_clusters,
-                labels,
-                center_shift,
-                n_threads,
-                update_centers=False,
-            )
+    if not strict_convergence:
+        # rerun E-step so that predicted labels match cluster centers
+        lloyd_iter(
+            X,
+            sample_weight,
+            centers,
+            centers,
+            weight_in_clusters,
+            labels,
+            center_shift,
+            n_threads,
+            update_centers=False,
+        )
 
     inertia = _inertia(X, sample_weight, centers, labels, n_threads)
 
@@ -826,14 +826,10 @@ def _labels_inertia(X, sample_weight, centers, n_threads=1, return_inertia=True)
     return labels
 
 
-def _labels_inertia_threadpool_limit(
-    X, sample_weight, centers, n_threads=1, return_inertia=True
-):
-    """Same as _labels_inertia but in a threadpool_limits context."""
-    with threadpool_limits(limits=1, user_api="blas"):
-        result = _labels_inertia(X, sample_weight, centers, n_threads, return_inertia)
-
-    return result
+# Same as _labels_inertia but in a threadpool_limits context.
+_labels_inertia_threadpool_limit = _threadpool_controller.wrap(
+    limits=1, user_api="blas"
+)(_labels_inertia)
 
 
 class _BaseKMeans(
@@ -926,7 +922,7 @@ def _check_mkl_vcomp(self, X, n_samples):
 
         n_active_threads = int(np.ceil(n_samples / CHUNK_SIZE))
         if n_active_threads < self._n_threads:
-            modules = threadpool_info()
+            modules = _threadpool_controller.info()
             has_vcomp = "vcomp" in [module["prefix"] for module in modules]
             has_mkl = ("mkl", "intel") in [
                 (module["internal_api"], module.get("threading_layer", None))
@@ -2148,7 +2144,7 @@ def fit(self, X, y=None, sample_weight=None):
 
         n_steps = (self.max_iter * n_samples) // self._batch_size
 
-        with threadpool_limits(limits=1, user_api="blas"):
+        with _threadpool_controller.limit(limits=1, user_api="blas"):
             # Perform the iterative optimization until convergence
             for i in range(n_steps):
                 # Sample a minibatch from the full dataset
@@ -2274,7 +2270,7 @@ def partial_fit(self, X, y=None, sample_weight=None):
             # Initialize number of samples seen since last reassignment
             self._n_since_last_reassign = 0
 
-        with threadpool_limits(limits=1, user_api="blas"):
+        with _threadpool_controller.limit(limits=1, user_api="blas"):
             _mini_batch_step(
                 X,
                 sample_weight=sample_weight,
diff --git a/sklearn/cluster/_mean_shift.py b/sklearn/cluster/_mean_shift.py
index fae11cca7df23..a99a607f3cf0d 100644
--- a/sklearn/cluster/_mean_shift.py
+++ b/sklearn/cluster/_mean_shift.py
@@ -122,7 +122,7 @@ def _mean_shift_single_seed(my_mean, X, nbrs, max_iter):
         my_mean = np.mean(points_within, axis=0)
         # If converged or at max_iter, adds the cluster
         if (
-            np.linalg.norm(my_mean - my_old_mean) < stop_thresh
+            np.linalg.norm(my_mean - my_old_mean) <= stop_thresh
             or completed_iterations == max_iter
         ):
             break
diff --git a/sklearn/cluster/tests/test_feature_agglomeration.py b/sklearn/cluster/tests/test_feature_agglomeration.py
index abeb81dca50aa..488dd638ad125 100644
--- a/sklearn/cluster/tests/test_feature_agglomeration.py
+++ b/sklearn/cluster/tests/test_feature_agglomeration.py
@@ -59,23 +59,23 @@ def test_feature_agglomeration_feature_names_out():
     )
 
 
-# TODO(1.5): remove this test
-def test_inverse_transform_Xred_deprecation():
+# TODO(1.7): remove this test
+def test_inverse_transform_Xt_deprecation():
     X = np.array([0, 0, 1]).reshape(1, 3)  # (n_samples, n_features)
 
     est = FeatureAgglomeration(n_clusters=1, pooling_func=np.mean)
     est.fit(X)
-    Xt = est.transform(X)
+    X = est.transform(X)
 
     with pytest.raises(TypeError, match="Missing required positional argument"):
         est.inverse_transform()
 
-    with pytest.raises(ValueError, match="Please provide only"):
-        est.inverse_transform(Xt=Xt, Xred=Xt)
+    with pytest.raises(TypeError, match="Cannot use both X and Xt. Use X only."):
+        est.inverse_transform(X=X, Xt=X)
 
     with warnings.catch_warnings(record=True):
         warnings.simplefilter("error")
-        est.inverse_transform(Xt)
+        est.inverse_transform(X)
 
-    with pytest.warns(FutureWarning, match="Input argument `Xred` was renamed to `Xt`"):
-        est.inverse_transform(Xred=Xt)
+    with pytest.warns(FutureWarning, match="Xt was renamed X in version 1.5"):
+        est.inverse_transform(Xt=X)
diff --git a/sklearn/cluster/tests/test_k_means.py b/sklearn/cluster/tests/test_k_means.py
index b6f718dce03e0..c3a41a65de632 100644
--- a/sklearn/cluster/tests/test_k_means.py
+++ b/sklearn/cluster/tests/test_k_means.py
@@ -8,6 +8,7 @@
 import pytest
 from scipy import sparse as sp
 
+from sklearn import _threadpool_controller
 from sklearn.base import clone
 from sklearn.cluster import KMeans, MiniBatchKMeans, k_means, kmeans_plusplus
 from sklearn.cluster._k_means_common import (
@@ -31,7 +32,7 @@
     create_memmap_backed_data,
 )
 from sklearn.utils.extmath import row_norms
-from sklearn.utils.fixes import CSR_CONTAINERS, threadpool_limits
+from sklearn.utils.fixes import CSR_CONTAINERS
 
 # non centered, sparse centers to check the
 centers = np.array(
@@ -967,13 +968,13 @@ def test_result_equal_in_diff_n_threads(Estimator, global_random_seed):
     rnd = np.random.RandomState(global_random_seed)
     X = rnd.normal(size=(50, 10))
 
-    with threadpool_limits(limits=1, user_api="openmp"):
+    with _threadpool_controller.limit(limits=1, user_api="openmp"):
         result_1 = (
             Estimator(n_clusters=n_clusters, random_state=global_random_seed)
             .fit(X)
             .labels_
         )
-    with threadpool_limits(limits=2, user_api="openmp"):
+    with _threadpool_controller.limit(limits=2, user_api="openmp"):
         result_2 = (
             Estimator(n_clusters=n_clusters, random_state=global_random_seed)
             .fit(X)
diff --git a/sklearn/cluster/tests/test_mean_shift.py b/sklearn/cluster/tests/test_mean_shift.py
index 265c72d0c4ce1..d2d73ba11a3ec 100644
--- a/sklearn/cluster/tests/test_mean_shift.py
+++ b/sklearn/cluster/tests/test_mean_shift.py
@@ -25,6 +25,15 @@
 )
 
 
+def test_convergence_of_1d_constant_data():
+    # Test convergence using 1D constant data
+    # Non-regression test for:
+    # https://github.com/scikit-learn/scikit-learn/issues/28926
+    model = MeanShift()
+    n_iter = model.fit(np.ones(10).reshape(-1, 1)).n_iter_
+    assert n_iter < model.max_iter
+
+
 def test_estimate_bandwidth():
     # Test estimate_bandwidth
     bandwidth = estimate_bandwidth(X, n_samples=200)
diff --git a/sklearn/compose/__init__.py b/sklearn/compose/__init__.py
index 7b137cdf9e07f..e6e8fc6fd2c1b 100644
--- a/sklearn/compose/__init__.py
+++ b/sklearn/compose/__init__.py
@@ -1,8 +1,8 @@
-"""Meta-estimators for building composite models with transformers
+"""Meta-estimators for building composite models with transformers.
 
 In addition to its current contents, this module will eventually be home to
-refurbished versions of Pipeline and FeatureUnion.
-
+refurbished versions of :class:`~sklearn.pipeline.Pipeline` and
+:class:`~sklearn.pipeline.FeatureUnion`.
 """
 
 from ._column_transformer import (
diff --git a/sklearn/compose/_column_transformer.py b/sklearn/compose/_column_transformer.py
index 96b0ff527b1f8..e594df3da92e7 100644
--- a/sklearn/compose/_column_transformer.py
+++ b/sklearn/compose/_column_transformer.py
@@ -8,7 +8,7 @@
 #         Joris Van den Bossche
 # License: BSD
 import warnings
-from collections import Counter
+from collections import Counter, UserList
 from itertools import chain
 from numbers import Integral, Real
 
@@ -18,9 +18,9 @@
 from ..base import TransformerMixin, _fit_context, clone
 from ..pipeline import _fit_transform_one, _name_estimators, _transform_one
 from ..preprocessing import FunctionTransformer
-from ..utils import Bunch, _safe_indexing
+from ..utils import Bunch
 from ..utils._estimator_html_repr import _VisualBlock
-from ..utils._indexing import _get_column_indices
+from ..utils._indexing import _determine_key_type, _get_column_indices
 from ..utils._metadata_requests import METHODS
 from ..utils._param_validation import HasMethods, Hidden, Interval, StrOptions
 from ..utils._set_output import (
@@ -143,6 +143,23 @@ class ColumnTransformer(TransformerMixin, _BaseComposition):
 
         .. versionadded:: 1.0
 
+    force_int_remainder_cols : bool, default=True
+        Force the columns of the last entry of `transformers_`, which
+        corresponds to the "remainder" transformer, to always be stored as
+        indices (int) rather than column names (str). See description of the
+        `transformers_` attribute for details.
+
+        .. note::
+            If you do not access the list of columns for the remainder columns
+            in the `transformers_` fitted attribute, you do not need to set
+            this parameter.
+
+        .. versionadded:: 1.5
+
+        .. versionchanged:: 1.7
+           The default value for `force_int_remainder_cols` will change from
+           `True` to `False` in version 1.7.
+
     Attributes
     ----------
     transformers_ : list
@@ -157,6 +174,17 @@ class ColumnTransformer(TransformerMixin, _BaseComposition):
         ``len(transformers_)==len(transformers)+1``, otherwise
         ``len(transformers_)==len(transformers)``.
 
+        .. versionchanged:: 1.5
+            If there are remaining columns and `force_int_remainder_cols` is
+            True, the remaining columns are always represented by their
+            positional indices in the input `X` (as in older versions). If
+            `force_int_remainder_cols` is False, the format attempts to match
+            that of the other transformers: if all columns were provided as
+            column names (`str`), the remaining columns are stored as column
+            names; if all columns were provided as mask arrays (`bool`), so are
+            the remaining columns; in all other cases the remaining columns are
+            stored as indices (`int`).
+
     named_transformers_ : :class:`~sklearn.utils.Bunch`
         Read-only attribute to access any transformer by given name.
         Keys are transformer names and values are the fitted transformer
@@ -256,6 +284,7 @@ class ColumnTransformer(TransformerMixin, _BaseComposition):
         "transformer_weights": [dict, None],
         "verbose": ["verbose"],
         "verbose_feature_names_out": ["boolean"],
+        "force_int_remainder_cols": ["boolean"],
     }
 
     def __init__(
@@ -268,6 +297,7 @@ def __init__(
         transformer_weights=None,
         verbose=False,
         verbose_feature_names_out=True,
+        force_int_remainder_cols=True,
     ):
         self.transformers = transformers
         self.remainder = remainder
@@ -276,6 +306,7 @@ def __init__(
         self.transformer_weights = transformer_weights
         self.verbose = verbose
         self.verbose_feature_names_out = verbose_feature_names_out
+        self.force_int_remainder_cols = force_int_remainder_cols
 
     @property
     def _transformers(self):
@@ -314,7 +345,7 @@ def set_output(self, *, transform=None):
 
         Parameters
         ----------
-        transform : {"default", "pandas"}, default=None
+        transform : {"default", "pandas", "polars"}, default=None
             Configure output of `transform` and `fit_transform`.
 
             - `"default"`: Default output format of a transformer
@@ -389,7 +420,7 @@ def set_params(self, **kwargs):
 
     def _iter(self, fitted, column_as_labels, skip_drop, skip_empty_columns):
         """
-        Generate (name, trans, column, weight) tuples.
+        Generate (name, trans, columns, weight) tuples.
 
 
         Parameters
@@ -429,6 +460,14 @@ def _iter(self, fitted, column_as_labels, skip_drop, skip_empty_columns):
             # add transformer tuple for remainder
             if self._remainder[2]:
                 transformers = chain(transformers, [self._remainder])
+
+        # We want the warning about the future change of the remainder
+        # columns dtype to be shown only when a user accesses them
+        # directly, not when they are used by the ColumnTransformer itself.
+        # We disable warnings here; they are enabled when setting
+        # self.transformers_.
+        transformers = _with_dtype_warning_enabled_set_to(False, transformers)
+
         get_weight = (self.transformer_weights or {}).get
 
         for name, trans, columns in transformers:
@@ -506,8 +545,30 @@ def _validate_remainder(self, X):
         """
         cols = set(chain(*self._transformer_to_input_indices.values()))
         remaining = sorted(set(range(self.n_features_in_)) - cols)
-        self._remainder = ("remainder", self.remainder, remaining)
         self._transformer_to_input_indices["remainder"] = remaining
+        remainder_cols = self._get_remainder_cols(remaining)
+        self._remainder = ("remainder", self.remainder, remainder_cols)
+
+    def _get_remainder_cols_dtype(self):
+        try:
+            all_dtypes = {_determine_key_type(c) for (*_, c) in self.transformers}
+            if len(all_dtypes) == 1:
+                return next(iter(all_dtypes))
+        except ValueError:
+            # _determine_key_type raises a ValueError if some transformer
+            # columns are Callables
+            return "int"
+        return "int"
+
+    def _get_remainder_cols(self, indices):
+        dtype = self._get_remainder_cols_dtype()
+        if self.force_int_remainder_cols and dtype != "int":
+            return _RemainderColsList(indices, future_dtype=dtype)
+        if dtype == "str":
+            return list(self.feature_names_in_[indices])
+        if dtype == "bool":
+            return [i in indices for i in range(self.n_features_in_)]
+        return indices
 
     @property
     def named_transformers_(self):
@@ -662,7 +723,7 @@ def _update_fitted_transformers(self, transformers):
 
         # sanity check that transformers is exhausted
         assert not list(fitted_transformers)
-        self.transformers_ = transformers_
+        self.transformers_ = _with_dtype_warning_enabled_set_to(True, transformers_)
 
     def _validate_output(self, result):
         """
@@ -794,7 +855,7 @@ def _call_func_on_transformers(self, X, y, func, column_as_labels, routed_params
         )
         try:
             jobs = []
-            for idx, (name, trans, column, weight) in enumerate(transformers, start=1):
+            for idx, (name, trans, columns, weight) in enumerate(transformers, start=1):
                 if func is _fit_transform_one:
                     if trans == "passthrough":
                         output_config = _get_output_config("transform", self)
@@ -813,9 +874,10 @@ def _call_func_on_transformers(self, X, y, func, column_as_labels, routed_params
                 jobs.append(
                     delayed(func)(
                         transformer=clone(trans) if not fitted else trans,
-                        X=_safe_indexing(X, column, axis=1),
+                        X=X,
                         y=y,
                         weight=weight,
+                        columns=columns,
                         **extra_args,
                         params=routed_params[name],
                     )
@@ -1277,6 +1339,7 @@ def make_column_transformer(
     n_jobs=None,
     verbose=False,
     verbose_feature_names_out=True,
+    force_int_remainder_cols=True,
 ):
     """Construct a ColumnTransformer from the given transformers.
 
@@ -1349,6 +1412,23 @@ def make_column_transformer(
 
         .. versionadded:: 1.0
 
+    force_int_remainder_cols : bool, default=True
+        Force the columns of the last entry of `transformers_`, which
+        corresponds to the "remainder" transformer, to always be stored as
+        indices (int) rather than column names (str). See description of the
+        :attr:`ColumnTransformer.transformers_` attribute for details.
+
+        .. note::
+            If you do not access the list of columns for the remainder columns
+            in the :attr:`ColumnTransformer.transformers_` fitted attribute,
+            you do not need to set this parameter.
+
+        .. versionadded:: 1.5
+
+        .. versionchanged:: 1.7
+           The default value for `force_int_remainder_cols` will change from
+           `True` to `False` in version 1.7.
+
     Returns
     -------
     ct : ColumnTransformer
@@ -1382,6 +1462,7 @@ def make_column_transformer(
         sparse_threshold=sparse_threshold,
         verbose=verbose,
         verbose_feature_names_out=verbose_feature_names_out,
+        force_int_remainder_cols=force_int_remainder_cols,
     )
 
 
@@ -1472,3 +1553,102 @@ def __call__(self, df):
         if self.pattern is not None:
             cols = cols[cols.str.contains(self.pattern, regex=True)]
         return cols.tolist()
+
+
+class _RemainderColsList(UserList):
+    """A list that raises a warning whenever items are accessed.
+
+    It is used to store the columns handled by the "remainder" entry of
+    ``ColumnTransformer.transformers_``, ie ``transformers_[-1][-1]``.
+
+    For some values of the ``ColumnTransformer`` ``transformers`` parameter,
+    this list of indices will be replaced by either a list of column names or a
+    boolean mask; in those cases we emit a ``FutureWarning`` the first time an
+    element is accessed.
+
+    Parameters
+    ----------
+    columns : list of int
+        The remainder columns.
+
+    future_dtype : {'str', 'bool'}, default=None
+        The dtype that will be used by a ColumnTransformer with the same inputs
+        in a future release. There is a default value because providing a
+        constructor that takes a single argument is a requirement for
+        subclasses of UserList, but we do not use it in practice. It would only
+        be used if a user called methods that return a new list such are
+        copying or concatenating `_RemainderColsList`.
+
+    warning_was_emitted : bool, default=False
+       Whether the warning for that particular list was already shown, so we
+       only emit it once.
+
+    warning_enabled : bool, default=True
+        When False, the list never emits the warning nor updates
+        `warning_was_emitted``. This is used to obtain a quiet copy of the list
+        for use by the `ColumnTransformer` itself, so that the warning is only
+        shown when a user accesses it directly.
+    """
+
+    def __init__(
+        self,
+        columns,
+        *,
+        future_dtype=None,
+        warning_was_emitted=False,
+        warning_enabled=True,
+    ):
+        super().__init__(columns)
+        self.future_dtype = future_dtype
+        self.warning_was_emitted = warning_was_emitted
+        self.warning_enabled = warning_enabled
+
+    def __getitem__(self, index):
+        self._show_remainder_cols_warning()
+        return super().__getitem__(index)
+
+    def _show_remainder_cols_warning(self):
+        if self.warning_was_emitted or not self.warning_enabled:
+            return
+        self.warning_was_emitted = True
+        future_dtype_description = {
+            "str": "column names (of type str)",
+            "bool": "a mask array (of type bool)",
+            # shouldn't happen because we always initialize it with a
+            # non-default future_dtype
+            None: "a different type depending on the ColumnTransformer inputs",
+        }.get(self.future_dtype, self.future_dtype)
+
+        # TODO(1.7) Update the warning to say that the old behavior will be
+        # removed in 1.9.
+        warnings.warn(
+            (
+                "\nThe format of the columns of the 'remainder' transformer in"
+                " ColumnTransformer.transformers_ will change in version 1.7 to"
+                " match the format of the other transformers.\nAt the moment the"
+                " remainder columns are stored as indices (of type int). With the same"
+                " ColumnTransformer configuration, in the future they will be stored"
+                f" as {future_dtype_description}.\nTo use the new behavior now and"
+                " suppress this warning, use"
+                " ColumnTransformer(force_int_remainder_cols=False).\n"
+            ),
+            category=FutureWarning,
+        )
+
+    def _repr_pretty_(self, printer, *_):
+        """Override display in ipython console, otherwise the class name is shown."""
+        printer.text(repr(self.data))
+
+
+def _with_dtype_warning_enabled_set_to(warning_enabled, transformers):
+    result = []
+    for name, trans, columns in transformers:
+        if isinstance(columns, _RemainderColsList):
+            columns = _RemainderColsList(
+                columns.data,
+                future_dtype=columns.future_dtype,
+                warning_was_emitted=columns.warning_was_emitted,
+                warning_enabled=warning_enabled,
+            )
+        result.append((name, trans, columns))
+    return result
diff --git a/sklearn/compose/tests/test_column_transformer.py b/sklearn/compose/tests/test_column_transformer.py
index 56c4cd459aab5..d0f2274272230 100644
--- a/sklearn/compose/tests/test_column_transformer.py
+++ b/sklearn/compose/tests/test_column_transformer.py
@@ -5,7 +5,9 @@
 import pickle
 import re
 import warnings
+from unittest.mock import Mock
 
+import joblib
 import numpy as np
 import pytest
 from numpy.testing import assert_allclose
@@ -17,6 +19,7 @@
     make_column_selector,
     make_column_transformer,
 )
+from sklearn.compose._column_transformer import _RemainderColsList
 from sklearn.exceptions import NotFittedError
 from sklearn.feature_selection import VarianceThreshold
 from sklearn.preprocessing import (
@@ -36,7 +39,7 @@
     assert_almost_equal,
     assert_array_equal,
 )
-from sklearn.utils.fixes import CSR_CONTAINERS
+from sklearn.utils.fixes import CSR_CONTAINERS, parse_version
 
 
 class Trans(TransformerMixin, BaseEstimator):
@@ -787,6 +790,7 @@ def test_column_transformer_get_set_params():
         "transformer_weights": None,
         "verbose_feature_names_out": True,
         "verbose": False,
+        "force_int_remainder_cols": True,
     }
 
     assert ct.get_params() == exp
@@ -808,6 +812,7 @@ def test_column_transformer_get_set_params():
         "transformer_weights": None,
         "verbose_feature_names_out": True,
         "verbose": False,
+        "force_int_remainder_cols": True,
     }
 
     assert ct.get_params() == exp
@@ -937,38 +942,135 @@ def test_column_transformer_remainder():
     assert ct.remainder == "drop"
 
 
+# TODO(1.7): check for deprecated force_int_remainder_cols
+# TODO(1.9): remove force_int but keep the test
 @pytest.mark.parametrize(
-    "key", [[0], np.array([0]), slice(0, 1), np.array([True, False])]
+    "cols1, cols2",
+    [
+        ([0], [False, True, False]),  # mix types
+        ([0], [1]),  # ints
+        (lambda x: [0], lambda x: [1]),  # callables
+    ],
+)
+@pytest.mark.parametrize("force_int", [False, True])
+def test_column_transformer_remainder_dtypes_ints(force_int, cols1, cols2):
+    """Check that the remainder columns are always stored as indices when
+    other columns are not all specified as column names or masks, regardless of
+    `force_int_remainder_cols`.
+    """
+    X = np.ones((1, 3))
+
+    ct = make_column_transformer(
+        (Trans(), cols1),
+        (Trans(), cols2),
+        remainder="passthrough",
+        force_int_remainder_cols=force_int,
+    )
+    with warnings.catch_warnings():
+        warnings.simplefilter("error")
+        ct.fit_transform(X)
+        assert ct.transformers_[-1][-1][0] == 2
+
+
+# TODO(1.7): check for deprecated force_int_remainder_cols
+# TODO(1.9): remove force_int but keep the test
+@pytest.mark.parametrize(
+    "force_int, cols1, cols2, expected_cols",
+    [
+        (True, ["A"], ["B"], [2]),
+        (False, ["A"], ["B"], ["C"]),
+        (True, [True, False, False], [False, True, False], [2]),
+        (False, [True, False, False], [False, True, False], [False, False, True]),
+    ],
 )
-def test_column_transformer_remainder_numpy(key):
+def test_column_transformer_remainder_dtypes(force_int, cols1, cols2, expected_cols):
+    """Check that the remainder columns format matches the format of the other
+    columns when they're all strings or masks, unless `force_int = True`.
+    """
+    X = np.ones((1, 3))
+
+    if isinstance(cols1[0], str):
+        pd = pytest.importorskip("pandas")
+        X = pd.DataFrame(X, columns=["A", "B", "C"])
+
+    # if inputs are column names store remainder columns as column names unless
+    # force_int_remainder_cols is True
+    ct = make_column_transformer(
+        (Trans(), cols1),
+        (Trans(), cols2),
+        remainder="passthrough",
+        force_int_remainder_cols=force_int,
+    )
+    with warnings.catch_warnings():
+        warnings.simplefilter("error")
+        ct.fit_transform(X)
+
+    if force_int:
+        # If we forced using ints and we access the remainder columns a warning is shown
+        match = "The format of the columns of the 'remainder' transformer"
+        cols = ct.transformers_[-1][-1]
+        with pytest.warns(FutureWarning, match=match):
+            cols[0]
+    else:
+        with warnings.catch_warnings():
+            warnings.simplefilter("error")
+            cols = ct.transformers_[-1][-1]
+            cols[0]
+
+    assert cols == expected_cols
+
+
+def test_remainder_list_repr():
+    cols = _RemainderColsList([0, 1], warning_enabled=False)
+    assert str(cols) == "[0, 1]"
+    assert repr(cols) == "[0, 1]"
+    mock = Mock()
+    cols._repr_pretty_(mock, False)
+    mock.text.assert_called_once_with("[0, 1]")
+
+
+@pytest.mark.parametrize(
+    "key, expected_cols",
+    [
+        ([0], [1]),
+        (np.array([0]), [1]),
+        (slice(0, 1), [1]),
+        (np.array([True, False]), [False, True]),
+    ],
+)
+def test_column_transformer_remainder_numpy(key, expected_cols):
     # test different ways that columns are specified with passthrough
     X_array = np.array([[0, 1, 2], [2, 4, 6]]).T
     X_res_both = X_array
 
-    ct = ColumnTransformer([("trans1", Trans(), key)], remainder="passthrough")
+    ct = ColumnTransformer(
+        [("trans1", Trans(), key)],
+        remainder="passthrough",
+        force_int_remainder_cols=False,
+    )
     assert_array_equal(ct.fit_transform(X_array), X_res_both)
     assert_array_equal(ct.fit(X_array).transform(X_array), X_res_both)
     assert len(ct.transformers_) == 2
     assert ct.transformers_[-1][0] == "remainder"
     assert isinstance(ct.transformers_[-1][1], FunctionTransformer)
-    assert_array_equal(ct.transformers_[-1][2], [1])
+    assert ct.transformers_[-1][2] == expected_cols
 
 
 @pytest.mark.parametrize(
-    "key",
+    "key, expected_cols",
     [
-        [0],
-        slice(0, 1),
-        np.array([True, False]),
-        ["first"],
-        "pd-index",
-        np.array(["first"]),
-        np.array(["first"], dtype=object),
-        slice(None, "first"),
-        slice("first", "first"),
+        ([0], [1]),
+        (slice(0, 1), [1]),
+        (np.array([True, False]), [False, True]),
+        (["first"], ["second"]),
+        ("pd-index", ["second"]),
+        (np.array(["first"]), ["second"]),
+        (np.array(["first"], dtype=object), ["second"]),
+        (slice(None, "first"), ["second"]),
+        (slice("first", "first"), ["second"]),
     ],
 )
-def test_column_transformer_remainder_pandas(key):
+def test_column_transformer_remainder_pandas(key, expected_cols):
     # test different ways that columns are specified with passthrough
     pd = pytest.importorskip("pandas")
     if isinstance(key, str) and key == "pd-index":
@@ -978,33 +1080,47 @@ def test_column_transformer_remainder_pandas(key):
     X_df = pd.DataFrame(X_array, columns=["first", "second"])
     X_res_both = X_array
 
-    ct = ColumnTransformer([("trans1", Trans(), key)], remainder="passthrough")
+    ct = ColumnTransformer(
+        [("trans1", Trans(), key)],
+        remainder="passthrough",
+        force_int_remainder_cols=False,
+    )
     assert_array_equal(ct.fit_transform(X_df), X_res_both)
     assert_array_equal(ct.fit(X_df).transform(X_df), X_res_both)
     assert len(ct.transformers_) == 2
     assert ct.transformers_[-1][0] == "remainder"
     assert isinstance(ct.transformers_[-1][1], FunctionTransformer)
-    assert_array_equal(ct.transformers_[-1][2], [1])
+    assert ct.transformers_[-1][2] == expected_cols
 
 
 @pytest.mark.parametrize(
-    "key", [[0], np.array([0]), slice(0, 1), np.array([True, False, False])]
+    "key, expected_cols",
+    [
+        ([0], [1, 2]),
+        (np.array([0]), [1, 2]),
+        (slice(0, 1), [1, 2]),
+        (np.array([True, False, False]), [False, True, True]),
+    ],
 )
-def test_column_transformer_remainder_transformer(key):
+def test_column_transformer_remainder_transformer(key, expected_cols):
     X_array = np.array([[0, 1, 2], [2, 4, 6], [8, 6, 4]]).T
     X_res_both = X_array.copy()
 
     # second and third columns are doubled when remainder = DoubleTrans
     X_res_both[:, 1:3] *= 2
 
-    ct = ColumnTransformer([("trans1", Trans(), key)], remainder=DoubleTrans())
+    ct = ColumnTransformer(
+        [("trans1", Trans(), key)],
+        remainder=DoubleTrans(),
+        force_int_remainder_cols=False,
+    )
 
     assert_array_equal(ct.fit_transform(X_array), X_res_both)
     assert_array_equal(ct.fit(X_array).transform(X_array), X_res_both)
     assert len(ct.transformers_) == 2
     assert ct.transformers_[-1][0] == "remainder"
     assert isinstance(ct.transformers_[-1][1], DoubleTrans)
-    assert_array_equal(ct.transformers_[-1][2], [1, 2])
+    assert ct.transformers_[-1][2] == expected_cols
 
 
 def test_column_transformer_no_remaining_remainder_transformer():
@@ -1099,6 +1215,7 @@ def test_column_transformer_get_set_params_with_remainder():
         "transformer_weights": None,
         "verbose_feature_names_out": True,
         "verbose": False,
+        "force_int_remainder_cols": True,
     }
 
     assert ct.get_params() == exp
@@ -1119,6 +1236,7 @@ def test_column_transformer_get_set_params_with_remainder():
         "transformer_weights": None,
         "verbose_feature_names_out": True,
         "verbose": False,
+        "force_int_remainder_cols": True,
     }
     assert ct.get_params() == exp
 
@@ -1475,7 +1593,9 @@ def test_sk_visual_block_remainder_fitted_pandas(remainder):
     pd = pytest.importorskip("pandas")
     ohe = OneHotEncoder()
     ct = ColumnTransformer(
-        transformers=[("ohe", ohe, ["col1", "col2"])], remainder=remainder
+        transformers=[("ohe", ohe, ["col1", "col2"])],
+        remainder=remainder,
+        force_int_remainder_cols=False,
     )
     df = pd.DataFrame(
         {
@@ -2447,6 +2567,30 @@ def test_column_transformer_error_with_duplicated_columns(dataframe_lib):
         transformer.fit_transform(df)
 
 
+@pytest.mark.skipif(
+    parse_version(joblib.__version__) < parse_version("1.3"),
+    reason="requires joblib >= 1.3",
+)
+def test_column_transformer_auto_memmap():
+    """Check that ColumnTransformer works in parallel with joblib's auto-memmapping.
+
+    non-regression test for issue #28781
+    """
+    X = np.random.RandomState(0).uniform(size=(3, 4))
+
+    scaler = StandardScaler(copy=False)
+
+    transformer = ColumnTransformer(
+        transformers=[("scaler", scaler, [0])],
+        n_jobs=2,
+    )
+
+    with joblib.parallel_backend("loky", max_nbytes=1):
+        Xt = transformer.fit_transform(X)
+
+    assert_allclose(Xt, StandardScaler().fit_transform(X[:, [0]]))
+
+
 # Metadata Routing Tests
 # ======================
 
diff --git a/sklearn/covariance/__init__.py b/sklearn/covariance/__init__.py
index 8fcf8c68444e5..19ee1d8a09759 100644
--- a/sklearn/covariance/__init__.py
+++ b/sklearn/covariance/__init__.py
@@ -1,9 +1,8 @@
-"""
-The :mod:`sklearn.covariance` module includes methods and algorithms to
-robustly estimate the covariance of features given a set of points. The
-precision matrix defined as the inverse of the covariance is also estimated.
-Covariance estimation is closely related to the theory of Gaussian Graphical
-Models.
+"""Methods and algorithms to robustly estimate covariance.
+
+They estimate the covariance of features at given sets of points, as well as the
+precision matrix defined as the inverse of the covariance. Covariance estimation is
+closely related to the theory of Gaussian graphical models.
 """
 
 from ._elliptic_envelope import EllipticEnvelope
diff --git a/sklearn/covariance/_shrunk_covariance.py b/sklearn/covariance/_shrunk_covariance.py
index 2c8248d0f6502..caf23ffaf687e 100644
--- a/sklearn/covariance/_shrunk_covariance.py
+++ b/sklearn/covariance/_shrunk_covariance.py
@@ -617,7 +617,7 @@ def fit(self, X, y=None):
     prefer_skip_nested_validation=False,
 )
 def oas(X, *, assume_centered=False):
-    """Estimate covariance with the Oracle Approximating Shrinkage as proposed in [1]_.
+    """Estimate covariance with the Oracle Approximating Shrinkage.
 
     Read more in the :ref:`User Guide <shrunk_covariance>`.
 
@@ -684,7 +684,7 @@ def oas(X, *, assume_centered=False):
 
 
 class OAS(EmpiricalCovariance):
-    """Oracle Approximating Shrinkage Estimator as proposed in [1]_.
+    """Oracle Approximating Shrinkage Estimator.
 
     Read more in the :ref:`User Guide <shrunk_covariance>`.
 
diff --git a/sklearn/cross_decomposition/__init__.py b/sklearn/cross_decomposition/__init__.py
index 47b78783caf9c..15eb21467daee 100644
--- a/sklearn/cross_decomposition/__init__.py
+++ b/sklearn/cross_decomposition/__init__.py
@@ -1,3 +1,5 @@
+"""Algorithms for cross decomposition."""
+
 from ._pls import CCA, PLSSVD, PLSCanonical, PLSRegression
 
 __all__ = ["PLSCanonical", "PLSRegression", "PLSSVD", "CCA"]
diff --git a/sklearn/cross_decomposition/_pls.py b/sklearn/cross_decomposition/_pls.py
index 858f123427fab..b6f7dd663724e 100644
--- a/sklearn/cross_decomposition/_pls.py
+++ b/sklearn/cross_decomposition/_pls.py
@@ -388,7 +388,7 @@ def fit(self, X, y=None, Y=None):
             pinv2(np.dot(self.y_loadings_.T, self.y_weights_), check_finite=False),
         )
         self.coef_ = np.dot(self.x_rotations_, self.y_loadings_.T)
-        self.coef_ = (self.coef_ * self._y_std).T
+        self.coef_ = (self.coef_ * self._y_std).T / self._x_std
         self.intercept_ = self._y_mean
         self._n_features_out = self.x_rotations_.shape[1]
         return self
@@ -517,9 +517,8 @@ def predict(self, X, copy=True):
         """
         check_is_fitted(self)
         X = self._validate_data(X, copy=copy, dtype=FLOAT_DTYPES, reset=False)
-        # Normalize
+        # Only center X but do not scale it since the coefficients are already scaled
         X -= self._x_mean
-        X /= self._x_std
         Ypred = X @ self.coef_.T + self.intercept_
         return Ypred.ravel() if self._predict_1d else Ypred
 
diff --git a/sklearn/cross_decomposition/tests/test_pls.py b/sklearn/cross_decomposition/tests/test_pls.py
index 12d60cfef3194..c8de4ad8a78de 100644
--- a/sklearn/cross_decomposition/tests/test_pls.py
+++ b/sklearn/cross_decomposition/tests/test_pls.py
@@ -589,8 +589,6 @@ def test_pls_prediction(PLSEstimator, scale):
 
     y_mean = Y.mean(axis=0)
     X_trans = X - X.mean(axis=0)
-    if scale:
-        X_trans /= X.std(axis=0, ddof=1)
 
     assert_allclose(pls.intercept_, y_mean)
     assert_allclose(Y_pred, X_trans @ pls.coef_.T + pls.intercept_)
@@ -646,6 +644,28 @@ def test_pls_regression_fit_1d_y():
     assert_allclose(y_pred, expected)
 
 
+def test_pls_regression_scaling_coef():
+    """Check that when using `scale=True`, the coefficients are using the std. dev. from
+    both `X` and `Y`.
+
+    Non-regression test for:
+    https://github.com/scikit-learn/scikit-learn/issues/27964
+    """
+    # handcrafted data where we can predict Y from X with an additional scaling factor
+    rng = np.random.RandomState(0)
+    coef = rng.uniform(size=(3, 5))
+    X = rng.normal(scale=10, size=(30, 5))  # add a std of 10
+    Y = X @ coef.T
+
+    # we need to make sure that the dimension of the latent space is large enough to
+    # perfectly predict `Y` from `X` (no information loss)
+    pls = PLSRegression(n_components=5, scale=True).fit(X, Y)
+    assert_allclose(pls.coef_, coef)
+
+    # we therefore should be able to predict `Y` from `X`
+    assert_allclose(pls.predict(X), Y)
+
+
 # TODO(1.7): Remove
 @pytest.mark.parametrize("Klass", [PLSRegression, CCA, PLSSVD, PLSCanonical])
 def test_pls_fit_warning_on_deprecated_Y_argument(Klass):
diff --git a/sklearn/datasets/__init__.py b/sklearn/datasets/__init__.py
index 6f61e027dceaa..58cddb099faff 100644
--- a/sklearn/datasets/__init__.py
+++ b/sklearn/datasets/__init__.py
@@ -1,8 +1,4 @@
-"""
-The :mod:`sklearn.datasets` module includes utilities to load datasets,
-including methods to load and fetch popular reference datasets. It also
-features some artificial data generators.
-"""
+"""Utilities to load popular datasets and artificial data generators."""
 
 import textwrap
 
diff --git a/sklearn/datasets/_lfw.py b/sklearn/datasets/_lfw.py
index cb62288646d23..be72baa981da7 100644
--- a/sklearn/datasets/_lfw.py
+++ b/sklearn/datasets/_lfw.py
@@ -281,6 +281,9 @@ def fetch_lfw_people(
     Features            real, between 0 and 255
     =================   =======================
 
+    For a usage example of this dataset, see
+    :ref:`sphx_glr_auto_examples_applications_plot_face_recognition.py`.
+
     Read more in the :ref:`User Guide <labeled_faces_in_the_wild_dataset>`.
 
     Parameters
diff --git a/sklearn/datasets/descr/breast_cancer.rst b/sklearn/datasets/descr/breast_cancer.rst
index ceabd33e14ddc..cedfa9b4ff0f4 100644
--- a/sklearn/datasets/descr/breast_cancer.rst
+++ b/sklearn/datasets/descr/breast_cancer.rst
@@ -104,19 +104,15 @@ This database is also available through the UW CS ftp server:
 ftp ftp.cs.wisc.edu
 cd math-prog/cpo-dataset/machine-learn/WDBC/
 
-|details-start|
-**References**
-|details-split|
-
-- W.N. Street, W.H. Wolberg and O.L. Mangasarian. Nuclear feature extraction
-  for breast tumor diagnosis. IS&T/SPIE 1993 International Symposium on
-  Electronic Imaging: Science and Technology, volume 1905, pages 861-870,
-  San Jose, CA, 1993.
-- O.L. Mangasarian, W.N. Street and W.H. Wolberg. Breast cancer diagnosis and
-  prognosis via linear programming. Operations Research, 43(4), pages 570-577,
-  July-August 1995.
-- W.H. Wolberg, W.N. Street, and O.L. Mangasarian. Machine learning techniques
-  to diagnose breast cancer from fine-needle aspirates. Cancer Letters 77 (1994)
-  163-171.
-
-|details-end|
+.. dropdown:: References
+
+  - W.N. Street, W.H. Wolberg and O.L. Mangasarian. Nuclear feature extraction
+    for breast tumor diagnosis. IS&T/SPIE 1993 International Symposium on
+    Electronic Imaging: Science and Technology, volume 1905, pages 861-870,
+    San Jose, CA, 1993.
+  - O.L. Mangasarian, W.N. Street and W.H. Wolberg. Breast cancer diagnosis and
+    prognosis via linear programming. Operations Research, 43(4), pages 570-577,
+    July-August 1995.
+  - W.H. Wolberg, W.N. Street, and O.L. Mangasarian. Machine learning techniques
+    to diagnose breast cancer from fine-needle aspirates. Cancer Letters 77 (1994)
+    163-171.
diff --git a/sklearn/datasets/descr/california_housing.rst b/sklearn/datasets/descr/california_housing.rst
index 33ff111fef541..3173a057d1d5a 100644
--- a/sklearn/datasets/descr/california_housing.rst
+++ b/sklearn/datasets/descr/california_housing.rst
@@ -40,7 +40,7 @@ and many empty houses, such as vacation resorts.
 It can be downloaded/loaded using the
 :func:`sklearn.datasets.fetch_california_housing` function.
 
-.. topic:: References
+.. rubric:: References
 
-    - Pace, R. Kelley and Ronald Barry, Sparse Spatial Autoregressions,
-      Statistics and Probability Letters, 33 (1997) 291-297
+- Pace, R. Kelley and Ronald Barry, Sparse Spatial Autoregressions,
+  Statistics and Probability Letters, 33 (1997) 291-297
diff --git a/sklearn/datasets/descr/digits.rst b/sklearn/datasets/descr/digits.rst
index 3b07233721d69..7297584a1b4ac 100644
--- a/sklearn/datasets/descr/digits.rst
+++ b/sklearn/datasets/descr/digits.rst
@@ -32,19 +32,15 @@ T. Candela, D. L. Dimmick, J. Geist, P. J. Grother, S. A. Janet, and C.
 L. Wilson, NIST Form-Based Handprint Recognition System, NISTIR 5469,
 1994.
 
-|details-start|
-**References**
-|details-split|
-
-- C. Kaynak (1995) Methods of Combining Multiple Classifiers and Their
-  Applications to Handwritten Digit Recognition, MSc Thesis, Institute of
-  Graduate Studies in Science and Engineering, Bogazici University.
-- E. Alpaydin, C. Kaynak (1998) Cascading Classifiers, Kybernetika.
-- Ken Tang and Ponnuthurai N. Suganthan and Xi Yao and A. Kai Qin.
-  Linear dimensionalityreduction using relevance weighted LDA. School of
-  Electrical and Electronic Engineering Nanyang Technological University.
-  2005.
-- Claudio Gentile. A New Approximate Maximal Margin Classification
-  Algorithm. NIPS. 2000.
-
-|details-end|
+.. dropdown:: References
+
+  - C. Kaynak (1995) Methods of Combining Multiple Classifiers and Their
+    Applications to Handwritten Digit Recognition, MSc Thesis, Institute of
+    Graduate Studies in Science and Engineering, Bogazici University.
+  - E. Alpaydin, C. Kaynak (1998) Cascading Classifiers, Kybernetika.
+  - Ken Tang and Ponnuthurai N. Suganthan and Xi Yao and A. Kai Qin.
+    Linear dimensionalityreduction using relevance weighted LDA. School of
+    Electrical and Electronic Engineering Nanyang Technological University.
+    2005.
+  - Claudio Gentile. A New Approximate Maximal Margin Classification
+    Algorithm. NIPS. 2000.
diff --git a/sklearn/datasets/descr/iris.rst b/sklearn/datasets/descr/iris.rst
index 771c92faa9899..98651543620e6 100644
--- a/sklearn/datasets/descr/iris.rst
+++ b/sklearn/datasets/descr/iris.rst
@@ -45,23 +45,19 @@ data set contains 3 classes of 50 instances each, where each class refers to a
 type of iris plant.  One class is linearly separable from the other 2; the
 latter are NOT linearly separable from each other.
 
-|details-start|
-**References**
-|details-split|
-
-- Fisher, R.A. "The use of multiple measurements in taxonomic problems"
-  Annual Eugenics, 7, Part II, 179-188 (1936); also in "Contributions to
-  Mathematical Statistics" (John Wiley, NY, 1950).
-- Duda, R.O., & Hart, P.E. (1973) Pattern Classification and Scene Analysis.
-  (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.
-- Dasarathy, B.V. (1980) "Nosing Around the Neighborhood: A New System
-  Structure and Classification Rule for Recognition in Partially Exposed
-  Environments".  IEEE Transactions on Pattern Analysis and Machine
-  Intelligence, Vol. PAMI-2, No. 1, 67-71.
-- Gates, G.W. (1972) "The Reduced Nearest Neighbor Rule".  IEEE Transactions
-  on Information Theory, May 1972, 431-433.
-- See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al"s AUTOCLASS II
-  conceptual clustering system finds 3 classes in the data.
-- Many, many more ...
-
-|details-end|
+.. dropdown:: References
+
+  - Fisher, R.A. "The use of multiple measurements in taxonomic problems"
+    Annual Eugenics, 7, Part II, 179-188 (1936); also in "Contributions to
+    Mathematical Statistics" (John Wiley, NY, 1950).
+  - Duda, R.O., & Hart, P.E. (1973) Pattern Classification and Scene Analysis.
+    (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.
+  - Dasarathy, B.V. (1980) "Nosing Around the Neighborhood: A New System
+    Structure and Classification Rule for Recognition in Partially Exposed
+    Environments".  IEEE Transactions on Pattern Analysis and Machine
+    Intelligence, Vol. PAMI-2, No. 1, 67-71.
+  - Gates, G.W. (1972) "The Reduced Nearest Neighbor Rule".  IEEE Transactions
+    on Information Theory, May 1972, 431-433.
+  - See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al"s AUTOCLASS II
+    conceptual clustering system finds 3 classes in the data.
+  - Many, many more ...
diff --git a/sklearn/datasets/descr/kddcup99.rst b/sklearn/datasets/descr/kddcup99.rst
index fe8a0c8f4168c..0eae813be27a9 100644
--- a/sklearn/datasets/descr/kddcup99.rst
+++ b/sklearn/datasets/descr/kddcup99.rst
@@ -81,14 +81,14 @@ and the target values in ``target``. The "as_frame" optional argument converts
 ``data`` into a pandas DataFrame and ``target`` into a pandas Series. The
 dataset will be downloaded from the web if necessary.
 
-.. topic:: References
+.. rubric:: References
 
-    .. [2] Analysis and Results of the 1999 DARPA Off-Line Intrusion
-           Detection Evaluation, Richard Lippmann, Joshua W. Haines,
-           David J. Fried, Jonathan Korba, Kumar Das.
+.. [2] Analysis and Results of the 1999 DARPA Off-Line Intrusion
+       Detection Evaluation, Richard Lippmann, Joshua W. Haines,
+       David J. Fried, Jonathan Korba, Kumar Das.
 
-    .. [3] K. Yamanishi, J.-I. Takeuchi, G. Williams, and P. Milne. Online
-           unsupervised outlier detection using finite mixtures with
-           discounting learning algorithms. In Proceedings of the sixth
-           ACM SIGKDD international conference on Knowledge discovery
-           and data mining, pages 320-324. ACM Press, 2000.
+.. [3] K. Yamanishi, J.-I. Takeuchi, G. Williams, and P. Milne. Online
+       unsupervised outlier detection using finite mixtures with
+       discounting learning algorithms. In Proceedings of the sixth
+       ACM SIGKDD international conference on Knowledge discovery
+       and data mining, pages 320-324. ACM Press, 2000.
diff --git a/sklearn/datasets/descr/lfw.rst b/sklearn/datasets/descr/lfw.rst
index f7d80558be373..fc23c9566bd64 100644
--- a/sklearn/datasets/descr/lfw.rst
+++ b/sklearn/datasets/descr/lfw.rst
@@ -32,97 +32,93 @@ Dimensionality                         5828
 Features            real, between 0 and 255
 =================   =======================
 
-|details-start|
-**Usage**
-|details-split|
+.. dropdown:: Usage
 
-``scikit-learn`` provides two loaders that will automatically download,
-cache, parse the metadata files, decode the jpeg and convert the
-interesting slices into memmapped numpy arrays. This dataset size is more
-than 200 MB. The first load typically takes more than a couple of minutes
-to fully decode the relevant part of the JPEG files into numpy arrays. If
-the dataset has  been loaded once, the following times the loading times
-less than 200ms by using a memmapped version memoized on the disk in the
-``~/scikit_learn_data/lfw_home/`` folder using ``joblib``.
+  ``scikit-learn`` provides two loaders that will automatically download,
+  cache, parse the metadata files, decode the jpeg and convert the
+  interesting slices into memmapped numpy arrays. This dataset size is more
+  than 200 MB. The first load typically takes more than a couple of minutes
+  to fully decode the relevant part of the JPEG files into numpy arrays. If
+  the dataset has  been loaded once, the following times the loading times
+  less than 200ms by using a memmapped version memoized on the disk in the
+  ``~/scikit_learn_data/lfw_home/`` folder using ``joblib``.
 
-The first loader is used for the Face Identification task: a multi-class
-classification task (hence supervised learning)::
+  The first loader is used for the Face Identification task: a multi-class
+  classification task (hence supervised learning)::
 
-  >>> from sklearn.datasets import fetch_lfw_people
-  >>> lfw_people = fetch_lfw_people(min_faces_per_person=70, resize=0.4)
+    >>> from sklearn.datasets import fetch_lfw_people
+    >>> lfw_people = fetch_lfw_people(min_faces_per_person=70, resize=0.4)
 
-  >>> for name in lfw_people.target_names:
-  ...     print(name)
-  ...
-  Ariel Sharon
-  Colin Powell
-  Donald Rumsfeld
-  George W Bush
-  Gerhard Schroeder
-  Hugo Chavez
-  Tony Blair
+    >>> for name in lfw_people.target_names:
+    ...     print(name)
+    ...
+    Ariel Sharon
+    Colin Powell
+    Donald Rumsfeld
+    George W Bush
+    Gerhard Schroeder
+    Hugo Chavez
+    Tony Blair
 
-The default slice is a rectangular shape around the face, removing
-most of the background::
+  The default slice is a rectangular shape around the face, removing
+  most of the background::
 
-  >>> lfw_people.data.dtype
-  dtype('float32')
+    >>> lfw_people.data.dtype
+    dtype('float32')
 
-  >>> lfw_people.data.shape
-  (1288, 1850)
+    >>> lfw_people.data.shape
+    (1288, 1850)
 
-  >>> lfw_people.images.shape
-  (1288, 50, 37)
+    >>> lfw_people.images.shape
+    (1288, 50, 37)
 
-Each of the ``1140`` faces is assigned to a single person id in the ``target``
-array::
+  Each of the ``1140`` faces is assigned to a single person id in the ``target``
+  array::
 
-  >>> lfw_people.target.shape
-  (1288,)
+    >>> lfw_people.target.shape
+    (1288,)
 
-  >>> list(lfw_people.target[:10])
-  [5, 6, 3, 1, 0, 1, 3, 4, 3, 0]
+    >>> list(lfw_people.target[:10])
+    [5, 6, 3, 1, 0, 1, 3, 4, 3, 0]
 
-The second loader is typically used for the face verification task: each sample
-is a pair of two picture belonging or not to the same person::
+  The second loader is typically used for the face verification task: each sample
+  is a pair of two picture belonging or not to the same person::
 
-  >>> from sklearn.datasets import fetch_lfw_pairs
-  >>> lfw_pairs_train = fetch_lfw_pairs(subset='train')
+    >>> from sklearn.datasets import fetch_lfw_pairs
+    >>> lfw_pairs_train = fetch_lfw_pairs(subset='train')
 
-  >>> list(lfw_pairs_train.target_names)
-  ['Different persons', 'Same person']
+    >>> list(lfw_pairs_train.target_names)
+    ['Different persons', 'Same person']
 
-  >>> lfw_pairs_train.pairs.shape
-  (2200, 2, 62, 47)
+    >>> lfw_pairs_train.pairs.shape
+    (2200, 2, 62, 47)
 
-  >>> lfw_pairs_train.data.shape
-  (2200, 5828)
+    >>> lfw_pairs_train.data.shape
+    (2200, 5828)
 
-  >>> lfw_pairs_train.target.shape
-  (2200,)
+    >>> lfw_pairs_train.target.shape
+    (2200,)
 
-Both for the :func:`sklearn.datasets.fetch_lfw_people` and
-:func:`sklearn.datasets.fetch_lfw_pairs` function it is
-possible to get an additional dimension with the RGB color channels by
-passing ``color=True``, in that case the shape will be
-``(2200, 2, 62, 47, 3)``.
+  Both for the :func:`sklearn.datasets.fetch_lfw_people` and
+  :func:`sklearn.datasets.fetch_lfw_pairs` function it is
+  possible to get an additional dimension with the RGB color channels by
+  passing ``color=True``, in that case the shape will be
+  ``(2200, 2, 62, 47, 3)``.
 
-The :func:`sklearn.datasets.fetch_lfw_pairs` datasets is subdivided into
-3 subsets: the development ``train`` set, the development ``test`` set and
-an evaluation ``10_folds`` set meant to compute performance metrics using a
-10-folds cross validation scheme.
+  The :func:`sklearn.datasets.fetch_lfw_pairs` datasets is subdivided into
+  3 subsets: the development ``train`` set, the development ``test`` set and
+  an evaluation ``10_folds`` set meant to compute performance metrics using a
+  10-folds cross validation scheme.
 
-|details-end|
+.. rubric:: References
 
-.. topic:: References:
+* `Labeled Faces in the Wild: A Database for Studying Face Recognition
+  in Unconstrained Environments.
+  <http://vis-www.cs.umass.edu/lfw/lfw.pdf>`_
+  Gary B. Huang, Manu Ramesh, Tamara Berg, and Erik Learned-Miller.
+  University of Massachusetts, Amherst, Technical Report 07-49, October, 2007.
 
- * `Labeled Faces in the Wild: A Database for Studying Face Recognition
-   in Unconstrained Environments.
-   <http://vis-www.cs.umass.edu/lfw/lfw.pdf>`_
-   Gary B. Huang, Manu Ramesh, Tamara Berg, and Erik Learned-Miller.
-   University of Massachusetts, Amherst, Technical Report 07-49, October, 2007.
 
+.. rubric:: Examples
 
-.. topic:: Examples:
-
-   * :ref:`sphx_glr_auto_examples_applications_plot_face_recognition.py`
+* :ref:`sphx_glr_auto_examples_applications_plot_face_recognition.py`
diff --git a/sklearn/datasets/descr/linnerud.rst b/sklearn/datasets/descr/linnerud.rst
index 108611a4722ad..6deb231fe67a9 100644
--- a/sklearn/datasets/descr/linnerud.rst
+++ b/sklearn/datasets/descr/linnerud.rst
@@ -18,11 +18,7 @@ twenty middle-aged men in a fitness club:
 - *exercise* - CSV containing 20 observations on 3 exercise variables:
    Chins, Situps and Jumps.
 
-|details-start|
-**References**
-|details-split|
+.. dropdown:: References
 
-* Tenenhaus, M. (1998). La regression PLS: theorie et pratique. Paris:
-  Editions Technic.
-
-|details-end|
+   * Tenenhaus, M. (1998). La regression PLS: theorie et pratique. Paris:
+     Editions Technic.
diff --git a/sklearn/datasets/descr/rcv1.rst b/sklearn/datasets/descr/rcv1.rst
index 7cf3730a17554..3f14cf01934a0 100644
--- a/sklearn/datasets/descr/rcv1.rst
+++ b/sklearn/datasets/descr/rcv1.rst
@@ -65,8 +65,8 @@ The compressed size is about 656 MB.
 .. _rcv1 homepage: http://jmlr.csail.mit.edu/papers/volume5/lewis04a/
 
 
-.. topic:: References
+.. rubric:: References
 
-    .. [1] Lewis, D. D., Yang, Y., Rose, T. G., & Li, F. (2004).
-           RCV1: A new benchmark collection for text categorization research.
-           The Journal of Machine Learning Research, 5, 361-397.
+.. [1] Lewis, D. D., Yang, Y., Rose, T. G., & Li, F. (2004).
+       RCV1: A new benchmark collection for text categorization research.
+       The Journal of Machine Learning Research, 5, 361-397.
diff --git a/sklearn/datasets/descr/species_distributions.rst b/sklearn/datasets/descr/species_distributions.rst
index a2c2243de5567..c2c0014b875e9 100644
--- a/sklearn/datasets/descr/species_distributions.rst
+++ b/sklearn/datasets/descr/species_distributions.rst
@@ -29,8 +29,8 @@ of a species at a specific location.
 
 The dataset is provided by Phillips et. al. (2006).
 
-.. topic:: References
+.. rubric:: References
 
- * `"Maximum entropy modeling of species geographic distributions"
-   <http://rob.schapire.net/papers/ecolmod.pdf>`_ S. J. Phillips,
-   R. P. Anderson, R. E. Schapire - Ecological Modelling, 190:231-259, 2006.
+* `"Maximum entropy modeling of species geographic distributions"
+  <http://rob.schapire.net/papers/ecolmod.pdf>`_ S. J. Phillips,
+  R. P. Anderson, R. E. Schapire - Ecological Modelling, 190:231-259, 2006.
diff --git a/sklearn/datasets/descr/twenty_newsgroups.rst b/sklearn/datasets/descr/twenty_newsgroups.rst
index d1a049869dd7f..182272b888d45 100644
--- a/sklearn/datasets/descr/twenty_newsgroups.rst
+++ b/sklearn/datasets/descr/twenty_newsgroups.rst
@@ -27,204 +27,191 @@ Dimensionality               1
 Features                  text
 =================   ==========
 
-|details-start|
-**Usage**
-|details-split|
-
-The :func:`sklearn.datasets.fetch_20newsgroups` function is a data
-fetching / caching functions that downloads the data archive from
-the original `20 newsgroups website`_, extracts the archive contents
-in the ``~/scikit_learn_data/20news_home`` folder and calls the
-:func:`sklearn.datasets.load_files` on either the training or
-testing set folder, or both of them::
-
-  >>> from sklearn.datasets import fetch_20newsgroups
-  >>> newsgroups_train = fetch_20newsgroups(subset='train')
-
-  >>> from pprint import pprint
-  >>> pprint(list(newsgroups_train.target_names))
-  ['alt.atheism',
-   'comp.graphics',
-   'comp.os.ms-windows.misc',
-   'comp.sys.ibm.pc.hardware',
-   'comp.sys.mac.hardware',
-   'comp.windows.x',
-   'misc.forsale',
-   'rec.autos',
-   'rec.motorcycles',
-   'rec.sport.baseball',
-   'rec.sport.hockey',
-   'sci.crypt',
-   'sci.electronics',
-   'sci.med',
-   'sci.space',
-   'soc.religion.christian',
-   'talk.politics.guns',
-   'talk.politics.mideast',
-   'talk.politics.misc',
-   'talk.religion.misc']
-
-The real data lies in the ``filenames`` and ``target`` attributes. The target
-attribute is the integer index of the category::
-
-  >>> newsgroups_train.filenames.shape
-  (11314,)
-  >>> newsgroups_train.target.shape
-  (11314,)
-  >>> newsgroups_train.target[:10]
-  array([ 7,  4,  4,  1, 14, 16, 13,  3,  2,  4])
-
-It is possible to load only a sub-selection of the categories by passing the
-list of the categories to load to the
-:func:`sklearn.datasets.fetch_20newsgroups` function::
-
-  >>> cats = ['alt.atheism', 'sci.space']
-  >>> newsgroups_train = fetch_20newsgroups(subset='train', categories=cats)
-
-  >>> list(newsgroups_train.target_names)
-  ['alt.atheism', 'sci.space']
-  >>> newsgroups_train.filenames.shape
-  (1073,)
-  >>> newsgroups_train.target.shape
-  (1073,)
-  >>> newsgroups_train.target[:10]
-  array([0, 1, 1, 1, 0, 1, 1, 0, 0, 0])
-
-|details-end|
-
-|details-start|
-**Converting text to vectors**
-|details-split|
-
-In order to feed predictive or clustering models with the text data,
-one first need to turn the text into vectors of numerical values suitable
-for statistical analysis. This can be achieved with the utilities of the
-``sklearn.feature_extraction.text`` as demonstrated in the following
-example that extract `TF-IDF`_ vectors of unigram tokens
-from a subset of 20news::
-
-  >>> from sklearn.feature_extraction.text import TfidfVectorizer
-  >>> categories = ['alt.atheism', 'talk.religion.misc',
-  ...               'comp.graphics', 'sci.space']
-  >>> newsgroups_train = fetch_20newsgroups(subset='train',
-  ...                                       categories=categories)
-  >>> vectorizer = TfidfVectorizer()
-  >>> vectors = vectorizer.fit_transform(newsgroups_train.data)
-  >>> vectors.shape
-  (2034, 34118)
-
-The extracted TF-IDF vectors are very sparse, with an average of 159 non-zero
-components by sample in a more than 30000-dimensional space
-(less than .5% non-zero features)::
-
-  >>> vectors.nnz / float(vectors.shape[0])
-  159.01327...
-
-:func:`sklearn.datasets.fetch_20newsgroups_vectorized` is a function which
-returns ready-to-use token counts features instead of file names.
-
-.. _`20 newsgroups website`: http://people.csail.mit.edu/jrennie/20Newsgroups/
-.. _`TF-IDF`: https://en.wikipedia.org/wiki/Tf-idf
-
-|details-end|
-
-|details-start|
-**Filtering text for more realistic training**
-|details-split|
-
-It is easy for a classifier to overfit on particular things that appear in the
-20 Newsgroups data, such as newsgroup headers. Many classifiers achieve very
-high F-scores, but their results would not generalize to other documents that
-aren't from this window of time.
-
-For example, let's look at the results of a multinomial Naive Bayes classifier,
-which is fast to train and achieves a decent F-score::
-
-  >>> from sklearn.naive_bayes import MultinomialNB
-  >>> from sklearn import metrics
-  >>> newsgroups_test = fetch_20newsgroups(subset='test',
-  ...                                      categories=categories)
-  >>> vectors_test = vectorizer.transform(newsgroups_test.data)
-  >>> clf = MultinomialNB(alpha=.01)
-  >>> clf.fit(vectors, newsgroups_train.target)
-  MultinomialNB(alpha=0.01, class_prior=None, fit_prior=True)
-
-  >>> pred = clf.predict(vectors_test)
-  >>> metrics.f1_score(newsgroups_test.target, pred, average='macro')
-  0.88213...
-
-(The example :ref:`sphx_glr_auto_examples_text_plot_document_classification_20newsgroups.py` shuffles
-the training and test data, instead of segmenting by time, and in that case
-multinomial Naive Bayes gets a much higher F-score of 0.88. Are you suspicious
-yet of what's going on inside this classifier?)
-
-Let's take a look at what the most informative features are:
-
-  >>> import numpy as np
-  >>> def show_top10(classifier, vectorizer, categories):
-  ...     feature_names = vectorizer.get_feature_names_out()
-  ...     for i, category in enumerate(categories):
-  ...         top10 = np.argsort(classifier.coef_[i])[-10:]
-  ...         print("%s: %s" % (category, " ".join(feature_names[top10])))
-  ...
-  >>> show_top10(clf, vectorizer, newsgroups_train.target_names)
-  alt.atheism: edu it and in you that is of to the
-  comp.graphics: edu in graphics it is for and of to the
-  sci.space: edu it that is in and space to of the
-  talk.religion.misc: not it you in is that and to of the
-
-
-You can now see many things that these features have overfit to:
-
-- Almost every group is distinguished by whether headers such as
-  ``NNTP-Posting-Host:`` and ``Distribution:`` appear more or less often.
-- Another significant feature involves whether the sender is affiliated with
-  a university, as indicated either by their headers or their signature.
-- The word "article" is a significant feature, based on how often people quote
-  previous posts like this: "In article [article ID], [name] <[e-mail address]>
-  wrote:"
-- Other features match the names and e-mail addresses of particular people who
-  were posting at the time.
-
-With such an abundance of clues that distinguish newsgroups, the classifiers
-barely have to identify topics from text at all, and they all perform at the
-same high level.
-
-For this reason, the functions that load 20 Newsgroups data provide a
-parameter called **remove**, telling it what kinds of information to strip out
-of each file. **remove** should be a tuple containing any subset of
-``('headers', 'footers', 'quotes')``, telling it to remove headers, signature
-blocks, and quotation blocks respectively.
-
-  >>> newsgroups_test = fetch_20newsgroups(subset='test',
-  ...                                      remove=('headers', 'footers', 'quotes'),
-  ...                                      categories=categories)
-  >>> vectors_test = vectorizer.transform(newsgroups_test.data)
-  >>> pred = clf.predict(vectors_test)
-  >>> metrics.f1_score(pred, newsgroups_test.target, average='macro')
-  0.77310...
-
-This classifier lost over a lot of its F-score, just because we removed
-metadata that has little to do with topic classification.
-It loses even more if we also strip this metadata from the training data:
-
-  >>> newsgroups_train = fetch_20newsgroups(subset='train',
-  ...                                       remove=('headers', 'footers', 'quotes'),
-  ...                                       categories=categories)
-  >>> vectors = vectorizer.fit_transform(newsgroups_train.data)
-  >>> clf = MultinomialNB(alpha=.01)
-  >>> clf.fit(vectors, newsgroups_train.target)
-  MultinomialNB(alpha=0.01, class_prior=None, fit_prior=True)
-
-  >>> vectors_test = vectorizer.transform(newsgroups_test.data)
-  >>> pred = clf.predict(vectors_test)
-  >>> metrics.f1_score(newsgroups_test.target, pred, average='macro')
-  0.76995...
-
-Some other classifiers cope better with this harder version of the task. Try the
-:ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_text_feature_extraction.py`
-example with and without the `remove` option to compare the results.
-|details-end|
+.. dropdown:: Usage
+
+  The :func:`sklearn.datasets.fetch_20newsgroups` function is a data
+  fetching / caching functions that downloads the data archive from
+  the original `20 newsgroups website <http://people.csail.mit.edu/jrennie/20Newsgroups/>`__,
+  extracts the archive contents
+  in the ``~/scikit_learn_data/20news_home`` folder and calls the
+  :func:`sklearn.datasets.load_files` on either the training or
+  testing set folder, or both of them::
+
+    >>> from sklearn.datasets import fetch_20newsgroups
+    >>> newsgroups_train = fetch_20newsgroups(subset='train')
+
+    >>> from pprint import pprint
+    >>> pprint(list(newsgroups_train.target_names))
+    ['alt.atheism',
+     'comp.graphics',
+     'comp.os.ms-windows.misc',
+     'comp.sys.ibm.pc.hardware',
+     'comp.sys.mac.hardware',
+     'comp.windows.x',
+     'misc.forsale',
+     'rec.autos',
+     'rec.motorcycles',
+     'rec.sport.baseball',
+     'rec.sport.hockey',
+     'sci.crypt',
+     'sci.electronics',
+     'sci.med',
+     'sci.space',
+     'soc.religion.christian',
+     'talk.politics.guns',
+     'talk.politics.mideast',
+     'talk.politics.misc',
+     'talk.religion.misc']
+
+  The real data lies in the ``filenames`` and ``target`` attributes. The target
+  attribute is the integer index of the category::
+
+    >>> newsgroups_train.filenames.shape
+    (11314,)
+    >>> newsgroups_train.target.shape
+    (11314,)
+    >>> newsgroups_train.target[:10]
+    array([ 7,  4,  4,  1, 14, 16, 13,  3,  2,  4])
+
+  It is possible to load only a sub-selection of the categories by passing the
+  list of the categories to load to the
+  :func:`sklearn.datasets.fetch_20newsgroups` function::
+
+    >>> cats = ['alt.atheism', 'sci.space']
+    >>> newsgroups_train = fetch_20newsgroups(subset='train', categories=cats)
+
+    >>> list(newsgroups_train.target_names)
+    ['alt.atheism', 'sci.space']
+    >>> newsgroups_train.filenames.shape
+    (1073,)
+    >>> newsgroups_train.target.shape
+    (1073,)
+    >>> newsgroups_train.target[:10]
+    array([0, 1, 1, 1, 0, 1, 1, 0, 0, 0])
+
+.. dropdown:: Converting text to vectors
+
+  In order to feed predictive or clustering models with the text data,
+  one first need to turn the text into vectors of numerical values suitable
+  for statistical analysis. This can be achieved with the utilities of the
+  ``sklearn.feature_extraction.text`` as demonstrated in the following
+  example that extract `TF-IDF <https://en.wikipedia.org/wiki/Tf-idf>`__ vectors
+  of unigram tokens from a subset of 20news::
+
+    >>> from sklearn.feature_extraction.text import TfidfVectorizer
+    >>> categories = ['alt.atheism', 'talk.religion.misc',
+    ...               'comp.graphics', 'sci.space']
+    >>> newsgroups_train = fetch_20newsgroups(subset='train',
+    ...                                       categories=categories)
+    >>> vectorizer = TfidfVectorizer()
+    >>> vectors = vectorizer.fit_transform(newsgroups_train.data)
+    >>> vectors.shape
+    (2034, 34118)
+
+  The extracted TF-IDF vectors are very sparse, with an average of 159 non-zero
+  components by sample in a more than 30000-dimensional space
+  (less than .5% non-zero features)::
+
+    >>> vectors.nnz / float(vectors.shape[0])
+    159.01327...
+
+  :func:`sklearn.datasets.fetch_20newsgroups_vectorized` is a function which
+  returns ready-to-use token counts features instead of file names.
+
+.. dropdown:: Filtering text for more realistic training
+
+  It is easy for a classifier to overfit on particular things that appear in the
+  20 Newsgroups data, such as newsgroup headers. Many classifiers achieve very
+  high F-scores, but their results would not generalize to other documents that
+  aren't from this window of time.
+
+  For example, let's look at the results of a multinomial Naive Bayes classifier,
+  which is fast to train and achieves a decent F-score::
+
+    >>> from sklearn.naive_bayes import MultinomialNB
+    >>> from sklearn import metrics
+    >>> newsgroups_test = fetch_20newsgroups(subset='test',
+    ...                                      categories=categories)
+    >>> vectors_test = vectorizer.transform(newsgroups_test.data)
+    >>> clf = MultinomialNB(alpha=.01)
+    >>> clf.fit(vectors, newsgroups_train.target)
+    MultinomialNB(alpha=0.01, class_prior=None, fit_prior=True)
+
+    >>> pred = clf.predict(vectors_test)
+    >>> metrics.f1_score(newsgroups_test.target, pred, average='macro')
+    0.88213...
+
+  (The example :ref:`sphx_glr_auto_examples_text_plot_document_classification_20newsgroups.py` shuffles
+  the training and test data, instead of segmenting by time, and in that case
+  multinomial Naive Bayes gets a much higher F-score of 0.88. Are you suspicious
+  yet of what's going on inside this classifier?)
+
+  Let's take a look at what the most informative features are:
+
+    >>> import numpy as np
+    >>> def show_top10(classifier, vectorizer, categories):
+    ...     feature_names = vectorizer.get_feature_names_out()
+    ...     for i, category in enumerate(categories):
+    ...         top10 = np.argsort(classifier.coef_[i])[-10:]
+    ...         print("%s: %s" % (category, " ".join(feature_names[top10])))
+    ...
+    >>> show_top10(clf, vectorizer, newsgroups_train.target_names)
+    alt.atheism: edu it and in you that is of to the
+    comp.graphics: edu in graphics it is for and of to the
+    sci.space: edu it that is in and space to of the
+    talk.religion.misc: not it you in is that and to of the
+
+
+  You can now see many things that these features have overfit to:
+
+  - Almost every group is distinguished by whether headers such as
+    ``NNTP-Posting-Host:`` and ``Distribution:`` appear more or less often.
+  - Another significant feature involves whether the sender is affiliated with
+    a university, as indicated either by their headers or their signature.
+  - The word "article" is a significant feature, based on how often people quote
+    previous posts like this: "In article [article ID], [name] <[e-mail address]>
+    wrote:"
+  - Other features match the names and e-mail addresses of particular people who
+    were posting at the time.
+
+  With such an abundance of clues that distinguish newsgroups, the classifiers
+  barely have to identify topics from text at all, and they all perform at the
+  same high level.
+
+  For this reason, the functions that load 20 Newsgroups data provide a
+  parameter called **remove**, telling it what kinds of information to strip out
+  of each file. **remove** should be a tuple containing any subset of
+  ``('headers', 'footers', 'quotes')``, telling it to remove headers, signature
+  blocks, and quotation blocks respectively.
+
+    >>> newsgroups_test = fetch_20newsgroups(subset='test',
+    ...                                      remove=('headers', 'footers', 'quotes'),
+    ...                                      categories=categories)
+    >>> vectors_test = vectorizer.transform(newsgroups_test.data)
+    >>> pred = clf.predict(vectors_test)
+    >>> metrics.f1_score(pred, newsgroups_test.target, average='macro')
+    0.77310...
+
+  This classifier lost over a lot of its F-score, just because we removed
+  metadata that has little to do with topic classification.
+  It loses even more if we also strip this metadata from the training data:
+
+    >>> newsgroups_train = fetch_20newsgroups(subset='train',
+    ...                                       remove=('headers', 'footers', 'quotes'),
+    ...                                       categories=categories)
+    >>> vectors = vectorizer.fit_transform(newsgroups_train.data)
+    >>> clf = MultinomialNB(alpha=.01)
+    >>> clf.fit(vectors, newsgroups_train.target)
+    MultinomialNB(alpha=0.01, class_prior=None, fit_prior=True)
+
+    >>> vectors_test = vectorizer.transform(newsgroups_test.data)
+    >>> pred = clf.predict(vectors_test)
+    >>> metrics.f1_score(newsgroups_test.target, pred, average='macro')
+    0.76995...
+
+  Some other classifiers cope better with this harder version of the task. Try the
+  :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_text_feature_extraction.py`
+  example with and without the `remove` option to compare the results.
 
 .. topic:: Data Considerations
 
@@ -253,12 +240,9 @@ example with and without the `remove` option to compare the results.
     should be taken into consideration when using the dataset, reviewing the
     output, and the bias should be documented.
 
-.. topic:: Examples
+.. rubric:: Examples
 
-   * :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_text_feature_extraction.py`
-
-   * :ref:`sphx_glr_auto_examples_text_plot_document_classification_20newsgroups.py`
-
-   * :ref:`sphx_glr_auto_examples_text_plot_hashing_vs_dict_vectorizer.py`
-
-   * :ref:`sphx_glr_auto_examples_text_plot_document_clustering.py`
+* :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_text_feature_extraction.py`
+* :ref:`sphx_glr_auto_examples_text_plot_document_classification_20newsgroups.py`
+* :ref:`sphx_glr_auto_examples_text_plot_hashing_vs_dict_vectorizer.py`
+* :ref:`sphx_glr_auto_examples_text_plot_document_clustering.py`
diff --git a/sklearn/datasets/descr/wine_data.rst b/sklearn/datasets/descr/wine_data.rst
index 0325af6233c17..64efe49900ebf 100644
--- a/sklearn/datasets/descr/wine_data.rst
+++ b/sklearn/datasets/descr/wine_data.rst
@@ -73,26 +73,22 @@ Lichman, M. (2013). UCI Machine Learning Repository
 [https://archive.ics.uci.edu/ml]. Irvine, CA: University of California,
 School of Information and Computer Science.
 
-|details-start|
-**References**
-|details-split|
-
-(1) S. Aeberhard, D. Coomans and O. de Vel,
-Comparison of Classifiers in High Dimensional Settings,
-Tech. Rep. no. 92-02, (1992), Dept. of Computer Science and Dept. of
-Mathematics and Statistics, James Cook University of North Queensland.
-(Also submitted to Technometrics).
-
-The data was used with many others for comparing various
-classifiers. The classes are separable, though only RDA
-has achieved 100% correct classification.
-(RDA : 100%, QDA 99.4%, LDA 98.9%, 1NN 96.1% (z-transformed data))
-(All results using the leave-one-out technique)
-
-(2) S. Aeberhard, D. Coomans and O. de Vel,
-"THE CLASSIFICATION PERFORMANCE OF RDA"
-Tech. Rep. no. 92-01, (1992), Dept. of Computer Science and Dept. of
-Mathematics and Statistics, James Cook University of North Queensland.
-(Also submitted to Journal of Chemometrics).
-
-|details-end|
+.. dropdown:: References
+
+    (1) S. Aeberhard, D. Coomans and O. de Vel,
+    Comparison of Classifiers in High Dimensional Settings,
+    Tech. Rep. no. 92-02, (1992), Dept. of Computer Science and Dept. of
+    Mathematics and Statistics, James Cook University of North Queensland.
+    (Also submitted to Technometrics).
+
+    The data was used with many others for comparing various
+    classifiers. The classes are separable, though only RDA
+    has achieved 100% correct classification.
+    (RDA : 100%, QDA 99.4%, LDA 98.9%, 1NN 96.1% (z-transformed data))
+    (All results using the leave-one-out technique)
+
+    (2) S. Aeberhard, D. Coomans and O. de Vel,
+    "THE CLASSIFICATION PERFORMANCE OF RDA"
+    Tech. Rep. no. 92-01, (1992), Dept. of Computer Science and Dept. of
+    Mathematics and Statistics, James Cook University of North Queensland.
+    (Also submitted to Journal of Chemometrics).
diff --git a/sklearn/decomposition/__init__.py b/sklearn/decomposition/__init__.py
index 3d33938a755a7..4730bda31ebbd 100644
--- a/sklearn/decomposition/__init__.py
+++ b/sklearn/decomposition/__init__.py
@@ -1,7 +1,7 @@
-"""
-The :mod:`sklearn.decomposition` module includes matrix decomposition
-algorithms, including among others PCA, NMF or ICA. Most of the algorithms of
-this module can be regarded as dimensionality reduction techniques.
+"""Matrix decomposition algorithms.
+
+These include PCA, NMF, ICA, and more. Most of the algorithms of this module can be
+regarded as dimensionality reduction techniques.
 """
 
 from ..utils.extmath import randomized_svd
diff --git a/sklearn/decomposition/_kernel_pca.py b/sklearn/decomposition/_kernel_pca.py
index edfd49c2e87a0..88b1bb63a4fb0 100644
--- a/sklearn/decomposition/_kernel_pca.py
+++ b/sklearn/decomposition/_kernel_pca.py
@@ -30,10 +30,10 @@
 
 
 class KernelPCA(ClassNamePrefixFeaturesOutMixin, TransformerMixin, BaseEstimator):
-    """Kernel Principal component analysis (KPCA) [1]_.
+    """Kernel Principal component analysis (KPCA).
 
-    Non-linear dimensionality reduction through the use of kernels (see
-    :ref:`metrics`).
+    Non-linear dimensionality reduction through the use of kernels [1]_, see also
+    :ref:`metrics`.
 
     It uses the :func:`scipy.linalg.eigh` LAPACK implementation of the full SVD
     or the :func:`scipy.sparse.linalg.eigsh` ARPACK implementation of the
@@ -41,9 +41,13 @@ class KernelPCA(ClassNamePrefixFeaturesOutMixin, TransformerMixin, BaseEstimator
     components to extract. It can also use a randomized truncated SVD by the
     method proposed in [3]_, see `eigen_solver`.
 
-    For a usage example, see
+    For a usage example and comparison between
+    Principal Components Analysis (PCA) and its kernelized version (KPCA), see
     :ref:`sphx_glr_auto_examples_decomposition_plot_kernel_pca.py`.
 
+    For a usage example in denoising images using KPCA, see
+    :ref:`sphx_glr_auto_examples_applications_plot_digits_denoising.py`.
+
     Read more in the :ref:`User Guide <kernel_PCA>`.
 
     Parameters
diff --git a/sklearn/decomposition/_nmf.py b/sklearn/decomposition/_nmf.py
index 75266c5f64b2b..0970c93deb1ec 100644
--- a/sklearn/decomposition/_nmf.py
+++ b/sklearn/decomposition/_nmf.py
@@ -32,6 +32,7 @@
     StrOptions,
     validate_params,
 )
+from ..utils.deprecation import _deprecate_Xt_in_inverse_transform
 from ..utils.extmath import randomized_svd, safe_sparse_dot, squared_norm
 from ..utils.validation import (
     check_is_fitted,
@@ -1139,9 +1140,9 @@ class _BaseNMF(ClassNamePrefixFeaturesOutMixin, TransformerMixin, BaseEstimator,
     """Base class for NMF and MiniBatchNMF."""
 
     # This prevents ``set_split_inverse_transform`` to be generated for the
-    # non-standard ``W`` arg on ``inverse_transform``.
-    # TODO: remove when W is removed in v1.5 for inverse_transform
-    __metadata_request__inverse_transform = {"W": metadata_routing.UNUSED}
+    # non-standard ``Xt`` arg on ``inverse_transform``.
+    # TODO(1.7): remove when Xt is removed in v1.7 for inverse_transform
+    __metadata_request__inverse_transform = {"Xt": metadata_routing.UNUSED}
 
     _parameter_constraints: dict = {
         "n_components": [
@@ -1310,44 +1311,32 @@ def fit(self, X, y=None, **params):
         self.fit_transform(X, **params)
         return self
 
-    def inverse_transform(self, Xt=None, W=None):
+    def inverse_transform(self, X=None, *, Xt=None):
         """Transform data back to its original space.
 
         .. versionadded:: 0.18
 
         Parameters
         ----------
-        Xt : {ndarray, sparse matrix} of shape (n_samples, n_components)
+        X : {ndarray, sparse matrix} of shape (n_samples, n_components)
             Transformed data matrix.
 
-        W : deprecated
-            Use `Xt` instead.
+        Xt : {ndarray, sparse matrix} of shape (n_samples, n_components)
+            Transformed data matrix.
 
-            .. deprecated:: 1.3
+            .. deprecated:: 1.5
+                `Xt` was deprecated in 1.5 and will be removed in 1.7. Use `X` instead.
 
         Returns
         -------
         X : ndarray of shape (n_samples, n_features)
             Returns a data matrix of the original shape.
         """
-        if Xt is None and W is None:
-            raise TypeError("Missing required positional argument: Xt")
 
-        if W is not None and Xt is not None:
-            raise ValueError("Please provide only `Xt`, and not `W`.")
-
-        if W is not None:
-            warnings.warn(
-                (
-                    "Input argument `W` was renamed to `Xt` in v1.3 and will be removed"
-                    " in v1.5."
-                ),
-                FutureWarning,
-            )
-            Xt = W
+        X = _deprecate_Xt_in_inverse_transform(X, Xt)
 
         check_is_fitted(self)
-        return Xt @ self.components_
+        return X @ self.components_
 
     @property
     def _n_features_out(self):
diff --git a/sklearn/decomposition/tests/test_nmf.py b/sklearn/decomposition/tests/test_nmf.py
index 2112b59129e25..b6eb4f9b1becc 100644
--- a/sklearn/decomposition/tests/test_nmf.py
+++ b/sklearn/decomposition/tests/test_nmf.py
@@ -933,30 +933,31 @@ def test_minibatch_nmf_verbose():
         sys.stdout = old_stdout
 
 
-# TODO(1.5): remove this test
-def test_NMF_inverse_transform_W_deprecation():
-    rng = np.random.mtrand.RandomState(42)
+# TODO(1.7): remove this test
+@pytest.mark.parametrize("Estimator", [NMF, MiniBatchNMF])
+def test_NMF_inverse_transform_Xt_deprecation(Estimator):
+    rng = np.random.RandomState(42)
     A = np.abs(rng.randn(6, 5))
-    est = NMF(
+    est = Estimator(
         n_components=3,
         init="random",
         random_state=0,
         tol=1e-6,
     )
-    Xt = est.fit_transform(A)
+    X = est.fit_transform(A)
 
     with pytest.raises(TypeError, match="Missing required positional argument"):
         est.inverse_transform()
 
-    with pytest.raises(ValueError, match="Please provide only"):
-        est.inverse_transform(Xt=Xt, W=Xt)
+    with pytest.raises(TypeError, match="Cannot use both X and Xt. Use X only"):
+        est.inverse_transform(X=X, Xt=X)
 
     with warnings.catch_warnings(record=True):
         warnings.simplefilter("error")
-        est.inverse_transform(Xt)
+        est.inverse_transform(X)
 
-    with pytest.warns(FutureWarning, match="Input argument `W` was renamed to `Xt`"):
-        est.inverse_transform(W=Xt)
+    with pytest.warns(FutureWarning, match="Xt was renamed X in version 1.5"):
+        est.inverse_transform(Xt=X)
 
 
 @pytest.mark.parametrize("Estimator", [NMF, MiniBatchNMF])
diff --git a/sklearn/decomposition/tests/test_pca.py b/sklearn/decomposition/tests/test_pca.py
index d099bf9a91e00..bd7f60061abdc 100644
--- a/sklearn/decomposition/tests/test_pca.py
+++ b/sklearn/decomposition/tests/test_pca.py
@@ -34,15 +34,18 @@
 SPARSE_MAX_COMPONENTS = min(SPARSE_M, SPARSE_N)
 
 
-def _check_fitted_pca_close(pca1, pca2, rtol):
-    assert_allclose(pca1.components_, pca2.components_, rtol=rtol)
-    assert_allclose(pca1.explained_variance_, pca2.explained_variance_, rtol=rtol)
-    assert_allclose(pca1.singular_values_, pca2.singular_values_, rtol=rtol)
-    assert_allclose(pca1.mean_, pca2.mean_, rtol=rtol)
-    assert_allclose(pca1.n_components_, pca2.n_components_, rtol=rtol)
-    assert_allclose(pca1.n_samples_, pca2.n_samples_, rtol=rtol)
-    assert_allclose(pca1.noise_variance_, pca2.noise_variance_, rtol=rtol)
-    assert_allclose(pca1.n_features_in_, pca2.n_features_in_, rtol=rtol)
+def _check_fitted_pca_close(pca1, pca2, rtol=1e-7, atol=1e-12):
+    assert_allclose(pca1.components_, pca2.components_, rtol=rtol, atol=atol)
+    assert_allclose(
+        pca1.explained_variance_, pca2.explained_variance_, rtol=rtol, atol=atol
+    )
+    assert_allclose(pca1.singular_values_, pca2.singular_values_, rtol=rtol, atol=atol)
+    assert_allclose(pca1.mean_, pca2.mean_, rtol=rtol, atol=atol)
+    assert_allclose(pca1.noise_variance_, pca2.noise_variance_, rtol=rtol, atol=atol)
+
+    assert pca1.n_components_ == pca2.n_components_
+    assert pca1.n_samples_ == pca2.n_samples_
+    assert pca1.n_features_in_ == pca2.n_features_in_
 
 
 @pytest.mark.parametrize("svd_solver", PCA_SOLVERS)
@@ -75,9 +78,12 @@ def test_pca(svd_solver, n_components):
 def test_pca_sparse(
     global_random_seed, svd_solver, sparse_container, n_components, density, scale
 ):
-    # Make sure any tolerance changes pass with SKLEARN_TESTS_GLOBAL_RANDOM_SEED="all"
-    rtol = 5e-07
-    transform_rtol = 3e-05
+    """Check that the results are the same for sparse and dense input."""
+
+    # Set atol in addition of the default rtol to account for the very wide range of
+    # result values (1e-8 to 1e0).
+    atol = 1e-12
+    transform_atol = 1e-10
 
     random_state = np.random.default_rng(global_random_seed)
     X = sparse_container(
@@ -108,7 +114,7 @@ def test_pca_sparse(
     pcad.fit(Xd)
 
     # Fitted attributes equality
-    _check_fitted_pca_close(pca, pcad, rtol=rtol)
+    _check_fitted_pca_close(pca, pcad, atol=atol)
 
     # Test transform
     X2 = sparse_container(
@@ -121,8 +127,8 @@ def test_pca_sparse(
     )
     X2d = X2.toarray()
 
-    assert_allclose(pca.transform(X2), pca.transform(X2d), rtol=transform_rtol)
-    assert_allclose(pca.transform(X2), pcad.transform(X2d), rtol=transform_rtol)
+    assert_allclose(pca.transform(X2), pca.transform(X2d), atol=transform_atol)
+    assert_allclose(pca.transform(X2), pcad.transform(X2d), atol=transform_atol)
 
 
 @pytest.mark.parametrize("sparse_container", CSR_CONTAINERS + CSC_CONTAINERS)
@@ -153,10 +159,10 @@ def test_pca_sparse_fit_transform(global_random_seed, sparse_container):
     pca_fit.fit(X)
     transformed_X = pca_fit_transform.fit_transform(X)
 
-    _check_fitted_pca_close(pca_fit, pca_fit_transform, rtol=1e-10)
-    assert_allclose(transformed_X, pca_fit_transform.transform(X), rtol=2e-9)
-    assert_allclose(transformed_X, pca_fit.transform(X), rtol=2e-9)
-    assert_allclose(pca_fit.transform(X2), pca_fit_transform.transform(X2), rtol=2e-9)
+    _check_fitted_pca_close(pca_fit, pca_fit_transform)
+    assert_allclose(transformed_X, pca_fit_transform.transform(X))
+    assert_allclose(transformed_X, pca_fit.transform(X))
+    assert_allclose(pca_fit.transform(X2), pca_fit_transform.transform(X2))
 
 
 @pytest.mark.parametrize("svd_solver", ["randomized", "full"])
@@ -303,7 +309,7 @@ def test_pca_solver_equivalence(
     X_train, X_test = X[:n_samples], X[n_samples:]
 
     if global_dtype == np.float32:
-        tols = dict(atol=1e-2, rtol=1e-5)
+        tols = dict(atol=3e-2, rtol=1e-5)
         variance_threshold = 1e-5
     else:
         tols = dict(atol=1e-10, rtol=1e-12)
diff --git a/sklearn/discriminant_analysis.py b/sklearn/discriminant_analysis.py
index 01a1004012787..e5eaee2db11bb 100644
--- a/sklearn/discriminant_analysis.py
+++ b/sklearn/discriminant_analysis.py
@@ -1,6 +1,4 @@
-"""
-Linear Discriminant Analysis and Quadratic Discriminant Analysis
-"""
+"""Linear and quadratic discriminant analysis."""
 
 # Authors: Clemens Brunner
 #          Martin Billinger
diff --git a/sklearn/dummy.py b/sklearn/dummy.py
index 17812fe1b3d05..9a30e6a84118a 100644
--- a/sklearn/dummy.py
+++ b/sklearn/dummy.py
@@ -1,3 +1,5 @@
+"""Dummy estimators that implement simple rules of thumb."""
+
 # Author: Mathieu Blondel <mathieu@mblondel.org>
 #         Arnaud Joly <a.joly@ulg.ac.be>
 #         Maheshakya Wijewardena <maheshakya.10@cse.mrt.ac.lk>
diff --git a/sklearn/ensemble/__init__.py b/sklearn/ensemble/__init__.py
index 8ddf05084f1be..e49d744ed6391 100644
--- a/sklearn/ensemble/__init__.py
+++ b/sklearn/ensemble/__init__.py
@@ -1,7 +1,4 @@
-"""
-The :mod:`sklearn.ensemble` module includes ensemble-based methods for
-classification, regression and anomaly detection.
-"""
+"""Ensemble-based methods for classification, regression and anomaly detection."""
 
 from ._bagging import BaggingClassifier, BaggingRegressor
 from ._base import BaseEnsemble
diff --git a/sklearn/ensemble/_base.py b/sklearn/ensemble/_base.py
index 5483206de51d5..18079b02c49f1 100644
--- a/sklearn/ensemble/_base.py
+++ b/sklearn/ensemble/_base.py
@@ -21,7 +21,7 @@ def _fit_single_estimator(
     estimator, X, y, fit_params, message_clsname=None, message=None
 ):
     """Private function used to fit an estimator within a job."""
-    # TODO(SLEP6): remove if condition for unrouted sample_weight when metadata
+    # TODO(SLEP6): remove if-condition for unrouted sample_weight when metadata
     # routing can't be disabled.
     if not _routing_enabled() and "sample_weight" in fit_params:
         try:
diff --git a/sklearn/ensemble/_forest.py b/sklearn/ensemble/_forest.py
index 6b1b842f5367b..28c404c3e406b 100644
--- a/sklearn/ensemble/_forest.py
+++ b/sklearn/ensemble/_forest.py
@@ -1308,7 +1308,7 @@ class RandomForestClassifier(ForestClassifier):
         When set to ``True``, reuse the solution of the previous call to fit
         and add more estimators to the ensemble, otherwise, just fit a whole
         new forest. See :term:`Glossary <warm_start>` and
-        :ref:`gradient_boosting_warm_start` for details.
+        :ref:`tree_ensemble_warm_start` for details.
 
     class_weight : {"balanced", "balanced_subsample"}, dict or list of dicts, \
             default=None
@@ -1710,7 +1710,7 @@ class RandomForestRegressor(ForestRegressor):
         When set to ``True``, reuse the solution of the previous call to fit
         and add more estimators to the ensemble, otherwise, just fit a whole
         new forest. See :term:`Glossary <warm_start>` and
-        :ref:`gradient_boosting_warm_start` for details.
+        :ref:`tree_ensemble_warm_start` for details.
 
     ccp_alpha : non-negative float, default=0.0
         Complexity parameter used for Minimal Cost-Complexity Pruning. The
@@ -2049,7 +2049,7 @@ class ExtraTreesClassifier(ForestClassifier):
         When set to ``True``, reuse the solution of the previous call to fit
         and add more estimators to the ensemble, otherwise, just fit a whole
         new forest. See :term:`Glossary <warm_start>` and
-        :ref:`gradient_boosting_warm_start` for details.
+        :ref:`tree_ensemble_warm_start` for details.
 
     class_weight : {"balanced", "balanced_subsample"}, dict or list of dicts, \
             default=None
@@ -2434,7 +2434,7 @@ class ExtraTreesRegressor(ForestRegressor):
         When set to ``True``, reuse the solution of the previous call to fit
         and add more estimators to the ensemble, otherwise, just fit a whole
         new forest. See :term:`Glossary <warm_start>` and
-        :ref:`gradient_boosting_warm_start` for details.
+        :ref:`tree_ensemble_warm_start` for details.
 
     ccp_alpha : non-negative float, default=0.0
         Complexity parameter used for Minimal Cost-Complexity Pruning. The
@@ -2727,7 +2727,7 @@ class RandomTreesEmbedding(TransformerMixin, BaseForest):
         When set to ``True``, reuse the solution of the previous call to fit
         and add more estimators to the ensemble, otherwise, just fit a whole
         new forest. See :term:`Glossary <warm_start>` and
-        :ref:`gradient_boosting_warm_start` for details.
+        :ref:`tree_ensemble_warm_start` for details.
 
     Attributes
     ----------
diff --git a/sklearn/ensemble/_gb.py b/sklearn/ensemble/_gb.py
index 960e469a090cd..18d89b21f5512 100644
--- a/sklearn/ensemble/_gb.py
+++ b/sklearn/ensemble/_gb.py
@@ -2032,6 +2032,11 @@ class GradientBoostingRegressor(RegressorMixin, BaseGradientBoosting):
     array([-61...])
     >>> reg.score(X_test, y_test)
     0.4...
+
+    For a detailed example of utilizing
+    :class:`~sklearn.ensemble.GradientBoostingRegressor`
+    to fit an ensemble of weak predictive models, please refer to
+    :ref:`sphx_glr_auto_examples_ensemble_plot_gradient_boosting_regression.py`.
     """
 
     _parameter_constraints: dict = {
diff --git a/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py b/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py
index d3929480552f9..78f8456e969de 100644
--- a/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py
+++ b/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py
@@ -583,6 +583,17 @@ def fit(self, X, y, sample_weight=None):
 
         self._validate_parameters()
         monotonic_cst = _check_monotonic_cst(self, self.monotonic_cst)
+        # _preprocess_X places the categorical features at the beginning,
+        # change the order of monotonic_cst accordingly
+        if self.is_categorical_ is not None:
+            monotonic_cst_remapped = np.concatenate(
+                (
+                    monotonic_cst[self.is_categorical_],
+                    monotonic_cst[~self.is_categorical_],
+                )
+            )
+        else:
+            monotonic_cst_remapped = monotonic_cst
 
         # used for validation in predict
         n_samples, self._n_features = X.shape
@@ -895,7 +906,7 @@ def fit(self, X, y, sample_weight=None):
                     n_bins_non_missing=self._bin_mapper.n_bins_non_missing_,
                     has_missing_values=has_missing_values,
                     is_categorical=self._is_categorical_remapped,
-                    monotonic_cst=monotonic_cst,
+                    monotonic_cst=monotonic_cst_remapped,
                     interaction_cst=interaction_cst,
                     max_leaf_nodes=self.max_leaf_nodes,
                     max_depth=self.max_depth,
diff --git a/sklearn/ensemble/_hist_gradient_boosting/tests/test_monotonic_contraints.py b/sklearn/ensemble/_hist_gradient_boosting/tests/test_monotonic_contraints.py
index 7782b5b32eb68..56b6068d794e8 100644
--- a/sklearn/ensemble/_hist_gradient_boosting/tests/test_monotonic_contraints.py
+++ b/sklearn/ensemble/_hist_gradient_boosting/tests/test_monotonic_contraints.py
@@ -206,9 +206,9 @@ def test_nodes_values(monotonic_cst, seed):
 
 @pytest.mark.parametrize("use_feature_names", (True, False))
 def test_predictions(global_random_seed, use_feature_names):
-    # Train a model with a POS constraint on the first feature and a NEG
-    # constraint on the second feature, and make sure the constraints are
-    # respected by checking the predictions.
+    # Train a model with a POS constraint on the first non-categorical feature
+    # and a NEG constraint on the second non-categorical feature, and make sure
+    # the constraints are respected by checking the predictions.
     # test adapted from lightgbm's test_monotone_constraint(), itself inspired
     # by https://xgboost.readthedocs.io/en/latest/tutorials/monotonic.html
 
@@ -216,9 +216,16 @@ def test_predictions(global_random_seed, use_feature_names):
 
     n_samples = 1000
     f_0 = rng.rand(n_samples)  # positive correlation with y
-    f_1 = rng.rand(n_samples)  # negative correslation with y
-    X = np.c_[f_0, f_1]
-    columns_name = ["f_0", "f_1"]
+    f_1 = rng.rand(n_samples)  # negative correlation with y
+
+    # extra categorical features, no correlation with y,
+    # to check the correctness of monotonicity constraint remapping, see issue #28898
+    f_a = rng.randint(low=0, high=9, size=n_samples)
+    f_b = rng.randint(low=0, high=9, size=n_samples)
+    f_c = rng.randint(low=0, high=9, size=n_samples)
+
+    X = np.c_[f_a, f_0, f_b, f_1, f_c]
+    columns_name = ["f_a", "f_0", "f_b", "f_1", "f_c"]
     constructor_name = "dataframe" if use_feature_names else "array"
     X = _convert_container(X, constructor_name, columns_name=columns_name)
 
@@ -227,10 +234,14 @@ def test_predictions(global_random_seed, use_feature_names):
 
     if use_feature_names:
         monotonic_cst = {"f_0": +1, "f_1": -1}
+        categorical_features = ["f_a", "f_b", "f_c"]
     else:
-        monotonic_cst = [+1, -1]
+        monotonic_cst = [0, +1, 0, -1, 0]
+        categorical_features = [0, 2, 4]
 
-    gbdt = HistGradientBoostingRegressor(monotonic_cst=monotonic_cst)
+    gbdt = HistGradientBoostingRegressor(
+        monotonic_cst=monotonic_cst, categorical_features=categorical_features
+    )
     gbdt.fit(X, y)
 
     linspace = np.linspace(0, 1, 100)
@@ -247,26 +258,26 @@ def test_predictions(global_random_seed, use_feature_names):
     # The constraint does not guanrantee that
     # x0 < x0' => f(x0, x1) < f(x0', x1')
 
-    # First feature (POS)
+    # First non-categorical feature (POS)
     # assert pred is all increasing when f_0 is all increasing
-    X = np.c_[linspace, constant]
+    X = np.c_[constant, linspace, constant, constant, constant]
     X = _convert_container(X, constructor_name, columns_name=columns_name)
     pred = gbdt.predict(X)
     assert is_increasing(pred)
     # assert pred actually follows the variations of f_0
-    X = np.c_[sin, constant]
+    X = np.c_[constant, sin, constant, constant, constant]
     X = _convert_container(X, constructor_name, columns_name=columns_name)
     pred = gbdt.predict(X)
     assert np.all((np.diff(pred) >= 0) == (np.diff(sin) >= 0))
 
-    # Second feature (NEG)
+    # Second non-categorical feature (NEG)
     # assert pred is all decreasing when f_1 is all increasing
-    X = np.c_[constant, linspace]
+    X = np.c_[constant, constant, constant, linspace, constant]
     X = _convert_container(X, constructor_name, columns_name=columns_name)
     pred = gbdt.predict(X)
     assert is_decreasing(pred)
     # assert pred actually follows the inverse variations of f_1
-    X = np.c_[constant, sin]
+    X = np.c_[constant, constant, constant, sin, constant]
     X = _convert_container(X, constructor_name, columns_name=columns_name)
     pred = gbdt.predict(X)
     assert ((np.diff(pred) <= 0) == (np.diff(sin) >= 0)).all()
diff --git a/sklearn/ensemble/_stacking.py b/sklearn/ensemble/_stacking.py
index a18803d507ffa..9dc93b6c35975 100644
--- a/sklearn/ensemble/_stacking.py
+++ b/sklearn/ensemble/_stacking.py
@@ -27,8 +27,11 @@
 from ..utils._estimator_html_repr import _VisualBlock
 from ..utils._param_validation import HasMethods, StrOptions
 from ..utils.metadata_routing import (
-    _raise_for_unsupported_routing,
-    _RoutingNotSupportedMixin,
+    MetadataRouter,
+    MethodMapping,
+    _raise_for_params,
+    _routing_enabled,
+    process_routing,
 )
 from ..utils.metaestimators import available_if
 from ..utils.multiclass import check_classification_targets, type_of_target
@@ -36,6 +39,7 @@
 from ..utils.validation import (
     _check_feature_names_in,
     _check_response_method,
+    _deprecate_positional_args,
     check_is_fitted,
     column_or_1d,
 )
@@ -171,7 +175,7 @@ def _method_name(name, estimator, method):
         # estimators in Stacking*.estimators are not validated yet
         prefer_skip_nested_validation=False
     )
-    def fit(self, X, y, sample_weight=None):
+    def fit(self, X, y, **fit_params):
         """Fit the estimators.
 
         Parameters
@@ -183,14 +187,13 @@ def fit(self, X, y, sample_weight=None):
         y : array-like of shape (n_samples,)
             Target values.
 
-        sample_weight : array-like of shape (n_samples,) or default=None
-            Sample weights. If None, then samples are equally weighted.
-            Note that this is supported only if all underlying estimators
-            support sample weights.
+        **fit_params : dict
+            Dict of metadata, potentially containing sample_weight as a
+            key-value pair. If sample_weight is not present, then samples are
+            equally weighted. Note that sample_weight is supported only if all
+            underlying estimators support sample weights.
 
-            .. versionchanged:: 0.23
-               when not None, `sample_weight` is passed to all underlying
-               estimators
+            .. versionadded:: 1.6
 
         Returns
         -------
@@ -201,16 +204,19 @@ def fit(self, X, y, sample_weight=None):
         names, all_estimators = self._validate_estimators()
         self._validate_final_estimator()
 
-        # FIXME: when adding support for metadata routing in Stacking*.
-        # This is a hotfix to make StackingClassifier and StackingRegressor
-        # pass the tests despite not supporting metadata routing but sharing
-        # the same base class with VotingClassifier and VotingRegressor.
-        fit_params = dict()
-        if sample_weight is not None:
-            fit_params["sample_weight"] = sample_weight
-
         stack_method = [self.stack_method] * len(all_estimators)
 
+        if _routing_enabled():
+            routed_params = process_routing(self, "fit", **fit_params)
+        else:
+            routed_params = Bunch()
+            for name in names:
+                routed_params[name] = Bunch(fit={})
+                if "sample_weight" in fit_params:
+                    routed_params[name].fit["sample_weight"] = fit_params[
+                        "sample_weight"
+                    ]
+
         if self.cv == "prefit":
             self.estimators_ = []
             for estimator in all_estimators:
@@ -222,8 +228,10 @@ def fit(self, X, y, sample_weight=None):
             # base estimators will be used in transform, predict, and
             # predict_proba. They are exposed publicly.
             self.estimators_ = Parallel(n_jobs=self.n_jobs)(
-                delayed(_fit_single_estimator)(clone(est), X, y, fit_params)
-                for est in all_estimators
+                delayed(_fit_single_estimator)(
+                    clone(est), X, y, routed_params[name]["fit"]
+                )
+                for name, est in zip(names, all_estimators)
                 if est != "drop"
             )
 
@@ -269,10 +277,10 @@ def fit(self, X, y, sample_weight=None):
                     cv=deepcopy(cv),
                     method=meth,
                     n_jobs=self.n_jobs,
-                    params=fit_params,
+                    params=routed_params[name]["fit"],
                     verbose=self.verbose,
                 )
-                for est, meth in zip(all_estimators, self.stack_method_)
+                for name, est, meth in zip(names, all_estimators, self.stack_method_)
                 if est != "drop"
             )
 
@@ -370,7 +378,7 @@ def predict(self, X, **predict_params):
             Parameters to the `predict` called by the `final_estimator`. Note
             that this may be used to return uncertainties from some estimators
             with `return_std` or `return_cov`. Be aware that it will only
-            accounts for uncertainty in the final estimator.
+            account for uncertainty in the final estimator.
 
         Returns
         -------
@@ -392,8 +400,43 @@ def _sk_visual_block_with_final_estimator(self, final_estimator):
         )
         return _VisualBlock("serial", (parallel, final_block), dash_wrapped=False)
 
+    def get_metadata_routing(self):
+        """Get metadata routing of this object.
+
+        Please check :ref:`User Guide <metadata_routing>` on how the routing
+        mechanism works.
+
+        .. versionadded:: 1.6
+
+        Returns
+        -------
+        routing : MetadataRouter
+            A :class:`~sklearn.utils.metadata_routing.MetadataRouter` encapsulating
+            routing information.
+        """
+        router = MetadataRouter(owner=self.__class__.__name__)
+
+        # `self.estimators` is a list of (name, est) tuples
+        for name, estimator in self.estimators:
+            router.add(
+                **{name: estimator},
+                method_mapping=MethodMapping().add(callee="fit", caller="fit"),
+            )
+
+        try:
+            final_estimator_ = self.final_estimator_
+        except AttributeError:
+            final_estimator_ = self.final_estimator
+
+        router.add(
+            final_estimator_=final_estimator_,
+            method_mapping=MethodMapping().add(caller="predict", callee="predict"),
+        )
+
+        return router
+
 
-class StackingClassifier(_RoutingNotSupportedMixin, ClassifierMixin, _BaseStacking):
+class StackingClassifier(ClassifierMixin, _BaseStacking):
     """Stack of estimators with a final classifier.
 
     Stacked generalization consists in stacking the output of individual
@@ -528,7 +571,7 @@ class StackingClassifier(_RoutingNotSupportedMixin, ClassifierMixin, _BaseStacki
     -----
     When `predict_proba` is used by each estimator (i.e. most of the time for
     `stack_method='auto'` or specifically for `stack_method='predict_proba'`),
-    The first column predicted by each estimator will be dropped in the case
+    the first column predicted by each estimator will be dropped in the case
     of a binary classification problem. Indeed, both feature will be perfectly
     collinear.
 
@@ -629,7 +672,11 @@ def _validate_estimators(self):
 
         return names, estimators
 
-    def fit(self, X, y, sample_weight=None):
+    # TODO(1.7): remove `sample_weight` from the signature after deprecation
+    # cycle; pop it from `fit_params` before the `_raise_for_params` check and
+    # reinsert afterwards, for backwards compatibility
+    @_deprecate_positional_args(version="1.7")
+    def fit(self, X, y, *, sample_weight=None, **fit_params):
         """Fit the estimators.
 
         Parameters
@@ -649,12 +696,22 @@ def fit(self, X, y, sample_weight=None):
             Note that this is supported only if all underlying estimators
             support sample weights.
 
+        **fit_params : dict
+            Parameters to pass to the underlying estimators.
+
+            .. versionadded:: 1.6
+
+                Only available if `enable_metadata_routing=True`, which can be
+                set by using ``sklearn.set_config(enable_metadata_routing=True)``.
+                See :ref:`Metadata Routing User Guide <metadata_routing>` for
+                more details.
+
         Returns
         -------
         self : object
             Returns a fitted instance of estimator.
         """
-        _raise_for_unsupported_routing(self, "fit", sample_weight=sample_weight)
+        _raise_for_params(fit_params, self, "fit")
         check_classification_targets(y)
         if type_of_target(y) == "multilabel-indicator":
             self._label_encoder = [LabelEncoder().fit(yk) for yk in y.T]
@@ -669,7 +726,10 @@ def fit(self, X, y, sample_weight=None):
             self._label_encoder = LabelEncoder().fit(y)
             self.classes_ = self._label_encoder.classes_
             y_encoded = self._label_encoder.transform(y)
-        return super().fit(X, y_encoded, sample_weight)
+
+        if sample_weight is not None:
+            fit_params["sample_weight"] = sample_weight
+        return super().fit(X, y_encoded, **fit_params)
 
     @available_if(_estimator_has("predict"))
     def predict(self, X, **predict_params):
@@ -685,14 +745,33 @@ def predict(self, X, **predict_params):
             Parameters to the `predict` called by the `final_estimator`. Note
             that this may be used to return uncertainties from some estimators
             with `return_std` or `return_cov`. Be aware that it will only
-            accounts for uncertainty in the final estimator.
+            account for uncertainty in the final estimator.
+
+            - If `enable_metadata_routing=False` (default):
+              Parameters directly passed to the `predict` method of the
+              `final_estimator`.
+
+            - If `enable_metadata_routing=True`: Parameters safely routed to
+              the `predict` method of the `final_estimator`. See :ref:`Metadata
+              Routing User Guide <metadata_routing>` for more details.
+
+            .. versionchanged:: 1.6
+                `**predict_params` can be routed via metadata routing API.
 
         Returns
         -------
         y_pred : ndarray of shape (n_samples,) or (n_samples, n_output)
             Predicted targets.
         """
-        y_pred = super().predict(X, **predict_params)
+        if _routing_enabled():
+            routed_params = process_routing(self, "predict", **predict_params)
+        else:
+            # TODO(SLEP6): remove when metadata routing cannot be disabled.
+            routed_params = Bunch()
+            routed_params.final_estimator_ = Bunch(predict={})
+            routed_params.final_estimator_.predict = predict_params
+
+        y_pred = super().predict(X, **routed_params.final_estimator_["predict"])
         if isinstance(self._label_encoder, list):
             # Handle the multilabel-indicator case
             y_pred = np.array(
@@ -775,7 +854,7 @@ def _sk_visual_block_(self):
         return super()._sk_visual_block_with_final_estimator(final_estimator)
 
 
-class StackingRegressor(_RoutingNotSupportedMixin, RegressorMixin, _BaseStacking):
+class StackingRegressor(RegressorMixin, _BaseStacking):
     """Stack of estimators with a final regressor.
 
     Stacked generalization consists in stacking the output of individual
@@ -944,7 +1023,11 @@ def _validate_final_estimator(self):
                 )
             )
 
-    def fit(self, X, y, sample_weight=None):
+    # TODO(1.7): remove `sample_weight` from the signature after deprecation
+    # cycle; pop it from `fit_params` before the `_raise_for_params` check and
+    # reinsert afterwards, for backwards compatibility
+    @_deprecate_positional_args(version="1.7")
+    def fit(self, X, y, *, sample_weight=None, **fit_params):
         """Fit the estimators.
 
         Parameters
@@ -961,14 +1044,26 @@ def fit(self, X, y, sample_weight=None):
             Note that this is supported only if all underlying estimators
             support sample weights.
 
+        **fit_params : dict
+            Parameters to pass to the underlying estimators.
+
+            .. versionadded:: 1.6
+
+                Only available if `enable_metadata_routing=True`, which can be
+                set by using ``sklearn.set_config(enable_metadata_routing=True)``.
+                See :ref:`Metadata Routing User Guide <metadata_routing>` for
+                more details.
+
         Returns
         -------
         self : object
             Returns a fitted instance.
         """
-        _raise_for_unsupported_routing(self, "fit", sample_weight=sample_weight)
+        _raise_for_params(fit_params, self, "fit")
         y = column_or_1d(y, warn=True)
-        return super().fit(X, y, sample_weight)
+        if sample_weight is not None:
+            fit_params["sample_weight"] = sample_weight
+        return super().fit(X, y, **fit_params)
 
     def transform(self, X):
         """Return the predictions for X for each estimator.
@@ -986,7 +1081,11 @@ def transform(self, X):
         """
         return self._transform(X)
 
-    def fit_transform(self, X, y, sample_weight=None):
+    # TODO(1.7): remove `sample_weight` from the signature after deprecation
+    # cycle; pop it from `fit_params` before the `_raise_for_params` check and
+    # reinsert afterwards, for backwards compatibility
+    @_deprecate_positional_args(version="1.7")
+    def fit_transform(self, X, y, *, sample_weight=None, **fit_params):
         """Fit the estimators and return the predictions for X for each estimator.
 
         Parameters
@@ -1003,12 +1102,69 @@ def fit_transform(self, X, y, sample_weight=None):
             Note that this is supported only if all underlying estimators
             support sample weights.
 
+        **fit_params : dict
+            Parameters to pass to the underlying estimators.
+
+            .. versionadded:: 1.6
+
+                Only available if `enable_metadata_routing=True`, which can be
+                set by using ``sklearn.set_config(enable_metadata_routing=True)``.
+                See :ref:`Metadata Routing User Guide <metadata_routing>` for
+                more details.
+
         Returns
         -------
         y_preds : ndarray of shape (n_samples, n_estimators)
             Prediction outputs for each estimator.
         """
-        return super().fit_transform(X, y, sample_weight=sample_weight)
+        _raise_for_params(fit_params, self, "fit")
+        if sample_weight is not None:
+            fit_params["sample_weight"] = sample_weight
+        return super().fit_transform(X, y, **fit_params)
+
+    @available_if(_estimator_has("predict"))
+    def predict(self, X, **predict_params):
+        """Predict target for X.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+            Training vectors, where `n_samples` is the number of samples and
+            `n_features` is the number of features.
+
+        **predict_params : dict of str -> obj
+            Parameters to the `predict` called by the `final_estimator`. Note
+            that this may be used to return uncertainties from some estimators
+            with `return_std` or `return_cov`. Be aware that it will only
+            account for uncertainty in the final estimator.
+
+            - If `enable_metadata_routing=False` (default):
+              Parameters directly passed to the `predict` method of the
+              `final_estimator`.
+
+            - If `enable_metadata_routing=True`: Parameters safely routed to
+              the `predict` method of the `final_estimator`. See :ref:`Metadata
+              Routing User Guide <metadata_routing>` for more details.
+
+            .. versionchanged:: 1.6
+                `**predict_params` can be routed via metadata routing API.
+
+        Returns
+        -------
+        y_pred : ndarray of shape (n_samples,) or (n_samples, n_output)
+            Predicted targets.
+        """
+        if _routing_enabled():
+            routed_params = process_routing(self, "predict", **predict_params)
+        else:
+            # TODO(SLEP6): remove when metadata routing cannot be disabled.
+            routed_params = Bunch()
+            routed_params.final_estimator_ = Bunch(predict={})
+            routed_params.final_estimator_.predict = predict_params
+
+        y_pred = super().predict(X, **routed_params.final_estimator_["predict"])
+
+        return y_pred
 
     def _sk_visual_block_(self):
         # If final_estimator's default changes then this should be
diff --git a/sklearn/ensemble/_voting.py b/sklearn/ensemble/_voting.py
index 4e7c7af369ab0..7c54be40dc013 100644
--- a/sklearn/ensemble/_voting.py
+++ b/sklearn/ensemble/_voting.py
@@ -287,7 +287,7 @@ class VotingClassifier(ClassifierMixin, _BaseVoting):
     >>> from sklearn.linear_model import LogisticRegression
     >>> from sklearn.naive_bayes import GaussianNB
     >>> from sklearn.ensemble import RandomForestClassifier, VotingClassifier
-    >>> clf1 = LogisticRegression(multi_class='multinomial', random_state=1)
+    >>> clf1 = LogisticRegression(random_state=1)
     >>> clf2 = RandomForestClassifier(n_estimators=50, random_state=1)
     >>> clf3 = GaussianNB()
     >>> X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])
diff --git a/sklearn/ensemble/_weight_boosting.py b/sklearn/ensemble/_weight_boosting.py
index 0461a397983be..5af3c22bde52c 100644
--- a/sklearn/ensemble/_weight_boosting.py
+++ b/sklearn/ensemble/_weight_boosting.py
@@ -482,6 +482,10 @@ class AdaBoostClassifier(
     For a detailed example of using AdaBoost to fit a sequence of DecisionTrees
     as weaklearners, please refer to
     :ref:`sphx_glr_auto_examples_ensemble_plot_adaboost_multiclass.py`.
+
+    For a detailed example of using AdaBoost to fit a non-linearly seperable
+    classification dataset composed of two Gaussian quantiles clusters, please
+    refer to :ref:`sphx_glr_auto_examples_ensemble_plot_adaboost_twoclass.py`.
     """
 
     # TODO(1.6): Modify _parameter_constraints for "algorithm" to only check
@@ -1076,6 +1080,10 @@ class AdaBoostRegressor(_RoutingNotSupportedMixin, RegressorMixin, BaseWeightBoo
     array([4.7972...])
     >>> regr.score(X, y)
     0.9771...
+
+    For a detailed example of utilizing :class:`~sklearn.ensemble.AdaBoostRegressor`
+    to fit a sequence of decision trees as weak learners, please refer to
+    :ref:`sphx_glr_auto_examples_ensemble_plot_adaboost_regression.py`.
     """
 
     _parameter_constraints: dict = {
diff --git a/sklearn/ensemble/tests/test_stacking.py b/sklearn/ensemble/tests/test_stacking.py
index 300b011f661d4..1c038cd469216 100644
--- a/sklearn/ensemble/tests/test_stacking.py
+++ b/sklearn/ensemble/tests/test_stacking.py
@@ -3,6 +3,7 @@
 # Authors: Guillaume Lemaitre <g.lemaitre58@gmail.com>
 # License: BSD 3 clause
 
+import re
 from unittest.mock import Mock
 
 import numpy as np
@@ -38,6 +39,12 @@
 from sklearn.neural_network import MLPClassifier
 from sklearn.preprocessing import scale
 from sklearn.svm import SVC, LinearSVC, LinearSVR
+from sklearn.tests.metadata_routing_common import (
+    ConsumingClassifier,
+    ConsumingRegressor,
+    _Registry,
+    check_recorded_metadata,
+)
 from sklearn.utils._mocking import CheckingClassifier
 from sklearn.utils._testing import (
     assert_allclose,
@@ -888,3 +895,116 @@ def test_stacking_final_estimator_attribute_error():
         clf.fit(X, y).decision_function(X)
     assert isinstance(exec_info.value.__cause__, AttributeError)
     assert inner_msg in str(exec_info.value.__cause__)
+
+
+# Metadata Routing Tests
+# ======================
+
+
+@pytest.mark.parametrize(
+    "Estimator, Child",
+    [
+        (StackingClassifier, ConsumingClassifier),
+        (StackingRegressor, ConsumingRegressor),
+    ],
+)
+def test_routing_passed_metadata_not_supported(Estimator, Child):
+    """Test that the right error message is raised when metadata is passed while
+    not supported when `enable_metadata_routing=False`."""
+
+    with pytest.raises(
+        ValueError, match="is only supported if enable_metadata_routing=True"
+    ):
+        Estimator(["clf", Child()]).fit(
+            X_iris, y_iris, sample_weight=[1, 1, 1, 1, 1], metadata="a"
+        )
+
+
+@pytest.mark.usefixtures("enable_slep006")
+@pytest.mark.parametrize(
+    "Estimator, Child",
+    [
+        (StackingClassifier, ConsumingClassifier),
+        (StackingRegressor, ConsumingRegressor),
+    ],
+)
+def test_get_metadata_routing_without_fit(Estimator, Child):
+    # Test that metadata_routing() doesn't raise when called before fit.
+    est = Estimator([("sub_est", Child())])
+    est.get_metadata_routing()
+
+
+@pytest.mark.usefixtures("enable_slep006")
+@pytest.mark.parametrize(
+    "Estimator, Child",
+    [
+        (StackingClassifier, ConsumingClassifier),
+        (StackingRegressor, ConsumingRegressor),
+    ],
+)
+@pytest.mark.parametrize(
+    "prop, prop_value", [("sample_weight", np.ones(X_iris.shape[0])), ("metadata", "a")]
+)
+def test_metadata_routing_for_stacking_estimators(Estimator, Child, prop, prop_value):
+    """Test that metadata is routed correctly for Stacking*."""
+
+    est = Estimator(
+        [
+            (
+                "sub_est1",
+                Child(registry=_Registry()).set_fit_request(**{prop: True}),
+            ),
+            (
+                "sub_est2",
+                Child(registry=_Registry()).set_fit_request(**{prop: True}),
+            ),
+        ],
+        final_estimator=Child(registry=_Registry()).set_predict_request(**{prop: True}),
+    )
+
+    est.fit(X_iris, y_iris, **{prop: prop_value})
+    est.fit_transform(X_iris, y_iris, **{prop: prop_value})
+
+    est.predict(X_iris, **{prop: prop_value})
+
+    for estimator in est.estimators:
+        # access sub-estimator in (name, est) with estimator[1]:
+        registry = estimator[1].registry
+        assert len(registry)
+        for sub_est in registry:
+            check_recorded_metadata(
+                obj=sub_est, method="fit", split_params=(prop), **{prop: prop_value}
+            )
+    # access final_estimator:
+    registry = est.final_estimator_.registry
+    assert len(registry)
+    check_recorded_metadata(
+        obj=registry[-1], method="predict", split_params=(prop), **{prop: prop_value}
+    )
+
+
+@pytest.mark.usefixtures("enable_slep006")
+@pytest.mark.parametrize(
+    "Estimator, Child",
+    [
+        (StackingClassifier, ConsumingClassifier),
+        (StackingRegressor, ConsumingRegressor),
+    ],
+)
+def test_metadata_routing_error_for_stacking_estimators(Estimator, Child):
+    """Test that the right error is raised when metadata is not requested."""
+    sample_weight, metadata = np.ones(X_iris.shape[0]), "a"
+
+    est = Estimator([("sub_est", Child())])
+
+    error_message = (
+        "[sample_weight, metadata] are passed but are not explicitly set as requested"
+        f" or not requested for {Child.__name__}.fit"
+    )
+
+    with pytest.raises(ValueError, match=re.escape(error_message)):
+        est.fit(X_iris, y_iris, sample_weight=sample_weight, metadata=metadata)
+
+
+# End of Metadata Routing Tests
+# =============================
diff --git a/sklearn/exceptions.py b/sklearn/exceptions.py
index 1466ce783ee00..7f6b26163cc87 100644
--- a/sklearn/exceptions.py
+++ b/sklearn/exceptions.py
@@ -1,7 +1,4 @@
-"""
-The :mod:`sklearn.exceptions` module includes all custom warnings and error
-classes used across scikit-learn.
-"""
+"""Custom warnings and errors used across scikit-learn."""
 
 __all__ = [
     "NotFittedError",
diff --git a/sklearn/experimental/__init__.py b/sklearn/experimental/__init__.py
index 0effaf5b05fa0..205a11a3d524b 100644
--- a/sklearn/experimental/__init__.py
+++ b/sklearn/experimental/__init__.py
@@ -1,7 +1,7 @@
-"""
-The :mod:`sklearn.experimental` module provides importable modules that enable
-the use of experimental features or estimators.
+"""Importable modules that enable the use of experimental features or estimators.
+
+.. warning::
 
-The features and estimators that are experimental aren't subject to
-deprecation cycles. Use them at your own risks!
+    The features and estimators that are experimental aren't subject to
+    deprecation cycles. Use them at your own risks!
 """
diff --git a/sklearn/feature_extraction/__init__.py b/sklearn/feature_extraction/__init__.py
index f4db85303f4b6..dced9d46b4cc7 100644
--- a/sklearn/feature_extraction/__init__.py
+++ b/sklearn/feature_extraction/__init__.py
@@ -1,8 +1,4 @@
-"""
-The :mod:`sklearn.feature_extraction` module deals with feature extraction
-from raw data. It currently includes methods to extract features from text and
-images.
-"""
+"""Feature extraction from raw data."""
 
 from . import text
 from ._dict_vectorizer import DictVectorizer
diff --git a/sklearn/feature_extraction/image.py b/sklearn/feature_extraction/image.py
index 3f64ff11e246f..66dfc6f872b59 100644
--- a/sklearn/feature_extraction/image.py
+++ b/sklearn/feature_extraction/image.py
@@ -1,7 +1,4 @@
-"""
-The :mod:`sklearn.feature_extraction.image` submodule gathers utilities to
-extract features from images.
-"""
+"""Utilities to extract features from images."""
 
 # Authors: Emmanuelle Gouillart <emmanuelle.gouillart@normalesup.org>
 #          Gael Varoquaux <gael.varoquaux@normalesup.org>
diff --git a/sklearn/feature_extraction/tests/test_text.py b/sklearn/feature_extraction/tests/test_text.py
index a513b94436d93..6b14d0dd8f271 100644
--- a/sklearn/feature_extraction/tests/test_text.py
+++ b/sklearn/feature_extraction/tests/test_text.py
@@ -756,21 +756,11 @@ def test_feature_names():
 @pytest.mark.parametrize("Vectorizer", (CountVectorizer, TfidfVectorizer))
 def test_vectorizer_max_features(Vectorizer):
     expected_vocabulary = {"burger", "beer", "salad", "pizza"}
-    expected_stop_words = {
-        "celeri",
-        "tomato",
-        "copyright",
-        "coke",
-        "sparkling",
-        "water",
-        "the",
-    }
 
     # test bounded number of extracted features
     vectorizer = Vectorizer(max_df=0.6, max_features=4)
     vectorizer.fit(ALL_FOOD_DOCS)
     assert set(vectorizer.vocabulary_) == expected_vocabulary
-    assert vectorizer.stop_words_ == expected_stop_words
 
 
 def test_count_vectorizer_max_features():
@@ -805,21 +795,16 @@ def test_vectorizer_max_df():
     vect.fit(test_data)
     assert "a" in vect.vocabulary_.keys()
     assert len(vect.vocabulary_.keys()) == 6
-    assert len(vect.stop_words_) == 0
 
     vect.max_df = 0.5  # 0.5 * 3 documents -> max_doc_count == 1.5
     vect.fit(test_data)
     assert "a" not in vect.vocabulary_.keys()  # {ae} ignored
     assert len(vect.vocabulary_.keys()) == 4  # {bcdt} remain
-    assert "a" in vect.stop_words_
-    assert len(vect.stop_words_) == 2
 
     vect.max_df = 1
     vect.fit(test_data)
     assert "a" not in vect.vocabulary_.keys()  # {ae} ignored
     assert len(vect.vocabulary_.keys()) == 4  # {bcdt} remain
-    assert "a" in vect.stop_words_
-    assert len(vect.stop_words_) == 2
 
 
 def test_vectorizer_min_df():
@@ -828,21 +813,16 @@ def test_vectorizer_min_df():
     vect.fit(test_data)
     assert "a" in vect.vocabulary_.keys()
     assert len(vect.vocabulary_.keys()) == 6
-    assert len(vect.stop_words_) == 0
 
     vect.min_df = 2
     vect.fit(test_data)
     assert "c" not in vect.vocabulary_.keys()  # {bcdt} ignored
     assert len(vect.vocabulary_.keys()) == 2  # {ae} remain
-    assert "c" in vect.stop_words_
-    assert len(vect.stop_words_) == 4
 
     vect.min_df = 0.8  # 0.8 * 3 documents -> min_doc_count == 2.4
     vect.fit(test_data)
     assert "c" not in vect.vocabulary_.keys()  # {bcdet} ignored
     assert len(vect.vocabulary_.keys()) == 1  # {a} remains
-    assert "c" in vect.stop_words_
-    assert len(vect.stop_words_) == 5
 
 
 def test_count_binary_occurrences():
@@ -1155,28 +1135,6 @@ def test_countvectorizer_vocab_dicts_when_pickling():
         )
 
 
-def test_stop_words_removal():
-    # Ensure that deleting the stop_words_ attribute doesn't affect transform
-
-    fitted_vectorizers = (
-        TfidfVectorizer().fit(JUNK_FOOD_DOCS),
-        CountVectorizer(preprocessor=strip_tags).fit(JUNK_FOOD_DOCS),
-        CountVectorizer(strip_accents=strip_eacute).fit(JUNK_FOOD_DOCS),
-    )
-
-    for vect in fitted_vectorizers:
-        vect_transform = vect.transform(JUNK_FOOD_DOCS).toarray()
-
-        vect.stop_words_ = None
-        stop_None_transform = vect.transform(JUNK_FOOD_DOCS).toarray()
-
-        delattr(vect, "stop_words_")
-        stop_del_transform = vect.transform(JUNK_FOOD_DOCS).toarray()
-
-        assert_array_equal(stop_None_transform, vect_transform)
-        assert_array_equal(stop_del_transform, vect_transform)
-
-
 def test_pickling_transformer():
     X = CountVectorizer().fit_transform(JUNK_FOOD_DOCS)
     orig = TfidfTransformer().fit(X)
diff --git a/sklearn/feature_extraction/text.py b/sklearn/feature_extraction/text.py
index c8cf37ac322e9..8607d6dab4373 100644
--- a/sklearn/feature_extraction/text.py
+++ b/sklearn/feature_extraction/text.py
@@ -1,3 +1,5 @@
+"""Utilities to build feature vectors from text documents."""
+
 # Authors: Olivier Grisel <olivier.grisel@ensta.org>
 #          Mathieu Blondel <mathieu@mblondel.org>
 #          Lars Buitinck
@@ -6,10 +8,6 @@
 #          Roman Sinayev <roman.sinayev@gmail.com>
 #
 # License: BSD 3 clause
-"""
-The :mod:`sklearn.feature_extraction.text` submodule gathers utilities to
-build feature vectors from text documents.
-"""
 
 import array
 import re
@@ -603,6 +601,10 @@ class HashingVectorizer(
     For an efficiency comparison of the different feature extractors, see
     :ref:`sphx_glr_auto_examples_text_plot_hashing_vs_dict_vectorizer.py`.
 
+    For an example of document clustering and comparison with
+    :class:`~sklearn.feature_extraction.text.TfidfVectorizer`, see
+    :ref:`sphx_glr_auto_examples_text_plot_document_clustering.py`.
+
     Read more in the :ref:`User Guide <text_feature_extraction>`.
 
     Parameters
@@ -1079,15 +1081,6 @@ class CountVectorizer(_VectorizerMixin, BaseEstimator):
         True if a fixed vocabulary of term to indices mapping
         is provided by the user.
 
-    stop_words_ : set
-        Terms that were ignored because they either:
-
-          - occurred in too many documents (`max_df`)
-          - occurred in too few documents (`min_df`)
-          - were cut off by feature selection (`max_features`).
-
-        This is only available if no vocabulary was given.
-
     See Also
     --------
     HashingVectorizer : Convert a collection of text documents to a
@@ -1096,12 +1089,6 @@ class CountVectorizer(_VectorizerMixin, BaseEstimator):
     TfidfVectorizer : Convert a collection of raw documents to a matrix
         of TF-IDF features.
 
-    Notes
-    -----
-    The ``stop_words_`` attribute can get large and increase the model size
-    when pickling. This attribute is provided only for introspection and can
-    be safely removed using delattr or set to None before pickling.
-
     Examples
     --------
     >>> from sklearn.feature_extraction.text import CountVectorizer
@@ -1240,19 +1227,17 @@ def _limit_features(self, X, vocabulary, high=None, low=None, limit=None):
             mask = new_mask
 
         new_indices = np.cumsum(mask) - 1  # maps old indices to new
-        removed_terms = set()
         for term, old_index in list(vocabulary.items()):
             if mask[old_index]:
                 vocabulary[term] = new_indices[old_index]
             else:
                 del vocabulary[term]
-                removed_terms.add(term)
         kept_indices = np.where(mask)[0]
         if len(kept_indices) == 0:
             raise ValueError(
                 "After pruning, no terms remain. Try a lower min_df or a higher max_df."
             )
-        return X[:, kept_indices], removed_terms
+        return X[:, kept_indices]
 
     def _count_vocab(self, raw_documents, fixed_vocab):
         """Create sparse feature matrix, and vocabulary where fixed_vocab=False"""
@@ -1397,7 +1382,7 @@ def fit_transform(self, raw_documents, y=None):
                 raise ValueError("max_df corresponds to < documents than min_df")
             if max_features is not None:
                 X = self._sort_features(X, vocabulary)
-            X, self.stop_words_ = self._limit_features(
+            X = self._limit_features(
                 X, vocabulary, max_doc_count, min_doc_count, max_features
             )
             if max_features is None:
@@ -1745,6 +1730,10 @@ class TfidfVectorizer(CountVectorizer):
     For an efficiency comparison of the different feature extractors, see
     :ref:`sphx_glr_auto_examples_text_plot_hashing_vs_dict_vectorizer.py`.
 
+    For an example of document clustering and comparison with
+    :class:`~sklearn.feature_extraction.text.HashingVectorizer`, see
+    :ref:`sphx_glr_auto_examples_text_plot_document_clustering.py`.
+
     Read more in the :ref:`User Guide <text_feature_extraction>`.
 
     Parameters
@@ -1911,15 +1900,6 @@ class TfidfVectorizer(CountVectorizer):
         The inverse document frequency (IDF) vector; only defined
         if ``use_idf`` is True.
 
-    stop_words_ : set
-        Terms that were ignored because they either:
-
-          - occurred in too many documents (`max_df`)
-          - occurred in too few documents (`min_df`)
-          - were cut off by feature selection (`max_features`).
-
-        This is only available if no vocabulary was given.
-
     See Also
     --------
     CountVectorizer : Transforms text into a sparse matrix of n-gram counts.
@@ -1927,12 +1907,6 @@ class TfidfVectorizer(CountVectorizer):
     TfidfTransformer : Performs the TF-IDF transformation from a provided
         matrix of counts.
 
-    Notes
-    -----
-    The ``stop_words_`` attribute can get large and increase the model size
-    when pickling. This attribute is provided only for introspection and can
-    be safely removed using delattr or set to None before pickling.
-
     Examples
     --------
     >>> from sklearn.feature_extraction.text import TfidfVectorizer
diff --git a/sklearn/feature_selection/__init__.py b/sklearn/feature_selection/__init__.py
index 4fbc631155078..d431a5b016807 100644
--- a/sklearn/feature_selection/__init__.py
+++ b/sklearn/feature_selection/__init__.py
@@ -1,7 +1,7 @@
-"""
-The :mod:`sklearn.feature_selection` module implements feature selection
-algorithms. It currently includes univariate filter selection methods and the
-recursive feature elimination algorithm.
+"""Feature selection algorithms.
+
+These include univariate filter selection methods and the recursive feature elimination
+algorithm.
 """
 
 from ._base import SelectorMixin
diff --git a/sklearn/feature_selection/_from_model.py b/sklearn/feature_selection/_from_model.py
index 5610121f152bf..46c2b9ebbb163 100644
--- a/sklearn/feature_selection/_from_model.py
+++ b/sklearn/feature_selection/_from_model.py
@@ -513,8 +513,8 @@ def get_metadata_routing(self):
         router = MetadataRouter(owner=self.__class__.__name__).add(
             estimator=self.estimator,
             method_mapping=MethodMapping()
-            .add(callee="partial_fit", caller="partial_fit")
-            .add(callee="fit", caller="fit"),
+            .add(caller="partial_fit", callee="partial_fit")
+            .add(caller="fit", callee="fit"),
         )
         return router
 
diff --git a/sklearn/feature_selection/_rfe.py b/sklearn/feature_selection/_rfe.py
index 7c5cd8d45b8d1..a0f4b74a44671 100644
--- a/sklearn/feature_selection/_rfe.py
+++ b/sklearn/feature_selection/_rfe.py
@@ -539,7 +539,7 @@ class RFECV(RFE):
             ``cv`` default value of None changed from 3-fold to 5-fold.
 
     scoring : str, callable or None, default=None
-        A string (see model evaluation documentation) or
+        A string (see :ref:`scoring_parameter`) or
         a scorer callable object / function with signature
         ``scorer(estimator, X, y)``.
 
diff --git a/sklearn/gaussian_process/__init__.py b/sklearn/gaussian_process/__init__.py
index bc0d902b45b18..b1488a8c3e663 100644
--- a/sklearn/gaussian_process/__init__.py
+++ b/sklearn/gaussian_process/__init__.py
@@ -1,13 +1,10 @@
+"""Gaussian process based regression and classification."""
+
 # Author: Jan Hendrik Metzen <jhm@informatik.uni-bremen.de>
 #         Vincent Dubourg <vincent.dubourg@gmail.com>
 #         (mostly translation, see implementation details)
 # License: BSD 3 clause
 
-"""
-The :mod:`sklearn.gaussian_process` module implements Gaussian Process
-based regression and classification.
-"""
-
 from . import kernels
 from ._gpc import GaussianProcessClassifier
 from ._gpr import GaussianProcessRegressor
diff --git a/sklearn/gaussian_process/_gpr.py b/sklearn/gaussian_process/_gpr.py
index 67bba2e29c857..829c1e2fad2d8 100644
--- a/sklearn/gaussian_process/_gpr.py
+++ b/sklearn/gaussian_process/_gpr.py
@@ -384,7 +384,7 @@ def predict(self, X, return_std=False, return_cov=False):
         Returns
         -------
         y_mean : ndarray of shape (n_samples,) or (n_samples, n_targets)
-            Mean of predictive distribution a query points.
+            Mean of predictive distribution at query points.
 
         y_std : ndarray of shape (n_samples,) or (n_samples, n_targets), optional
             Standard deviation of predictive distribution at query points.
@@ -392,7 +392,7 @@ def predict(self, X, return_std=False, return_cov=False):
 
         y_cov : ndarray of shape (n_samples, n_samples) or \
                 (n_samples, n_samples, n_targets), optional
-            Covariance of joint predictive distribution a query points.
+            Covariance of joint predictive distribution at query points.
             Only returned when `return_cov` is True.
         """
         if return_std and return_cov:
diff --git a/sklearn/gaussian_process/kernels.py b/sklearn/gaussian_process/kernels.py
index c31335696944c..9c26d4fc466ee 100644
--- a/sklearn/gaussian_process/kernels.py
+++ b/sklearn/gaussian_process/kernels.py
@@ -1,7 +1,4 @@
-"""
-The :mod:`sklearn.gaussian_process.kernels` module implements a set of kernels that
-can be combined by operators and used in Gaussian processes.
-"""
+"""A set of kernels that can be combined by operators and used in Gaussian processes."""
 
 # Kernels for Gaussian process regression and classification.
 #
diff --git a/sklearn/impute/__init__.py b/sklearn/impute/__init__.py
index 380bcecaf65b5..a40c6b432d6d5 100644
--- a/sklearn/impute/__init__.py
+++ b/sklearn/impute/__init__.py
@@ -1,4 +1,4 @@
-"""Transformers for missing value imputation"""
+"""Transformers for missing value imputation."""
 
 import typing
 
diff --git a/sklearn/inspection/__init__.py b/sklearn/inspection/__init__.py
index 1af20d6f22b25..71425f281dee6 100644
--- a/sklearn/inspection/__init__.py
+++ b/sklearn/inspection/__init__.py
@@ -1,4 +1,4 @@
-"""The :mod:`sklearn.inspection` module includes tools for model inspection."""
+"""Tools for model inspection."""
 
 from ._h_statistic import h_statistic
 from ._partial_dependence import partial_dependence
diff --git a/sklearn/isotonic.py b/sklearn/isotonic.py
index 04456b1763791..66acd3edb29b3 100644
--- a/sklearn/isotonic.py
+++ b/sklearn/isotonic.py
@@ -1,3 +1,5 @@
+"""Isotonic regression for obtaining monotonic fit to data."""
+
 # Authors: Fabian Pedregosa <fabian@fseoane.net>
 #          Alexandre Gramfort <alexandre.gramfort@inria.fr>
 #          Nelle Varoquaux <nelle.varoquaux@gmail.com>
@@ -8,13 +10,14 @@
 from numbers import Real
 
 import numpy as np
-from scipy import interpolate
+from scipy import interpolate, optimize
 from scipy.stats import spearmanr
 
 from ._isotonic import _inplace_contiguous_isotonic_regression, _make_unique
 from .base import BaseEstimator, RegressorMixin, TransformerMixin, _fit_context
 from .utils import check_array, check_consistent_length
 from .utils._param_validation import Interval, StrOptions, validate_params
+from .utils.fixes import parse_version, sp_base_version
 from .utils.validation import _check_sample_weight, check_is_fitted
 
 __all__ = ["check_increasing", "isotonic_regression", "IsotonicRegression"]
@@ -151,13 +154,22 @@ def isotonic_regression(
     array([2.75   , 2.75   , 2.75   , 2.75   , 7.33...,
            7.33..., 7.33..., 7.33..., 7.33..., 7.33...])
     """
-    order = np.s_[:] if increasing else np.s_[::-1]
     y = check_array(y, ensure_2d=False, input_name="y", dtype=[np.float64, np.float32])
-    y = np.array(y[order], dtype=y.dtype)
-    sample_weight = _check_sample_weight(sample_weight, y, dtype=y.dtype, copy=True)
-    sample_weight = np.ascontiguousarray(sample_weight[order])
+    if sp_base_version >= parse_version("1.12.0"):
+        res = optimize.isotonic_regression(
+            y=y, weights=sample_weight, increasing=increasing
+        )
+        y = np.asarray(res.x, dtype=y.dtype)
+    else:
+        # TODO: remove this branch when Scipy 1.12 is the minimum supported version
+        # Also remove _inplace_contiguous_isotonic_regression.
+        order = np.s_[:] if increasing else np.s_[::-1]
+        y = np.array(y[order], dtype=y.dtype)
+        sample_weight = _check_sample_weight(sample_weight, y, dtype=y.dtype, copy=True)
+        sample_weight = np.ascontiguousarray(sample_weight[order])
+        _inplace_contiguous_isotonic_regression(y, sample_weight)
+        y = y[order]
 
-    _inplace_contiguous_isotonic_regression(y, sample_weight)
     if y_min is not None or y_max is not None:
         # Older versions of np.clip don't accept None as a bound, so use np.inf
         if y_min is None:
@@ -165,7 +177,7 @@ def isotonic_regression(
         if y_max is None:
             y_max = np.inf
         np.clip(y, y_min, y_max, y)
-    return y[order]
+    return y
 
 
 class IsotonicRegression(RegressorMixin, TransformerMixin, BaseEstimator):
diff --git a/sklearn/kernel_approximation.py b/sklearn/kernel_approximation.py
index 3df9d318fe2c8..118b37a9b03f2 100644
--- a/sklearn/kernel_approximation.py
+++ b/sklearn/kernel_approximation.py
@@ -1,11 +1,7 @@
-"""
-The :mod:`sklearn.kernel_approximation` module implements several
-approximate kernel feature maps based on Fourier transforms and Count Sketches.
-"""
+"""Approximate kernel feature maps based on Fourier transforms and count sketches."""
 
 # Author: Andreas Mueller <amueller@ais.uni-bonn.de>
 #         Daniel Lopez-Sanchez (TensorSketch) <lope@usal.es>
-
 # License: BSD 3 clause
 
 import warnings
@@ -27,7 +23,7 @@
     _fit_context,
 )
 from .metrics.pairwise import KERNEL_PARAMS, PAIRWISE_KERNEL_FUNCTIONS, pairwise_kernels
-from .utils import check_random_state, deprecated
+from .utils import check_random_state
 from .utils._param_validation import Interval, StrOptions
 from .utils.extmath import safe_sparse_dot
 from .utils.validation import (
@@ -600,13 +596,6 @@ class AdditiveChi2Sampler(TransformerMixin, BaseEstimator):
 
     Attributes
     ----------
-    sample_interval_ : float
-        Stored sampling interval. Specified as a parameter if `sample_steps`
-        not in {1,2,3}.
-
-        .. deprecated:: 1.3
-           `sample_interval_` serves internal purposes only and will be removed in 1.5.
-
     n_features_in_ : int
         Number of features seen during :term:`fit`.
 
@@ -693,37 +682,14 @@ def fit(self, X, y=None):
         X = self._validate_data(X, accept_sparse="csr")
         check_non_negative(X, "X in AdditiveChi2Sampler.fit")
 
-        # TODO(1.5): remove the setting of _sample_interval from fit
-        if self.sample_interval is None:
-            # See figure 2 c) of "Efficient additive kernels via explicit feature maps"
-            # <http://www.robots.ox.ac.uk/~vedaldi/assets/pubs/vedaldi11efficient.pdf>
-            # A. Vedaldi and A. Zisserman, Pattern Analysis and Machine Intelligence,
-            # 2011
-            if self.sample_steps == 1:
-                self._sample_interval = 0.8
-            elif self.sample_steps == 2:
-                self._sample_interval = 0.5
-            elif self.sample_steps == 3:
-                self._sample_interval = 0.4
-            else:
-                raise ValueError(
-                    "If sample_steps is not in [1, 2, 3],"
-                    " you need to provide sample_interval"
-                )
-        else:
-            self._sample_interval = self.sample_interval
+        if self.sample_interval is None and self.sample_steps not in (1, 2, 3):
+            raise ValueError(
+                "If sample_steps is not in [1, 2, 3],"
+                " you need to provide sample_interval"
+            )
 
         return self
 
-    # TODO(1.5): remove
-    @deprecated(  # type: ignore
-        "The ``sample_interval_`` attribute was deprecated in version 1.3 and "
-        "will be removed 1.5."
-    )
-    @property
-    def sample_interval_(self):
-        return self._sample_interval
-
     def transform(self, X):
         """Apply approximate feature map to X.
 
@@ -744,29 +710,24 @@ def transform(self, X):
         check_non_negative(X, "X in AdditiveChi2Sampler.transform")
         sparse = sp.issparse(X)
 
-        if hasattr(self, "_sample_interval"):
-            # TODO(1.5): remove this branch
-            sample_interval = self._sample_interval
-
-        else:
-            if self.sample_interval is None:
-                # See figure 2 c) of "Efficient additive kernels via explicit feature maps" # noqa
-                # <http://www.robots.ox.ac.uk/~vedaldi/assets/pubs/vedaldi11efficient.pdf>
-                # A. Vedaldi and A. Zisserman, Pattern Analysis and Machine Intelligence, # noqa
-                # 2011
-                if self.sample_steps == 1:
-                    sample_interval = 0.8
-                elif self.sample_steps == 2:
-                    sample_interval = 0.5
-                elif self.sample_steps == 3:
-                    sample_interval = 0.4
-                else:
-                    raise ValueError(
-                        "If sample_steps is not in [1, 2, 3],"
-                        " you need to provide sample_interval"
-                    )
+        if self.sample_interval is None:
+            # See figure 2 c) of "Efficient additive kernels via explicit feature maps" # noqa
+            # <http://www.robots.ox.ac.uk/~vedaldi/assets/pubs/vedaldi11efficient.pdf>
+            # A. Vedaldi and A. Zisserman, Pattern Analysis and Machine Intelligence, # noqa
+            # 2011
+            if self.sample_steps == 1:
+                sample_interval = 0.8
+            elif self.sample_steps == 2:
+                sample_interval = 0.5
+            elif self.sample_steps == 3:
+                sample_interval = 0.4
             else:
-                sample_interval = self.sample_interval
+                raise ValueError(
+                    "If sample_steps is not in [1, 2, 3],"
+                    " you need to provide sample_interval"
+                )
+        else:
+            sample_interval = self.sample_interval
 
         # zeroth component
         # 1/cosh = sech
diff --git a/sklearn/kernel_ridge.py b/sklearn/kernel_ridge.py
index 23890f3a68cd7..bdd31d5105336 100644
--- a/sklearn/kernel_ridge.py
+++ b/sklearn/kernel_ridge.py
@@ -1,4 +1,4 @@
-"""Module :mod:`sklearn.kernel_ridge` implements kernel ridge regression."""
+"""Kernel ridge regression."""
 
 # Authors: Mathieu Blondel <mathieu@mblondel.org>
 #          Jan Hendrik Metzen <jhm@informatik.uni-bremen.de>
diff --git a/sklearn/linear_model/__init__.py b/sklearn/linear_model/__init__.py
index 45c99d4d36df1..a2b0ffba0e728 100644
--- a/sklearn/linear_model/__init__.py
+++ b/sklearn/linear_model/__init__.py
@@ -1,6 +1,4 @@
-"""
-The :mod:`sklearn.linear_model` module implements a variety of linear models.
-"""
+"""A variety of linear models."""
 
 # See http://scikit-learn.sourceforge.net/modules/sgd.html and
 # http://scikit-learn.sourceforge.net/modules/linear_model.html for
diff --git a/sklearn/linear_model/_base.py b/sklearn/linear_model/_base.py
index be8c9097332eb..eac754f3f88b4 100644
--- a/sklearn/linear_model/_base.py
+++ b/sklearn/linear_model/_base.py
@@ -33,7 +33,14 @@
     _fit_context,
 )
 from ..utils import check_array, check_random_state
-from ..utils._array_api import get_namespace, indexing_dtype
+from ..utils._array_api import (
+    _asarray_with_order,
+    _average,
+    get_namespace,
+    get_namespace_and_device,
+    indexing_dtype,
+    supported_float_dtypes,
+)
 from ..utils._seq_dataset import (
     ArrayDataset32,
     ArrayDataset64,
@@ -43,7 +50,7 @@
 from ..utils.extmath import safe_sparse_dot
 from ..utils.parallel import Parallel, delayed
 from ..utils.sparsefuncs import mean_variance_axis
-from ..utils.validation import FLOAT_DTYPES, _check_sample_weight, check_is_fitted
+from ..utils.validation import _check_sample_weight, check_is_fitted
 
 # TODO: bayesian_ridge_regression and bayesian_regression_ard
 # should be squashed into its respective objects.
@@ -155,43 +162,51 @@ def _preprocess_data(
         Always an array of ones. TODO: refactor the code base to make it
         possible to remove this unused variable.
     """
+    xp, _, device_ = get_namespace_and_device(X, y, sample_weight)
+    n_samples, n_features = X.shape
+    X_is_sparse = sp.issparse(X)
+
     if isinstance(sample_weight, numbers.Number):
         sample_weight = None
     if sample_weight is not None:
-        sample_weight = np.asarray(sample_weight)
+        sample_weight = xp.asarray(sample_weight)
 
     if check_input:
-        X = check_array(X, copy=copy, accept_sparse=["csr", "csc"], dtype=FLOAT_DTYPES)
+        X = check_array(
+            X, copy=copy, accept_sparse=["csr", "csc"], dtype=supported_float_dtypes(xp)
+        )
         y = check_array(y, dtype=X.dtype, copy=copy_y, ensure_2d=False)
     else:
-        y = y.astype(X.dtype, copy=copy_y)
+        y = xp.astype(y, X.dtype, copy=copy_y)
         if copy:
-            if sp.issparse(X):
+            if X_is_sparse:
                 X = X.copy()
             else:
-                X = X.copy(order="K")
+                X = _asarray_with_order(X, order="K", copy=True, xp=xp)
+
+    dtype_ = X.dtype
 
     if fit_intercept:
-        if sp.issparse(X):
+        if X_is_sparse:
             X_offset, X_var = mean_variance_axis(X, axis=0, weights=sample_weight)
         else:
-            X_offset = np.average(X, axis=0, weights=sample_weight)
+            X_offset = _average(X, axis=0, weights=sample_weight, xp=xp)
 
-            X_offset = X_offset.astype(X.dtype, copy=False)
+            X_offset = xp.astype(X_offset, X.dtype, copy=False)
             X -= X_offset
 
-        y_offset = np.average(y, axis=0, weights=sample_weight)
+        y_offset = _average(y, axis=0, weights=sample_weight, xp=xp)
         y -= y_offset
     else:
-        X_offset = np.zeros(X.shape[1], dtype=X.dtype)
+        X_offset = xp.zeros(n_features, dtype=X.dtype, device=device_)
         if y.ndim == 1:
-            y_offset = X.dtype.type(0)
+            y_offset = xp.asarray(0.0, dtype=dtype_, device=device_)
         else:
-            y_offset = np.zeros(y.shape[1], dtype=X.dtype)
+            y_offset = xp.zeros(y.shape[1], dtype=dtype_, device=device_)
 
     # XXX: X_scale is no longer needed. It is an historic artifact from the
     # time where linear model exposed the normalize parameter.
-    X_scale = np.ones(X.shape[1], dtype=X.dtype)
+    X_scale = xp.ones(n_features, dtype=X.dtype, device=device_)
     return X, y, X_offset, y_offset, X_scale
 
 
@@ -224,8 +239,9 @@ def _rescale_data(X, y, sample_weight, inplace=False):
     """
     # Assume that _validate_data and _check_sample_weight have been called by
     # the caller.
+    xp, _ = get_namespace(X, y, sample_weight)
     n_samples = X.shape[0]
-    sample_weight_sqrt = np.sqrt(sample_weight)
+    sample_weight_sqrt = xp.sqrt(sample_weight)
 
     if sp.issparse(X) or sp.issparse(y):
         sw_matrix = sparse.dia_matrix(
@@ -236,9 +252,9 @@ def _rescale_data(X, y, sample_weight, inplace=False):
         X = safe_sparse_dot(sw_matrix, X)
     else:
         if inplace:
-            X *= sample_weight_sqrt[:, np.newaxis]
+            X *= sample_weight_sqrt[:, None]
         else:
-            X = X * sample_weight_sqrt[:, np.newaxis]
+            X = X * sample_weight_sqrt[:, None]
 
     if sp.issparse(y):
         y = safe_sparse_dot(sw_matrix, y)
@@ -247,12 +263,12 @@ def _rescale_data(X, y, sample_weight, inplace=False):
             if y.ndim == 1:
                 y *= sample_weight_sqrt
             else:
-                y *= sample_weight_sqrt[:, np.newaxis]
+                y *= sample_weight_sqrt[:, None]
         else:
             if y.ndim == 1:
                 y = y * sample_weight_sqrt
             else:
-                y = y * sample_weight_sqrt[:, np.newaxis]
+                y = y * sample_weight_sqrt[:, None]
     return X, y, sample_weight_sqrt
 
 
@@ -267,7 +283,11 @@ def _decision_function(self, X):
         check_is_fitted(self)
 
         X = self._validate_data(X, accept_sparse=["csr", "csc", "coo"], reset=False)
-        return safe_sparse_dot(X, self.coef_.T, dense_output=True) + self.intercept_
+        coef_ = self.coef_
+        if coef_.ndim == 1:
+            return X @ coef_ + self.intercept_
+        else:
+            return X @ coef_.T + self.intercept_
 
     def predict(self, X):
         """
@@ -287,11 +307,22 @@ def predict(self, X):
 
     def _set_intercept(self, X_offset, y_offset, X_scale):
         """Set the intercept_"""
+
+        xp, _ = get_namespace(X_offset, y_offset, X_scale)
+
         if self.fit_intercept:
             # We always want coef_.dtype=X.dtype. For instance, X.dtype can differ from
             # coef_.dtype if warm_start=True.
-            self.coef_ = np.divide(self.coef_, X_scale, dtype=X_scale.dtype)
-            self.intercept_ = y_offset - np.dot(X_offset, self.coef_.T)
+            coef_ = xp.astype(self.coef_, X_scale.dtype, copy=False)
+            coef_ = self.coef_ = xp.divide(coef_, X_scale)
+
+            if coef_.ndim == 1:
+                intercept_ = y_offset - X_offset @ coef_
+            else:
+                intercept_ = y_offset - X_offset @ coef_.T
+
+            self.intercept_ = intercept_
+
         else:
             self.intercept_ = 0.0
 
diff --git a/sklearn/linear_model/_bayes.py b/sklearn/linear_model/_bayes.py
index bf11f36cec599..a572c82e6e158 100644
--- a/sklearn/linear_model/_bayes.py
+++ b/sklearn/linear_model/_bayes.py
@@ -31,6 +31,9 @@ class BayesianRidge(RegressorMixin, LinearModel):
     lambda (precision of the weights) and alpha (precision of the noise).
 
     Read more in the :ref:`User Guide <bayesian_regression>`.
+    For an intuitive visualization of how the sinusoid is approximated by
+    a polynomial using different pairs of initial values, see
+    :ref:`sphx_glr_auto_examples_linear_model_plot_bayesian_ridge_curvefit.py`.
 
     Parameters
     ----------
diff --git a/sklearn/linear_model/_coordinate_descent.py b/sklearn/linear_model/_coordinate_descent.py
index 05d7b93f3e090..6a62fa1e245e2 100644
--- a/sklearn/linear_model/_coordinate_descent.py
+++ b/sklearn/linear_model/_coordinate_descent.py
@@ -776,6 +776,9 @@ class ElasticNet(MultiOutputMixin, RegressorMixin, LinearModel):
         Whether to use a precomputed Gram matrix to speed up
         calculations. The Gram matrix can also be passed as argument.
         For sparse input this option is always ``False`` to preserve sparsity.
+        Check :ref:`an example on how to use a precomputed Gram Matrix in ElasticNet
+        <sphx_glr_auto_examples_linear_model_plot_elastic_net_precomputed_gram_matrix_with_weighted_samples.py>`
+        for details.
 
     max_iter : int, default=1000
         The maximum number of iterations.
@@ -1860,7 +1863,7 @@ def get_metadata_routing(self):
             .add_self_request(self)
             .add(
                 splitter=check_cv(self.cv),
-                method_mapping=MethodMapping().add(callee="split", caller="fit"),
+                method_mapping=MethodMapping().add(caller="fit", callee="split"),
             )
         )
         return router
diff --git a/sklearn/linear_model/_glm/__init__.py b/sklearn/linear_model/_glm/__init__.py
index 1b82bbd77bcf9..199b938b023d0 100644
--- a/sklearn/linear_model/_glm/__init__.py
+++ b/sklearn/linear_model/_glm/__init__.py
@@ -1,5 +1,5 @@
-# License: BSD 3 clause
-
+# Authors: The scikit-learn developers
+# SPDX-License-Identifier: BSD-3-Clause
 from .glm import (
     GammaRegressor,
     PoissonRegressor,
diff --git a/sklearn/linear_model/_glm/_newton_solver.py b/sklearn/linear_model/_glm/_newton_solver.py
index 20df35e6b48c2..b2be604d931c5 100644
--- a/sklearn/linear_model/_glm/_newton_solver.py
+++ b/sklearn/linear_model/_glm/_newton_solver.py
@@ -1,10 +1,9 @@
+# Authors: The scikit-learn developers
+# SPDX-License-Identifier: BSD-3-Clause
 """
 Newton solver for Generalized Linear Models
 """
 
-# Author: Christian Lorentzen <lorentzen.ch@gmail.com>
-# License: BSD 3 clause
-
 import warnings
 from abc import ABC, abstractmethod
 
diff --git a/sklearn/linear_model/_glm/glm.py b/sklearn/linear_model/_glm/glm.py
index 4cac889a4da51..14caa4fd733c2 100644
--- a/sklearn/linear_model/_glm/glm.py
+++ b/sklearn/linear_model/_glm/glm.py
@@ -1,11 +1,9 @@
+# Authors: The scikit-learn developers
+# SPDX-License-Identifier: BSD-3-Clause
 """
 Generalized Linear Models with Exponential Dispersion Family
 """
 
-# Author: Christian Lorentzen <lorentzen.ch@gmail.com>
-# some parts and tricks stolen from other sklearn files.
-# License: BSD 3 clause
-
 from numbers import Integral, Real
 
 import numpy as np
diff --git a/sklearn/linear_model/_glm/tests/__init__.py b/sklearn/linear_model/_glm/tests/__init__.py
index 588cf7e93eef0..67dd18fb94b59 100644
--- a/sklearn/linear_model/_glm/tests/__init__.py
+++ b/sklearn/linear_model/_glm/tests/__init__.py
@@ -1 +1,2 @@
-# License: BSD 3 clause
+# Authors: The scikit-learn developers
+# SPDX-License-Identifier: BSD-3-Clause
diff --git a/sklearn/linear_model/_glm/tests/test_glm.py b/sklearn/linear_model/_glm/tests/test_glm.py
index 26f6bdc08d254..7f6ec64c15ad4 100644
--- a/sklearn/linear_model/_glm/tests/test_glm.py
+++ b/sklearn/linear_model/_glm/tests/test_glm.py
@@ -1,7 +1,5 @@
-# Authors: Christian Lorentzen <lorentzen.ch@gmail.com>
-#
-# License: BSD 3 clause
-
+# Authors: The scikit-learn developers
+# SPDX-License-Identifier: BSD-3-Clause
 import itertools
 import warnings
 from functools import partial
diff --git a/sklearn/linear_model/_least_angle.py b/sklearn/linear_model/_least_angle.py
index f29bcb4c8915c..fae9f523847ad 100644
--- a/sklearn/linear_model/_least_angle.py
+++ b/sklearn/linear_model/_least_angle.py
@@ -79,14 +79,14 @@ def lars_path(
     return_n_iter=False,
     positive=False,
 ):
-    """Compute Least Angle Regression or Lasso path using the LARS algorithm [1].
+    """Compute Least Angle Regression or Lasso path using the LARS algorithm.
 
     The optimization objective for the case method='lasso' is::
 
     (1 / (2 * n_samples)) * ||y - Xw||^2_2 + alpha * ||w||_1
 
     in the case of method='lar', the objective function is only known in
-    the form of an implicit equation (see discussion in [1]).
+    the form of an implicit equation (see discussion in [1]_).
 
     Read more in the :ref:`User Guide <least_angle_regression>`.
 
@@ -268,14 +268,14 @@ def lars_path_gram(
     return_n_iter=False,
     positive=False,
 ):
-    """The lars_path in the sufficient stats mode [1].
+    """The lars_path in the sufficient stats mode.
 
     The optimization objective for the case method='lasso' is::
 
     (1 / (2 * n_samples)) * ||y - Xw||^2_2 + alpha * ||w||_1
 
     in the case of method='lar', the objective function is only known in
-    the form of an implicit equation (see discussion in [1])
+    the form of an implicit equation (see discussion in [1]_).
 
     Read more in the :ref:`User Guide <least_angle_regression>`.
 
@@ -1821,7 +1821,7 @@ def get_metadata_routing(self):
         """
         router = MetadataRouter(owner=self.__class__.__name__).add(
             splitter=check_cv(self.cv),
-            method_mapping=MethodMapping().add(callee="split", caller="fit"),
+            method_mapping=MethodMapping().add(caller="fit", callee="split"),
         )
         return router
 
diff --git a/sklearn/linear_model/_logistic.py b/sklearn/linear_model/_logistic.py
index 129d3f6cc9494..e8755c659af6e 100644
--- a/sklearn/linear_model/_logistic.py
+++ b/sklearn/linear_model/_logistic.py
@@ -33,7 +33,7 @@
     check_random_state,
     compute_class_weight,
 )
-from ..utils._param_validation import Interval, StrOptions
+from ..utils._param_validation import Hidden, Interval, StrOptions
 from ..utils.extmath import row_norms, softmax
 from ..utils.metadata_routing import (
     MetadataRouter,
@@ -636,7 +636,7 @@ def _log_reg_scoring_path(
         values are chosen in a logarithmic scale between 1e-4 and 1e4.
 
     scoring : callable
-        A string (see model evaluation documentation) or
+        A string (see :ref:`scoring_parameter`) or
         a scorer callable object / function with signature
         ``scorer(estimator, X, y)``. For a list of scoring functions
         that can be used, look at :mod:`sklearn.metrics`.
@@ -905,28 +905,33 @@ class LogisticRegression(LinearClassifierMixin, SparseCoefMixin, BaseEstimator):
         Algorithm to use in the optimization problem. Default is 'lbfgs'.
         To choose a solver, you might want to consider the following aspects:
 
-            - For small datasets, 'liblinear' is a good choice, whereas 'sag'
-              and 'saga' are faster for large ones;
-            - For multiclass problems, only 'newton-cg', 'sag', 'saga' and
-              'lbfgs' handle multinomial loss;
-            - 'liblinear' is limited to one-versus-rest schemes.
-            - 'newton-cholesky' is a good choice for `n_samples` >> `n_features`,
-              especially with one-hot encoded categorical features with rare
-              categories. Note that it is limited to binary classification and the
-              one-versus-rest reduction for multiclass classification. Be aware that
-              the memory usage of this solver has a quadratic dependency on
-              `n_features` because it explicitly computes the Hessian matrix.
+        - For small datasets, 'liblinear' is a good choice, whereas 'sag'
+          and 'saga' are faster for large ones;
+        - For multiclass problems, only 'newton-cg', 'sag', 'saga' and
+          'lbfgs' handle multinomial loss;
+        - 'liblinear' and 'newton-cholesky' can only handle binary classification
+          by default. To apply a one-versus-rest scheme for the multiclass setting
+          one can wrapt it with the `OneVsRestClassifier`.
+        - 'newton-cholesky' is a good choice for `n_samples` >> `n_features`,
+          especially with one-hot encoded categorical features with rare
+          categories. Be aware that the memory usage of this solver has a quadratic
+          dependency on `n_features` because it explicitly computes the Hessian
+          matrix.
 
         .. warning::
-           The choice of the algorithm depends on the penalty chosen.
-           Supported penalties by solver:
-
-           - 'lbfgs'           -   ['l2', None]
-           - 'liblinear'       -   ['l1', 'l2']
-           - 'newton-cg'       -   ['l2', None]
-           - 'newton-cholesky' -   ['l2', None]
-           - 'sag'             -   ['l2', None]
-           - 'saga'            -   ['elasticnet', 'l1', 'l2', None]
+           The choice of the algorithm depends on the penalty chosen and on
+           (multinomial) multiclass support:
+
+           ================= ============================== ======================
+           solver            penalty                        multinomial multiclass
+           ================= ============================== ======================
+           'lbfgs'           'l2', None                     yes
+           'liblinear'       'l1', 'l2'                     no
+           'newton-cg'       'l2', None                     yes
+           'newton-cholesky' 'l2', None                     no
+           'sag'             'l2', None                     yes
+           'saga'            'elasticnet', 'l1', 'l2', None yes
+           ================= ============================== ======================
 
         .. note::
            'sag' and 'saga' fast convergence is only guaranteed on features
@@ -963,6 +968,13 @@ class LogisticRegression(LinearClassifierMixin, SparseCoefMixin, BaseEstimator):
            Stochastic Average Gradient descent solver for 'multinomial' case.
         .. versionchanged:: 0.22
             Default changed from 'ovr' to 'auto' in 0.22.
+        .. deprecated:: 1.5
+           ``multi_class`` was deprecated in version 1.5 and will be removed in 1.7.
+           From then on, the recommended 'multinomial' will always be used for
+           `n_classes >= 3`.
+           Solvers that do not support 'multinomial' will raise an error.
+           Use `sklearn.multiclass.OneVsRestClassifier(LogisticRegression())` if you
+           still want to use OvR.
 
     verbose : int, default=0
         For the liblinear and lbfgs solvers set verbose to any positive
@@ -1104,11 +1116,14 @@ class LogisticRegression(LinearClassifierMixin, SparseCoefMixin, BaseEstimator):
             )
         ],
         "max_iter": [Interval(Integral, 0, None, closed="left")],
-        "multi_class": [StrOptions({"auto", "ovr", "multinomial"})],
         "verbose": ["verbose"],
         "warm_start": ["boolean"],
         "n_jobs": [None, Integral],
         "l1_ratio": [Interval(Real, 0, 1, closed="both"), None],
+        "multi_class": [
+            StrOptions({"auto", "ovr", "multinomial"}),
+            Hidden(StrOptions({"deprecated"})),
+        ],
     }
 
     def __init__(
@@ -1124,7 +1139,7 @@ def __init__(
         random_state=None,
         solver="lbfgs",
         max_iter=100,
-        multi_class="auto",
+        multi_class="deprecated",
         verbose=0,
         warm_start=False,
         n_jobs=None,
@@ -1216,7 +1231,40 @@ def fit(self, X, y, sample_weight=None):
         check_classification_targets(y)
         self.classes_ = np.unique(y)
 
-        multi_class = _check_multi_class(self.multi_class, solver, len(self.classes_))
+        # TODO(1.7) remove multi_class
+        multi_class = self.multi_class
+        if self.multi_class == "multinomial" and len(self.classes_) == 2:
+            warnings.warn(
+                (
+                    "'multi_class' was deprecated in version 1.5 and will be removed in"
+                    " 1.7. From then on, binary problems will be fit as proper binary "
+                    " logistic regression models (as if multi_class='ovr' were set)."
+                    " Leave it to its default value to avoid this warning."
+                ),
+                FutureWarning,
+            )
+        elif self.multi_class in ("multinomial", "auto"):
+            warnings.warn(
+                (
+                    "'multi_class' was deprecated in version 1.5 and will be removed in"
+                    " 1.7. From then on, it will always use 'multinomial'."
+                    " Leave it to its default value to avoid this warning."
+                ),
+                FutureWarning,
+            )
+        elif self.multi_class == "ovr":
+            warnings.warn(
+                (
+                    "'multi_class' was deprecated in version 1.5 and will be removed in"
+                    " 1.7. Use OneVsRestClassifier(LogisticRegression(..)) instead."
+                    " Leave it to its default value to avoid this warning."
+                ),
+                FutureWarning,
+            )
+        else:
+            # Set to old default value.
+            multi_class = "auto"
+        multi_class = _check_multi_class(multi_class, solver, len(self.classes_))
 
         if solver == "liblinear":
             if effective_n_jobs(self.n_jobs) != 1:
@@ -1373,7 +1421,7 @@ def predict_proba(self, X):
         check_is_fitted(self)
 
         ovr = self.multi_class in ["ovr", "warn"] or (
-            self.multi_class == "auto"
+            self.multi_class in ["auto", "deprecated"]
             and (
                 self.classes_.size <= 2
                 or self.solver in ("liblinear", "newton-cholesky")
@@ -1473,7 +1521,7 @@ class LogisticRegressionCV(LogisticRegression, LinearClassifierMixin, BaseEstima
            solver.
 
     scoring : str or callable, default=None
-        A string (see model evaluation documentation) or
+        A string (see :ref:`scoring_parameter`) or
         a scorer callable object / function with signature
         ``scorer(estimator, X, y)``. For a list of scoring functions
         that can be used, look at :mod:`sklearn.metrics`. The
@@ -1485,30 +1533,35 @@ class LogisticRegressionCV(LogisticRegression, LinearClassifierMixin, BaseEstima
         Algorithm to use in the optimization problem. Default is 'lbfgs'.
         To choose a solver, you might want to consider the following aspects:
 
-            - For small datasets, 'liblinear' is a good choice, whereas 'sag'
-              and 'saga' are faster for large ones;
-            - For multiclass problems, only 'newton-cg', 'sag', 'saga' and
-              'lbfgs' handle multinomial loss;
-            - 'liblinear' might be slower in :class:`LogisticRegressionCV`
-              because it does not handle warm-starting. 'liblinear' is
-              limited to one-versus-rest schemes.
-            - 'newton-cholesky' is a good choice for `n_samples` >> `n_features`,
-              especially with one-hot encoded categorical features with rare
-              categories. Note that it is limited to binary classification and the
-              one-versus-rest reduction for multiclass classification. Be aware that
-              the memory usage of this solver has a quadratic dependency on
-              `n_features` because it explicitly computes the Hessian matrix.
+        - For small datasets, 'liblinear' is a good choice, whereas 'sag'
+          and 'saga' are faster for large ones;
+        - For multiclass problems, only 'newton-cg', 'sag', 'saga' and
+          'lbfgs' handle multinomial loss;
+        - 'liblinear' might be slower in :class:`LogisticRegressionCV`
+          because it does not handle warm-starting.
+        - 'liblinear' and 'newton-cholesky' can only handle binary classification
+          by default. To apply a one-versus-rest scheme for the multiclass setting
+          one can wrapt it with the `OneVsRestClassifier`.
+        - 'newton-cholesky' is a good choice for `n_samples` >> `n_features`,
+          especially with one-hot encoded categorical features with rare
+          categories. Be aware that the memory usage of this solver has a quadratic
+          dependency on `n_features` because it explicitly computes the Hessian
+          matrix.
 
         .. warning::
-           The choice of the algorithm depends on the penalty chosen.
-           Supported penalties by solver:
-
-           - 'lbfgs'           -   ['l2']
-           - 'liblinear'       -   ['l1', 'l2']
-           - 'newton-cg'       -   ['l2']
-           - 'newton-cholesky' -   ['l2']
-           - 'sag'             -   ['l2']
-           - 'saga'            -   ['elasticnet', 'l1', 'l2']
+           The choice of the algorithm depends on the penalty chosen and on
+           (multinomial) multiclass support:
+
+           ================= ============================== ======================
+           solver            penalty                        multinomial multiclass
+           ================= ============================== ======================
+           'lbfgs'           'l2'                           yes
+           'liblinear'       'l1', 'l2'                     no
+           'newton-cg'       'l2'                           yes
+           'newton-cholesky' 'l2',                          no
+           'sag'             'l2',                          yes
+           'saga'            'elasticnet', 'l1', 'l2'       yes
+           ================= ============================== ======================
 
         .. note::
            'sag' and 'saga' fast convergence is only guaranteed on features
@@ -1584,6 +1637,13 @@ class LogisticRegressionCV(LogisticRegression, LinearClassifierMixin, BaseEstima
            Stochastic Average Gradient descent solver for 'multinomial' case.
         .. versionchanged:: 0.22
             Default changed from 'ovr' to 'auto' in 0.22.
+        .. deprecated:: 1.5
+           ``multi_class`` was deprecated in version 1.5 and will be removed in 1.7.
+           From then on, the recommended 'multinomial' will always be used for
+           `n_classes >= 3`.
+           Solvers that do not support 'multinomial' will raise an error.
+           Use `sklearn.multiclass.OneVsRestClassifier(LogisticRegressionCV())` if you
+           still want to use OvR.
 
     random_state : int, RandomState instance, default=None
         Used when `solver='sag'`, 'saga' or 'liblinear' to shuffle the data.
@@ -1725,7 +1785,7 @@ def __init__(
         verbose=0,
         refit=True,
         intercept_scaling=1.0,
-        multi_class="auto",
+        multi_class="deprecated",
         random_state=None,
         l1_ratios=None,
     ):
@@ -1829,7 +1889,40 @@ def fit(self, X, y, sample_weight=None, **params):
         classes = self.classes_ = label_encoder.classes_
         encoded_labels = label_encoder.transform(label_encoder.classes_)
 
-        multi_class = _check_multi_class(self.multi_class, solver, len(classes))
+        # TODO(1.7) remove multi_class
+        multi_class = self.multi_class
+        if self.multi_class == "multinomial" and len(self.classes_) == 2:
+            warnings.warn(
+                (
+                    "'multi_class' was deprecated in version 1.5 and will be removed in"
+                    " 1.7. From then on, binary problems will be fit as proper binary "
+                    " logistic regression models (as if multi_class='ovr' were set)."
+                    " Leave it to its default value to avoid this warning."
+                ),
+                FutureWarning,
+            )
+        elif self.multi_class in ("multinomial", "auto"):
+            warnings.warn(
+                (
+                    "'multi_class' was deprecated in version 1.5 and will be removed in"
+                    " 1.7. From then on, it will always use 'multinomial'."
+                    " Leave it to its default value to avoid this warning."
+                ),
+                FutureWarning,
+            )
+        elif self.multi_class == "ovr":
+            warnings.warn(
+                (
+                    "'multi_class' was deprecated in version 1.5 and will be removed in"
+                    " 1.7. Use OneVsRestClassifier(LogisticRegressionCV(..)) instead."
+                    " Leave it to its default value to avoid this warning."
+                ),
+                FutureWarning,
+            )
+        else:
+            # Set to old default value.
+            multi_class = "auto"
+        multi_class = _check_multi_class(multi_class, solver, len(classes))
 
         if solver in ["sag", "saga"]:
             max_squared_sum = row_norms(X, squared=True).max()
@@ -2166,13 +2259,13 @@ def get_metadata_routing(self):
             .add_self_request(self)
             .add(
                 splitter=self.cv,
-                method_mapping=MethodMapping().add(callee="split", caller="fit"),
+                method_mapping=MethodMapping().add(caller="fit", callee="split"),
             )
             .add(
                 scorer=self._get_scorer(),
                 method_mapping=MethodMapping()
-                .add(callee="score", caller="score")
-                .add(callee="score", caller="fit"),
+                .add(caller="score", callee="score")
+                .add(caller="fit", callee="score"),
             )
         )
         return router
diff --git a/sklearn/linear_model/_omp.py b/sklearn/linear_model/_omp.py
index 2d6fe48869742..f52ef553eab4c 100644
--- a/sklearn/linear_model/_omp.py
+++ b/sklearn/linear_model/_omp.py
@@ -1116,6 +1116,6 @@ def get_metadata_routing(self):
 
         router = MetadataRouter(owner=self.__class__.__name__).add(
             splitter=self.cv,
-            method_mapping=MethodMapping().add(callee="split", caller="fit"),
+            method_mapping=MethodMapping().add(caller="fit", callee="split"),
         )
         return router
diff --git a/sklearn/linear_model/_passive_aggressive.py b/sklearn/linear_model/_passive_aggressive.py
index 2de019b6d986c..3a7ffbf9bfaea 100644
--- a/sklearn/linear_model/_passive_aggressive.py
+++ b/sklearn/linear_model/_passive_aggressive.py
@@ -145,6 +145,10 @@ class PassiveAggressiveClassifier(BaseSGDClassifier):
     loss_function_ : callable
         Loss function used by the algorithm.
 
+         .. deprecated:: 1.4
+            Attribute `loss_function_` was deprecated in version 1.4 and will be
+            removed in 1.6.
+
     See Also
     --------
     SGDClassifier : Incrementally trained logistic regression.
diff --git a/sklearn/linear_model/_ridge.py b/sklearn/linear_model/_ridge.py
index 584302f927ab6..b336565cff1f6 100644
--- a/sklearn/linear_model/_ridge.py
+++ b/sklearn/linear_model/_ridge.py
@@ -31,8 +31,16 @@
     check_scalar,
     column_or_1d,
     compute_sample_weight,
+    deprecated,
 )
-from ..utils._param_validation import Interval, StrOptions, validate_params
+from ..utils._array_api import (
+    _is_numpy_namespace,
+    _ravel,
+    device,
+    get_namespace,
+    get_namespace_and_device,
+)
+from ..utils._param_validation import Hidden, Interval, StrOptions, validate_params
 from ..utils.extmath import row_norms, safe_sparse_dot
 from ..utils.fixes import _sparse_linalg_cg
 from ..utils.metadata_routing import (
@@ -277,15 +285,16 @@ def _solve_cholesky_kernel(K, y, alpha, sample_weight=None, copy=False):
         return dual_coefs.T
 
 
-def _solve_svd(X, y, alpha):
-    U, s, Vt = linalg.svd(X, full_matrices=False)
+def _solve_svd(X, y, alpha, xp=None):
+    xp, _ = get_namespace(X, xp=xp)
+    U, s, Vt = xp.linalg.svd(X, full_matrices=False)
     idx = s > 1e-15  # same default value as scipy.linalg.pinv
-    s_nnz = s[idx][:, np.newaxis]
-    UTy = np.dot(U.T, y)
-    d = np.zeros((s.size, alpha.size), dtype=X.dtype)
+    s_nnz = s[idx][:, None]
+    UTy = U.T @ y
+    d = xp.zeros((s.shape[0], alpha.shape[0]), dtype=X.dtype, device=device(X))
     d[idx] = s_nnz / (s_nnz**2 + alpha)
     d_UT_y = d * UTy
-    return np.dot(Vt.T, d_UT_y).T
+    return (Vt.T @ d_UT_y).T
 
 
 def _solve_lbfgs(
@@ -600,28 +609,29 @@ def _ridge_regression(
     random_state=None,
     return_n_iter=False,
     return_intercept=False,
+    return_solver=False,
     X_scale=None,
     X_offset=None,
     check_input=True,
     fit_intercept=False,
 ):
+    xp, is_array_api_compliant, device_ = get_namespace_and_device(
+        X, y, sample_weight, X_scale, X_offset
+    )
+    is_numpy_namespace = _is_numpy_namespace(xp)
+    X_is_sparse = sparse.issparse(X)
+
     has_sw = sample_weight is not None
 
-    if solver == "auto":
-        if positive:
-            solver = "lbfgs"
-        elif return_intercept:
-            # sag supports fitting intercept directly
-            solver = "sag"
-        elif not sparse.issparse(X):
-            solver = "cholesky"
-        else:
-            solver = "sparse_cg"
+    solver = resolve_solver(solver, positive, return_intercept, X_is_sparse, xp)
 
-    if solver not in ("sparse_cg", "cholesky", "svd", "lsqr", "sag", "saga", "lbfgs"):
+    if is_numpy_namespace and not X_is_sparse:
+        X = np.asarray(X)
+
+    if not is_numpy_namespace and solver != "svd":
         raise ValueError(
-            "Known solvers are 'sparse_cg', 'cholesky', 'svd'"
-            " 'lsqr', 'sag', 'saga' or 'lbfgs'. Got %s." % solver
+            f"Array API dispatch to namespace {xp.__name__} only supports "
+            f"solver 'svd'. Got '{solver}'."
         )
 
     if positive and solver != "lbfgs":
@@ -645,8 +655,8 @@ def _ridge_regression(
         )
 
     if check_input:
-        _dtype = [np.float64, np.float32]
-        _accept_sparse = _get_valid_accept_sparse(sparse.issparse(X), solver)
+        _dtype = [xp.float64, xp.float32]
+        _accept_sparse = _get_valid_accept_sparse(X_is_sparse, solver)
         X = check_array(X, accept_sparse=_accept_sparse, dtype=_dtype, order="C")
         y = check_array(y, dtype=X.dtype, ensure_2d=False, order=None)
     check_consistent_length(X, y)
@@ -658,7 +668,7 @@ def _ridge_regression(
 
     ravel = False
     if y.ndim == 1:
-        y = y.reshape(-1, 1)
+        y = xp.reshape(y, (-1, 1))
         ravel = True
 
     n_samples_, n_targets = y.shape
@@ -679,7 +689,7 @@ def _ridge_regression(
 
     # Some callers of this method might pass alpha as single
     # element array which already has been validated.
-    if alpha is not None and not isinstance(alpha, np.ndarray):
+    if alpha is not None and not isinstance(alpha, type(xp.asarray([0.0]))):
         alpha = check_scalar(
             alpha,
             "alpha",
@@ -689,15 +699,17 @@ def _ridge_regression(
         )
 
     # There should be either 1 or n_targets penalties
-    alpha = np.asarray(alpha, dtype=X.dtype).ravel()
-    if alpha.size not in [1, n_targets]:
+    alpha = _ravel(xp.asarray(alpha, device=device_, dtype=X.dtype), xp=xp)
+    if alpha.shape[0] not in [1, n_targets]:
         raise ValueError(
             "Number of targets and number of penalties do not correspond: %d != %d"
-            % (alpha.size, n_targets)
+            % (alpha.shape[0], n_targets)
         )
 
-    if alpha.size == 1 and n_targets > 1:
-        alpha = np.repeat(alpha, n_targets)
+    if alpha.shape[0] == 1 and n_targets > 1:
+        alpha = xp.full(
+            shape=(n_targets,), fill_value=alpha[0], dtype=alpha.dtype, device=device_
+        )
 
     n_iter = None
     if solver == "sparse_cg":
@@ -779,7 +791,6 @@ def _ridge_regression(
 
         if intercept.shape[0] == 1:
             intercept = intercept[0]
-        coef = np.asarray(coef)
 
     elif solver == "lbfgs":
         coef = _solve_lbfgs(
@@ -795,22 +806,71 @@ def _ridge_regression(
         )
 
     if solver == "svd":
-        if sparse.issparse(X):
+        if X_is_sparse:
             raise TypeError("SVD solver does not support sparse inputs currently")
-        coef = _solve_svd(X, y, alpha)
+        coef = _solve_svd(X, y, alpha, xp)
 
     if ravel:
-        # When y was passed as a 1d-array, we flatten the coefficients.
-        coef = coef.ravel()
+        coef = _ravel(coef)
+
+    coef = xp.asarray(coef)
 
     if return_n_iter and return_intercept:
-        return coef, n_iter, intercept
+        res = coef, n_iter, intercept
     elif return_intercept:
-        return coef, intercept
+        res = coef, intercept
     elif return_n_iter:
-        return coef, n_iter
+        res = coef, n_iter
     else:
-        return coef
+        res = coef
+
+    return (*res, solver) if return_solver else res
+
+
+def resolve_solver(solver, positive, return_intercept, is_sparse, xp):
+    if solver != "auto":
+        return solver
+
+    is_numpy_namespace = _is_numpy_namespace(xp)
+
+    auto_solver_np = resolve_solver_for_numpy(positive, return_intercept, is_sparse)
+    if is_numpy_namespace:
+        return auto_solver_np
+
+    if positive:
+        raise ValueError(
+            "The solvers that support positive fitting do not support "
+            f"Array API dispatch to namespace {xp.__name__}. Please "
+            "either disable Array API dispatch, or use a numpy-like "
+            "namespace, or set `positive=False`."
+        )
+
+    # At the moment, Array API dispatch only supports the "svd" solver.
+    solver = "svd"
+    if solver != auto_solver_np:
+        warnings.warn(
+            f"Using Array API dispatch to namespace {xp.__name__} with "
+            f"`solver='auto'` will result in using the solver '{solver}'. "
+            "The results may differ from those when using a Numpy array, "
+            f"because in that case the preferred solver would be {auto_solver_np}. "
+            f"Set `solver='{solver}'` to suppress this warning."
+        )
+
+    return solver
+
+
+def resolve_solver_for_numpy(positive, return_intercept, is_sparse):
+    if positive:
+        return "lbfgs"
+
+    if return_intercept:
+        # sag supports fitting intercept directly
+        return "sag"
+
+    if not is_sparse:
+        return "cholesky"
+
+    return "sparse_cg"
 
 
 class _BaseRidge(LinearModel, metaclass=ABCMeta):
@@ -852,6 +912,8 @@ def __init__(
         self.random_state = random_state
 
     def fit(self, X, y, sample_weight=None):
+        xp, is_array_api_compliant = get_namespace(X, y, sample_weight)
+
         if self.solver == "lbfgs" and not self.positive:
             raise ValueError(
                 "'lbfgs' solver can be used only when positive=True. "
@@ -903,7 +965,7 @@ def fit(self, X, y, sample_weight=None):
         )
 
         if solver == "sag" and sparse.issparse(X) and self.fit_intercept:
-            self.coef_, self.n_iter_, self.intercept_ = _ridge_regression(
+            self.coef_, self.n_iter_, self.intercept_, self.solver_ = _ridge_regression(
                 X,
                 y,
                 alpha=self.alpha,
@@ -915,6 +977,7 @@ def fit(self, X, y, sample_weight=None):
                 random_state=self.random_state,
                 return_n_iter=True,
                 return_intercept=True,
+                return_solver=True,
                 check_input=False,
             )
             # add the offset which was subtracted by _preprocess_data
@@ -928,7 +991,7 @@ def fit(self, X, y, sample_weight=None):
                 # for dense matrices or when intercept is set to 0
                 params = {}
 
-            self.coef_, self.n_iter_ = _ridge_regression(
+            self.coef_, self.n_iter_, self.solver_ = _ridge_regression(
                 X,
                 y,
                 alpha=self.alpha,
@@ -940,6 +1003,7 @@ def fit(self, X, y, sample_weight=None):
                 random_state=self.random_state,
                 return_n_iter=True,
                 return_intercept=False,
+                return_solver=True,
                 check_input=False,
                 fit_intercept=self.fit_intercept,
                 **params,
@@ -1095,6 +1159,12 @@ class Ridge(MultiOutputMixin, RegressorMixin, _BaseRidge):
 
         .. versionadded:: 1.0
 
+    solver_ : str
+        The solver that was used at fit time by the computational
+        routines.
+
+        .. versionadded:: 1.5
+
     See Also
     --------
     RidgeClassifier : Ridge classifier.
@@ -1168,16 +1238,20 @@ def fit(self, X, y, sample_weight=None):
             Fitted estimator.
         """
         _accept_sparse = _get_valid_accept_sparse(sparse.issparse(X), self.solver)
+        xp, _ = get_namespace(X, y, sample_weight)
         X, y = self._validate_data(
             X,
             y,
             accept_sparse=_accept_sparse,
-            dtype=[np.float64, np.float32],
+            dtype=[xp.float64, xp.float32],
             multi_output=True,
             y_numeric=True,
         )
         return super().fit(X, y, sample_weight=sample_weight)
 
+    def _more_tags(self):
+        return {"array_api_support": True}
+
 
 class _RidgeClassifierMixin(LinearClassifierMixin):
     def _prepare_data(self, X, y, sample_weight, solver):
@@ -1403,6 +1477,12 @@ class RidgeClassifier(_RidgeClassifierMixin, _BaseRidge):
 
         .. versionadded:: 1.0
 
+    solver_ : str
+        The solver that was used at fit time by the computational
+        routines.
+
+        .. versionadded:: 1.5
+
     See Also
     --------
     Ridge : Ridge regression.
@@ -1652,7 +1732,7 @@ def __init__(
         scoring=None,
         copy_X=True,
         gcv_mode=None,
-        store_cv_values=False,
+        store_cv_results=False,
         is_clf=False,
         alpha_per_target=False,
     ):
@@ -1661,7 +1741,7 @@ def __init__(
         self.scoring = scoring
         self.copy_X = copy_X
         self.gcv_mode = gcv_mode
-        self.store_cv_values = store_cv_values
+        self.store_cv_results = store_cv_results
         self.is_clf = is_clf
         self.alpha_per_target = alpha_per_target
 
@@ -2056,8 +2136,8 @@ def fit(self, X, y, sample_weight=None, score_params=None):
         n_y = 1 if len(y.shape) == 1 else y.shape[1]
         n_alphas = 1 if np.ndim(self.alphas) == 0 else len(self.alphas)
 
-        if self.store_cv_values:
-            self.cv_values_ = np.empty((n_samples * n_y, n_alphas), dtype=X.dtype)
+        if self.store_cv_results:
+            self.cv_results_ = np.empty((n_samples * n_y, n_alphas), dtype=X.dtype)
 
         best_coef, best_score, best_alpha = None, None, None
 
@@ -2066,12 +2146,12 @@ def fit(self, X, y, sample_weight=None, score_params=None):
             if scorer is None:
                 squared_errors = (c / G_inverse_diag) ** 2
                 alpha_score = self._score_without_scorer(squared_errors=squared_errors)
-                if self.store_cv_values:
-                    self.cv_values_[:, i] = squared_errors.ravel()
+                if self.store_cv_results:
+                    self.cv_results_[:, i] = squared_errors.ravel()
             else:
                 predictions = y - (c / G_inverse_diag)
-                if self.store_cv_values:
-                    self.cv_values_[:, i] = predictions.ravel()
+                if self.store_cv_results:
+                    self.cv_results_[:, i] = predictions.ravel()
 
                 score_params = score_params or {}
                 alpha_score = self._score(
@@ -2114,12 +2194,12 @@ def fit(self, X, y, sample_weight=None, score_params=None):
             X_offset += X_mean * X_scale
         self._set_intercept(X_offset, y_offset, X_scale)
 
-        if self.store_cv_values:
+        if self.store_cv_results:
             if len(y.shape) == 1:
-                cv_values_shape = n_samples, n_alphas
+                cv_results_shape = n_samples, n_alphas
             else:
-                cv_values_shape = n_samples, n_y, n_alphas
-            self.cv_values_ = self.cv_values_.reshape(cv_values_shape)
+                cv_results_shape = n_samples, n_y, n_alphas
+            self.cv_results_ = self.cv_results_.reshape(cv_results_shape)
 
         return self
 
@@ -2179,8 +2259,9 @@ class _BaseRidgeCV(LinearModel):
         "scoring": [StrOptions(set(get_scorer_names())), callable, None],
         "cv": ["cv_object"],
         "gcv_mode": [StrOptions({"auto", "svd", "eigen"}), None],
-        "store_cv_values": ["boolean"],
+        "store_cv_results": ["boolean", Hidden(None)],
         "alpha_per_target": ["boolean"],
+        "store_cv_values": ["boolean", Hidden(StrOptions({"deprecated"}))],
     }
 
     def __init__(
@@ -2191,16 +2272,18 @@ def __init__(
         scoring=None,
         cv=None,
         gcv_mode=None,
-        store_cv_values=False,
+        store_cv_results=None,
         alpha_per_target=False,
+        store_cv_values="deprecated",
     ):
         self.alphas = alphas
         self.fit_intercept = fit_intercept
         self.scoring = scoring
         self.cv = cv
         self.gcv_mode = gcv_mode
-        self.store_cv_values = store_cv_values
+        self.store_cv_results = store_cv_results
         self.alpha_per_target = alpha_per_target
+        self.store_cv_values = store_cv_values
 
     def fit(self, X, y, sample_weight=None, **params):
         """Fit Ridge regression model with cv.
@@ -2244,6 +2327,28 @@ def fit(self, X, y, sample_weight=None, **params):
         _raise_for_params(params, self, "fit")
         cv = self.cv
 
+        # TODO(1.7): Remove in 1.7
+        # Also change `store_cv_results` default back to False
+        if self.store_cv_values != "deprecated":
+            if self.store_cv_results is not None:
+                raise ValueError(
+                    "Both 'store_cv_values' and 'store_cv_results' were set. "
+                    "'store_cv_values' is deprecated in version 1.5 and will be "
+                    "removed in 1.7. To avoid this error, only set 'store_cv_results'."
+                )
+            warnings.warn(
+                (
+                    "'store_cv_values' is deprecated in version 1.5 and will be "
+                    "removed in 1.7. Use 'store_cv_results' instead."
+                ),
+                FutureWarning,
+            )
+            self._store_cv_results = self.store_cv_values
+        elif self.store_cv_results is None:
+            self._store_cv_results = False
+        else:
+            self._store_cv_results = self.store_cv_results
+
         # `_RidgeGCV` does not work for alpha = 0
         if cv is None:
             check_scalar_alpha = partial(
@@ -2289,7 +2394,7 @@ def fit(self, X, y, sample_weight=None, **params):
                 fit_intercept=self.fit_intercept,
                 scoring=self.scoring,
                 gcv_mode=self.gcv_mode,
-                store_cv_values=self.store_cv_values,
+                store_cv_results=self._store_cv_results,
                 is_clf=is_classifier(self),
                 alpha_per_target=self.alpha_per_target,
             )
@@ -2301,11 +2406,11 @@ def fit(self, X, y, sample_weight=None, **params):
             )
             self.alpha_ = estimator.alpha_
             self.best_score_ = estimator.best_score_
-            if self.store_cv_values:
-                self.cv_values_ = estimator.cv_values_
+            if self._store_cv_results:
+                self.cv_results_ = estimator.cv_results_
         else:
-            if self.store_cv_values:
-                raise ValueError("cv!=None and store_cv_values=True are incompatible")
+            if self._store_cv_results:
+                raise ValueError("cv!=None and store_cv_results=True are incompatible")
             if self.alpha_per_target:
                 raise ValueError("cv!=None and alpha_per_target=True are incompatible")
 
@@ -2366,6 +2471,16 @@ def get_metadata_routing(self):
     def _get_scorer(self):
         return check_scoring(self, scoring=self.scoring, allow_none=True)
 
+    # TODO(1.7): Remove
+    # mypy error: Decorated property not supported
+    @deprecated(  # type: ignore
+        "Attribute `cv_values_` is deprecated in version 1.5 and will be removed "
+        "in 1.7. Use `cv_results_` instead."
+    )
+    @property
+    def cv_values_(self):
+        return self.cv_results_
+
 
 class RidgeCV(MultiOutputMixin, RegressorMixin, _BaseRidgeCV):
     """Ridge regression with built-in cross-validation.
@@ -2394,12 +2509,10 @@ class RidgeCV(MultiOutputMixin, RegressorMixin, _BaseRidgeCV):
         (i.e. data is expected to be centered).
 
     scoring : str, callable, default=None
-        A string (see model evaluation documentation) or
-        a scorer callable object / function with signature
-        ``scorer(estimator, X, y)``.
-        If None, the negative mean squared error if cv is 'auto' or None
-        (i.e. when using leave-one-out cross-validation), and r2 score
-        otherwise.
+        A string (see :ref:`scoring_parameter`) or a scorer callable object /
+        function with signature ``scorer(estimator, X, y)``. If None, the
+        negative mean squared error if cv is 'auto' or None (i.e. when using
+        leave-one-out cross-validation), and r2 score otherwise.
 
     cv : int, cross-validation generator or an iterable, default=None
         Determines the cross-validation splitting strategy.
@@ -2429,12 +2542,15 @@ class RidgeCV(MultiOutputMixin, RegressorMixin, _BaseRidgeCV):
         The 'auto' mode is the default and is intended to pick the cheaper
         option of the two depending on the shape of the training data.
 
-    store_cv_values : bool, default=False
+    store_cv_results : bool, default=False
         Flag indicating if the cross-validation values corresponding to
         each alpha should be stored in the ``cv_values_`` attribute (see
         below). This flag is only compatible with ``cv=None`` (i.e. using
         Leave-One-Out Cross-Validation).
 
+        .. versionchanged:: 1.5
+            Parameter name changed from `store_cv_values` to `store_cv_results`.
+
     alpha_per_target : bool, default=False
         Flag indicating whether to optimize the alpha value (picked from the
         `alphas` parameter list) for each target separately (for multi-output
@@ -2444,16 +2560,29 @@ class RidgeCV(MultiOutputMixin, RegressorMixin, _BaseRidgeCV):
 
         .. versionadded:: 0.24
 
+    store_cv_values : bool
+        Flag indicating if the cross-validation values corresponding to
+        each alpha should be stored in the ``cv_values_`` attribute (see
+        below). This flag is only compatible with ``cv=None`` (i.e. using
+        Leave-One-Out Cross-Validation).
+
+        .. deprecated:: 1.5
+            `store_cv_values` is deprecated in version 1.5 in favor of
+            `store_cv_results` and will be removed in version 1.7.
+
     Attributes
     ----------
-    cv_values_ : ndarray of shape (n_samples, n_alphas) or \
+    cv_results_ : ndarray of shape (n_samples, n_alphas) or \
             shape (n_samples, n_targets, n_alphas), optional
         Cross-validation values for each alpha (only available if
-        ``store_cv_values=True`` and ``cv=None``). After ``fit()`` has been
+        ``store_cv_results=True`` and ``cv=None``). After ``fit()`` has been
         called, this attribute will contain the mean squared errors if
         `scoring is None` otherwise it will contain standardized per point
         prediction values.
 
+        .. versionchanged:: 1.5
+            `cv_values_` changed to `cv_results_`.
+
     coef_ : ndarray of shape (n_features) or (n_targets, n_features)
         Weight vector(s).
 
@@ -2570,9 +2699,8 @@ class RidgeClassifierCV(_RidgeClassifierMixin, _BaseRidgeCV):
         (i.e. data is expected to be centered).
 
     scoring : str, callable, default=None
-        A string (see model evaluation documentation) or
-        a scorer callable object / function with signature
-        ``scorer(estimator, X, y)``.
+        A string (see :ref:`scoring_parameter`) or a scorer callable object /
+        function with signature ``scorer(estimator, X, y)``.
 
     cv : int, cross-validation generator or an iterable, default=None
         Determines the cross-validation splitting strategy.
@@ -2594,20 +2722,36 @@ class RidgeClassifierCV(_RidgeClassifierMixin, _BaseRidgeCV):
         weights inversely proportional to class frequencies in the input data
         as ``n_samples / (n_classes * np.bincount(y))``.
 
-    store_cv_values : bool, default=False
+    store_cv_results : bool, default=False
+        Flag indicating if the cross-validation results corresponding to
+        each alpha should be stored in the ``cv_results_`` attribute (see
+        below). This flag is only compatible with ``cv=None`` (i.e. using
+        Leave-One-Out Cross-Validation).
+
+        .. versionchanged:: 1.5
+            Parameter name changed from `store_cv_values` to `store_cv_results`.
+
+    store_cv_values : bool
         Flag indicating if the cross-validation values corresponding to
         each alpha should be stored in the ``cv_values_`` attribute (see
         below). This flag is only compatible with ``cv=None`` (i.e. using
         Leave-One-Out Cross-Validation).
 
+        .. deprecated:: 1.5
+            `store_cv_values` is deprecated in version 1.5 in favor of
+            `store_cv_results` and will be removed in version 1.7.
+
     Attributes
     ----------
-    cv_values_ : ndarray of shape (n_samples, n_targets, n_alphas), optional
-        Cross-validation values for each alpha (only if ``store_cv_values=True`` and
+    cv_results_ : ndarray of shape (n_samples, n_targets, n_alphas), optional
+        Cross-validation results for each alpha (only if ``store_cv_results=True`` and
         ``cv=None``). After ``fit()`` has been called, this attribute will
         contain the mean squared errors if `scoring is None` otherwise it
         will contain standardized per point prediction values.
 
+        .. versionchanged:: 1.5
+            `cv_values_` changed to `cv_results_`.
+
     coef_ : ndarray of shape (1, n_features) or (n_targets, n_features)
         Coefficient of the features in the decision function.
 
@@ -2676,13 +2820,15 @@ def __init__(
         scoring=None,
         cv=None,
         class_weight=None,
-        store_cv_values=False,
+        store_cv_results=None,
+        store_cv_values="deprecated",
     ):
         super().__init__(
             alphas=alphas,
             fit_intercept=fit_intercept,
             scoring=scoring,
             cv=cv,
+            store_cv_results=store_cv_results,
             store_cv_values=store_cv_values,
         )
         self.class_weight = class_weight
diff --git a/sklearn/linear_model/_sag.py b/sklearn/linear_model/_sag.py
index 2626955ec2a7f..758e361fc1ad9 100644
--- a/sklearn/linear_model/_sag.py
+++ b/sklearn/linear_model/_sag.py
@@ -220,10 +220,9 @@ def sag_solver(
 
     >>> X = np.array([[-1, -1], [-2, -1], [1, 1], [2, 1]])
     >>> y = np.array([1, 1, 2, 2])
-    >>> clf = linear_model.LogisticRegression(
-    ...     solver='sag', multi_class='multinomial')
+    >>> clf = linear_model.LogisticRegression(solver='sag')
     >>> clf.fit(X, y)
-    LogisticRegression(multi_class='multinomial', solver='sag')
+    LogisticRegression(solver='sag')
 
     References
     ----------
diff --git a/sklearn/linear_model/_theil_sen.py b/sklearn/linear_model/_theil_sen.py
index cc774e8783762..b1ebd6524cb27 100644
--- a/sklearn/linear_model/_theil_sen.py
+++ b/sklearn/linear_model/_theil_sen.py
@@ -20,7 +20,7 @@
 from ..base import RegressorMixin, _fit_context
 from ..exceptions import ConvergenceWarning
 from ..utils import check_random_state
-from ..utils._param_validation import Interval
+from ..utils._param_validation import Hidden, Interval, StrOptions
 from ..utils.parallel import Parallel, delayed
 from ._base import LinearModel
 
@@ -228,6 +228,10 @@ class TheilSenRegressor(RegressorMixin, LinearModel):
     copy_X : bool, default=True
         If True, X will be copied; else, it may be overwritten.
 
+        .. deprecated:: 1.6
+            `copy_X` was deprecated in 1.6 and will be removed in 1.8.
+            It has no effect as a copy is always made.
+
     max_subpopulation : int, default=1e4
         Instead of computing with a set of cardinality 'n choose k', where n is
         the number of samples and k is the number of subsamples (at least
@@ -324,7 +328,7 @@ class TheilSenRegressor(RegressorMixin, LinearModel):
 
     _parameter_constraints: dict = {
         "fit_intercept": ["boolean"],
-        "copy_X": ["boolean"],
+        "copy_X": ["boolean", Hidden(StrOptions({"deprecated"}))],
         # target_type should be Integral but can accept Real for backward compatibility
         "max_subpopulation": [Interval(Real, 1, None, closed="left")],
         "n_subsamples": [None, Integral],
@@ -339,7 +343,7 @@ def __init__(
         self,
         *,
         fit_intercept=True,
-        copy_X=True,
+        copy_X="deprecated",
         max_subpopulation=1e4,
         n_subsamples=None,
         max_iter=300,
@@ -411,6 +415,14 @@ def fit(self, X, y):
         self : returns an instance of self.
             Fitted `TheilSenRegressor` estimator.
         """
+        if self.copy_X != "deprecated":
+            warnings.warn(
+                "`copy_X` was deprecated in 1.6 and will be removed in 1.8 since it "
+                "has no effect internally. Simply leave this parameter to its default "
+                "value to avoid this warning.",
+                FutureWarning,
+            )
+
         random_state = check_random_state(self.random_state)
         X, y = self._validate_data(X, y, y_numeric=True)
         n_samples, n_features = X.shape
diff --git a/sklearn/linear_model/tests/test_logistic.py b/sklearn/linear_model/tests/test_logistic.py
index deda8a9984048..f47b06a0cc4b1 100644
--- a/sklearn/linear_model/tests/test_logistic.py
+++ b/sklearn/linear_model/tests/test_logistic.py
@@ -35,6 +35,7 @@
     cross_val_score,
     train_test_split,
 )
+from sklearn.multiclass import OneVsRestClassifier
 from sklearn.preprocessing import LabelEncoder, StandardScaler, scale
 from sklearn.svm import l1_min_c
 from sklearn.utils import compute_class_weight, shuffle
@@ -144,14 +145,14 @@ def test_predict_3_classes(csr_container):
     check_predictions(LogisticRegression(C=10), csr_container(X), Y2)
 
 
+# TODO(1.7): remove filterwarnings after the deprecation of multi_class
+@pytest.mark.filterwarnings("ignore:.*'multi_class' was deprecated.*:FutureWarning")
 @pytest.mark.parametrize(
     "clf",
     [
         LogisticRegression(C=len(iris.data), solver="liblinear", multi_class="ovr"),
-        LogisticRegression(C=len(iris.data), solver="lbfgs", multi_class="multinomial"),
-        LogisticRegression(
-            C=len(iris.data), solver="newton-cg", multi_class="multinomial"
-        ),
+        LogisticRegression(C=len(iris.data), solver="lbfgs"),
+        LogisticRegression(C=len(iris.data), solver="newton-cg"),
         LogisticRegression(
             C=len(iris.data), solver="sag", tol=1e-2, multi_class="ovr", random_state=42
         ),
@@ -195,6 +196,8 @@ def test_predict_iris(clf):
     assert np.mean(pred == target) > 0.95
 
 
+# TODO(1.7): remove filterwarnings after the deprecation of multi_class
+@pytest.mark.filterwarnings("ignore:.*'multi_class' was deprecated.*:FutureWarning")
 @pytest.mark.parametrize("LR", [LogisticRegression, LogisticRegressionCV])
 def test_check_solver_option(LR):
     X, y = iris.data, iris.target
@@ -245,6 +248,8 @@ def test_elasticnet_l1_ratio_err_helpful(LR):
         model.fit(np.array([[1, 2], [3, 4]]), np.array([0, 1]))
 
 
+# TODO(1.7): remove whole test with deprecation of multi_class
+@pytest.mark.filterwarnings("ignore:.*'multi_class' was deprecated.*:FutureWarning")
 @pytest.mark.parametrize("solver", ["lbfgs", "newton-cg", "sag", "saga"])
 def test_multinomial_binary(solver):
     # Test multinomial LR on a binary problem.
@@ -268,6 +273,10 @@ def test_multinomial_binary(solver):
     assert np.mean(pred == target) > 0.9
 
 
+# TODO(1.7): remove filterwarnings after the deprecation of multi_class
+# Maybe even remove this whole test as correctness of multinomial loss is tested
+# elsewhere.
+@pytest.mark.filterwarnings("ignore:.*'multi_class' was deprecated.*:FutureWarning")
 def test_multinomial_binary_probabilities(global_random_seed):
     # Test multinomial LR gives expected probabilities based on the
     # decision function, for a binary problem.
@@ -373,7 +382,6 @@ def test_consistency_path():
             tol=1e-5,
             solver=solver,
             max_iter=1000,
-            multi_class="ovr",
             random_state=0,
         )
         for i, C in enumerate(Cs):
@@ -382,7 +390,6 @@ def test_consistency_path():
                 fit_intercept=False,
                 tol=1e-5,
                 solver=solver,
-                multi_class="ovr",
                 random_state=0,
                 max_iter=1000,
             )
@@ -403,14 +410,12 @@ def test_consistency_path():
             solver=solver,
             intercept_scaling=10000.0,
             random_state=0,
-            multi_class="ovr",
         )
         lr = LogisticRegression(
             C=Cs[0],
             tol=1e-6,
             intercept_scaling=10000.0,
             random_state=0,
-            multi_class="ovr",
             solver=solver,
         )
         lr.fit(X, y)
@@ -450,7 +455,6 @@ def test_liblinear_dual_random_state():
         dual=True,
         tol=1e-3,
         solver="liblinear",
-        multi_class="ovr",
     )
     lr1.fit(X, y)
     lr2 = LogisticRegression(
@@ -458,7 +462,6 @@ def test_liblinear_dual_random_state():
         dual=True,
         tol=1e-3,
         solver="liblinear",
-        multi_class="ovr",
     )
     lr2.fit(X, y)
     lr3 = LogisticRegression(
@@ -466,7 +469,6 @@ def test_liblinear_dual_random_state():
         dual=True,
         tol=1e-3,
         solver="liblinear",
-        multi_class="ovr",
     )
     lr3.fit(X, y)
 
@@ -487,12 +489,10 @@ def test_logistic_cv():
     X_ref -= X_ref.mean()
     X_ref /= X_ref.std()
     lr_cv = LogisticRegressionCV(
-        Cs=[1.0], fit_intercept=False, solver="liblinear", multi_class="ovr", cv=3
+        Cs=[1.0], fit_intercept=False, solver="liblinear", cv=3
     )
     lr_cv.fit(X_ref, y)
-    lr = LogisticRegression(
-        C=1.0, fit_intercept=False, solver="liblinear", multi_class="ovr"
-    )
+    lr = LogisticRegression(C=1.0, fit_intercept=False, solver="liblinear")
     lr.fit(X_ref, y)
     assert_array_almost_equal(lr.coef_, lr_cv.coef_)
 
@@ -530,7 +530,7 @@ def test_logistic_cv_multinomial_score(scoring, multiclass_agg_list):
         n_samples=100, random_state=0, n_classes=3, n_informative=6
     )
     train, test = np.arange(80), np.arange(80, 100)
-    lr = LogisticRegression(C=1.0, multi_class="multinomial")
+    lr = LogisticRegression(C=1.0)
     # we use lbfgs to support multinomial
     params = lr.get_params()
     # we store the params to set them further in _log_reg_scoring_path
@@ -551,7 +551,7 @@ def test_logistic_cv_multinomial_score(scoring, multiclass_agg_list):
                 max_squared_sum=None,
                 sample_weight=None,
                 score_params=None,
-                **params,
+                **(params | {"multi_class": "multinomial"}),
             )[2][0],
             scorer(lr, X[test], y[test]),
         )
@@ -571,10 +571,10 @@ def test_multinomial_logistic_regression_string_inputs():
     # For numerical labels, let y values be taken from set (-1, 0, 1)
     y = np.array(y) - 1
     # Test for string labels
-    lr = LogisticRegression(multi_class="multinomial")
-    lr_cv = LogisticRegressionCV(multi_class="multinomial", Cs=3)
-    lr_str = LogisticRegression(multi_class="multinomial")
-    lr_cv_str = LogisticRegressionCV(multi_class="multinomial", Cs=3)
+    lr = LogisticRegression()
+    lr_cv = LogisticRegressionCV(Cs=3)
+    lr_str = LogisticRegression()
+    lr_cv_str = LogisticRegressionCV(Cs=3)
 
     lr.fit(X_ref, y)
     lr_cv.fit(X_ref, y)
@@ -592,9 +592,9 @@ def test_multinomial_logistic_regression_string_inputs():
     assert sorted(np.unique(lr_cv_str.predict(X_ref))) == ["bar", "baz", "foo"]
 
     # Make sure class weights can be given with string labels
-    lr_cv_str = LogisticRegression(
-        class_weight={"bar": 1, "baz": 2, "foo": 0}, multi_class="multinomial"
-    ).fit(X_ref, y_str)
+    lr_cv_str = LogisticRegression(class_weight={"bar": 1, "baz": 2, "foo": 0}).fit(
+        X_ref, y_str
+    )
     assert sorted(np.unique(lr_cv_str.predict(X_ref))) == ["bar", "baz"]
 
 
@@ -613,6 +613,9 @@ def test_logistic_cv_sparse(csr_container):
     assert clfs.C_ == clf.C_
 
 
+# TODO(1.7): remove filterwarnings after the deprecation of multi_class
+# Best remove this whole test.
+@pytest.mark.filterwarnings("ignore:.*'multi_class' was deprecated.*:FutureWarning")
 def test_ovr_multinomial_iris():
     # Test that OvR and multinomial are correct using the iris dataset.
     train, target = iris.data, iris.target
@@ -655,7 +658,6 @@ def test_ovr_multinomial_iris():
         max_iter = 500 if solver in ["sag", "saga"] else 30
         clf_multi = LogisticRegressionCV(
             solver=solver,
-            multi_class="multinomial",
             max_iter=max_iter,
             random_state=42,
             tol=1e-3 if solver in ["sag", "saga"] else 1e-2,
@@ -684,7 +686,7 @@ def test_logistic_regression_solvers():
     """Test solvers converge to the same result."""
     X, y = make_classification(n_features=10, n_informative=5, random_state=0)
 
-    params = dict(fit_intercept=False, random_state=42, multi_class="ovr")
+    params = dict(fit_intercept=False, random_state=42)
 
     regressors = {
         solver: LogisticRegression(solver=solver, **params).fit(X, y)
@@ -702,18 +704,18 @@ def test_logistic_regression_solvers_multiclass():
     X, y = make_classification(
         n_samples=20, n_features=20, n_informative=10, n_classes=3, random_state=0
     )
-    tol = 1e-7
-    params = dict(fit_intercept=False, tol=tol, random_state=42, multi_class="ovr")
+    tol = 1e-8
+    params = dict(fit_intercept=False, tol=tol, random_state=42)
 
     # Override max iteration count for specific solvers to allow for
     # proper convergence.
-    solver_max_iter = {"sag": 1000, "saga": 10000}
+    solver_max_iter = {"sag": 10_000, "saga": 10_000}
 
     regressors = {
         solver: LogisticRegression(
             solver=solver, max_iter=solver_max_iter.get(solver, 100), **params
         ).fit(X, y)
-        for solver in SOLVERS
+        for solver in set(SOLVERS) - set(["liblinear", "newton-cholesky"])
     }
 
     for solver_1, solver_2 in itertools.combinations(regressors, r=2):
@@ -745,7 +747,6 @@ def test_logistic_regressioncv_class_weights(weight, class_weight, global_random
     params = dict(
         Cs=1,
         fit_intercept=False,
-        multi_class="ovr",
         class_weight=class_weight,
         tol=1e-8,
     )
@@ -761,7 +762,7 @@ def test_logistic_regressioncv_class_weights(weight, class_weight, global_random
     with ignore_warnings(category=ConvergenceWarning):
         clf_lbfgs.fit(X, y)
 
-    for solver in set(SOLVERS) - set(["lbfgs"]):
+    for solver in set(SOLVERS) - set(["lbfgs", "liblinear", "newton-cholesky"]):
         clf = LogisticRegressionCV(solver=solver, **params)
         if solver in ("sag", "saga"):
             clf.set_params(
@@ -781,7 +782,7 @@ def test_logistic_regression_sample_weights():
     sample_weight = y + 1
 
     for LR in [LogisticRegression, LogisticRegressionCV]:
-        kw = {"random_state": 42, "fit_intercept": False, "multi_class": "ovr"}
+        kw = {"random_state": 42, "fit_intercept": False}
         if LR is LogisticRegressionCV:
             kw.update({"Cs": 3, "cv": 3})
 
@@ -798,7 +799,7 @@ def test_logistic_regression_sample_weights():
         # newton-cg, newton-cholesky and 'sag' solvers
         clf_sw_lbfgs = LR(**kw, tol=1e-5)
         clf_sw_lbfgs.fit(X, y, sample_weight=sample_weight)
-        for solver in set(SOLVERS) - set(("lbfgs", "saga")):
+        for solver in set(SOLVERS) - set(["lbfgs"]):
             clf_sw = LR(solver=solver, tol=1e-10 if solver == "sag" else 1e-5, **kw)
             # ignore convergence warning due to small dataset with sag
             with ignore_warnings():
@@ -824,7 +825,6 @@ def test_logistic_regression_sample_weights():
         penalty="l1",
         tol=1e-5,
         random_state=42,
-        multi_class="ovr",
     )
     clf_cw.fit(X, y)
     clf_sw = LogisticRegression(
@@ -833,7 +833,6 @@ def test_logistic_regression_sample_weights():
         penalty="l1",
         tol=1e-5,
         random_state=42,
-        multi_class="ovr",
     )
     clf_sw.fit(X, y, sample_weight)
     assert_array_almost_equal(clf_cw.coef_, clf_sw.coef_, decimal=4)
@@ -845,7 +844,6 @@ def test_logistic_regression_sample_weights():
         penalty="l2",
         dual=True,
         random_state=42,
-        multi_class="ovr",
     )
     clf_cw.fit(X, y)
     clf_sw = LogisticRegression(
@@ -854,7 +852,6 @@ def test_logistic_regression_sample_weights():
         penalty="l2",
         dual=True,
         random_state=42,
-        multi_class="ovr",
     )
     clf_sw.fit(X, y, sample_weight)
     assert_array_almost_equal(clf_cw.coef_, clf_sw.coef_, decimal=4)
@@ -868,38 +865,40 @@ def _compute_class_weight_dictionary(y):
     return class_weight_dict
 
 
-def test_logistic_regression_class_weights():
+@pytest.mark.parametrize("csr_container", [lambda x: x] + CSR_CONTAINERS)
+def test_logistic_regression_class_weights(csr_container):
     # Scale data to avoid convergence warnings with the lbfgs solver
     X_iris = scale(iris.data)
     # Multinomial case: remove 90% of class 0
     X = X_iris[45:, :]
+    X = csr_container(X)
     y = iris.target[45:]
-    solvers = ("lbfgs", "newton-cg")
     class_weight_dict = _compute_class_weight_dictionary(y)
 
-    for solver in solvers:
-        clf1 = LogisticRegression(
-            solver=solver, multi_class="multinomial", class_weight="balanced"
-        )
-        clf2 = LogisticRegression(
-            solver=solver, multi_class="multinomial", class_weight=class_weight_dict
-        )
+    for solver in set(SOLVERS) - set(["liblinear", "newton-cholesky"]):
+        params = dict(solver=solver, max_iter=1000)
+        clf1 = LogisticRegression(class_weight="balanced", **params)
+        clf2 = LogisticRegression(class_weight=class_weight_dict, **params)
         clf1.fit(X, y)
         clf2.fit(X, y)
-        assert_array_almost_equal(clf1.coef_, clf2.coef_, decimal=4)
+        assert len(clf1.classes_) == 3
+        assert_allclose(clf1.coef_, clf2.coef_, rtol=1e-4)
+        # Same as appropriate sample_weight.
+        sw = np.ones(X.shape[0])
+        for c in clf1.classes_:
+            sw[y == c] *= class_weight_dict[c]
+        clf3 = LogisticRegression(**params).fit(X, y, sample_weight=sw)
+        assert_allclose(clf3.coef_, clf2.coef_, rtol=1e-4)
 
     # Binary case: remove 90% of class 0 and 100% of class 2
     X = X_iris[45:100, :]
     y = iris.target[45:100]
     class_weight_dict = _compute_class_weight_dictionary(y)
 
-    for solver in set(SOLVERS) - set(("sag", "saga")):
-        clf1 = LogisticRegression(
-            solver=solver, multi_class="ovr", class_weight="balanced"
-        )
-        clf2 = LogisticRegression(
-            solver=solver, multi_class="ovr", class_weight=class_weight_dict
-        )
+    for solver in SOLVERS:
+        params = dict(solver=solver, max_iter=1000)
+        clf1 = LogisticRegression(class_weight="balanced", **params)
+        clf2 = LogisticRegression(class_weight=class_weight_dict, **params)
         clf1.fit(X, y)
         clf2.fit(X, y)
         assert_array_almost_equal(clf1.coef_, clf2.coef_, decimal=6)
@@ -922,10 +921,8 @@ def test_logistic_regression_multinomial():
 
     # 'lbfgs' is used as a referenced
     solver = "lbfgs"
-    ref_i = LogisticRegression(solver=solver, multi_class="multinomial", tol=1e-6)
-    ref_w = LogisticRegression(
-        solver=solver, multi_class="multinomial", fit_intercept=False, tol=1e-6
-    )
+    ref_i = LogisticRegression(solver=solver, tol=1e-6)
+    ref_w = LogisticRegression(solver=solver, fit_intercept=False, tol=1e-6)
     ref_i.fit(X, y)
     ref_w.fit(X, y)
     assert ref_i.coef_.shape == (n_classes, n_features)
@@ -933,14 +930,12 @@ def test_logistic_regression_multinomial():
     for solver in ["sag", "saga", "newton-cg"]:
         clf_i = LogisticRegression(
             solver=solver,
-            multi_class="multinomial",
             random_state=42,
             max_iter=2000,
             tol=1e-7,
         )
         clf_w = LogisticRegression(
             solver=solver,
-            multi_class="multinomial",
             random_state=42,
             max_iter=2000,
             tol=1e-7,
@@ -961,7 +956,7 @@ def test_logistic_regression_multinomial():
     # folds, it need not be exactly the same.
     for solver in ["lbfgs", "newton-cg", "sag", "saga"]:
         clf_path = LogisticRegressionCV(
-            solver=solver, max_iter=2000, tol=1e-6, multi_class="multinomial", Cs=[1.0]
+            solver=solver, max_iter=2000, tol=1e-6, Cs=[1.0]
         )
         clf_path.fit(X, y)
         assert_allclose(clf_path.coef_, ref_i.coef_, rtol=1e-2)
@@ -975,7 +970,7 @@ def test_liblinear_decision_function_zero():
     # See Issue: https://github.com/scikit-learn/scikit-learn/issues/3600
     # and the PR https://github.com/scikit-learn/scikit-learn/pull/3623
     X, y = make_classification(n_samples=5, n_features=5, random_state=0)
-    clf = LogisticRegression(fit_intercept=False, solver="liblinear", multi_class="ovr")
+    clf = LogisticRegression(fit_intercept=False, solver="liblinear")
     clf.fit(X, y)
 
     # Dummy data such that the decision function becomes zero.
@@ -988,7 +983,7 @@ def test_liblinear_logregcv_sparse(csr_container):
     # Test LogRegCV with solver='liblinear' works for sparse matrices
 
     X, y = make_classification(n_samples=10, n_features=5, random_state=0)
-    clf = LogisticRegressionCV(solver="liblinear", multi_class="ovr")
+    clf = LogisticRegressionCV(solver="liblinear")
     clf.fit(csr_container(X), y)
 
 
@@ -1024,7 +1019,6 @@ def test_logreg_l1():
         C=1.0,
         solver="liblinear",
         fit_intercept=False,
-        multi_class="ovr",
         tol=1e-10,
     )
     lr_liblinear.fit(X, y)
@@ -1034,7 +1028,6 @@ def test_logreg_l1():
         C=1.0,
         solver="saga",
         fit_intercept=False,
-        multi_class="ovr",
         max_iter=1000,
         tol=1e-10,
     )
@@ -1066,7 +1059,6 @@ def test_logreg_l1_sparse_data(csr_container):
         C=1.0,
         solver="liblinear",
         fit_intercept=False,
-        multi_class="ovr",
         tol=1e-10,
     )
     lr_liblinear.fit(X, y)
@@ -1076,7 +1068,6 @@ def test_logreg_l1_sparse_data(csr_container):
         C=1.0,
         solver="saga",
         fit_intercept=False,
-        multi_class="ovr",
         max_iter=1000,
         tol=1e-10,
     )
@@ -1093,7 +1084,6 @@ def test_logreg_l1_sparse_data(csr_container):
         C=1.0,
         solver="saga",
         fit_intercept=False,
-        multi_class="ovr",
         max_iter=1000,
         tol=1e-10,
     )
@@ -1134,10 +1124,10 @@ def test_logreg_predict_proba_multinomial():
 
     # Predicted probabilities using the true-entropy loss should give a
     # smaller loss than those using the ovr method.
-    clf_multi = LogisticRegression(multi_class="multinomial", solver="lbfgs")
+    clf_multi = LogisticRegression(solver="lbfgs")
     clf_multi.fit(X, y)
     clf_multi_loss = log_loss(y, clf_multi.predict_proba(X))
-    clf_ovr = LogisticRegression(multi_class="ovr", solver="lbfgs")
+    clf_ovr = OneVsRestClassifier(LogisticRegression(solver="lbfgs"))
     clf_ovr.fit(X, y)
     clf_ovr_loss = log_loss(y, clf_ovr.predict_proba(X))
     assert clf_ovr_loss > clf_multi_loss
@@ -1149,6 +1139,8 @@ def test_logreg_predict_proba_multinomial():
     assert clf_wrong_loss > clf_multi_loss
 
 
+# TODO(1.7): remove filterwarnings after the deprecation of multi_class
+@pytest.mark.filterwarnings("ignore:.*'multi_class' was deprecated.*:FutureWarning")
 @pytest.mark.parametrize("max_iter", np.arange(1, 5))
 @pytest.mark.parametrize("multi_class", ["ovr", "multinomial"])
 @pytest.mark.parametrize(
@@ -1191,6 +1183,8 @@ def test_max_iter(max_iter, multi_class, solver, message):
     assert lr.n_iter_[0] == max_iter
 
 
+# TODO(1.7): remove filterwarnings after the deprecation of multi_class
+@pytest.mark.filterwarnings("ignore:.*'multi_class' was deprecated.*:FutureWarning")
 @pytest.mark.parametrize("solver", SOLVERS)
 def test_n_iter(solver):
     # Test that self.n_iter_ has the correct format.
@@ -1241,23 +1235,19 @@ def test_n_iter(solver):
     assert clf_cv.n_iter_.shape == (1, n_cv_fold, n_Cs)
 
 
-@pytest.mark.parametrize("solver", sorted(set(SOLVERS) - set(["liblinear"])))
+@pytest.mark.parametrize(
+    "solver", sorted(set(SOLVERS) - set(["liblinear", "newton-cholesky"]))
+)
 @pytest.mark.parametrize("warm_start", (True, False))
 @pytest.mark.parametrize("fit_intercept", (True, False))
-@pytest.mark.parametrize("multi_class", ["ovr", "multinomial"])
-def test_warm_start(solver, warm_start, fit_intercept, multi_class):
+def test_warm_start(solver, warm_start, fit_intercept):
     # A 1-iteration second fit on same data should give almost same result
     # with warm starting, and quite different result without warm starting.
     # Warm starting does not work with liblinear solver.
     X, y = iris.data, iris.target
 
-    if solver == "newton-cholesky" and multi_class == "multinomial":
-        # solver does only support OvR
-        return
-
     clf = LogisticRegression(
         tol=1e-4,
-        multi_class=multi_class,
         warm_start=warm_start,
         solver=solver,
         random_state=42,
@@ -1271,9 +1261,8 @@ def test_warm_start(solver, warm_start, fit_intercept, multi_class):
         clf.fit(X, y)
     cum_diff = np.sum(np.abs(coef_1 - clf.coef_))
     msg = (
-        "Warm starting issue with %s solver in %s mode "
-        "with fit_intercept=%s and warm_start=%s"
-        % (solver, multi_class, str(fit_intercept), str(warm_start))
+        f"Warm starting issue with solver {solver}"
+        f"with {fit_intercept=} and {warm_start=}"
     )
     if warm_start:
         assert 2.0 > cum_diff, msg
@@ -1304,7 +1293,6 @@ def test_saga_vs_liblinear(csr_container):
                 saga = LogisticRegression(
                     C=1.0 / (n_samples * alpha),
                     solver="saga",
-                    multi_class="ovr",
                     max_iter=200,
                     fit_intercept=False,
                     penalty=penalty,
@@ -1315,7 +1303,6 @@ def test_saga_vs_liblinear(csr_container):
                 liblinear = LogisticRegression(
                     C=1.0 / (n_samples * alpha),
                     solver="liblinear",
-                    multi_class="ovr",
                     max_iter=200,
                     fit_intercept=False,
                     penalty=penalty,
@@ -1329,6 +1316,8 @@ def test_saga_vs_liblinear(csr_container):
                 assert_array_almost_equal(saga.coef_, liblinear.coef_, 3)
 
 
+# TODO(1.7): remove filterwarnings after the deprecation of multi_class
+@pytest.mark.filterwarnings("ignore:.*'multi_class' was deprecated.*:FutureWarning")
 @pytest.mark.parametrize("multi_class", ["ovr", "multinomial"])
 @pytest.mark.parametrize(
     "solver", ["liblinear", "newton-cg", "newton-cholesky", "saga"]
@@ -1414,12 +1403,8 @@ def test_warm_start_converge_LR():
     rng = np.random.RandomState(0)
     X = np.concatenate((rng.randn(100, 2) + [1, 1], rng.randn(100, 2)))
     y = np.array([1] * 100 + [-1] * 100)
-    lr_no_ws = LogisticRegression(
-        multi_class="multinomial", solver="sag", warm_start=False, random_state=0
-    )
-    lr_ws = LogisticRegression(
-        multi_class="multinomial", solver="sag", warm_start=True, random_state=0
-    )
+    lr_no_ws = LogisticRegression(solver="sag", warm_start=False, random_state=0)
+    lr_ws = LogisticRegression(solver="sag", warm_start=True, random_state=0)
 
     lr_no_ws_loss = log_loss(y, lr_no_ws.fit(X, y).predict_proba(X))
     for i in range(5):
@@ -1552,19 +1537,14 @@ def enet_objective(lr):
     assert enet_objective(lr_enet) < enet_objective(lr_l2)
 
 
-@pytest.mark.parametrize("multi_class", ("ovr", "multinomial"))
-def test_LogisticRegressionCV_GridSearchCV_elastic_net(multi_class):
+@pytest.mark.parametrize("n_classes", (2, 3))
+def test_LogisticRegressionCV_GridSearchCV_elastic_net(n_classes):
     # make sure LogisticRegressionCV gives same best params (l1 and C) as
     # GridSearchCV when penalty is elasticnet
 
-    if multi_class == "ovr":
-        # This is actually binary classification, ovr multiclass is treated in
-        # test_LogisticRegressionCV_GridSearchCV_elastic_net_ovr
-        X, y = make_classification(random_state=0)
-    else:
-        X, y = make_classification(
-            n_samples=100, n_classes=3, n_informative=3, random_state=0
-        )
+    X, y = make_classification(
+        n_samples=100, n_classes=n_classes, n_informative=3, random_state=0
+    )
 
     cv = StratifiedKFold(5)
 
@@ -1578,7 +1558,6 @@ def test_LogisticRegressionCV_GridSearchCV_elastic_net(multi_class):
         cv=cv,
         l1_ratios=l1_ratios,
         random_state=0,
-        multi_class=multi_class,
         tol=1e-2,
     )
     lrcv.fit(X, y)
@@ -1588,7 +1567,6 @@ def test_LogisticRegressionCV_GridSearchCV_elastic_net(multi_class):
         penalty="elasticnet",
         solver="saga",
         random_state=0,
-        multi_class=multi_class,
         tol=1e-2,
     )
     gs = GridSearchCV(lr, param_grid, cv=cv)
@@ -1598,6 +1576,9 @@ def test_LogisticRegressionCV_GridSearchCV_elastic_net(multi_class):
     assert gs.best_params_["C"] == lrcv.C_[0]
 
 
+# TODO(1.7): remove filterwarnings after the deprecation of multi_class
+# Maybe remove whole test after removal of the deprecated multi_class.
+@pytest.mark.filterwarnings("ignore:.*'multi_class' was deprecated.*:FutureWarning")
 def test_LogisticRegressionCV_GridSearchCV_elastic_net_ovr():
     # make sure LogisticRegressionCV gives same best params (l1 and C) as
     # GridSearchCV when penalty is elasticnet and multiclass is ovr. We can't
@@ -1643,6 +1624,8 @@ def test_LogisticRegressionCV_GridSearchCV_elastic_net_ovr():
     assert (lrcv.predict(X_test) == gs.predict(X_test)).mean() >= 0.8
 
 
+# TODO(1.7): remove filterwarnings after the deprecation of multi_class
+@pytest.mark.filterwarnings("ignore:.*'multi_class' was deprecated.*:FutureWarning")
 @pytest.mark.parametrize("penalty", ("l2", "elasticnet"))
 @pytest.mark.parametrize("multi_class", ("ovr", "multinomial", "auto"))
 def test_LogisticRegressionCV_no_refit(penalty, multi_class):
@@ -1680,6 +1663,10 @@ def test_LogisticRegressionCV_no_refit(penalty, multi_class):
     assert lrcv.coef_.shape == (n_classes, n_features)
 
 
+# TODO(1.7): remove filterwarnings after the deprecation of multi_class
+# Remove multi_class an change first element of the expected n_iter_.shape from
+# n_classes to 1 (according to the docstring).
+@pytest.mark.filterwarnings("ignore:.*'multi_class' was deprecated.*:FutureWarning")
 def test_LogisticRegressionCV_elasticnet_attribute_shapes():
     # Make sure the shapes of scores_ and coefs_paths_ attributes are correct
     # when using elasticnet (added one dimension for l1_ratios)
@@ -1806,6 +1793,8 @@ def test_logistic_regression_path_coefs_multinomial():
         assert_array_almost_equal(coefs[1], coefs[2], decimal=1)
 
 
+# TODO(1.7): remove filterwarnings after the deprecation of multi_class
+@pytest.mark.filterwarnings("ignore:.*'multi_class' was deprecated.*:FutureWarning")
 @pytest.mark.parametrize(
     "est",
     [
@@ -2005,7 +1994,6 @@ def test_multinomial_identifiability_on_iris(fit_intercept):
     clf = LogisticRegression(
         C=len(iris.data),
         solver="lbfgs",
-        multi_class="multinomial",
         fit_intercept=fit_intercept,
     )
     # Scaling X to ease convergence.
@@ -2018,6 +2006,8 @@ def test_multinomial_identifiability_on_iris(fit_intercept):
         clf.intercept_.sum(axis=0) == pytest.approx(0, abs=1e-15)
 
 
+# TODO(1.7): remove filterwarnings after the deprecation of multi_class
+@pytest.mark.filterwarnings("ignore:.*'multi_class' was deprecated.*:FutureWarning")
 @pytest.mark.parametrize("multi_class", ["ovr", "multinomial", "auto"])
 @pytest.mark.parametrize("class_weight", [{0: 1.0, 1: 10.0, 2: 1.0}, "balanced"])
 def test_sample_weight_not_modified(multi_class, class_weight):
@@ -2192,3 +2182,28 @@ def test_passing_params_without_enabling_metadata_routing():
 
         with pytest.raises(ValueError, match=msg):
             lr_cv.score(X, y, **params)
+
+
+# TODO(1.7): remove
+def test_multi_class_deprecated():
+    """Check `multi_class` parameter deprecated."""
+    X, y = make_classification(n_classes=3, n_samples=50, n_informative=6)
+    lr = LogisticRegression(multi_class="ovr")
+    msg = "'multi_class' was deprecated"
+    with pytest.warns(FutureWarning, match=msg):
+        lr.fit(X, y)
+
+    lrCV = LogisticRegressionCV(multi_class="ovr")
+    with pytest.warns(FutureWarning, match=msg):
+        lrCV.fit(X, y)
+
+    # Special warning for "binary multinomial"
+    X, y = make_classification(n_classes=2, n_samples=50, n_informative=6)
+    lr = LogisticRegression(multi_class="multinomial")
+    msg = "'multi_class' was deprecated.*binary problems"
+    with pytest.warns(FutureWarning, match=msg):
+        lr.fit(X, y)
+
+    lrCV = LogisticRegressionCV(multi_class="multinomial")
+    with pytest.warns(FutureWarning, match=msg):
+        lrCV.fit(X, y)
diff --git a/sklearn/linear_model/tests/test_ridge.py b/sklearn/linear_model/tests/test_ridge.py
index 3a015defd59b1..167ce0bac4cba 100644
--- a/sklearn/linear_model/tests/test_ridge.py
+++ b/sklearn/linear_model/tests/test_ridge.py
@@ -5,7 +5,8 @@
 import pytest
 from scipy import linalg
 
-from sklearn import datasets
+from sklearn import config_context, datasets
+from sklearn.base import clone
 from sklearn.datasets import (
     make_classification,
     make_low_rank_matrix,
@@ -40,6 +41,13 @@
 )
 from sklearn.preprocessing import minmax_scale
 from sklearn.utils import check_random_state
+from sklearn.utils._array_api import (
+    _NUMPY_NAMESPACE_NAMES,
+    _atol_for_type,
+    _convert_to_numpy,
+    yield_namespace_device_dtype_combinations,
+    yield_namespaces,
+)
 from sklearn.utils._testing import (
     assert_allclose,
     assert_almost_equal,
@@ -47,6 +55,11 @@
     assert_array_equal,
     ignore_warnings,
 )
+from sklearn.utils.estimator_checks import (
+    _array_api_for_tests,
+    _get_check_estimator_ids,
+    check_array_api_input_and_values,
+)
 from sklearn.utils.fixes import (
     _IS_32BIT,
     COO_CONTAINERS,
@@ -194,6 +207,8 @@ def test_ridge_regression(solver, fit_intercept, ols_ridge_dataset, global_rando
     assert_allclose(model.coef_, coef)
     assert model.score(X, y) == pytest.approx(R2_Ridge)
 
+    assert model.solver_ == solver
+
 
 @pytest.mark.parametrize("solver", SOLVERS)
 @pytest.mark.parametrize("fit_intercept", [True, False])
@@ -905,15 +920,15 @@ def test_ridge_gcv_sample_weights(
     X_gcv = X_container(X)
     gcv_ridge = RidgeCV(
         alphas=alphas,
-        store_cv_values=True,
+        store_cv_results=True,
         gcv_mode=gcv_mode,
         fit_intercept=fit_intercept,
     )
     gcv_ridge.fit(X_gcv, y, sample_weight=sample_weight)
     if len(y_shape) == 2:
-        gcv_errors = gcv_ridge.cv_values_[:, :, alphas.index(kfold.alpha_)]
+        gcv_errors = gcv_ridge.cv_results_[:, :, alphas.index(kfold.alpha_)]
     else:
-        gcv_errors = gcv_ridge.cv_values_[:, alphas.index(kfold.alpha_)]
+        gcv_errors = gcv_ridge.cv_results_[:, alphas.index(kfold.alpha_)]
 
     assert kfold.alpha_ == pytest.approx(gcv_ridge.alpha_)
     assert_allclose(gcv_errors, kfold_errors, rtol=1e-3)
@@ -1019,15 +1034,15 @@ def _test_ridge_cv(sparse_container):
 @pytest.mark.parametrize(
     "ridge, make_dataset",
     [
-        (RidgeCV(store_cv_values=False), make_regression),
-        (RidgeClassifierCV(store_cv_values=False), make_classification),
+        (RidgeCV(store_cv_results=False), make_regression),
+        (RidgeClassifierCV(store_cv_results=False), make_classification),
     ],
 )
-def test_ridge_gcv_cv_values_not_stored(ridge, make_dataset):
-    # Check that `cv_values_` is not stored when store_cv_values is False
+def test_ridge_gcv_cv_results_not_stored(ridge, make_dataset):
+    # Check that `cv_results_` is not stored when store_cv_results is False
     X, y = make_dataset(n_samples=6, random_state=42)
     ridge.fit(X, y)
-    assert not hasattr(ridge, "cv_values_")
+    assert not hasattr(ridge, "cv_results_")
 
 
 @pytest.mark.parametrize(
@@ -1038,7 +1053,7 @@ def test_ridge_gcv_cv_values_not_stored(ridge, make_dataset):
 def test_ridge_best_score(ridge, make_dataset, cv):
     # check that the best_score_ is store
     X, y = make_dataset(n_samples=6, random_state=42)
-    ridge.set_params(store_cv_values=False, cv=cv)
+    ridge.set_params(store_cv_results=False, cv=cv)
     ridge.fit(X, y)
     assert hasattr(ridge, "best_score_")
     assert isinstance(ridge.best_score_, float)
@@ -1075,27 +1090,27 @@ def test_ridge_cv_individual_penalties():
         Ridge(alpha=ridge_cv.alpha_).fit(X, y).coef_, ridge_cv.coef_
     )
 
-    # Test shape of alpha_ and cv_values_
-    ridge_cv = RidgeCV(alphas=alphas, alpha_per_target=True, store_cv_values=True).fit(
+    # Test shape of alpha_ and cv_results_
+    ridge_cv = RidgeCV(alphas=alphas, alpha_per_target=True, store_cv_results=True).fit(
         X, y
     )
     assert ridge_cv.alpha_.shape == (n_targets,)
     assert ridge_cv.best_score_.shape == (n_targets,)
-    assert ridge_cv.cv_values_.shape == (n_samples, len(alphas), n_targets)
+    assert ridge_cv.cv_results_.shape == (n_samples, len(alphas), n_targets)
 
     # Test edge case of there being only one alpha value
-    ridge_cv = RidgeCV(alphas=1, alpha_per_target=True, store_cv_values=True).fit(X, y)
+    ridge_cv = RidgeCV(alphas=1, alpha_per_target=True, store_cv_results=True).fit(X, y)
     assert ridge_cv.alpha_.shape == (n_targets,)
     assert ridge_cv.best_score_.shape == (n_targets,)
-    assert ridge_cv.cv_values_.shape == (n_samples, n_targets, 1)
+    assert ridge_cv.cv_results_.shape == (n_samples, n_targets, 1)
 
     # Test edge case of there being only one target
-    ridge_cv = RidgeCV(alphas=alphas, alpha_per_target=True, store_cv_values=True).fit(
+    ridge_cv = RidgeCV(alphas=alphas, alpha_per_target=True, store_cv_results=True).fit(
         X, y[:, 0]
     )
     assert np.isscalar(ridge_cv.alpha_)
     assert np.isscalar(ridge_cv.best_score_)
-    assert ridge_cv.cv_values_.shape == (n_samples, len(alphas))
+    assert ridge_cv.cv_results_.shape == (n_samples, len(alphas))
 
     # Try with a custom scoring function
     ridge_cv = RidgeCV(alphas=alphas, alpha_per_target=True, scoring="r2").fit(X, y)
@@ -1201,6 +1216,130 @@ def _test_tolerance(sparse_container):
     assert score >= score2
 
 
+def check_array_api_attributes(name, estimator, array_namespace, device, dtype_name):
+    xp = _array_api_for_tests(array_namespace, device)
+
+    X_iris_np = X_iris.astype(dtype_name)
+    y_iris_np = y_iris.astype(dtype_name)
+
+    X_iris_xp = xp.asarray(X_iris_np, device=device)
+    y_iris_xp = xp.asarray(y_iris_np, device=device)
+
+    estimator.fit(X_iris_np, y_iris_np)
+    coef_np = estimator.coef_
+    intercept_np = estimator.intercept_
+
+    with config_context(array_api_dispatch=True):
+        estimator_xp = clone(estimator).fit(X_iris_xp, y_iris_xp)
+        coef_xp = estimator_xp.coef_
+        assert coef_xp.shape == (4,)
+        assert coef_xp.dtype == X_iris_xp.dtype
+
+        assert_allclose(
+            _convert_to_numpy(coef_xp, xp=xp),
+            coef_np,
+            atol=_atol_for_type(dtype_name),
+        )
+        intercept_xp = estimator_xp.intercept_
+        assert intercept_xp.shape == ()
+        assert intercept_xp.dtype == X_iris_xp.dtype
+
+        assert_allclose(
+            _convert_to_numpy(intercept_xp, xp=xp),
+            intercept_np,
+            atol=_atol_for_type(dtype_name),
+        )
+
+
+@pytest.mark.parametrize(
+    "array_namespace, device, dtype_name", yield_namespace_device_dtype_combinations()
+)
+@pytest.mark.parametrize(
+    "check",
+    [check_array_api_input_and_values, check_array_api_attributes],
+    ids=_get_check_estimator_ids,
+)
+@pytest.mark.parametrize(
+    "estimator",
+    [Ridge(solver="svd")],
+    ids=_get_check_estimator_ids,
+)
+def test_ridge_array_api_compliance(
+    estimator, check, array_namespace, device, dtype_name
+):
+    name = estimator.__class__.__name__
+    check(name, estimator, array_namespace, device=device, dtype_name=dtype_name)
+
+
+@pytest.mark.parametrize(
+    "array_namespace", yield_namespaces(include_numpy_namespaces=False)
+)
+def test_array_api_error_and_warnings_for_solver_parameter(array_namespace):
+    xp = _array_api_for_tests(array_namespace, device=None)
+
+    X_iris_xp = xp.asarray(X_iris[:5])
+    y_iris_xp = xp.asarray(y_iris[:5])
+
+    available_solvers = Ridge._parameter_constraints["solver"][0].options
+    for solver in available_solvers - {"auto", "svd"}:
+        ridge = Ridge(solver=solver, positive=solver == "lbfgs")
+        expected_msg = (
+            f"Array API dispatch to namespace {xp.__name__} only supports "
+            f"solver 'svd'. Got '{solver}'."
+        )
+
+        with pytest.raises(ValueError, match=expected_msg):
+            with config_context(array_api_dispatch=True):
+                ridge.fit(X_iris_xp, y_iris_xp)
+
+    ridge = Ridge(solver="auto", positive=True)
+    expected_msg = (
+        "The solvers that support positive fitting do not support "
+        f"Array API dispatch to namespace {xp.__name__}. Please "
+        "either disable Array API dispatch, or use a numpy-like "
+        "namespace, or set `positive=False`."
+    )
+
+    with pytest.raises(ValueError, match=expected_msg):
+        with config_context(array_api_dispatch=True):
+            ridge.fit(X_iris_xp, y_iris_xp)
+
+    ridge = Ridge()
+    expected_msg = (
+        f"Using Array API dispatch to namespace {xp.__name__} with `solver='auto'` "
+        "will result in using the solver 'svd'. The results may differ from those "
+        "when using a Numpy array, because in that case the preferred solver would "
+        "be cholesky. Set `solver='svd'` to suppress this warning."
+    )
+    with pytest.warns(UserWarning, match=expected_msg):
+        with config_context(array_api_dispatch=True):
+            ridge.fit(X_iris_xp, y_iris_xp)
+
+
+@pytest.mark.parametrize("array_namespace", sorted(_NUMPY_NAMESPACE_NAMES))
+def test_array_api_numpy_namespace_no_warning(array_namespace):
+    xp = _array_api_for_tests(array_namespace, device=None)
+
+    X_iris_xp = xp.asarray(X_iris[:5])
+    y_iris_xp = xp.asarray(y_iris[:5])
+
+    ridge = Ridge()
+    expected_msg = (
+        "Results might be different than when Array API dispatch is "
+        "disabled, or when a numpy-like namespace is used"
+    )
+
+    with warnings.catch_warnings():
+        warnings.filterwarnings("error", message=expected_msg, category=UserWarning)
+        with config_context(array_api_dispatch=True):
+            ridge.fit(X_iris_xp, y_iris_xp)
+
+    # All numpy namespaces are compatible with all solver, in particular
+    # solvers that support `positive=True` (like 'lbfgs') should work.
+    with config_context(array_api_dispatch=True):
+        Ridge(solver="auto", positive=True).fit(X_iris_xp, y_iris_xp)
+
+
 @pytest.mark.parametrize(
     "test_func",
     (
@@ -1305,7 +1444,7 @@ def test_class_weights_cv():
 @pytest.mark.parametrize(
     "scoring", [None, "neg_mean_squared_error", _mean_squared_error_callable]
 )
-def test_ridgecv_store_cv_values(scoring):
+def test_ridgecv_store_cv_results(scoring):
     rng = np.random.RandomState(42)
 
     n_samples = 8
@@ -1316,26 +1455,26 @@ def test_ridgecv_store_cv_values(scoring):
 
     scoring_ = make_scorer(scoring) if callable(scoring) else scoring
 
-    r = RidgeCV(alphas=alphas, cv=None, store_cv_values=True, scoring=scoring_)
+    r = RidgeCV(alphas=alphas, cv=None, store_cv_results=True, scoring=scoring_)
 
     # with len(y.shape) == 1
     y = rng.randn(n_samples)
     r.fit(x, y)
-    assert r.cv_values_.shape == (n_samples, n_alphas)
+    assert r.cv_results_.shape == (n_samples, n_alphas)
 
     # with len(y.shape) == 2
     n_targets = 3
     y = rng.randn(n_samples, n_targets)
     r.fit(x, y)
-    assert r.cv_values_.shape == (n_samples, n_targets, n_alphas)
+    assert r.cv_results_.shape == (n_samples, n_targets, n_alphas)
 
-    r = RidgeCV(cv=3, store_cv_values=True, scoring=scoring)
-    with pytest.raises(ValueError, match="cv!=None and store_cv_values"):
+    r = RidgeCV(cv=3, store_cv_results=True, scoring=scoring)
+    with pytest.raises(ValueError, match="cv!=None and store_cv_results"):
         r.fit(x, y)
 
 
 @pytest.mark.parametrize("scoring", [None, "accuracy", _accuracy_callable])
-def test_ridge_classifier_cv_store_cv_values(scoring):
+def test_ridge_classifier_cv_store_cv_results(scoring):
     x = np.array([[-1.0, -1.0], [-1.0, 0], [-0.8, -1.0], [1.0, 1.0], [1.0, 0.0]])
     y = np.array([1, 1, 1, -1, -1])
 
@@ -1346,13 +1485,13 @@ def test_ridge_classifier_cv_store_cv_values(scoring):
     scoring_ = make_scorer(scoring) if callable(scoring) else scoring
 
     r = RidgeClassifierCV(
-        alphas=alphas, cv=None, store_cv_values=True, scoring=scoring_
+        alphas=alphas, cv=None, store_cv_results=True, scoring=scoring_
     )
 
     # with len(y.shape) == 1
     n_targets = 1
     r.fit(x, y)
-    assert r.cv_values_.shape == (n_samples, n_targets, n_alphas)
+    assert r.cv_results_.shape == (n_samples, n_targets, n_alphas)
 
     # with len(y.shape) == 2
     y = np.array(
@@ -1360,7 +1499,7 @@ def test_ridge_classifier_cv_store_cv_values(scoring):
     ).transpose()
     n_targets = y.shape[1]
     r.fit(x, y)
-    assert r.cv_values_.shape == (n_samples, n_targets, n_alphas)
+    assert r.cv_results_.shape == (n_samples, n_targets, n_alphas)
 
 
 @pytest.mark.parametrize("Estimator", [RidgeCV, RidgeClassifierCV])
@@ -2085,3 +2224,46 @@ def test_ridge_sample_weight_consistency(
     assert_allclose(reg1.coef_, reg2.coef_)
     if fit_intercept:
         assert_allclose(reg1.intercept_, reg2.intercept_)
+
+
+# TODO(1.7): Remove
+def test_ridge_store_cv_values_deprecated():
+    """Check `store_cv_values` parameter deprecated."""
+    X, y = make_regression(n_samples=6, random_state=42)
+    ridge = RidgeCV(store_cv_values=True)
+    msg = "'store_cv_values' is deprecated"
+    with pytest.warns(FutureWarning, match=msg):
+        ridge.fit(X, y)
+
+    # Error when both set
+    ridge = RidgeCV(store_cv_results=True, store_cv_values=True)
+    msg = "Both 'store_cv_values' and 'store_cv_results' were"
+    with pytest.raises(ValueError, match=msg):
+        ridge.fit(X, y)
+
+
+def test_ridge_cv_values_deprecated():
+    """Check `cv_values_` deprecated."""
+    X, y = make_regression(n_samples=6, random_state=42)
+    ridge = RidgeCV(store_cv_results=True)
+    msg = "Attribute `cv_values_` is deprecated"
+    with pytest.warns(FutureWarning, match=msg):
+        ridge.fit(X, y)
+        ridge.cv_values_
+
+
+# Metadata Routing Tests
+# ======================
+
+
+@pytest.mark.usefixtures("enable_slep006")
+@pytest.mark.parametrize("metaestimator", [RidgeCV, RidgeClassifierCV])
+def test_metadata_routing_with_default_scoring(metaestimator):
+    """Test that `RidgeCV` or `RidgeClassifierCV` with default `scoring`
+    argument (`None`), don't enter into `RecursionError` when metadata is routed.
+    """
+    metaestimator().get_metadata_routing()
+
+
+# End of Metadata Routing Tests
+# =============================
diff --git a/sklearn/linear_model/tests/test_sag.py b/sklearn/linear_model/tests/test_sag.py
index 96f8a79726833..a51d1406559ff 100644
--- a/sklearn/linear_model/tests/test_sag.py
+++ b/sklearn/linear_model/tests/test_sag.py
@@ -18,6 +18,7 @@
 from sklearn.linear_model._linear_loss import LinearModelLoss
 from sklearn.linear_model._sag import get_auto_step_size
 from sklearn.linear_model._sag_fast import _multinomial_grad_loss_all_samples
+from sklearn.multiclass import OneVsRestClassifier
 from sklearn.preprocessing import LabelBinarizer, LabelEncoder
 from sklearn.utils import check_random_state, compute_class_weight
 from sklearn.utils._testing import (
@@ -272,7 +273,6 @@ def test_classifier_matching():
             C=1.0 / alpha / n_samples,
             max_iter=n_iter,
             random_state=10,
-            multi_class="ovr",
         )
         clf.fit(X, y)
 
@@ -371,7 +371,6 @@ def test_sag_pobj_matches_logistic_regression(csr_container):
         C=1.0 / alpha / n_samples,
         max_iter=max_iter,
         random_state=10,
-        multi_class="ovr",
     )
     clf2 = clone(clf1)
     clf3 = LogisticRegression(
@@ -380,7 +379,6 @@ def test_sag_pobj_matches_logistic_regression(csr_container):
         C=1.0 / alpha / n_samples,
         max_iter=max_iter,
         random_state=10,
-        multi_class="ovr",
     )
 
     clf1.fit(X, y)
@@ -619,7 +617,6 @@ def test_sag_classifier_computed_correctly(csr_container):
         tol=tol,
         random_state=77,
         fit_intercept=fit_intercept,
-        multi_class="ovr",
     )
     clf2 = clone(clf1)
 
@@ -659,21 +656,22 @@ def test_sag_multiclass_computed_correctly(csr_container):
     """tests if the multiclass classifier is computed correctly"""
     alpha = 0.1
     n_samples = 20
-    tol = 0.00001
-    max_iter = 40
+    tol = 1e-5
+    max_iter = 70
     fit_intercept = True
     X, y = make_blobs(n_samples=n_samples, centers=3, random_state=0, cluster_std=0.1)
     step_size = get_step_size(X, alpha, fit_intercept, classification=True)
     classes = np.unique(y)
 
-    clf1 = LogisticRegression(
-        solver="sag",
-        C=1.0 / alpha / n_samples,
-        max_iter=max_iter,
-        tol=tol,
-        random_state=77,
-        fit_intercept=fit_intercept,
-        multi_class="ovr",
+    clf1 = OneVsRestClassifier(
+        LogisticRegression(
+            solver="sag",
+            C=1.0 / alpha / n_samples,
+            max_iter=max_iter,
+            tol=tol,
+            random_state=77,
+            fit_intercept=fit_intercept,
+        )
     )
     clf2 = clone(clf1)
 
@@ -719,11 +717,12 @@ def test_sag_multiclass_computed_correctly(csr_container):
     intercept2 = np.array(intercept2)
 
     for i, cl in enumerate(classes):
-        assert_array_almost_equal(clf1.coef_[i].ravel(), coef1[i].ravel(), decimal=2)
-        assert_almost_equal(clf1.intercept_[i], intercept1[i], decimal=1)
+        assert_allclose(clf1.estimators_[i].coef_.ravel(), coef1[i], rtol=1e-2)
+        assert_allclose(clf1.estimators_[i].intercept_, intercept1[i], rtol=1e-1)
 
-        assert_array_almost_equal(clf2.coef_[i].ravel(), coef2[i].ravel(), decimal=2)
-        assert_almost_equal(clf2.intercept_[i], intercept2[i], decimal=1)
+        assert_allclose(clf2.estimators_[i].coef_.ravel(), coef2[i], rtol=1e-2)
+        # Note the very crude accuracy, i.e. high rtol.
+        assert_allclose(clf2.estimators_[i].intercept_, intercept2[i], rtol=5e-1)
 
 
 @pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
@@ -780,7 +779,6 @@ def test_binary_classifier_class_weight(csr_container):
         tol=tol,
         random_state=77,
         fit_intercept=fit_intercept,
-        multi_class="ovr",
         class_weight=class_weight,
     )
     clf2 = clone(clf1)
@@ -820,83 +818,6 @@ def test_binary_classifier_class_weight(csr_container):
     assert_almost_equal(clf2.intercept_, spintercept2, decimal=1)
 
 
-@pytest.mark.filterwarnings("ignore:The max_iter was reached")
-@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
-def test_multiclass_classifier_class_weight(csr_container):
-    """tests multiclass with classweights for each class"""
-    alpha = 0.1
-    n_samples = 20
-    tol = 0.00001
-    max_iter = 50
-    class_weight = {0: 0.45, 1: 0.55, 2: 0.75}
-    fit_intercept = True
-    X, y = make_blobs(n_samples=n_samples, centers=3, random_state=0, cluster_std=0.1)
-    step_size = get_step_size(X, alpha, fit_intercept, classification=True)
-    classes = np.unique(y)
-
-    clf1 = LogisticRegression(
-        solver="sag",
-        C=1.0 / alpha / n_samples,
-        max_iter=max_iter,
-        tol=tol,
-        random_state=77,
-        fit_intercept=fit_intercept,
-        multi_class="ovr",
-        class_weight=class_weight,
-    )
-    clf2 = clone(clf1)
-    clf1.fit(X, y)
-    clf2.fit(csr_container(X), y)
-
-    le = LabelEncoder()
-    class_weight_ = compute_class_weight(class_weight, classes=np.unique(y), y=y)
-    sample_weight = class_weight_[le.fit_transform(y)]
-
-    coef1 = []
-    intercept1 = []
-    coef2 = []
-    intercept2 = []
-    for cl in classes:
-        y_encoded = np.ones(n_samples)
-        y_encoded[y != cl] = -1
-
-        spweights1, spintercept1 = sag_sparse(
-            X,
-            y_encoded,
-            step_size,
-            alpha,
-            n_iter=max_iter,
-            dloss=log_dloss,
-            sample_weight=sample_weight,
-        )
-        spweights2, spintercept2 = sag_sparse(
-            X,
-            y_encoded,
-            step_size,
-            alpha,
-            n_iter=max_iter,
-            dloss=log_dloss,
-            sample_weight=sample_weight,
-            sparse=True,
-        )
-        coef1.append(spweights1)
-        intercept1.append(spintercept1)
-        coef2.append(spweights2)
-        intercept2.append(spintercept2)
-
-    coef1 = np.vstack(coef1)
-    intercept1 = np.array(intercept1)
-    coef2 = np.vstack(coef2)
-    intercept2 = np.array(intercept2)
-
-    for i, cl in enumerate(classes):
-        assert_array_almost_equal(clf1.coef_[i].ravel(), coef1[i].ravel(), decimal=2)
-        assert_almost_equal(clf1.intercept_[i], intercept1[i], decimal=1)
-
-        assert_array_almost_equal(clf2.coef_[i].ravel(), coef2[i].ravel(), decimal=2)
-        assert_almost_equal(clf2.intercept_[i], intercept2[i], decimal=1)
-
-
 def test_classifier_single_class():
     """tests if ValueError is thrown with only one class"""
     X = [[1, 2], [3, 4]]
diff --git a/sklearn/linear_model/tests/test_theil_sen.py b/sklearn/linear_model/tests/test_theil_sen.py
index c8415d02be80a..f72c18c24a809 100644
--- a/sklearn/linear_model/tests/test_theil_sen.py
+++ b/sklearn/linear_model/tests/test_theil_sen.py
@@ -292,3 +292,11 @@ def test_less_samples_than_features():
     theil_sen = TheilSenRegressor(fit_intercept=True, random_state=0).fit(X, y)
     y_pred = theil_sen.predict(X)
     assert_array_almost_equal(y_pred, y, 12)
+
+
+# TODO(1.8): Remove
+def test_copy_X_deprecated():
+    X, y, _, _ = gen_toy_problem_1d()
+    theil_sen = TheilSenRegressor(copy_X=True, random_state=0)
+    with pytest.warns(FutureWarning, match="`copy_X` was deprecated"):
+        theil_sen.fit(X, y)
diff --git a/sklearn/manifold/__init__.py b/sklearn/manifold/__init__.py
index 1e8d96c7cf94b..b3f53e84b04fb 100644
--- a/sklearn/manifold/__init__.py
+++ b/sklearn/manifold/__init__.py
@@ -1,6 +1,4 @@
-"""
-The :mod:`sklearn.manifold` module implements data embedding techniques.
-"""
+"""Data embedding techniques."""
 
 from ._isomap import Isomap
 from ._locally_linear import LocallyLinearEmbedding, locally_linear_embedding
diff --git a/sklearn/manifold/_barnes_hut_tsne.pyx b/sklearn/manifold/_barnes_hut_tsne.pyx
index 485a14edce6c5..f0906fbf2bec8 100644
--- a/sklearn/manifold/_barnes_hut_tsne.pyx
+++ b/sklearn/manifold/_barnes_hut_tsne.pyx
@@ -10,6 +10,7 @@ cimport numpy as cnp
 from libc.stdio cimport printf
 from libc.math cimport log
 from libc.stdlib cimport malloc, free
+from libc.time cimport clock, clock_t
 from cython.parallel cimport prange, parallel
 
 from ..neighbors._quad_tree cimport _QuadTree
@@ -19,9 +20,6 @@ cnp.import_array()
 
 cdef char* EMPTY_STRING = ""
 
-cdef extern from "math.h":
-    float fabsf(float x) nogil
-
 # Smallest strictly positive value that can be represented by floating
 # point numbers for different precision levels. This is useful to avoid
 # taking the log of zero when computing the KL divergence.
@@ -36,13 +34,6 @@ cdef float FLOAT64_EPS = np.finfo(np.float64).eps
 cdef enum:
     DEBUGFLAG = 0
 
-cdef extern from "time.h":
-    # Declare only what is necessary from `tm` structure.
-    ctypedef long clock_t
-    clock_t clock() nogil
-    double CLOCKS_PER_SEC
-
-
 cdef float compute_gradient(float[:] val_P,
                             float[:, :] pos_reference,
                             cnp.int64_t[:] neighbors,
diff --git a/sklearn/metrics/__init__.py b/sklearn/metrics/__init__.py
index 8a818c885043c..81a67d9793bb0 100644
--- a/sklearn/metrics/__init__.py
+++ b/sklearn/metrics/__init__.py
@@ -1,7 +1,4 @@
-"""
-The :mod:`sklearn.metrics` module includes score functions, performance metrics
-and pairwise metrics and distance computations.
-"""
+"""Score functions, performance metrics, pairwise metrics and distance computations."""
 
 from . import cluster
 from ._classification import (
@@ -12,6 +9,7 @@
     classification_report,
     cohen_kappa_score,
     confusion_matrix,
+    d2_log_loss_score,
     f1_score,
     fbeta_score,
     hamming_loss,
@@ -113,6 +111,7 @@
     "coverage_error",
     "d2_tweedie_score",
     "d2_absolute_error_score",
+    "d2_log_loss_score",
     "d2_pinball_score",
     "dcg_score",
     "davies_bouldin_score",
diff --git a/sklearn/metrics/_classification.py b/sklearn/metrics/_classification.py
index 62a6c87428e9a..1fb4c1d694be0 100644
--- a/sklearn/metrics/_classification.py
+++ b/sklearn/metrics/_classification.py
@@ -53,7 +53,11 @@
 from ..utils.extmath import _nanaverage
 from ..utils.multiclass import type_of_target, unique_labels
 from ..utils.sparsefuncs import count_nonzero
-from ..utils.validation import _check_pos_label_consistency, _num_samples
+from ..utils.validation import (
+    _check_pos_label_consistency,
+    _check_sample_weight,
+    _num_samples,
+)
 
 
 def _check_zero_division(zero_division):
@@ -963,8 +967,8 @@ def matthews_corrcoef(y_true, y_pred, *, sample_weight=None):
        accuracy of prediction algorithms for classification: an overview.
        <10.1093/bioinformatics/16.5.412>`
 
-    .. [2] `Wikipedia entry for the Matthews Correlation Coefficient
-       <https://en.wikipedia.org/wiki/Matthews_correlation_coefficient>`_.
+    .. [2] `Wikipedia entry for the Matthews Correlation Coefficient (phi coefficient)
+       <https://en.wikipedia.org/wiki/Phi_coefficient>`_.
 
     .. [3] `Gorodkin, (2004). Comparing two K-category assignments by a
         K-category correlation coefficient
@@ -2816,16 +2820,13 @@ def hamming_loss(y_true, y_pred, *, sample_weight=None):
     {
         "y_true": ["array-like"],
         "y_pred": ["array-like"],
-        "eps": [StrOptions({"auto"}), Interval(Real, 0, 1, closed="both")],
         "normalize": ["boolean"],
         "sample_weight": ["array-like", None],
         "labels": ["array-like", None],
     },
     prefer_skip_nested_validation=True,
 )
-def log_loss(
-    y_true, y_pred, *, eps="auto", normalize=True, sample_weight=None, labels=None
-):
+def log_loss(y_true, y_pred, *, normalize=True, sample_weight=None, labels=None):
     r"""Log loss, aka logistic loss or cross-entropy loss.
 
     This is the loss function used in (multinomial) logistic regression
@@ -2855,19 +2856,8 @@ def log_loss(
         ordered alphabetically, as done by
         :class:`~sklearn.preprocessing.LabelBinarizer`.
 
-    eps : float or "auto", default="auto"
-        Log loss is undefined for p=0 or p=1, so probabilities are
-        clipped to `max(eps, min(1 - eps, p))`. The default will depend on the
-        data type of `y_pred` and is set to `np.finfo(y_pred.dtype).eps`.
-
-        .. versionadded:: 1.2
-
-        .. versionchanged:: 1.2
-           The default value changed from `1e-15` to `"auto"` that is
-           equivalent to `np.finfo(y_pred.dtype).eps`.
-
-        .. deprecated:: 1.3
-           `eps` is deprecated in 1.3 and will be removed in 1.5.
+        `y_pred` values are clipped to `[eps, 1-eps]` where `eps` is the machine
+        precision for `y_pred`'s dtype.
 
     normalize : bool, default=True
         If true, return the mean loss per sample.
@@ -2907,18 +2897,6 @@ def log_loss(
     y_pred = check_array(
         y_pred, ensure_2d=False, dtype=[np.float64, np.float32, np.float16]
     )
-    if eps == "auto":
-        eps = np.finfo(y_pred.dtype).eps
-    else:
-        # TODO: Remove user defined eps in 1.5
-        warnings.warn(
-            (
-                "Setting the eps parameter is deprecated and will "
-                "be removed in 1.5. Instead eps will always have"
-                "a default value of `np.finfo(y_pred.dtype).eps`."
-            ),
-            FutureWarning,
-        )
 
     check_consistent_length(y_pred, y_true, sample_weight)
     lb = LabelBinarizer()
@@ -2949,9 +2927,6 @@ def log_loss(
             1 - transformed_labels, transformed_labels, axis=1
         )
 
-    # Clipping
-    y_pred = np.clip(y_pred, eps, 1 - eps)
-
     # If y_pred is of single dimension, assume y_true to be binary
     # and then check.
     if y_pred.ndim == 1:
@@ -2959,6 +2934,19 @@ def log_loss(
     if y_pred.shape[1] == 1:
         y_pred = np.append(1 - y_pred, y_pred, axis=1)
 
+    eps = np.finfo(y_pred.dtype).eps
+
+    # Make sure y_pred is normalized
+    y_pred_sum = y_pred.sum(axis=1)
+    if not np.allclose(y_pred_sum, 1, rtol=np.sqrt(eps)):
+        warnings.warn(
+            "The y_pred values do not sum to one. Make sure to pass probabilities.",
+            UserWarning,
+        )
+
+    # Clipping
+    y_pred = np.clip(y_pred, eps, 1 - eps)
+
     # Check if dimensions are consistent.
     transformed_labels = check_array(transformed_labels)
     if len(lb.classes_) != y_pred.shape[1]:
@@ -2979,17 +2967,6 @@ def log_loss(
                 "labels: {0}".format(lb.classes_)
             )
 
-    # Renormalize
-    y_pred_sum = y_pred.sum(axis=1)
-    if not np.isclose(y_pred_sum, 1, rtol=1e-15, atol=5 * eps).all():
-        warnings.warn(
-            (
-                "The y_pred values do not sum to one. Starting from 1.5 this"
-                "will result in an error."
-            ),
-            UserWarning,
-        )
-    y_pred = y_pred / y_pred_sum[:, np.newaxis]
     loss = -xlogy(transformed_labels, y_pred).sum(axis=1)
 
     return float(_average(loss, weights=sample_weight, normalize=normalize))
@@ -3284,3 +3261,96 @@ def brier_score_loss(
             raise
     y_true = np.array(y_true == pos_label, int)
     return np.average((y_true - y_proba) ** 2, weights=sample_weight)
+
+
+@validate_params(
+    {
+        "y_true": ["array-like"],
+        "y_pred": ["array-like"],
+        "sample_weight": ["array-like", None],
+        "labels": ["array-like", None],
+    },
+    prefer_skip_nested_validation=True,
+)
+def d2_log_loss_score(y_true, y_pred, *, sample_weight=None, labels=None):
+    """
+    :math:`D^2` score function, fraction of log loss explained.
+
+    Best possible score is 1.0 and it can be negative (because the model can be
+    arbitrarily worse). A model that always predicts the per-class proportions
+    of `y_true`, disregarding the input features, gets a D^2 score of 0.0.
+
+    Read more in the :ref:`User Guide <d2_score_classification>`.
+
+    .. versionadded:: 1.5
+
+    Parameters
+    ----------
+    y_true : array-like or label indicator matrix
+        The actuals labels for the n_samples samples.
+
+    y_pred : array-like of shape (n_samples, n_classes) or (n_samples,)
+        Predicted probabilities, as returned by a classifier's
+        predict_proba method. If ``y_pred.shape = (n_samples,)``
+        the probabilities provided are assumed to be that of the
+        positive class. The labels in ``y_pred`` are assumed to be
+        ordered alphabetically, as done by
+        :class:`~sklearn.preprocessing.LabelBinarizer`.
+
+    sample_weight : array-like of shape (n_samples,), default=None
+        Sample weights.
+
+    labels : array-like, default=None
+        If not provided, labels will be inferred from y_true. If ``labels``
+        is ``None`` and ``y_pred`` has shape (n_samples,) the labels are
+        assumed to be binary and are inferred from ``y_true``.
+
+    Returns
+    -------
+    d2 : float or ndarray of floats
+        The D^2 score.
+
+    Notes
+    -----
+    This is not a symmetric function.
+
+    Like R^2, D^2 score may be negative (it need not actually be the square of
+    a quantity D).
+
+    This metric is not well-defined for a single sample and will return a NaN
+    value if n_samples is less than two.
+    """
+    y_pred = check_array(y_pred, ensure_2d=False, dtype="numeric")
+    check_consistent_length(y_pred, y_true, sample_weight)
+    if _num_samples(y_pred) < 2:
+        msg = "D^2 score is not well-defined with less than two samples."
+        warnings.warn(msg, UndefinedMetricWarning)
+        return float("nan")
+
+    # log loss of the fitted model
+    numerator = log_loss(
+        y_true=y_true,
+        y_pred=y_pred,
+        normalize=False,
+        sample_weight=sample_weight,
+        labels=labels,
+    )
+
+    # Proportion of labels in the dataset
+    weights = _check_sample_weight(sample_weight, y_true)
+
+    _, y_value_indices = np.unique(y_true, return_inverse=True)
+    counts = np.bincount(y_value_indices, weights=weights)
+    y_prob = counts / weights.sum()
+    y_pred_null = np.tile(y_prob, (len(y_true), 1))
+
+    # log loss of the null model
+    denominator = log_loss(
+        y_true=y_true,
+        y_pred=y_pred_null,
+        normalize=False,
+        sample_weight=sample_weight,
+        labels=labels,
+    )
+
+    return 1 - (numerator / denominator)
diff --git a/sklearn/metrics/_pairwise_distances_reduction/_argkmin.pyx.tp b/sklearn/metrics/_pairwise_distances_reduction/_argkmin.pyx.tp
index a686153c3ac9c..ef61158fedca8 100644
--- a/sklearn/metrics/_pairwise_distances_reduction/_argkmin.pyx.tp
+++ b/sklearn/metrics/_pairwise_distances_reduction/_argkmin.pyx.tp
@@ -14,7 +14,7 @@ from numbers import Integral
 from scipy.sparse import issparse
 from ...utils import check_array, check_scalar
 from ...utils.fixes import _in_unstable_openblas_configuration
-from ...utils.fixes import threadpool_limits
+from ... import _threadpool_controller
 
 {{for name_suffix in ['64', '32']}}
 
@@ -58,7 +58,7 @@ cdef class ArgKmin{{name_suffix}}(BaseDistancesReduction{{name_suffix}}):
         """
         # Limit the number of threads in second level of nested parallelism for BLAS
         # to avoid threads over-subscription (in DOT or GEMM for instance).
-        with threadpool_limits(limits=1, user_api='blas'):
+        with _threadpool_controller.limit(limits=1, user_api='blas'):
           if metric in ("euclidean", "sqeuclidean"):
               # Specialized implementation of ArgKmin for the Euclidean distance
               # for the dense-dense and sparse-sparse cases.
diff --git a/sklearn/metrics/_pairwise_distances_reduction/_argkmin_classmode.pyx.tp b/sklearn/metrics/_pairwise_distances_reduction/_argkmin_classmode.pyx.tp
index f9719f6959dfc..b875499f44ed4 100644
--- a/sklearn/metrics/_pairwise_distances_reduction/_argkmin_classmode.pyx.tp
+++ b/sklearn/metrics/_pairwise_distances_reduction/_argkmin_classmode.pyx.tp
@@ -4,10 +4,10 @@ from libcpp.map cimport map as cpp_map, pair as cpp_pair
 from libc.stdlib cimport free
 
 from ...utils._typedefs cimport intp_t, float64_t
+from ... import _threadpool_controller
 
 import numpy as np
 from scipy.sparse import issparse
-from sklearn.utils.fixes import threadpool_limits
 from ._classmode cimport WeightingStrategy
 
 {{for name_suffix in ["32", "64"]}}
@@ -66,7 +66,7 @@ cdef class ArgKminClassMode{{name_suffix}}(ArgKmin{{name_suffix}}):
 
         # Limit the number of threads in second level of nested parallelism for BLAS
         # to avoid threads over-subscription (in GEMM for instance).
-        with threadpool_limits(limits=1, user_api="blas"):
+        with _threadpool_controller.limit(limits=1, user_api="blas"):
             if pda.execute_in_parallel_on_Y:
                 pda._parallel_on_Y()
             else:
diff --git a/sklearn/metrics/_pairwise_distances_reduction/_radius_neighbors.pyx.tp b/sklearn/metrics/_pairwise_distances_reduction/_radius_neighbors.pyx.tp
index dcc97b4d32fd4..f4af378062bdc 100644
--- a/sklearn/metrics/_pairwise_distances_reduction/_radius_neighbors.pyx.tp
+++ b/sklearn/metrics/_pairwise_distances_reduction/_radius_neighbors.pyx.tp
@@ -17,7 +17,7 @@ from numbers import Real
 from scipy.sparse import issparse
 from ...utils import check_array, check_scalar
 from ...utils.fixes import _in_unstable_openblas_configuration
-from ...utils.fixes import threadpool_limits
+from ... import _threadpool_controller
 
 cnp.import_array()
 
@@ -110,7 +110,7 @@ cdef class RadiusNeighbors{{name_suffix}}(BaseDistancesReduction{{name_suffix}})
 
         # Limit the number of threads in second level of nested parallelism for BLAS
         # to avoid threads over-subscription (in GEMM for instance).
-        with threadpool_limits(limits=1, user_api="blas"):
+        with _threadpool_controller.limit(limits=1, user_api="blas"):
             if pda.execute_in_parallel_on_Y:
                 pda._parallel_on_Y()
             else:
diff --git a/sklearn/metrics/_pairwise_distances_reduction/_radius_neighbors_classmode.pyx.tp b/sklearn/metrics/_pairwise_distances_reduction/_radius_neighbors_classmode.pyx.tp
index 25067b43cd20c..ab12d7904c7fd 100644
--- a/sklearn/metrics/_pairwise_distances_reduction/_radius_neighbors_classmode.pyx.tp
+++ b/sklearn/metrics/_pairwise_distances_reduction/_radius_neighbors_classmode.pyx.tp
@@ -8,7 +8,7 @@ from ...utils._typedefs cimport intp_t, float64_t
 
 import numpy as np
 from scipy.sparse import issparse
-from ...utils.fixes import threadpool_limits
+from ... import _threadpool_controller
 
 
 {{for name_suffix in ["32", "64"]}}
@@ -60,7 +60,7 @@ cdef class RadiusNeighborsClassMode{{name_suffix}}(RadiusNeighbors{{name_suffix}
 
         # Limit the number of threads in second level of nested parallelism for BLAS
         # to avoid threads over-subscription (in GEMM for instance).
-        with threadpool_limits(limits=1, user_api="blas"):
+        with _threadpool_controller.limit(limits=1, user_api="blas"):
             if pda.execute_in_parallel_on_Y:
                 pda._parallel_on_Y()
             else:
diff --git a/sklearn/metrics/_pairwise_distances_reduction/meson.build b/sklearn/metrics/_pairwise_distances_reduction/meson.build
index e22cf70164f7f..ced94fd8b694e 100644
--- a/sklearn/metrics/_pairwise_distances_reduction/meson.build
+++ b/sklearn/metrics/_pairwise_distances_reduction/meson.build
@@ -11,6 +11,11 @@
 # needed for the cimport to work
 _pairwise_distances_reduction_cython_tree = [
   fs.copyfile('__init__.py'),
+  # We are in a sub-module of metrics, so we always need to have
+  # sklearn/metrics/__init__.py copied to the build directory to avoid the
+  # error:
+  # relative cimport beyond main package is not allowed
+  metrics_cython_tree
 ]
 
 _classmode_pxd = fs.copyfile('_classmode.pxd')
@@ -30,7 +35,7 @@ _datasets_pair_pyx = custom_target(
 _datasets_pair = py.extension_module(
   '_datasets_pair',
   [_datasets_pair_pxd, _datasets_pair_pyx,
-    _pairwise_distances_reduction_cython_tree, utils_cython_tree],
+   _pairwise_distances_reduction_cython_tree, utils_cython_tree],
   dependencies: [np_dep, openmp_dep],
   override_options: ['cython_language=cpp'],
   cython_args: cython_args,
diff --git a/sklearn/metrics/_ranking.py b/sklearn/metrics/_ranking.py
index e31b06a926dd1..6a53fb542fd32 100644
--- a/sklearn/metrics/_ranking.py
+++ b/sklearn/metrics/_ranking.py
@@ -1672,7 +1672,7 @@ def dcg_score(
     --------
     >>> import numpy as np
     >>> from sklearn.metrics import dcg_score
-    >>> # we have groud-truth relevance of some answers to a query:
+    >>> # we have ground-truth relevance of some answers to a query:
     >>> true_relevance = np.asarray([[10, 0, 0, 1, 5]])
     >>> # we predict scores for the answers
     >>> scores = np.asarray([[.1, .2, .3, 4, 70]])
@@ -1832,7 +1832,7 @@ def ndcg_score(y_true, y_score, *, k=None, sample_weight=None, ignore_ties=False
     --------
     >>> import numpy as np
     >>> from sklearn.metrics import ndcg_score
-    >>> # we have groud-truth relevance of some answers to a query:
+    >>> # we have ground-truth relevance of some answers to a query:
     >>> true_relevance = np.asarray([[10, 0, 0, 1, 5]])
     >>> # we predict some scores (relevance) for the answers
     >>> scores = np.asarray([[.1, .2, .3, 4, 70]])
diff --git a/sklearn/metrics/_regression.py b/sklearn/metrics/_regression.py
index ad5c76810f36a..61bb1caa2d9da 100644
--- a/sklearn/metrics/_regression.py
+++ b/sklearn/metrics/_regression.py
@@ -37,8 +37,9 @@
 from ..utils._array_api import (
     _average,
     _find_matching_floating_dtype,
-    device,
     get_namespace,
+    get_namespace_and_device,
+    size,
 )
 from ..utils._param_validation import Hidden, Interval, StrOptions, validate_params
 from ..utils.stats import _weighted_percentile
@@ -188,7 +189,7 @@ def mean_absolute_error(
 
     Returns
     -------
-    loss : float or ndarray of floats
+    loss : float or array of floats
         If multioutput is 'raw_values', then mean absolute error is returned
         for each output separately.
         If multioutput is 'uniform_average' or an ndarray of weights, then the
@@ -212,11 +213,19 @@ def mean_absolute_error(
     >>> mean_absolute_error(y_true, y_pred, multioutput=[0.3, 0.7])
     0.85...
     """
-    y_type, y_true, y_pred, multioutput = _check_reg_targets(
-        y_true, y_pred, multioutput
+    input_arrays = [y_true, y_pred, sample_weight, multioutput]
+    xp, _ = get_namespace(*input_arrays)
+
+    dtype = _find_matching_floating_dtype(y_true, y_pred, sample_weight, xp=xp)
+
+    _, y_true, y_pred, multioutput = _check_reg_targets(
+        y_true, y_pred, multioutput, dtype=dtype, xp=xp
     )
     check_consistent_length(y_true, y_pred, sample_weight)
-    output_errors = np.average(np.abs(y_pred - y_true), weights=sample_weight, axis=0)
+
+    output_errors = _average(
+        xp.abs(y_pred - y_true), weights=sample_weight, axis=0, xp=xp
+    )
     if isinstance(multioutput, str):
         if multioutput == "raw_values":
             return output_errors
@@ -224,7 +233,15 @@ def mean_absolute_error(
             # pass None as weights to np.average: uniform mean
             multioutput = None
 
-    return np.average(output_errors, weights=multioutput)
+    # Average across the outputs (if needed).
+    mean_absolute_error = _average(output_errors, weights=multioutput)
+
+    # Since `y_pred.ndim <= 2` and `y_true.ndim <= 2`, the second call to _average
+    # should always return a scalar array that we convert to a Python float to
+    # consistently return the same eager evaluated value, irrespective of the
+    # Array API implementation.
+    assert mean_absolute_error.shape == ()
+    return float(mean_absolute_error)
 
 
 @validate_params(
@@ -907,7 +924,7 @@ def _assemble_r2_explained_variance(
         avg_weights = multioutput
 
     result = _average(output_scores, weights=avg_weights)
-    if result.size == 1:
+    if size(result) == 1:
         return float(result)
     return result
 
@@ -1194,9 +1211,9 @@ def r2_score(
     >>> r2_score(y_true, y_pred, force_finite=False)
     -inf
     """
-    input_arrays = [y_true, y_pred, sample_weight, multioutput]
-    xp, _ = get_namespace(*input_arrays)
-    device_ = device(*input_arrays)
+    xp, _, device_ = get_namespace_and_device(
+        y_true, y_pred, sample_weight, multioutput
+    )
 
     dtype = _find_matching_floating_dtype(y_true, y_pred, sample_weight, xp=xp)
 
@@ -1275,13 +1292,14 @@ def max_error(y_true, y_pred):
 
 def _mean_tweedie_deviance(y_true, y_pred, sample_weight, power):
     """Mean Tweedie deviance regression loss."""
+    xp, _ = get_namespace(y_true, y_pred)
     p = power
     if p < 0:
         # 'Extreme stable', y any real number, y_pred > 0
         dev = 2 * (
-            np.power(np.maximum(y_true, 0), 2 - p) / ((1 - p) * (2 - p))
-            - y_true * np.power(y_pred, 1 - p) / (1 - p)
-            + np.power(y_pred, 2 - p) / (2 - p)
+            xp.pow(xp.where(y_true > 0, y_true, 0), 2 - p) / ((1 - p) * (2 - p))
+            - y_true * xp.pow(y_pred, 1 - p) / (1 - p)
+            + xp.pow(y_pred, 2 - p) / (2 - p)
         )
     elif p == 0:
         # Normal distribution, y and y_pred any real number
@@ -1291,15 +1309,14 @@ def _mean_tweedie_deviance(y_true, y_pred, sample_weight, power):
         dev = 2 * (xlogy(y_true, y_true / y_pred) - y_true + y_pred)
     elif p == 2:
         # Gamma distribution
-        dev = 2 * (np.log(y_pred / y_true) + y_true / y_pred - 1)
+        dev = 2 * (xp.log(y_pred / y_true) + y_true / y_pred - 1)
     else:
         dev = 2 * (
-            np.power(y_true, 2 - p) / ((1 - p) * (2 - p))
-            - y_true * np.power(y_pred, 1 - p) / (1 - p)
-            + np.power(y_pred, 2 - p) / (2 - p)
+            xp.pow(y_true, 2 - p) / ((1 - p) * (2 - p))
+            - y_true * xp.pow(y_pred, 1 - p) / (1 - p)
+            + xp.pow(y_pred, 2 - p) / (2 - p)
         )
-
-    return np.average(dev, weights=sample_weight)
+    return float(_average(dev, weights=sample_weight))
 
 
 @validate_params(
@@ -1362,8 +1379,9 @@ def mean_tweedie_deviance(y_true, y_pred, *, sample_weight=None, power=0):
     >>> mean_tweedie_deviance(y_true, y_pred, power=1)
     1.4260...
     """
+    xp, _ = get_namespace(y_true, y_pred)
     y_type, y_true, y_pred, _ = _check_reg_targets(
-        y_true, y_pred, None, dtype=[np.float64, np.float32]
+        y_true, y_pred, None, dtype=[xp.float64, xp.float32]
     )
     if y_type == "continuous-multioutput":
         raise ValueError("Multioutput not supported in mean_tweedie_deviance")
diff --git a/sklearn/metrics/_scorer.py b/sklearn/metrics/_scorer.py
index 391d19459a871..c1a916aa0b5f3 100644
--- a/sklearn/metrics/_scorer.py
+++ b/sklearn/metrics/_scorer.py
@@ -32,6 +32,7 @@
 from ..utils.metadata_routing import (
     MetadataRequest,
     MetadataRouter,
+    MethodMapping,
     _MetadataRequester,
     _raise_for_params,
     _routing_enabled,
@@ -188,11 +189,31 @@ def get_metadata_routing(self):
             routing information.
         """
         return MetadataRouter(owner=self.__class__.__name__).add(
-            **self._scorers, method_mapping="score"
+            **self._scorers,
+            method_mapping=MethodMapping().add(caller="score", callee="score"),
         )
 
 
 class _BaseScorer(_MetadataRequester):
+    """Base scorer that is used as `scorer(estimator, X, y_true)`.
+
+    Parameters
+    ----------
+    score_func : callable
+        The score function to use. It will be called as
+        `score_func(y_true, y_pred, **kwargs)`.
+
+    sign : int
+        Either 1 or -1 to returns the score with `sign * score_func(estimator, X, y)`.
+        Thus, `sign` defined if higher scores are better or worse.
+
+    kwargs : dict
+        Additional parameters to pass to the score function.
+
+    response_method : str
+        The method to call on the estimator to get the response values.
+    """
+
     def __init__(self, score_func, sign, kwargs, response_method="predict"):
         self._score_func = score_func
         self._sign = sign
@@ -411,14 +432,31 @@ def get_scorer(scoring):
     return scorer
 
 
-class _PassthroughScorer:
+class _PassthroughScorer(_MetadataRequester):
+    # Passes scoring of estimator's `score` method back to estimator if scoring
+    # is `None`.
+
     def __init__(self, estimator):
         self._estimator = estimator
 
+        requests = MetadataRequest(owner=self.__class__.__name__)
+        try:
+            requests.score = copy.deepcopy(estimator._metadata_request.score)
+        except AttributeError:
+            try:
+                requests.score = copy.deepcopy(estimator._get_default_requests().score)
+            except AttributeError:
+                pass
+
+        self._metadata_request = requests
+
     def __call__(self, estimator, *args, **kwargs):
         """Method that wraps estimator.score"""
         return estimator.score(*args, **kwargs)
 
+    def __repr__(self):
+        return f"{self._estimator.__class__}.score"
+
     def get_metadata_routing(self):
         """Get requested data properties.
 
@@ -433,13 +471,32 @@ def get_metadata_routing(self):
             A :class:`~utils.metadata_routing.MetadataRouter` encapsulating
             routing information.
         """
-        # This scorer doesn't do any validation or routing, it only exposes the
-        # requests of the given estimator. This object behaves as a consumer
-        # rather than a router. Ideally it only exposes the score requests to
-        # the parent object; however, that requires computing the routing for
-        # meta-estimators, which would be more time consuming than simply
-        # returning the child object's requests.
-        return get_routing_for_object(self._estimator)
+        return get_routing_for_object(self._metadata_request)
+
+    def set_score_request(self, **kwargs):
+        """Set requested parameters by the scorer.
+
+        Please see :ref:`User Guide <metadata_routing>` on how the routing
+        mechanism works.
+
+        .. versionadded:: 1.5
+
+        Parameters
+        ----------
+        kwargs : dict
+            Arguments should be of the form ``param_name=alias``, and `alias`
+            can be one of ``{True, False, None, str}``.
+        """
+        if not _routing_enabled():
+            raise RuntimeError(
+                "This method is only available when metadata routing is enabled."
+                " You can enable it using"
+                " sklearn.set_config(enable_metadata_routing=True)."
+            )
+
+        for param, alias in kwargs.items():
+            self._metadata_request.score.add_request(param=param, alias=alias)
+        return self
 
 
 def _check_multimetric_scoring(estimator, scoring):
@@ -898,10 +955,11 @@ def get_scorer_names():
             None,
         ],
         "allow_none": ["boolean"],
+        "raise_exc": ["boolean"],
     },
     prefer_skip_nested_validation=True,
 )
-def check_scoring(estimator=None, scoring=None, *, allow_none=False):
+def check_scoring(estimator=None, scoring=None, *, allow_none=False, raise_exc=True):
     """Determine scorer from user options.
 
     A TypeError will be thrown if the estimator cannot be scored.
@@ -912,7 +970,7 @@ def check_scoring(estimator=None, scoring=None, *, allow_none=False):
         The object to use to fit the data. If `None`, then this function may error
         depending on `allow_none`.
 
-    scoring : str, callable, list, tuple, or dict, default=None
+    scoring : str, callable, list, tuple, set, or dict, default=None
         Scorer to use. If `scoring` represents a single score, one can use:
 
         - a single string (see :ref:`scoring_parameter`);
@@ -920,22 +978,35 @@ def check_scoring(estimator=None, scoring=None, *, allow_none=False):
 
         If `scoring` represents multiple scores, one can use:
 
-        - a list or tuple of unique strings;
-        - a callable returning a dictionary where the keys are the metric
-          names and the values are the metric scorers;
-        - a dictionary with metric names as keys and callables a values.
+        - a list, tuple or set of unique strings;
+        - a callable returning a dictionary where the keys are the metric names and the
+          values are the metric scorers;
+        - a dictionary with metric names as keys and callables a values. The callables
+          need to have the signature `callable(estimator, X, y)`.
 
         If None, the provided estimator object's `score` method is used.
 
     allow_none : bool, default=False
-        If no scoring is specified and the estimator has no score function, we
-        can either return None or raise an exception.
+        Whether to return None or raise an error if no `scoring` is specified and the
+        estimator has no `score` method.
+
+    raise_exc : bool, default=True
+        Whether to raise an exception (if a subset of the scorers in multimetric scoring
+        fails) or to return an error code.
+
+        - If set to `True`, raises the failing scorer's exception.
+        - If set to `False`, a formatted string of the exception details is passed as
+          result of the failing scorer(s).
+
+        This applies if `scoring` is list, tuple, set, or dict. Ignored if `scoring` is
+        a str or a callable.
+
+        .. versionadded:: 1.6
 
     Returns
     -------
     scoring : callable
-        A scorer callable object / function with signature
-        ``scorer(estimator, X, y)``.
+        A scorer callable object / function with signature ``scorer(estimator, X, y)``.
 
     Examples
     --------
@@ -947,6 +1018,19 @@ def check_scoring(estimator=None, scoring=None, *, allow_none=False):
     >>> scorer = check_scoring(classifier, scoring='accuracy')
     >>> scorer(classifier, X, y)
     0.96...
+
+    >>> from sklearn.metrics import make_scorer, accuracy_score, mean_squared_log_error
+    >>> X, y = load_iris(return_X_y=True)
+    >>> y *= -1
+    >>> clf = DecisionTreeClassifier().fit(X, y)
+    >>> scoring = {
+    ...     "accuracy": make_scorer(accuracy_score),
+    ...     "mean_squared_log_error": make_scorer(mean_squared_log_error),
+    ... }
+    >>> scoring_call = check_scoring(estimator=clf, scoring=scoring, raise_exc=False)
+    >>> scores = scoring_call(clf, X, y)
+    >>> scores
+    {'accuracy': 1.0, 'mean_squared_log_error': 'Traceback ...'}
     """
     if isinstance(scoring, str):
         return get_scorer(scoring)
@@ -969,7 +1053,7 @@ def check_scoring(estimator=None, scoring=None, *, allow_none=False):
         return get_scorer(scoring)
     if isinstance(scoring, (list, tuple, set, dict)):
         scorers = _check_multimetric_scoring(estimator, scoring=scoring)
-        return _MultimetricScorer(scorers=scorers)
+        return _MultimetricScorer(scorers=scorers, raise_exc=raise_exc)
     if scoring is None:
         if hasattr(estimator, "score"):
             return _PassthroughScorer(estimator)
diff --git a/sklearn/metrics/cluster/__init__.py b/sklearn/metrics/cluster/__init__.py
index 44da911061bc8..776b2f2dbd063 100644
--- a/sklearn/metrics/cluster/__init__.py
+++ b/sklearn/metrics/cluster/__init__.py
@@ -1,9 +1,8 @@
-"""
-The :mod:`sklearn.metrics.cluster` submodule contains evaluation metrics for
-cluster analysis results. There are two forms of evaluation:
+"""Evaluation metrics for cluster analysis results.
 
-- supervised, which uses a ground truth class values for each sample.
-- unsupervised, which does not and measures the 'quality' of the model itself.
+- Supervised evaluation uses a ground truth class values for each sample.
+- Unsupervised evaluation does use ground truths and measures the "quality" of the
+  model itself.
 """
 
 from ._bicluster import consensus_score
diff --git a/sklearn/metrics/cluster/_supervised.py b/sklearn/metrics/cluster/_supervised.py
index 992b460329302..1f72eae3725f6 100644
--- a/sklearn/metrics/cluster/_supervised.py
+++ b/sklearn/metrics/cluster/_supervised.py
@@ -188,12 +188,12 @@ def contingency_matrix(
     prefer_skip_nested_validation=True,
 )
 def pair_confusion_matrix(labels_true, labels_pred):
-    """Pair confusion matrix arising from two clusterings [1]_.
+    """Pair confusion matrix arising from two clusterings.
 
     The pair confusion matrix :math:`C` computes a 2 by 2 similarity matrix
     between two clusterings by considering all pairs of samples and counting
     pairs that are assigned into the same or into different clusters under
-    the true and predicted clusterings.
+    the true and predicted clusterings [1]_.
 
     Considering a pair of samples that is clustered together a positive pair,
     then as in binary classification the count of true negatives is
diff --git a/sklearn/metrics/pairwise.py b/sklearn/metrics/pairwise.py
index 6b2ea04002682..e3faffa77ae51 100644
--- a/sklearn/metrics/pairwise.py
+++ b/sklearn/metrics/pairwise.py
@@ -1,3 +1,5 @@
+"""Metrics for pairwise distances and affinity of sets of samples."""
+
 # Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
 #          Mathieu Blondel <mathieu@mblondel.org>
 #          Robert Layton <robertlayton@gmail.com>
@@ -25,6 +27,11 @@
     gen_batches,
     gen_even_slices,
 )
+from ..utils._array_api import (
+    _find_matching_floating_dtype,
+    _is_numpy_namespace,
+    get_namespace,
+)
 from ..utils._chunking import get_chunk_n_rows
 from ..utils._mask import _get_mask
 from ..utils._missing import is_scalar_nan
@@ -154,7 +161,11 @@ def check_pairwise_arrays(
         An array equal to Y if Y was not None, guaranteed to be a numpy array.
         If Y was None, safe_Y will be a pointer to X.
     """
-    X, Y, dtype_float = _return_float_dtype(X, Y)
+    xp, _ = get_namespace(X, Y)
+    if any([issparse(X), issparse(Y)]) or _is_numpy_namespace(xp):
+        X, Y, dtype_float = _return_float_dtype(X, Y)
+    else:
+        dtype_float = _find_matching_floating_dtype(X, Y, xp=xp)
 
     estimator = "check_pairwise_arrays"
     if dtype == "infer_float":
@@ -1651,7 +1662,7 @@ def cosine_similarity(X, Y=None, dense_output=True):
 
     Returns
     -------
-    similarities : ndarray of shape (n_samples_X, n_samples_Y)
+    similarities : ndarray or sparse matrix of shape (n_samples_X, n_samples_Y)
         Returns the cosine similarity between samples in X and Y.
 
     Examples
diff --git a/sklearn/metrics/tests/test_classification.py b/sklearn/metrics/tests/test_classification.py
index bbebe2cba2197..b87e76ba2fb42 100644
--- a/sklearn/metrics/tests/test_classification.py
+++ b/sklearn/metrics/tests/test_classification.py
@@ -35,7 +35,7 @@
     recall_score,
     zero_one_loss,
 )
-from sklearn.metrics._classification import _check_targets
+from sklearn.metrics._classification import _check_targets, d2_log_loss_score
 from sklearn.model_selection import cross_val_score
 from sklearn.preprocessing import LabelBinarizer, label_binarize
 from sklearn.tree import DecisionTreeClassifier
@@ -2624,62 +2624,37 @@ def test_log_loss():
     )
     loss = log_loss(y_true, y_pred)
     loss_true = -np.mean(bernoulli.logpmf(np.array(y_true) == "yes", y_pred[:, 1]))
-    assert_almost_equal(loss, loss_true)
+    assert_allclose(loss, loss_true)
 
     # multiclass case; adapted from http://bit.ly/RJJHWA
     y_true = [1, 0, 2]
     y_pred = [[0.2, 0.7, 0.1], [0.6, 0.2, 0.2], [0.6, 0.1, 0.3]]
     loss = log_loss(y_true, y_pred, normalize=True)
-    assert_almost_equal(loss, 0.6904911)
+    assert_allclose(loss, 0.6904911)
 
     # check that we got all the shapes and axes right
     # by doubling the length of y_true and y_pred
     y_true *= 2
     y_pred *= 2
     loss = log_loss(y_true, y_pred, normalize=False)
-    assert_almost_equal(loss, 0.6904911 * 6, decimal=6)
-
-    user_warning_msg = "y_pred values do not sum to one"
-    # check eps and handling of absolute zero and one probabilities
-    y_pred = np.asarray(y_pred) > 0.5
-    with pytest.warns(FutureWarning):
-        loss = log_loss(y_true, y_pred, normalize=True, eps=0.1)
-    with pytest.warns(UserWarning, match=user_warning_msg):
-        assert_almost_equal(loss, log_loss(y_true, np.clip(y_pred, 0.1, 0.9)))
-
-    # binary case: check correct boundary values for eps = 0
-    with pytest.warns(FutureWarning):
-        assert log_loss([0, 1], [0, 1], eps=0) == 0
-    with pytest.warns(FutureWarning):
-        assert log_loss([0, 1], [0, 0], eps=0) == np.inf
-    with pytest.warns(FutureWarning):
-        assert log_loss([0, 1], [1, 1], eps=0) == np.inf
-
-    # multiclass case: check correct boundary values for eps = 0
-    with pytest.warns(FutureWarning):
-        assert log_loss([0, 1, 2], [[1, 0, 0], [0, 1, 0], [0, 0, 1]], eps=0) == 0
-    with pytest.warns(FutureWarning):
-        assert (
-            log_loss([0, 1, 2], [[0, 0.5, 0.5], [0, 1, 0], [0, 0, 1]], eps=0) == np.inf
-        )
+    assert_allclose(loss, 0.6904911 * 6)
 
     # raise error if number of classes are not equal.
     y_true = [1, 0, 2]
-    y_pred = [[0.2, 0.7], [0.6, 0.5], [0.4, 0.1]]
+    y_pred = [[0.3, 0.7], [0.6, 0.4], [0.4, 0.6]]
     with pytest.raises(ValueError):
         log_loss(y_true, y_pred)
 
     # case when y_true is a string array object
     y_true = ["ham", "spam", "spam", "ham"]
-    y_pred = [[0.2, 0.7], [0.6, 0.5], [0.4, 0.1], [0.7, 0.2]]
-    with pytest.warns(UserWarning, match=user_warning_msg):
-        loss = log_loss(y_true, y_pred)
-    assert_almost_equal(loss, 1.0383217, decimal=6)
+    y_pred = [[0.3, 0.7], [0.6, 0.4], [0.4, 0.6], [0.7, 0.3]]
+    loss = log_loss(y_true, y_pred)
+    assert_allclose(loss, 0.7469410)
 
     # test labels option
 
     y_true = [2, 2]
-    y_pred = [[0.2, 0.7], [0.6, 0.5]]
+    y_pred = [[0.2, 0.8], [0.6, 0.4]]
     y_score = np.array([[0.1, 0.9], [0.1, 0.9]])
     error_str = (
         r"y_true contains only one label \(2\). Please provide "
@@ -2688,50 +2663,66 @@ def test_log_loss():
     with pytest.raises(ValueError, match=error_str):
         log_loss(y_true, y_pred)
 
-    y_pred = [[0.2, 0.7], [0.6, 0.5], [0.2, 0.3]]
-    error_str = "Found input variables with inconsistent numbers of samples: [3, 2]"
-    (ValueError, error_str, log_loss, y_true, y_pred)
+    y_pred = [[0.2, 0.8], [0.6, 0.4], [0.7, 0.3]]
+    error_str = r"Found input variables with inconsistent numbers of samples: \[3, 2\]"
+    with pytest.raises(ValueError, match=error_str):
+        log_loss(y_true, y_pred)
 
     # works when the labels argument is used
 
     true_log_loss = -np.mean(np.log(y_score[:, 1]))
     calculated_log_loss = log_loss(y_true, y_score, labels=[1, 2])
-    assert_almost_equal(calculated_log_loss, true_log_loss)
+    assert_allclose(calculated_log_loss, true_log_loss)
 
     # ensure labels work when len(np.unique(y_true)) != y_pred.shape[1]
     y_true = [1, 2, 2]
-    y_score2 = [[0.2, 0.7, 0.3], [0.6, 0.5, 0.3], [0.3, 0.9, 0.1]]
-    with pytest.warns(UserWarning, match=user_warning_msg):
-        loss = log_loss(y_true, y_score2, labels=[1, 2, 3])
-    assert_almost_equal(loss, 1.0630345, decimal=6)
+    y_score2 = [[0.7, 0.1, 0.2], [0.2, 0.7, 0.1], [0.1, 0.7, 0.2]]
+    loss = log_loss(y_true, y_score2, labels=[1, 2, 3])
+    assert_allclose(loss, -np.log(0.7))
 
 
-def test_log_loss_eps_auto(global_dtype):
-    """Check the behaviour of `eps="auto"` that changes depending on the input
-    array dtype.
+@pytest.mark.parametrize("dtype", [np.float64, np.float32, np.float16])
+def test_log_loss_eps(dtype):
+    """Check the behaviour internal eps that changes depending on the input dtype.
+
     Non-regression test for:
     https://github.com/scikit-learn/scikit-learn/issues/24315
     """
-    y_true = np.array([0, 1], dtype=global_dtype)
-    y_pred = y_true.copy()
+    y_true = np.array([0, 1], dtype=dtype)
+    y_pred = np.array([1, 0], dtype=dtype)
 
-    loss = log_loss(y_true, y_pred, eps="auto")
+    loss = log_loss(y_true, y_pred)
     assert np.isfinite(loss)
 
 
-def test_log_loss_eps_auto_float16():
-    """Check the behaviour of `eps="auto"` for np.float16"""
-    y_true = np.array([0, 1], dtype=np.float16)
-    y_pred = y_true.copy()
+@pytest.mark.parametrize("dtype", [np.float64, np.float32, np.float16])
+def test_log_loss_not_probabilities_warning(dtype):
+    """Check that log_loss raises a warning when y_pred values don't sum to 1."""
+    y_true = np.array([0, 1, 1, 0])
+    y_pred = np.array([[0.2, 0.7], [0.6, 0.3], [0.4, 0.7], [0.8, 0.3]], dtype=dtype)
 
-    loss = log_loss(y_true, y_pred, eps="auto")
-    assert np.isfinite(loss)
+    with pytest.warns(UserWarning, match="The y_pred values do not sum to one."):
+        log_loss(y_true, y_pred)
+
+
+@pytest.mark.parametrize(
+    "y_true, y_pred",
+    [
+        ([0, 1, 0], [0, 1, 0]),
+        ([0, 1, 0], [[1, 0], [0, 1], [1, 0]]),
+        ([0, 1, 2], [[1, 0, 0], [0, 1, 0], [0, 0, 1]]),
+    ],
+)
+def test_log_loss_perfect_predictions(y_true, y_pred):
+    """Check that log_loss returns 0 for perfect predictions."""
+    # Because of the clipping, the result is not exactly 0
+    assert log_loss(y_true, y_pred) == pytest.approx(0)
 
 
 def test_log_loss_pandas_input():
     # case when input is a pandas series and dataframe gh-5715
     y_tr = np.array(["ham", "spam", "spam", "ham"])
-    y_pr = np.array([[0.2, 0.7], [0.6, 0.5], [0.4, 0.1], [0.7, 0.2]])
+    y_pr = np.array([[0.3, 0.7], [0.6, 0.4], [0.4, 0.6], [0.7, 0.3]])
     types = [(MockDataFrame, MockDataFrame)]
     try:
         from pandas import DataFrame, Series
@@ -2742,9 +2733,8 @@ def test_log_loss_pandas_input():
     for TrueInputType, PredInputType in types:
         # y_pred dataframe, y_true series
         y_true, y_pred = TrueInputType(y_tr), PredInputType(y_pr)
-        with pytest.warns(UserWarning, match="y_pred values do not sum to one"):
-            loss = log_loss(y_true, y_pred)
-        assert_almost_equal(loss, 1.0383217, decimal=6)
+        loss = log_loss(y_true, y_pred)
+        assert_allclose(loss, 0.7469410)
 
 
 def test_brier_score_loss():
@@ -2905,3 +2895,202 @@ def test_brier_score_loss_deprecation_warning():
             y_prob=y_pred,
             y_proba=y_pred,
         )
+
+
+def test_d2_log_loss_score():
+    y_true = [0, 0, 0, 1, 1, 1]
+    y_true_string = ["no", "no", "no", "yes", "yes", "yes"]
+    y_pred = np.array(
+        [
+            [0.5, 0.5],
+            [0.9, 0.1],
+            [0.4, 0.6],
+            [0.6, 0.4],
+            [0.35, 0.65],
+            [0.01, 0.99],
+        ]
+    )
+    y_pred_null = np.array(
+        [
+            [0.5, 0.5],
+            [0.5, 0.5],
+            [0.5, 0.5],
+            [0.5, 0.5],
+            [0.5, 0.5],
+            [0.5, 0.5],
+        ]
+    )
+    d2_score = d2_log_loss_score(y_true=y_true, y_pred=y_pred)
+    log_likelihood = log_loss(y_true=y_true, y_pred=y_pred, normalize=False)
+    log_likelihood_null = log_loss(y_true=y_true, y_pred=y_pred_null, normalize=False)
+    d2_score_true = 1 - log_likelihood / log_likelihood_null
+    assert d2_score == pytest.approx(d2_score_true)
+
+    # check that using sample weight also gives the correct d2 score
+    sample_weight = np.array([2, 1, 3, 4, 3, 1])
+    y_pred_null[:, 0] = sample_weight[:3].sum() / sample_weight.sum()
+    y_pred_null[:, 1] = sample_weight[3:].sum() / sample_weight.sum()
+    d2_score = d2_log_loss_score(
+        y_true=y_true, y_pred=y_pred, sample_weight=sample_weight
+    )
+    log_likelihood = log_loss(
+        y_true=y_true,
+        y_pred=y_pred,
+        sample_weight=sample_weight,
+        normalize=False,
+    )
+    log_likelihood_null = log_loss(
+        y_true=y_true,
+        y_pred=y_pred_null,
+        sample_weight=sample_weight,
+        normalize=False,
+    )
+    d2_score_true = 1 - log_likelihood / log_likelihood_null
+    assert d2_score == pytest.approx(d2_score_true)
+
+    # check if good predictions give a relatively higher value for the d2 score
+    y_pred = np.array(
+        [
+            [0.9, 0.1],
+            [0.8, 0.2],
+            [0.9, 0.1],
+            [0.1, 0.9],
+            [0.2, 0.8],
+            [0.1, 0.9],
+        ]
+    )
+    d2_score = d2_log_loss_score(y_true, y_pred)
+    assert 0.5 < d2_score < 1.0
+    # check that a similar value is obtained for string labels
+    d2_score_string = d2_log_loss_score(y_true_string, y_pred)
+    assert d2_score_string == pytest.approx(d2_score)
+
+    # check if poor predictions gives a relatively low value for the d2 score
+    y_pred = np.array(
+        [
+            [0.5, 0.5],
+            [0.1, 0.9],
+            [0.1, 0.9],
+            [0.9, 0.1],
+            [0.75, 0.25],
+            [0.1, 0.9],
+        ]
+    )
+    d2_score = d2_log_loss_score(y_true, y_pred)
+    assert d2_score < 0
+    # check that a similar value is obtained for string labels
+    d2_score_string = d2_log_loss_score(y_true_string, y_pred)
+    assert d2_score_string == pytest.approx(d2_score)
+
+    # check if simply using the average of the classes as the predictions
+    # gives a d2 score of 0
+    y_true = [0, 0, 0, 1, 1, 1]
+    y_pred = np.array(
+        [
+            [0.5, 0.5],
+            [0.5, 0.5],
+            [0.5, 0.5],
+            [0.5, 0.5],
+            [0.5, 0.5],
+            [0.5, 0.5],
+        ]
+    )
+    d2_score = d2_log_loss_score(y_true, y_pred)
+    assert d2_score == 0
+    d2_score_string = d2_log_loss_score(y_true_string, y_pred)
+    assert d2_score_string == 0
+
+    # check if simply using the average of the classes as the predictions
+    # gives a d2 score of 0 when the positive class has a higher proportion
+    y_true = [0, 1, 1, 1]
+    y_true_string = ["no", "yes", "yes", "yes"]
+    y_pred = np.array([[0.25, 0.75], [0.25, 0.75], [0.25, 0.75], [0.25, 0.75]])
+    d2_score = d2_log_loss_score(y_true, y_pred)
+    assert d2_score == 0
+    d2_score_string = d2_log_loss_score(y_true_string, y_pred)
+    assert d2_score_string == 0
+    sample_weight = [2, 2, 2, 2]
+    d2_score_with_sample_weight = d2_log_loss_score(
+        y_true, y_pred, sample_weight=sample_weight
+    )
+    assert d2_score_with_sample_weight == 0
+
+    # check that the d2 scores seem correct when more than 2
+    # labels are specified
+    y_true = ["high", "high", "low", "neutral"]
+    sample_weight = [1.4, 0.6, 0.8, 0.2]
+
+    y_pred = np.array(
+        [
+            [0.8, 0.1, 0.1],
+            [0.8, 0.1, 0.1],
+            [0.1, 0.8, 0.1],
+            [0.1, 0.1, 0.8],
+        ]
+    )
+    d2_score = d2_log_loss_score(y_true, y_pred)
+    assert 0.5 < d2_score < 1.0
+    d2_score = d2_log_loss_score(y_true, y_pred, sample_weight=sample_weight)
+    assert 0.5 < d2_score < 1.0
+
+    y_pred = np.array(
+        [
+            [0.2, 0.5, 0.3],
+            [0.1, 0.7, 0.2],
+            [0.1, 0.1, 0.8],
+            [0.2, 0.7, 0.1],
+        ]
+    )
+    d2_score = d2_log_loss_score(y_true, y_pred)
+    assert d2_score < 0
+    d2_score = d2_log_loss_score(y_true, y_pred, sample_weight=sample_weight)
+    assert d2_score < 0
+
+
+def test_d2_log_loss_score_raises():
+    """Test that d2_log_loss_score raises the appropriate errors on
+    invalid inputs."""
+    y_true = [0, 1, 2]
+    y_pred = [[0.2, 0.8], [0.5, 0.5], [0.4, 0.6]]
+    err = "contain different number of classes"
+    with pytest.raises(ValueError, match=err):
+        d2_log_loss_score(y_true, y_pred)
+
+    # check error if the number of classes in labels do not match the number
+    # of classes in y_pred.
+    y_true = ["a", "b", "c"]
+    y_pred = [[0.5, 0.5], [0.5, 0.5], [0.5, 0.5]]
+    labels = [0, 1, 2]
+    err = "number of classes in labels is different"
+    with pytest.raises(ValueError, match=err):
+        d2_log_loss_score(y_true, y_pred, labels=labels)
+
+    # check error if y_true and y_pred do not have equal lengths
+    y_true = [0, 1, 2]
+    y_pred = [[0.5, 0.5, 0.5], [0.6, 0.3, 0.1]]
+    err = "inconsistent numbers of samples"
+    with pytest.raises(ValueError, match=err):
+        d2_log_loss_score(y_true, y_pred)
+
+    # check warning for samples < 2
+    y_true = [1]
+    y_pred = [[0.5, 0.5]]
+    err = "score is not well-defined"
+    with pytest.warns(UndefinedMetricWarning, match=err):
+        d2_log_loss_score(y_true, y_pred)
+
+    # check error when y_true only has 1 label
+    y_true = [1, 1, 1]
+    y_pred = [[0.5, 0.5], [0.5, 0.5], [0.5, 5]]
+    err = "y_true contains only one label"
+    with pytest.raises(ValueError, match=err):
+        d2_log_loss_score(y_true, y_pred)
+
+    # check error when y_true only has 1 label and labels also has
+    # only 1 label
+    y_true = [1, 1, 1]
+    labels = [1]
+    y_pred = [[0.5, 0.5], [0.5, 0.5], [0.5, 5]]
+    err = "The labels array needs to contain at least two"
+    with pytest.raises(ValueError, match=err):
+        d2_log_loss_score(y_true, y_pred, labels=labels)
diff --git a/sklearn/metrics/tests/test_common.py b/sklearn/metrics/tests/test_common.py
index e84ef1e358473..42f2a36445642 100644
--- a/sklearn/metrics/tests/test_common.py
+++ b/sklearn/metrics/tests/test_common.py
@@ -51,6 +51,7 @@
     zero_one_loss,
 )
 from sklearn.metrics._base import _average_binary_score
+from sklearn.metrics.pairwise import cosine_similarity
 from sklearn.preprocessing import LabelBinarizer
 from sklearn.utils import shuffle
 from sklearn.utils._array_api import (
@@ -637,7 +638,10 @@ def test_sample_order_invariance_multilabel_and_multioutput():
     # Generate some data
     y_true = random_state.randint(0, 2, size=(20, 25))
     y_pred = random_state.randint(0, 2, size=(20, 25))
-    y_score = random_state.normal(size=y_true.shape)
+    y_score = random_state.uniform(size=y_true.shape)
+
+    # Some metrics (e.g. log_loss) require y_score to be probabilities (sum to 1)
+    y_score /= y_score.sum(axis=1, keepdims=True)
 
     y_true_shuffle, y_pred_shuffle, y_score_shuffle = shuffle(
         y_true, y_pred, y_score, random_state=0
@@ -1566,7 +1570,10 @@ def test_multilabel_sample_weight_invariance(name):
     )
     y_true = np.vstack([ya, yb])
     y_pred = np.vstack([ya, ya])
-    y_score = random_state.randint(1, 4, size=y_true.shape)
+    y_score = random_state.uniform(size=y_true.shape)
+
+    # Some metrics (e.g. log_loss) require y_score to be probabilities (sum to 1)
+    y_score /= y_score.sum(axis=1, keepdims=True)
 
     metric = ALL_METRICS[name]
     if name in THRESHOLDED_METRICS:
@@ -1629,7 +1636,10 @@ def test_thresholded_multilabel_multioutput_permutations_invariance(name):
     random_state = check_random_state(0)
     n_samples, n_classes = 20, 4
     y_true = random_state.randint(0, 2, size=(n_samples, n_classes))
-    y_score = random_state.normal(size=y_true.shape)
+    y_score = random_state.uniform(size=y_true.shape)
+
+    # Some metrics (e.g. log_loss) require y_score to be probabilities (sum to 1)
+    y_score /= y_score.sum(axis=1, keepdims=True)
 
     # Makes sure all samples have at least one label. This works around errors
     # when running metrics where average="sample"
@@ -1734,20 +1744,22 @@ def test_metrics_pos_label_error_str(metric, y_pred_threshold, dtype_y_str):
 
 
 def check_array_api_metric(
-    metric, array_namespace, device, dtype_name, y_true_np, y_pred_np, sample_weight
+    metric, array_namespace, device, dtype_name, a_np, b_np, **metric_kwargs
 ):
     xp = _array_api_for_tests(array_namespace, device)
 
-    y_true_xp = xp.asarray(y_true_np, device=device)
-    y_pred_xp = xp.asarray(y_pred_np, device=device)
+    a_xp = xp.asarray(a_np, device=device)
+    b_xp = xp.asarray(b_np, device=device)
 
-    metric_np = metric(y_true_np, y_pred_np, sample_weight=sample_weight)
+    metric_np = metric(a_np, b_np, **metric_kwargs)
 
-    if sample_weight is not None:
-        sample_weight = xp.asarray(sample_weight, device=device)
+    if metric_kwargs.get("sample_weight") is not None:
+        metric_kwargs["sample_weight"] = xp.asarray(
+            metric_kwargs["sample_weight"], device=device
+        )
 
     with config_context(array_api_dispatch=True):
-        metric_xp = metric(y_true_xp, y_pred_xp, sample_weight=sample_weight)
+        metric_xp = metric(a_xp, b_xp, **metric_kwargs)
 
         assert_allclose(
             _convert_to_numpy(xp.asarray(metric_xp), xp),
@@ -1767,8 +1779,8 @@ def check_array_api_binary_classification_metric(
         array_namespace,
         device,
         dtype_name,
-        y_true_np=y_true_np,
-        y_pred_np=y_pred_np,
+        a_np=y_true_np,
+        b_np=y_pred_np,
         sample_weight=None,
     )
 
@@ -1779,8 +1791,8 @@ def check_array_api_binary_classification_metric(
         array_namespace,
         device,
         dtype_name,
-        y_true_np=y_true_np,
-        y_pred_np=y_pred_np,
+        a_np=y_true_np,
+        b_np=y_pred_np,
         sample_weight=sample_weight,
     )
 
@@ -1796,8 +1808,8 @@ def check_array_api_multiclass_classification_metric(
         array_namespace,
         device,
         dtype_name,
-        y_true_np=y_true_np,
-        y_pred_np=y_pred_np,
+        a_np=y_true_np,
+        b_np=y_pred_np,
         sample_weight=None,
     )
 
@@ -1808,13 +1820,42 @@ def check_array_api_multiclass_classification_metric(
         array_namespace,
         device,
         dtype_name,
-        y_true_np=y_true_np,
-        y_pred_np=y_pred_np,
+        a_np=y_true_np,
+        b_np=y_pred_np,
         sample_weight=sample_weight,
     )
 
 
 def check_array_api_regression_metric(metric, array_namespace, device, dtype_name):
+    y_true_np = np.array([2, 0, 1, 4], dtype=dtype_name)
+    y_pred_np = np.array([0.5, 0.5, 2, 2], dtype=dtype_name)
+
+    check_array_api_metric(
+        metric,
+        array_namespace,
+        device,
+        dtype_name,
+        a_np=y_true_np,
+        b_np=y_pred_np,
+        sample_weight=None,
+    )
+
+    sample_weight = np.array([0.1, 2.0, 1.5, 0.5], dtype=dtype_name)
+
+    check_array_api_metric(
+        metric,
+        array_namespace,
+        device,
+        dtype_name,
+        a_np=y_true_np,
+        b_np=y_pred_np,
+        sample_weight=sample_weight,
+    )
+
+
+def check_array_api_regression_metric_multioutput(
+    metric, array_namespace, device, dtype_name
+):
     y_true_np = np.array([[1, 3], [1, 2]], dtype=dtype_name)
     y_pred_np = np.array([[1, 4], [1, 1]], dtype=dtype_name)
 
@@ -1823,8 +1864,8 @@ def check_array_api_regression_metric(metric, array_namespace, device, dtype_nam
         array_namespace,
         device,
         dtype_name,
-        y_true_np=y_true_np,
-        y_pred_np=y_pred_np,
+        a_np=y_true_np,
+        b_np=y_pred_np,
         sample_weight=None,
     )
 
@@ -1835,12 +1876,49 @@ def check_array_api_regression_metric(metric, array_namespace, device, dtype_nam
         array_namespace,
         device,
         dtype_name,
-        y_true_np=y_true_np,
-        y_pred_np=y_pred_np,
+        a_np=y_true_np,
+        b_np=y_pred_np,
         sample_weight=sample_weight,
     )
 
 
+def check_array_api_multioutput_regression_metric(
+    metric, array_namespace, device, dtype_name
+):
+    metric = partial(metric, multioutput="raw_values")
+    check_array_api_regression_metric(metric, array_namespace, device, dtype_name)
+
+
+def check_array_api_metric_pairwise(metric, array_namespace, device, dtype_name):
+
+    X_np = np.array([[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]], dtype=dtype_name)
+    Y_np = np.array([[0.2, 0.3, 0.4], [0.5, 0.6, 0.7]], dtype=dtype_name)
+
+    metric_kwargs = {}
+    if "dense_output" in signature(metric).parameters:
+        metric_kwargs["dense_output"] = False
+        check_array_api_metric(
+            metric,
+            array_namespace,
+            device,
+            dtype_name,
+            a_np=X_np,
+            b_np=Y_np,
+            **metric_kwargs,
+        )
+        metric_kwargs["dense_output"] = True
+
+    check_array_api_metric(
+        metric,
+        array_namespace,
+        device,
+        dtype_name,
+        a_np=X_np,
+        b_np=Y_np,
+        **metric_kwargs,
+    )
+
+
 array_api_metric_checkers = {
     accuracy_score: [
         check_array_api_binary_classification_metric,
@@ -1850,7 +1928,16 @@ def check_array_api_regression_metric(metric, array_namespace, device, dtype_nam
         check_array_api_binary_classification_metric,
         check_array_api_multiclass_classification_metric,
     ],
-    r2_score: [check_array_api_regression_metric],
+    mean_tweedie_deviance: [check_array_api_regression_metric],
+    r2_score: [
+        check_array_api_regression_metric,
+        check_array_api_regression_metric_multioutput,
+    ],
+    cosine_similarity: [check_array_api_metric_pairwise],
+    mean_absolute_error: [
+        check_array_api_regression_metric,
+        check_array_api_multioutput_regression_metric,
+    ],
 }
 
 
diff --git a/sklearn/metrics/tests/test_pairwise_distances_reduction.py b/sklearn/metrics/tests/test_pairwise_distances_reduction.py
index e5983f9273d94..95dfa98178ee7 100644
--- a/sklearn/metrics/tests/test_pairwise_distances_reduction.py
+++ b/sklearn/metrics/tests/test_pairwise_distances_reduction.py
@@ -5,9 +5,9 @@
 
 import numpy as np
 import pytest
-import threadpoolctl
 from scipy.spatial.distance import cdist
 
+from sklearn import _threadpool_controller
 from sklearn.metrics import euclidean_distances, pairwise_distances
 from sklearn.metrics._pairwise_distances_reduction import (
     ArgKmin,
@@ -1200,7 +1200,7 @@ def test_n_threads_agnosticism(
         **compute_parameters,
     )
 
-    with threadpoolctl.threadpool_limits(limits=1, user_api="openmp"):
+    with _threadpool_controller.limit(limits=1, user_api="openmp"):
         dist, indices = Dispatcher.compute(
             X,
             Y,
diff --git a/sklearn/metrics/tests/test_ranking.py b/sklearn/metrics/tests/test_ranking.py
index 4beeea23446c8..ac3c3855a327e 100644
--- a/sklearn/metrics/tests/test_ranking.py
+++ b/sklearn/metrics/tests/test_ranking.py
@@ -875,7 +875,7 @@ def test_binary_clf_curve_implicit_pos_label(curve_func):
         np.testing.assert_allclose(int_curve_part, float_curve_part)
 
 
-# TODO(1.5): Update test to check for error when bytes support is removed.
+# TODO(1.7): Update test to check for error when bytes support is removed.
 @ignore_warnings(category=FutureWarning)
 @pytest.mark.parametrize("curve_func", [precision_recall_curve, roc_curve])
 @pytest.mark.parametrize("labels_type", ["list", "array"])
diff --git a/sklearn/metrics/tests/test_score_objects.py b/sklearn/metrics/tests/test_score_objects.py
index 6ce2e3b30dd8e..bfe8f57f92265 100644
--- a/sklearn/metrics/tests/test_score_objects.py
+++ b/sklearn/metrics/tests/test_score_objects.py
@@ -53,7 +53,6 @@
 from sklearn.pipeline import make_pipeline
 from sklearn.svm import LinearSVC
 from sklearn.tests.metadata_routing_common import (
-    assert_request_equal,
     assert_request_is_empty,
 )
 from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
@@ -62,7 +61,7 @@
     assert_array_equal,
     ignore_warnings,
 )
-from sklearn.utils.metadata_routing import MetadataRouter
+from sklearn.utils.metadata_routing import MetadataRouter, MethodMapping
 
 REGRESSION_SCORERS = [
     "d2_absolute_error_score",
@@ -927,7 +926,7 @@ def test_multiclass_roc_proba_scorer(scorer_name, metric):
     X, y = make_classification(
         n_classes=3, n_informative=3, n_samples=20, random_state=0
     )
-    lr = LogisticRegression(multi_class="multinomial").fit(X, y)
+    lr = LogisticRegression().fit(X, y)
     y_proba = lr.predict_proba(X)
     expected_score = metric(y, y_proba)
 
@@ -944,7 +943,7 @@ def test_multiclass_roc_proba_scorer_label():
     X, y = make_classification(
         n_classes=3, n_informative=3, n_samples=20, random_state=0
     )
-    lr = LogisticRegression(multi_class="multinomial").fit(X, y)
+    lr = LogisticRegression().fit(X, y)
     y_proba = lr.predict_proba(X)
 
     y_binary = y == 0
@@ -1234,7 +1233,8 @@ def test_scorer_metadata_request(name):
     # make sure putting the scorer in a router doesn't request anything by
     # default
     router = MetadataRouter(owner="test").add(
-        method_mapping="score", scorer=get_scorer(name)
+        scorer=get_scorer(name),
+        method_mapping=MethodMapping().add(caller="score", callee="score"),
     )
     # make sure `sample_weight` is refused if passed.
     with pytest.raises(TypeError, match="got unexpected argument"):
@@ -1245,7 +1245,8 @@ def test_scorer_metadata_request(name):
 
     # make sure putting weighted_scorer in a router requests sample_weight
     router = MetadataRouter(owner="test").add(
-        scorer=weighted_scorer, method_mapping="score"
+        scorer=weighted_scorer,
+        method_mapping=MethodMapping().add(caller="score", callee="score"),
     )
     router.validate_metadata(params={"sample_weight": 1}, method="score")
     routed_params = router.route_params(params={"sample_weight": 1}, caller="score")
@@ -1276,24 +1277,40 @@ def test_metadata_kwarg_conflict():
 
 
 @pytest.mark.usefixtures("enable_slep006")
-def test_PassthroughScorer_metadata_request():
-    """Test that _PassthroughScorer properly routes metadata.
+def test_PassthroughScorer_set_score_request():
+    """Test that _PassthroughScorer.set_score_request adds the correct metadata request
+    on itself and doesn't change its estimator's routing."""
+    est = LogisticRegression().set_score_request(sample_weight="estimator_weights")
+    # make a `_PassthroughScorer` with `check_scoring`:
+    scorer = check_scoring(est, None)
+    assert (
+        scorer.get_metadata_routing().score.requests["sample_weight"]
+        == "estimator_weights"
+    )
 
-    _PassthroughScorer should behave like a consumer, mirroring whatever is the
-    underlying score method.
-    """
-    scorer = _PassthroughScorer(
-        estimator=LinearSVC()
-        .set_score_request(sample_weight="alias")
-        .set_fit_request(sample_weight=True)
+    scorer.set_score_request(sample_weight="scorer_weights")
+    assert (
+        scorer.get_metadata_routing().score.requests["sample_weight"]
+        == "scorer_weights"
     )
-    # Test that _PassthroughScorer doesn't change estimator's routing.
-    assert_request_equal(
-        scorer.get_metadata_routing(),
-        {"fit": {"sample_weight": True}, "score": {"sample_weight": "alias"}},
+
+    # making sure changing the passthrough object doesn't affect the estimator.
+    assert (
+        est.get_metadata_routing().score.requests["sample_weight"]
+        == "estimator_weights"
     )
 
 
+def test_PassthroughScorer_set_score_request_raises_without_routing_enabled():
+    """Test that _PassthroughScorer.set_score_request raises if metadata routing is
+    disabled."""
+    scorer = check_scoring(LogisticRegression(), None)
+    msg = "This method is only available when metadata routing is enabled."
+
+    with pytest.raises(RuntimeError, match=msg):
+        scorer.set_score_request(sample_weight="my_weights")
+
+
 @pytest.mark.usefixtures("enable_slep006")
 def test_multimetric_scoring_metadata_routing():
     # Test that _MultimetricScorer properly routes metadata.
@@ -1540,6 +1557,34 @@ def test_multimetric_scorer_repr():
     assert str(multi_metric_scorer) == 'MultiMetricScorer("accuracy", "r2")'
 
 
+def test_check_scoring_multimetric_raise_exc():
+    """Test that check_scoring returns error code for a subset of scorers in
+    multimetric scoring if raise_exc=False and raises otherwise."""
+
+    def raising_scorer(estimator, X, y):
+        raise ValueError("That doesn't work.")
+
+    X, y = make_classification(n_samples=150, n_features=10, random_state=0)
+    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
+    clf = LogisticRegression().fit(X_train, y_train)
+
+    # "raising_scorer" is raising ValueError and should return an string representation
+    # of the error of the last scorer:
+    scoring = {
+        "accuracy": make_scorer(accuracy_score),
+        "raising_scorer": raising_scorer,
+    }
+    scoring_call = check_scoring(estimator=clf, scoring=scoring, raise_exc=False)
+    scores = scoring_call(clf, X_test, y_test)
+    assert "That doesn't work." in scores["raising_scorer"]
+
+    # should raise an error
+    scoring_call = check_scoring(estimator=clf, scoring=scoring, raise_exc=True)
+    err_msg = "That doesn't work."
+    with pytest.raises(ValueError, match=err_msg):
+        scores = scoring_call(clf, X_test, y_test)
+
+
 @pytest.mark.parametrize("enable_metadata_routing", [True, False])
 def test_metadata_routing_multimetric_metadata_routing(enable_metadata_routing):
     """Test multimetric scorer works with and without metadata routing enabled when
diff --git a/sklearn/mixture/__init__.py b/sklearn/mixture/__init__.py
index f0018196ffc98..9eb449226a9cb 100644
--- a/sklearn/mixture/__init__.py
+++ b/sklearn/mixture/__init__.py
@@ -1,6 +1,4 @@
-"""
-The :mod:`sklearn.mixture` module implements mixture modeling algorithms.
-"""
+"""Mixture modeling algorithms."""
 
 from ._bayesian_mixture import BayesianGaussianMixture
 from ._gaussian_mixture import GaussianMixture
diff --git a/sklearn/model_selection/__init__.py b/sklearn/model_selection/__init__.py
index d7d316d95ada4..8a50d032dc164 100644
--- a/sklearn/model_selection/__init__.py
+++ b/sklearn/model_selection/__init__.py
@@ -1,5 +1,11 @@
+"""Tools for model selection, such as cross validation and hyper-parameter tuning."""
+
 import typing
 
+from ._classification_threshold import (
+    FixedThresholdClassifier,
+    TunedThresholdClassifierCV,
+)
 from ._plot import LearningCurveDisplay, ValidationCurveDisplay
 from ._search import GridSearchCV, ParameterGrid, ParameterSampler, RandomizedSearchCV
 from ._split import (
@@ -63,6 +69,8 @@
     "StratifiedKFold",
     "StratifiedGroupKFold",
     "StratifiedShuffleSplit",
+    "FixedThresholdClassifier",
+    "TunedThresholdClassifierCV",
     "check_cv",
     "cross_val_predict",
     "cross_val_score",
diff --git a/sklearn/model_selection/_classification_threshold.py b/sklearn/model_selection/_classification_threshold.py
new file mode 100644
index 0000000000000..1d221d3388434
--- /dev/null
+++ b/sklearn/model_selection/_classification_threshold.py
@@ -0,0 +1,1019 @@
+from collections.abc import MutableMapping
+from numbers import Integral, Real
+
+import numpy as np
+
+from ..base import (
+    BaseEstimator,
+    ClassifierMixin,
+    MetaEstimatorMixin,
+    _fit_context,
+    clone,
+)
+from ..exceptions import NotFittedError
+from ..metrics import (
+    check_scoring,
+    get_scorer_names,
+)
+from ..metrics._scorer import _BaseScorer
+from ..utils import _safe_indexing
+from ..utils._param_validation import HasMethods, Interval, RealNotInt, StrOptions
+from ..utils._response import _get_response_values_binary
+from ..utils.metadata_routing import (
+    MetadataRouter,
+    MethodMapping,
+    _raise_for_params,
+    process_routing,
+)
+from ..utils.metaestimators import available_if
+from ..utils.multiclass import type_of_target
+from ..utils.parallel import Parallel, delayed
+from ..utils.validation import (
+    _check_method_params,
+    _num_samples,
+    check_is_fitted,
+    indexable,
+)
+from ._split import StratifiedShuffleSplit, check_cv
+
+
+def _estimator_has(attr):
+    """Check if we can delegate a method to the underlying estimator.
+
+    First, we check the fitted estimator if available, otherwise we
+    check the unfitted estimator.
+    """
+
+    def check(self):
+        if hasattr(self, "estimator_"):
+            getattr(self.estimator_, attr)
+        else:
+            getattr(self.estimator, attr)
+        return True
+
+    return check
+
+
+def _threshold_scores_to_class_labels(y_score, threshold, classes, pos_label):
+    """Threshold `y_score` and return the associated class labels."""
+    if pos_label is None:
+        map_thresholded_score_to_label = np.array([0, 1])
+    else:
+        pos_label_idx = np.flatnonzero(classes == pos_label)[0]
+        neg_label_idx = np.flatnonzero(classes != pos_label)[0]
+        map_thresholded_score_to_label = np.array([neg_label_idx, pos_label_idx])
+
+    return classes[map_thresholded_score_to_label[(y_score >= threshold).astype(int)]]
+
+
+class BaseThresholdClassifier(ClassifierMixin, MetaEstimatorMixin, BaseEstimator):
+    """Base class for binary classifiers that set a non-default decision threshold.
+
+    In this base class, we define the following interface:
+
+    - the validation of common parameters in `fit`;
+    - the different prediction methods that can be used with the classifier.
+
+    .. versionadded:: 1.5
+
+    Parameters
+    ----------
+    estimator : estimator instance
+        The binary classifier, fitted or not, for which we want to optimize
+        the decision threshold used during `predict`.
+
+    response_method : {"auto", "decision_function", "predict_proba"}, default="auto"
+        Methods by the classifier `estimator` corresponding to the
+        decision function for which we want to find a threshold. It can be:
+
+        * if `"auto"`, it will try to invoke, for each classifier,
+          `"predict_proba"` or `"decision_function"` in that order.
+        * otherwise, one of `"predict_proba"` or `"decision_function"`.
+          If the method is not implemented by the classifier, it will raise an
+          error.
+    """
+
+    _required_parameters = ["estimator"]
+    _parameter_constraints: dict = {
+        "estimator": [
+            HasMethods(["fit", "predict_proba"]),
+            HasMethods(["fit", "decision_function"]),
+        ],
+        "response_method": [StrOptions({"auto", "predict_proba", "decision_function"})],
+    }
+
+    def __init__(self, estimator, *, response_method="auto"):
+        self.estimator = estimator
+        self.response_method = response_method
+
+    def _get_response_method(self):
+        """Define the response method."""
+        if self.response_method == "auto":
+            response_method = ["predict_proba", "decision_function"]
+        else:
+            response_method = self.response_method
+        return response_method
+
+    @_fit_context(
+        # *ThresholdClassifier*.estimator is not validated yet
+        prefer_skip_nested_validation=False
+    )
+    def fit(self, X, y, **params):
+        """Fit the classifier.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+            Training data.
+
+        y : array-like of shape (n_samples,)
+            Target values.
+
+        **params : dict
+            Parameters to pass to the `fit` method of the underlying
+            classifier.
+
+        Returns
+        -------
+        self : object
+            Returns an instance of self.
+        """
+        _raise_for_params(params, self, None)
+
+        X, y = indexable(X, y)
+
+        y_type = type_of_target(y, input_name="y")
+        if y_type != "binary":
+            raise ValueError(
+                f"Only binary classification is supported. Unknown label type: {y_type}"
+            )
+
+        self._fit(X, y, **params)
+
+        if hasattr(self.estimator_, "n_features_in_"):
+            self.n_features_in_ = self.estimator_.n_features_in_
+        if hasattr(self.estimator_, "feature_names_in_"):
+            self.feature_names_in_ = self.estimator_.feature_names_in_
+
+        return self
+
+    @property
+    def classes_(self):
+        """Classes labels."""
+        return self.estimator_.classes_
+
+    @available_if(_estimator_has("predict_proba"))
+    def predict_proba(self, X):
+        """Predict class probabilities for `X` using the fitted estimator.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+            Training vectors, where `n_samples` is the number of samples and
+            `n_features` is the number of features.
+
+        Returns
+        -------
+        probabilities : ndarray of shape (n_samples, n_classes)
+            The class probabilities of the input samples.
+        """
+        check_is_fitted(self, "estimator_")
+        return self.estimator_.predict_proba(X)
+
+    @available_if(_estimator_has("predict_log_proba"))
+    def predict_log_proba(self, X):
+        """Predict logarithm class probabilities for `X` using the fitted estimator.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+            Training vectors, where `n_samples` is the number of samples and
+            `n_features` is the number of features.
+
+        Returns
+        -------
+        log_probabilities : ndarray of shape (n_samples, n_classes)
+            The logarithm class probabilities of the input samples.
+        """
+        check_is_fitted(self, "estimator_")
+        return self.estimator_.predict_log_proba(X)
+
+    @available_if(_estimator_has("decision_function"))
+    def decision_function(self, X):
+        """Decision function for samples in `X` using the fitted estimator.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+            Training vectors, where `n_samples` is the number of samples and
+            `n_features` is the number of features.
+
+        Returns
+        -------
+        decisions : ndarray of shape (n_samples,)
+            The decision function computed the fitted estimator.
+        """
+        check_is_fitted(self, "estimator_")
+        return self.estimator_.decision_function(X)
+
+    def _more_tags(self):
+        return {
+            "binary_only": True,
+            "_xfail_checks": {
+                "check_classifiers_train": "Threshold at probability 0.5 does not hold",
+                "check_sample_weights_invariance": (
+                    "Due to the cross-validation and sample ordering, removing a sample"
+                    " is not strictly equal to putting is weight to zero. Specific unit"
+                    " tests are added for TunedThresholdClassifierCV specifically."
+                ),
+            },
+        }
+
+
+class FixedThresholdClassifier(BaseThresholdClassifier):
+    """Binary classifier that manually sets the decision threshold.
+
+    This classifier allows to change the default decision threshold used for
+    converting posterior probability estimates (i.e. output of `predict_proba`) or
+    decision scores (i.e. output of `decision_function`) into a class label.
+
+    Here, the threshold is not optimized and is set to a constant value.
+
+    Read more in the :ref:`User Guide <FixedThresholdClassifier>`.
+
+    .. versionadded:: 1.5
+
+    Parameters
+    ----------
+    estimator : estimator instance
+        The binary classifier, fitted or not, for which we want to optimize
+        the decision threshold used during `predict`.
+
+    threshold : {"auto"} or float, default="auto"
+        The decision threshold to use when converting posterior probability estimates
+        (i.e. output of `predict_proba`) or decision scores (i.e. output of
+        `decision_function`) into a class label. When `"auto"`, the threshold is set
+        to 0.5 if `predict_proba` is used as `response_method`, otherwise it is set to
+        0 (i.e. the default threshold for `decision_function`).
+
+    pos_label : int, float, bool or str, default=None
+        The label of the positive class. Used to process the output of the
+        `response_method` method. When `pos_label=None`, if `y_true` is in `{-1, 1}` or
+        `{0, 1}`, `pos_label` is set to 1, otherwise an error will be raised.
+
+    response_method : {"auto", "decision_function", "predict_proba"}, default="auto"
+        Methods by the classifier `estimator` corresponding to the
+        decision function for which we want to find a threshold. It can be:
+
+        * if `"auto"`, it will try to invoke `"predict_proba"` or `"decision_function"`
+          in that order.
+        * otherwise, one of `"predict_proba"` or `"decision_function"`.
+          If the method is not implemented by the classifier, it will raise an
+          error.
+
+    prefit : bool, default=False
+        Whether a pre-fitted model is expected to be passed into the constructor
+        directly or not. If `True`, `estimator` must be a fitted estimator. If `False`,
+        `estimator` is fitted and updated by calling `fit`.
+
+        .. versionadded:: 1.6
+
+    Attributes
+    ----------
+    estimator_ : estimator instance
+        The fitted classifier used when predicting.
+
+    classes_ : ndarray of shape (n_classes,)
+        The class labels.
+
+    n_features_in_ : int
+        Number of features seen during :term:`fit`. Only defined if the
+        underlying estimator exposes such an attribute when fit.
+
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Only defined if the
+        underlying estimator exposes such an attribute when fit.
+
+    See Also
+    --------
+    sklearn.model_selection.TunedThresholdClassifierCV : Classifier that post-tunes
+        the decision threshold based on some metrics and using cross-validation.
+    sklearn.calibration.CalibratedClassifierCV : Estimator that calibrates
+        probabilities.
+
+    Examples
+    --------
+    >>> from sklearn.datasets import make_classification
+    >>> from sklearn.linear_model import LogisticRegression
+    >>> from sklearn.metrics import confusion_matrix
+    >>> from sklearn.model_selection import FixedThresholdClassifier, train_test_split
+    >>> X, y = make_classification(
+    ...     n_samples=1_000, weights=[0.9, 0.1], class_sep=0.8, random_state=42
+    ... )
+    >>> X_train, X_test, y_train, y_test = train_test_split(
+    ...     X, y, stratify=y, random_state=42
+    ... )
+    >>> classifier = LogisticRegression(random_state=0).fit(X_train, y_train)
+    >>> print(confusion_matrix(y_test, classifier.predict(X_test)))
+    [[217   7]
+     [ 19   7]]
+    >>> classifier_other_threshold = FixedThresholdClassifier(
+    ...     classifier, threshold=0.1, response_method="predict_proba"
+    ... ).fit(X_train, y_train)
+    >>> print(confusion_matrix(y_test, classifier_other_threshold.predict(X_test)))
+    [[184  40]
+     [  6  20]]
+    """
+
+    _parameter_constraints: dict = {
+        **BaseThresholdClassifier._parameter_constraints,
+        "threshold": [StrOptions({"auto"}), Real],
+        "pos_label": [Real, str, "boolean", None],
+        "prefit": ["boolean"],
+    }
+
+    def __init__(
+        self,
+        estimator,
+        *,
+        threshold="auto",
+        pos_label=None,
+        response_method="auto",
+        prefit=False,
+    ):
+        super().__init__(estimator=estimator, response_method=response_method)
+        self.pos_label = pos_label
+        self.threshold = threshold
+        self.prefit = prefit
+
+    def _fit(self, X, y, **params):
+        """Fit the classifier.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+            Training data.
+
+        y : array-like of shape (n_samples,)
+            Target values.
+
+        **params : dict
+            Parameters to pass to the `fit` method of the underlying
+            classifier.
+
+        Returns
+        -------
+        self : object
+            Returns an instance of self.
+        """
+        routed_params = process_routing(self, "fit", **params)
+        if self.prefit:
+            check_is_fitted(self.estimator)
+            self.estimator_ = self.estimator
+        else:
+            self.estimator_ = clone(self.estimator).fit(
+                X, y, **routed_params.estimator.fit
+            )
+        return self
+
+    def predict(self, X):
+        """Predict the target of new samples.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+            The samples, as accepted by `estimator.predict`.
+
+        Returns
+        -------
+        class_labels : ndarray of shape (n_samples,)
+            The predicted class.
+        """
+        check_is_fitted(self, "estimator_")
+        y_score, _, response_method_used = _get_response_values_binary(
+            self.estimator_,
+            X,
+            self._get_response_method(),
+            pos_label=self.pos_label,
+            return_response_method_used=True,
+        )
+
+        if self.threshold == "auto":
+            decision_threshold = 0.5 if response_method_used == "predict_proba" else 0.0
+        else:
+            decision_threshold = self.threshold
+
+        return _threshold_scores_to_class_labels(
+            y_score, decision_threshold, self.classes_, self.pos_label
+        )
+
+    def get_metadata_routing(self):
+        """Get metadata routing of this object.
+
+        Please check :ref:`User Guide <metadata_routing>` on how the routing
+        mechanism works.
+
+        Returns
+        -------
+        routing : MetadataRouter
+            A :class:`~sklearn.utils.metadata_routing.MetadataRouter` encapsulating
+            routing information.
+        """
+        router = MetadataRouter(owner=self.__class__.__name__).add(
+            estimator=self.estimator,
+            method_mapping=MethodMapping().add(callee="fit", caller="fit"),
+        )
+        return router
+
+
+class _CurveScorer(_BaseScorer):
+    """Scorer taking a continuous response and output a score for each threshold.
+
+    Parameters
+    ----------
+    score_func : callable
+        The score function to use. It will be called as
+        `score_func(y_true, y_pred, **kwargs)`.
+
+    sign : int
+        Either 1 or -1 to returns the score with `sign * score_func(estimator, X, y)`.
+        Thus, `sign` defined if higher scores are better or worse.
+
+    kwargs : dict
+        Additional parameters to pass to the score function.
+
+    thresholds : int or array-like
+        Related to the number of decision thresholds for which we want to compute the
+        score. If an integer, it will be used to generate `thresholds` thresholds
+        uniformly distributed between the minimum and maximum predicted scores. If an
+        array-like, it will be used as the thresholds.
+
+    response_method : str
+        The method to call on the estimator to get the response values.
+    """
+
+    def __init__(self, score_func, sign, kwargs, thresholds, response_method):
+        super().__init__(
+            score_func=score_func,
+            sign=sign,
+            kwargs=kwargs,
+            response_method=response_method,
+        )
+        self._thresholds = thresholds
+
+    @classmethod
+    def from_scorer(cls, scorer, response_method, thresholds):
+        """Create a continuous scorer from a normal scorer."""
+        instance = cls(
+            score_func=scorer._score_func,
+            sign=scorer._sign,
+            response_method=response_method,
+            thresholds=thresholds,
+            kwargs=scorer._kwargs,
+        )
+        # transfer the metadata request
+        instance._metadata_request = scorer._get_metadata_request()
+        return instance
+
+    def _score(self, method_caller, estimator, X, y_true, **kwargs):
+        """Evaluate predicted target values for X relative to y_true.
+
+        Parameters
+        ----------
+        method_caller : callable
+            Returns predictions given an estimator, method name, and other
+            arguments, potentially caching results.
+
+        estimator : object
+            Trained estimator to use for scoring.
+
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+            Test data that will be fed to estimator.predict.
+
+        y_true : array-like of shape (n_samples,)
+            Gold standard target values for X.
+
+        **kwargs : dict
+            Other parameters passed to the scorer. Refer to
+            :func:`set_score_request` for more details.
+
+        Returns
+        -------
+        scores : ndarray of shape (thresholds,)
+            The scores associated to each threshold.
+
+        potential_thresholds : ndarray of shape (thresholds,)
+            The potential thresholds used to compute the scores.
+        """
+        pos_label = self._get_pos_label()
+        y_score = method_caller(
+            estimator, self._response_method, X, pos_label=pos_label
+        )
+
+        scoring_kwargs = {**self._kwargs, **kwargs}
+        if isinstance(self._thresholds, Integral):
+            potential_thresholds = np.linspace(
+                np.min(y_score), np.max(y_score), self._thresholds
+            )
+        else:
+            potential_thresholds = np.asarray(self._thresholds)
+        score_thresholds = [
+            self._sign
+            * self._score_func(
+                y_true,
+                _threshold_scores_to_class_labels(
+                    y_score, th, estimator.classes_, pos_label
+                ),
+                **scoring_kwargs,
+            )
+            for th in potential_thresholds
+        ]
+        return np.array(score_thresholds), potential_thresholds
+
+
+def _fit_and_score_over_thresholds(
+    classifier,
+    X,
+    y,
+    *,
+    fit_params,
+    train_idx,
+    val_idx,
+    curve_scorer,
+    score_params,
+):
+    """Fit a classifier and compute the scores for different decision thresholds.
+
+    Parameters
+    ----------
+    classifier : estimator instance
+        The classifier to fit and use for scoring. If `classifier` is already fitted,
+        it will be used as is.
+
+    X : {array-like, sparse matrix} of shape (n_samples, n_features)
+        The entire dataset.
+
+    y : array-like of shape (n_samples,)
+        The entire target vector.
+
+    fit_params : dict
+        Parameters to pass to the `fit` method of the underlying classifier.
+
+    train_idx : ndarray of shape (n_train_samples,) or None
+        The indices of the training set. If `None`, `classifier` is expected to be
+        already fitted.
+
+    val_idx : ndarray of shape (n_val_samples,)
+        The indices of the validation set used to score `classifier`. If `train_idx`,
+        the entire set will be used.
+
+    curve_scorer : scorer instance
+        The scorer taking `classifier` and the validation set as input and outputting
+        decision thresholds and scores as a curve. Note that this is different from
+        the usual scorer that output a single score value:
+
+        * when `score_method` is one of the four constraint metrics, the curve scorer
+          will output a curve of two scores parametrized by the decision threshold, e.g.
+          TPR/TNR or precision/recall curves for each threshold;
+        * otherwise, the curve scorer will output a single score value for each
+          threshold.
+
+    score_params : dict
+        Parameters to pass to the `score` method of the underlying scorer.
+
+    Returns
+    -------
+    scores : ndarray of shape (thresholds,) or tuple of such arrays
+        The scores computed for each decision threshold. When TPR/TNR or precision/
+        recall are computed, `scores` is a tuple of two arrays.
+
+    potential_thresholds : ndarray of shape (thresholds,)
+        The decision thresholds used to compute the scores. They are returned in
+        ascending order.
+    """
+
+    if train_idx is not None:
+        X_train, X_val = _safe_indexing(X, train_idx), _safe_indexing(X, val_idx)
+        y_train, y_val = _safe_indexing(y, train_idx), _safe_indexing(y, val_idx)
+        fit_params_train = _check_method_params(X, fit_params, indices=train_idx)
+        score_params_val = _check_method_params(X, score_params, indices=val_idx)
+        classifier.fit(X_train, y_train, **fit_params_train)
+    else:  # prefit estimator, only a validation set is provided
+        X_val, y_val, score_params_val = X, y, score_params
+
+    return curve_scorer(classifier, X_val, y_val, **score_params_val)
+
+
+def _mean_interpolated_score(target_thresholds, cv_thresholds, cv_scores):
+    """Compute the mean interpolated score across folds by defining common thresholds.
+
+    Parameters
+    ----------
+    target_thresholds : ndarray of shape (thresholds,)
+        The thresholds to use to compute the mean score.
+
+    cv_thresholds : ndarray of shape (n_folds, thresholds_fold)
+        The thresholds used to compute the scores for each fold.
+
+    cv_scores : ndarray of shape (n_folds, thresholds_fold)
+        The scores computed for each threshold for each fold.
+
+    Returns
+    -------
+    mean_score : ndarray of shape (thresholds,)
+        The mean score across all folds for each target threshold.
+    """
+    return np.mean(
+        [
+            np.interp(target_thresholds, split_thresholds, split_score)
+            for split_thresholds, split_score in zip(cv_thresholds, cv_scores)
+        ],
+        axis=0,
+    )
+
+
+class TunedThresholdClassifierCV(BaseThresholdClassifier):
+    """Classifier that post-tunes the decision threshold using cross-validation.
+
+    This estimator post-tunes the decision threshold (cut-off point) that is
+    used for converting posterior probability estimates (i.e. output of
+    `predict_proba`) or decision scores (i.e. output of `decision_function`)
+    into a class label. The tuning is done by optimizing a binary metric,
+    potentially constrained by a another metric.
+
+    Read more in the :ref:`User Guide <TunedThresholdClassifierCV>`.
+
+    .. versionadded:: 1.5
+
+    Parameters
+    ----------
+    estimator : estimator instance
+        The classifier, fitted or not, for which we want to optimize
+        the decision threshold used during `predict`.
+
+    scoring : str or callable, default="balanced_accuracy"
+        The objective metric to be optimized. Can be one of:
+
+        * a string associated to a scoring function for binary classification
+          (see :ref:`scoring_parameter`);
+        * a scorer callable object created with :func:`~sklearn.metrics.make_scorer`;
+
+    response_method : {"auto", "decision_function", "predict_proba"}, default="auto"
+        Methods by the classifier `estimator` corresponding to the
+        decision function for which we want to find a threshold. It can be:
+
+        * if `"auto"`, it will try to invoke, for each classifier,
+          `"predict_proba"` or `"decision_function"` in that order.
+        * otherwise, one of `"predict_proba"` or `"decision_function"`.
+          If the method is not implemented by the classifier, it will raise an
+          error.
+
+    thresholds : int or array-like, default=100
+        The number of decision threshold to use when discretizing the output of the
+        classifier `method`. Pass an array-like to manually specify the thresholds
+        to use.
+
+    cv : int, float, cross-validation generator, iterable or "prefit", default=None
+        Determines the cross-validation splitting strategy to train classifier.
+        Possible inputs for cv are:
+
+        * `None`, to use the default 5-fold stratified K-fold cross validation;
+        * An integer number, to specify the number of folds in a stratified k-fold;
+        * A float number, to specify a single shuffle split. The floating number should
+          be in (0, 1) and represent the size of the validation set;
+        * An object to be used as a cross-validation generator;
+        * An iterable yielding train, test splits;
+        * `"prefit"`, to bypass the cross-validation.
+
+        Refer :ref:`User Guide <cross_validation>` for the various
+        cross-validation strategies that can be used here.
+
+        .. warning::
+            Using `cv="prefit"` and passing the same dataset for fitting `estimator`
+            and tuning the cut-off point is subject to undesired overfitting. You can
+            refer to :ref:`TunedThresholdClassifierCV_no_cv` for an example.
+
+            This option should only be used when the set used to fit `estimator` is
+            different from the one used to tune the cut-off point (by calling
+            :meth:`TunedThresholdClassifierCV.fit`).
+
+    refit : bool, default=True
+        Whether or not to refit the classifier on the entire training set once
+        the decision threshold has been found.
+        Note that forcing `refit=False` on cross-validation having more
+        than a single split will raise an error. Similarly, `refit=True` in
+        conjunction with `cv="prefit"` will raise an error.
+
+    n_jobs : int, default=None
+        The number of jobs to run in parallel. When `cv` represents a
+        cross-validation strategy, the fitting and scoring on each data split
+        is done in parallel. ``None`` means 1 unless in a
+        :obj:`joblib.parallel_backend` context. ``-1`` means using all
+        processors. See :term:`Glossary <n_jobs>` for more details.
+
+    random_state : int, RandomState instance or None, default=None
+        Controls the randomness of cross-validation when `cv` is a float.
+        See :term:`Glossary <random_state>`.
+
+    store_cv_results : bool, default=False
+        Whether to store all scores and thresholds computed during the cross-validation
+        process.
+
+    Attributes
+    ----------
+    estimator_ : estimator instance
+        The fitted classifier used when predicting.
+
+    best_threshold_ : float
+        The new decision threshold.
+
+    best_score_ : float or None
+        The optimal score of the objective metric, evaluated at `best_threshold_`.
+
+    cv_results_ : dict or None
+        A dictionary containing the scores and thresholds computed during the
+        cross-validation process. Only exist if `store_cv_results=True`. The
+        keys are `"thresholds"` and `"scores"`.
+
+    classes_ : ndarray of shape (n_classes,)
+        The class labels.
+
+    n_features_in_ : int
+        Number of features seen during :term:`fit`. Only defined if the
+        underlying estimator exposes such an attribute when fit.
+
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Only defined if the
+        underlying estimator exposes such an attribute when fit.
+
+    See Also
+    --------
+    sklearn.model_selection.FixedThresholdClassifier : Classifier that uses a
+        constant threshold.
+    sklearn.calibration.CalibratedClassifierCV : Estimator that calibrates
+        probabilities.
+
+    Examples
+    --------
+    >>> from sklearn.datasets import make_classification
+    >>> from sklearn.ensemble import RandomForestClassifier
+    >>> from sklearn.metrics import classification_report
+    >>> from sklearn.model_selection import TunedThresholdClassifierCV, train_test_split
+    >>> X, y = make_classification(
+    ...     n_samples=1_000, weights=[0.9, 0.1], class_sep=0.8, random_state=42
+    ... )
+    >>> X_train, X_test, y_train, y_test = train_test_split(
+    ...     X, y, stratify=y, random_state=42
+    ... )
+    >>> classifier = RandomForestClassifier(random_state=0).fit(X_train, y_train)
+    >>> print(classification_report(y_test, classifier.predict(X_test)))
+                  precision    recall  f1-score   support
+    <BLANKLINE>
+               0       0.94      0.99      0.96       224
+               1       0.80      0.46      0.59        26
+    <BLANKLINE>
+        accuracy                           0.93       250
+       macro avg       0.87      0.72      0.77       250
+    weighted avg       0.93      0.93      0.92       250
+    <BLANKLINE>
+    >>> classifier_tuned = TunedThresholdClassifierCV(
+    ...     classifier, scoring="balanced_accuracy"
+    ... ).fit(X_train, y_train)
+    >>> print(
+    ...     f"Cut-off point found at {classifier_tuned.best_threshold_:.3f}"
+    ... )
+    Cut-off point found at 0.342
+    >>> print(classification_report(y_test, classifier_tuned.predict(X_test)))
+                  precision    recall  f1-score   support
+    <BLANKLINE>
+               0       0.96      0.95      0.96       224
+               1       0.61      0.65      0.63        26
+    <BLANKLINE>
+        accuracy                           0.92       250
+       macro avg       0.78      0.80      0.79       250
+    weighted avg       0.92      0.92      0.92       250
+    <BLANKLINE>
+    """
+
+    _parameter_constraints: dict = {
+        **BaseThresholdClassifier._parameter_constraints,
+        "scoring": [
+            StrOptions(set(get_scorer_names())),
+            callable,
+            MutableMapping,
+        ],
+        "thresholds": [Interval(Integral, 1, None, closed="left"), "array-like"],
+        "cv": [
+            "cv_object",
+            StrOptions({"prefit"}),
+            Interval(RealNotInt, 0.0, 1.0, closed="neither"),
+        ],
+        "refit": ["boolean"],
+        "n_jobs": [Integral, None],
+        "random_state": ["random_state"],
+        "store_cv_results": ["boolean"],
+    }
+
+    def __init__(
+        self,
+        estimator,
+        *,
+        scoring="balanced_accuracy",
+        response_method="auto",
+        thresholds=100,
+        cv=None,
+        refit=True,
+        n_jobs=None,
+        random_state=None,
+        store_cv_results=False,
+    ):
+        super().__init__(estimator=estimator, response_method=response_method)
+        self.scoring = scoring
+        self.thresholds = thresholds
+        self.cv = cv
+        self.refit = refit
+        self.n_jobs = n_jobs
+        self.random_state = random_state
+        self.store_cv_results = store_cv_results
+
+    def _fit(self, X, y, **params):
+        """Fit the classifier and post-tune the decision threshold.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+            Training data.
+
+        y : array-like of shape (n_samples,)
+            Target values.
+
+        **params : dict
+            Parameters to pass to the `fit` method of the underlying
+            classifier and to the `scoring` scorer.
+
+        Returns
+        -------
+        self : object
+            Returns an instance of self.
+        """
+        if isinstance(self.cv, Real) and 0 < self.cv < 1:
+            cv = StratifiedShuffleSplit(
+                n_splits=1, test_size=self.cv, random_state=self.random_state
+            )
+        elif self.cv == "prefit":
+            if self.refit is True:
+                raise ValueError("When cv='prefit', refit cannot be True.")
+            try:
+                check_is_fitted(self.estimator, "classes_")
+            except NotFittedError as exc:
+                raise NotFittedError(
+                    """When cv='prefit', `estimator` must be fitted."""
+                ) from exc
+            cv = self.cv
+        else:
+            cv = check_cv(self.cv, y=y, classifier=True)
+            if self.refit is False and cv.get_n_splits() > 1:
+                raise ValueError("When cv has several folds, refit cannot be False.")
+
+        routed_params = process_routing(self, "fit", **params)
+        self._curve_scorer = self._get_curve_scorer()
+
+        # in the following block, we:
+        # - define the final classifier `self.estimator_` and train it if necessary
+        # - define `classifier` to be used to post-tune the decision threshold
+        # - define `split` to be used to fit/score `classifier`
+        if cv == "prefit":
+            self.estimator_ = self.estimator
+            classifier = self.estimator_
+            splits = [(None, range(_num_samples(X)))]
+        else:
+            self.estimator_ = clone(self.estimator)
+            classifier = clone(self.estimator)
+            splits = cv.split(X, y, **routed_params.splitter.split)
+
+            if self.refit:
+                # train on the whole dataset
+                X_train, y_train, fit_params_train = X, y, routed_params.estimator.fit
+            else:
+                # single split cross-validation
+                train_idx, _ = next(cv.split(X, y, **routed_params.splitter.split))
+                X_train = _safe_indexing(X, train_idx)
+                y_train = _safe_indexing(y, train_idx)
+                fit_params_train = _check_method_params(
+                    X, routed_params.estimator.fit, indices=train_idx
+                )
+
+            self.estimator_.fit(X_train, y_train, **fit_params_train)
+
+        cv_scores, cv_thresholds = zip(
+            *Parallel(n_jobs=self.n_jobs)(
+                delayed(_fit_and_score_over_thresholds)(
+                    clone(classifier) if cv != "prefit" else classifier,
+                    X,
+                    y,
+                    fit_params=routed_params.estimator.fit,
+                    train_idx=train_idx,
+                    val_idx=val_idx,
+                    curve_scorer=self._curve_scorer,
+                    score_params=routed_params.scorer.score,
+                )
+                for train_idx, val_idx in splits
+            )
+        )
+
+        if any(np.isclose(th[0], th[-1]) for th in cv_thresholds):
+            raise ValueError(
+                "The provided estimator makes constant predictions. Therefore, it is "
+                "impossible to optimize the decision threshold."
+            )
+
+        # find the global min and max thresholds across all folds
+        min_threshold = min(
+            split_thresholds.min() for split_thresholds in cv_thresholds
+        )
+        max_threshold = max(
+            split_thresholds.max() for split_thresholds in cv_thresholds
+        )
+        if isinstance(self.thresholds, Integral):
+            decision_thresholds = np.linspace(
+                min_threshold, max_threshold, num=self.thresholds
+            )
+        else:
+            decision_thresholds = np.asarray(self.thresholds)
+
+        objective_scores = _mean_interpolated_score(
+            decision_thresholds, cv_thresholds, cv_scores
+        )
+        best_idx = objective_scores.argmax()
+        self.best_score_ = objective_scores[best_idx]
+        self.best_threshold_ = decision_thresholds[best_idx]
+        if self.store_cv_results:
+            self.cv_results_ = {
+                "thresholds": decision_thresholds,
+                "scores": objective_scores,
+            }
+
+        return self
+
+    def predict(self, X):
+        """Predict the target of new samples.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+            The samples, as accepted by `estimator.predict`.
+
+        Returns
+        -------
+        class_labels : ndarray of shape (n_samples,)
+            The predicted class.
+        """
+        check_is_fitted(self, "estimator_")
+        pos_label = self._curve_scorer._get_pos_label()
+        y_score, _ = _get_response_values_binary(
+            self.estimator_,
+            X,
+            self._get_response_method(),
+            pos_label=pos_label,
+        )
+
+        return _threshold_scores_to_class_labels(
+            y_score, self.best_threshold_, self.classes_, pos_label
+        )
+
+    def get_metadata_routing(self):
+        """Get metadata routing of this object.
+
+        Please check :ref:`User Guide <metadata_routing>` on how the routing
+        mechanism works.
+
+        Returns
+        -------
+        routing : MetadataRouter
+            A :class:`~sklearn.utils.metadata_routing.MetadataRouter` encapsulating
+            routing information.
+        """
+        router = (
+            MetadataRouter(owner=self.__class__.__name__)
+            .add(
+                estimator=self.estimator,
+                method_mapping=MethodMapping().add(callee="fit", caller="fit"),
+            )
+            .add(
+                splitter=self.cv,
+                method_mapping=MethodMapping().add(callee="split", caller="fit"),
+            )
+            .add(
+                scorer=self._get_curve_scorer(),
+                method_mapping=MethodMapping().add(callee="score", caller="fit"),
+            )
+        )
+        return router
+
+    def _get_curve_scorer(self):
+        """Get the curve scorer based on the objective metric used."""
+        scoring = check_scoring(self.estimator, scoring=self.scoring)
+        curve_scorer = _CurveScorer.from_scorer(
+            scoring, self._get_response_method(), self.thresholds
+        )
+        return curve_scorer
diff --git a/sklearn/model_selection/_search.py b/sklearn/model_selection/_search.py
index 42fde09c16bce..edf492b84877a 100644
--- a/sklearn/model_selection/_search.py
+++ b/sklearn/model_selection/_search.py
@@ -36,6 +36,7 @@
 from ..utils._estimator_html_repr import _VisualBlock
 from ..utils._param_validation import HasMethods, Interval, StrOptions
 from ..utils._tags import _safe_tags
+from ..utils.deprecation import _deprecate_Xt_in_inverse_transform
 from ..utils.metadata_routing import (
     MetadataRouter,
     MethodMapping,
@@ -637,7 +638,7 @@ def transform(self, X):
         return self.best_estimator_.transform(X)
 
     @available_if(_estimator_has("inverse_transform"))
-    def inverse_transform(self, Xt):
+    def inverse_transform(self, X=None, Xt=None):
         """Call inverse_transform on the estimator with the best found params.
 
         Only available if the underlying estimator implements
@@ -645,18 +646,26 @@ def inverse_transform(self, Xt):
 
         Parameters
         ----------
+        X : indexable, length n_samples
+            Must fulfill the input assumptions of the
+            underlying estimator.
+
         Xt : indexable, length n_samples
             Must fulfill the input assumptions of the
             underlying estimator.
 
+            .. deprecated:: 1.5
+                `Xt` was deprecated in 1.5 and will be removed in 1.7. Use `X` instead.
+
         Returns
         -------
         X : {ndarray, sparse matrix} of shape (n_samples, n_features)
             Result of the `inverse_transform` function for `Xt` based on the
             estimator with the best found parameters.
         """
+        X = _deprecate_Xt_in_inverse_transform(X, Xt)
         check_is_fitted(self)
-        return self.best_estimator_.inverse_transform(Xt)
+        return self.best_estimator_.inverse_transform(X)
 
     @property
     def n_features_in_(self):
@@ -963,7 +972,7 @@ def evaluate_candidates(candidate_params, cv=None, more_results=None):
             first_test_score = all_out[0]["test_scores"]
             self.multimetric_ = isinstance(first_test_score, dict)
 
-            # check refit_metric now for a callabe scorer that is multimetric
+            # check refit_metric now for a callable scorer that is multimetric
             if callable(self.scoring) and self.multimetric_:
                 self._check_refit_for_multimetric(first_test_score)
                 refit_metric = self.refit
@@ -1081,7 +1090,7 @@ def _store(key_name, array, weights=None, splits=False, rank=False):
             param_list = list(param_result.values())
             try:
                 arr_dtype = np.result_type(*param_list)
-            except TypeError:
+            except (TypeError, ValueError):
                 arr_dtype = object
             if len(param_list) == n_candidates and arr_dtype != object:
                 # Exclude `object` else the numpy constructor might infer a list of
diff --git a/sklearn/model_selection/_split.py b/sklearn/model_selection/_split.py
index 53c11a665ccf4..1f9d78d3e4cbd 100644
--- a/sklearn/model_selection/_split.py
+++ b/sklearn/model_selection/_split.py
@@ -2596,7 +2596,7 @@ def check_cv(cv=5, y=None, *, classifier=False):
 
     Parameters
     ----------
-    cv : int, cross-validation generator or an iterable, default=None
+    cv : int, cross-validation generator, iterable or None, default=5
         Determines the cross-validation splitting strategy.
         Possible inputs for cv are:
         - None, to use the default 5-fold cross validation,
diff --git a/sklearn/model_selection/_validation.py b/sklearn/model_selection/_validation.py
index 176627ace91d4..e0858a2c4860a 100644
--- a/sklearn/model_selection/_validation.py
+++ b/sklearn/model_selection/_validation.py
@@ -27,7 +27,7 @@
 from ..base import clone, is_classifier
 from ..exceptions import FitFailedWarning, UnsetMetadataPassedError
 from ..metrics import check_scoring, get_scorer_names
-from ..metrics._scorer import _check_multimetric_scoring, _MultimetricScorer
+from ..metrics._scorer import _MultimetricScorer
 from ..preprocessing import LabelEncoder
 from ..utils import Bunch, _safe_indexing, check_random_state, indexable
 from ..utils._param_validation import (
@@ -58,21 +58,22 @@
 ]
 
 
-def _check_params_groups_deprecation(fit_params, params, groups):
+def _check_params_groups_deprecation(fit_params, params, groups, version):
     """A helper function to check deprecations on `groups` and `fit_params`.
 
-    To be removed when set_config(enable_metadata_routing=False) is not possible.
+    # TODO(SLEP6): To be removed when set_config(enable_metadata_routing=False) is not
+    # possible.
     """
     if params is not None and fit_params is not None:
         raise ValueError(
             "`params` and `fit_params` cannot both be provided. Pass parameters "
             "via `params`. `fit_params` is deprecated and will be removed in "
-            "version 1.6."
+            f"version {version}."
         )
     elif fit_params is not None:
         warnings.warn(
             (
-                "`fit_params` is deprecated and will be removed in version 1.6. "
+                "`fit_params` is deprecated and will be removed in version {version}. "
                 "Pass parameters via `params` instead."
             ),
             FutureWarning,
@@ -346,21 +347,15 @@ def cross_validate(
     >>> print(scores['train_r2'])
     [0.28009951 0.3908844  0.22784907]
     """
-    params = _check_params_groups_deprecation(fit_params, params, groups)
+    params = _check_params_groups_deprecation(fit_params, params, groups, "1.6")
 
     X, y = indexable(X, y)
 
     cv = check_cv(cv, y, classifier=is_classifier(estimator))
 
-    if callable(scoring):
-        scorers = scoring
-    elif scoring is None or isinstance(scoring, str):
-        scorers = check_scoring(estimator, scoring)
-    else:
-        scorers = _check_multimetric_scoring(estimator, scoring)
-        scorers = _MultimetricScorer(
-            scorers=scorers, raise_exc=(error_score == "raise")
-        )
+    scorers = check_scoring(
+        estimator, scoring=scoring, raise_exc=(error_score == "raise")
+    )
 
     if _routing_enabled():
         # For estimators, a MetadataRouter is created in get_metadata_routing
@@ -602,10 +597,8 @@ def cross_val_score(
             ``cross_val_score(..., params={'groups': groups})``.
 
     scoring : str or callable, default=None
-        A str (see model evaluation documentation) or
-        a scorer callable object / function with signature
-        ``scorer(estimator, X, y)`` which should return only
-        a single value.
+        A str (see :ref:`scoring_parameter`) or a scorer callable object / function with
+        signature ``scorer(estimator, X, y)`` which should return only a single value.
 
         Similar to :func:`cross_validate`
         but only a single metric is permitted.
@@ -1206,7 +1199,7 @@ def cross_val_predict(
     >>> lasso = linear_model.Lasso()
     >>> y_pred = cross_val_predict(lasso, X, y, cv=3)
     """
-    params = _check_params_groups_deprecation(fit_params, params, groups)
+    params = _check_params_groups_deprecation(fit_params, params, groups, "1.6")
     X, y = indexable(X, y)
 
     if _routing_enabled():
@@ -1718,6 +1711,7 @@ def _shuffle(y, groups, random_state):
         "error_score": [StrOptions({"raise"}), Real],
         "return_times": ["boolean"],
         "fit_params": [dict, None],
+        "params": [dict, None],
     },
     prefer_skip_nested_validation=False,  # estimator is not validated yet
 )
@@ -1739,6 +1733,7 @@ def learning_curve(
     error_score=np.nan,
     return_times=False,
     fit_params=None,
+    params=None,
 ):
     """Learning curve.
 
@@ -1773,6 +1768,13 @@ def learning_curve(
         train/test set. Only used in conjunction with a "Group" :term:`cv`
         instance (e.g., :class:`GroupKFold`).
 
+        .. versionchanged:: 1.6
+            ``groups`` can only be passed if metadata routing is not enabled
+            via ``sklearn.set_config(enable_metadata_routing=True)``. When routing
+            is enabled, pass ``groups`` alongside other metadata via the ``params``
+            argument instead. E.g.:
+            ``learning_curve(..., params={'groups': groups})``.
+
     train_sizes : array-like of shape (n_ticks,), \
             default=np.linspace(0.1, 1.0, 5)
         Relative or absolute numbers of training examples that will be used to
@@ -1780,7 +1782,7 @@ def learning_curve(
         fraction of the maximum size of the training set (that is determined
         by the selected validation method), i.e. it has to be within (0, 1].
         Otherwise it is interpreted as absolute sizes of the training sets.
-        Note that for classification the number of samples usually have to
+        Note that for classification the number of samples usually has to
         be big enough to contain at least one sample from each class.
 
     cv : int, cross-validation generator or an iterable, default=None
@@ -1804,9 +1806,8 @@ def learning_curve(
             ``cv`` default value if None changed from 3-fold to 5-fold.
 
     scoring : str or callable, default=None
-        A str (see model evaluation documentation) or
-        a scorer callable object / function with signature
-        ``scorer(estimator, X, y)``.
+        A str (see :ref:`scoring_parameter`) or a scorer callable object / function with
+        signature ``scorer(estimator, X, y)``.
 
     exploit_incremental_learning : bool, default=False
         If the estimator supports incremental learning, this will be
@@ -1849,7 +1850,22 @@ def learning_curve(
     fit_params : dict, default=None
         Parameters to pass to the fit method of the estimator.
 
-        .. versionadded:: 0.24
+        .. deprecated:: 1.6
+            This parameter is deprecated and will be removed in version 1.6. Use
+            ``params`` instead.
+
+    params : dict, default=None
+        Parameters to pass to the `fit` method of the estimator and to the scorer.
+
+            - If `enable_metadata_routing=False` (default):
+              Parameters directly passed to the `fit` method of the estimator.
+
+            - If `enable_metadata_routing=True`:
+              Parameters safely routed to the `fit` method of the estimator.
+              See :ref:`Metadata Routing User Guide <metadata_routing>` for more
+              details.
+
+            .. versionadded:: 1.6
 
     Returns
     -------
@@ -1903,14 +1919,69 @@ def learning_curve(
             "An estimator must support the partial_fit interface "
             "to exploit incremental learning"
         )
+
+    params = _check_params_groups_deprecation(fit_params, params, groups, "1.8")
+
     X, y, groups = indexable(X, y, groups)
 
     cv = check_cv(cv, y, classifier=is_classifier(estimator))
-    # Store it as list as we will be iterating over the list multiple times
-    cv_iter = list(cv.split(X, y, groups))
 
     scorer = check_scoring(estimator, scoring=scoring)
 
+    if _routing_enabled():
+        router = (
+            MetadataRouter(owner="learning_curve")
+            .add(
+                estimator=estimator,
+                # TODO(SLEP6): also pass metadata to the predict method for
+                # scoring?
+                method_mapping=MethodMapping()
+                .add(caller="fit", callee="fit")
+                .add(caller="fit", callee="partial_fit"),
+            )
+            .add(
+                splitter=cv,
+                method_mapping=MethodMapping().add(caller="fit", callee="split"),
+            )
+            .add(
+                scorer=scorer,
+                method_mapping=MethodMapping().add(caller="fit", callee="score"),
+            )
+        )
+
+        try:
+            routed_params = process_routing(router, "fit", **params)
+        except UnsetMetadataPassedError as e:
+            # The default exception would mention `fit` since in the above
+            # `process_routing` code, we pass `fit` as the caller. However,
+            # the user is not calling `fit` directly, so we change the message
+            # to make it more suitable for this case.
+            unrequested_params = sorted(e.unrequested_params)
+            raise UnsetMetadataPassedError(
+                message=(
+                    f"{unrequested_params} are passed to `learning_curve` but are not"
+                    " explicitly set as requested or not requested for learning_curve's"
+                    f" estimator: {estimator.__class__.__name__}. Call"
+                    " `.set_fit_request({{metadata}}=True)` on the estimator for"
+                    f" each metadata in {unrequested_params} that you"
+                    " want to use and `metadata=False` for not using it. See the"
+                    " Metadata Routing User guide"
+                    " <https://scikit-learn.org/stable/metadata_routing.html> for more"
+                    " information."
+                ),
+                unrequested_params=e.unrequested_params,
+                routed_params=e.routed_params,
+            )
+
+    else:
+        routed_params = Bunch()
+        routed_params.estimator = Bunch(fit=params, partial_fit=params)
+        routed_params.splitter = Bunch(split={"groups": groups})
+        routed_params.scorer = Bunch(score={})
+
+    # Store cv as list as we will be iterating over the list multiple times
+    cv_iter = list(cv.split(X, y, **routed_params.splitter.split))
+
     n_max_training_samples = len(cv_iter[0][0])
     # Because the lengths of folds can be significantly different, it is
     # not guaranteed that we use all of the available training data when we
@@ -1940,7 +2011,8 @@ def learning_curve(
                 scorer,
                 return_times,
                 error_score=error_score,
-                fit_params=fit_params,
+                fit_params=routed_params.estimator.partial_fit,
+                score_params=routed_params.scorer.score,
             )
             for train, test in cv_iter
         )
@@ -1961,9 +2033,8 @@ def learning_curve(
                 test=test,
                 verbose=verbose,
                 parameters=None,
-                fit_params=fit_params,
-                # TODO(SLEP6): support score params here
-                score_params=None,
+                fit_params=routed_params.estimator.fit,
+                score_params=routed_params.scorer.score,
                 return_train_score=True,
                 error_score=error_score,
                 return_times=return_times,
@@ -2069,6 +2140,7 @@ def _incremental_fit_estimator(
     return_times,
     error_score,
     fit_params,
+    score_params,
 ):
     """Train estimator on training subsets incrementally and compute scores."""
     train_scores, test_scores, fit_times, score_times = [], [], [], []
@@ -2079,6 +2151,9 @@ def _incremental_fit_estimator(
         partial_fit_func = partial(estimator.partial_fit, **fit_params)
     else:
         partial_fit_func = partial(estimator.partial_fit, classes=classes, **fit_params)
+    score_params = score_params if score_params is not None else {}
+    score_params_train = _check_method_params(X, params=score_params, indices=train)
+    score_params_test = _check_method_params(X, params=score_params, indices=test)
 
     for n_train_samples, partial_train in partitions:
         train_subset = train[:n_train_samples]
@@ -2095,14 +2170,13 @@ def _incremental_fit_estimator(
 
         start_score = time.time()
 
-        # TODO(SLEP6): support score params in the following two calls
         test_scores.append(
             _score(
                 estimator,
                 X_test,
                 y_test,
                 scorer,
-                score_params=None,
+                score_params=score_params_test,
                 error_score=error_score,
             )
         )
@@ -2112,7 +2186,7 @@ def _incremental_fit_estimator(
                 X_train,
                 y_train,
                 scorer,
-                score_params=None,
+                score_params=score_params_train,
                 error_score=error_score,
             )
         )
@@ -2220,9 +2294,8 @@ def validation_curve(
             ``cv`` default value if None changed from 3-fold to 5-fold.
 
     scoring : str or callable, default=None
-        A str (see model evaluation documentation) or
-        a scorer callable object / function with signature
-        ``scorer(estimator, X, y)``.
+        A str (see :ref:`scoring_parameter`) or a scorer callable object / function with
+        signature ``scorer(estimator, X, y)``.
 
     n_jobs : int, default=None
         Number of jobs to run in parallel. Training the estimator and computing
diff --git a/sklearn/model_selection/tests/test_classification_threshold.py b/sklearn/model_selection/tests/test_classification_threshold.py
new file mode 100644
index 0000000000000..77c4c20e99ef2
--- /dev/null
+++ b/sklearn/model_selection/tests/test_classification_threshold.py
@@ -0,0 +1,724 @@
+import numpy as np
+import pytest
+
+from sklearn.base import BaseEstimator, ClassifierMixin, clone
+from sklearn.datasets import (
+    load_breast_cancer,
+    load_iris,
+    make_classification,
+    make_multilabel_classification,
+)
+from sklearn.dummy import DummyClassifier
+from sklearn.ensemble import GradientBoostingClassifier
+from sklearn.exceptions import NotFittedError
+from sklearn.linear_model import LogisticRegression
+from sklearn.metrics import (
+    balanced_accuracy_score,
+    f1_score,
+    fbeta_score,
+    make_scorer,
+    recall_score,
+)
+from sklearn.model_selection import (
+    FixedThresholdClassifier,
+    StratifiedShuffleSplit,
+    TunedThresholdClassifierCV,
+)
+from sklearn.model_selection._classification_threshold import (
+    _CurveScorer,
+    _fit_and_score_over_thresholds,
+)
+from sklearn.pipeline import make_pipeline
+from sklearn.preprocessing import StandardScaler
+from sklearn.svm import SVC
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.utils._mocking import CheckingClassifier
+from sklearn.utils._testing import (
+    _convert_container,
+    assert_allclose,
+    assert_array_equal,
+)
+
+
+def test_curve_scorer():
+    """Check the behaviour of the `_CurveScorer` class."""
+    X, y = make_classification(random_state=0)
+    estimator = LogisticRegression().fit(X, y)
+    curve_scorer = _CurveScorer(
+        balanced_accuracy_score,
+        sign=1,
+        response_method="predict_proba",
+        thresholds=10,
+        kwargs={},
+    )
+    scores, thresholds = curve_scorer(estimator, X, y)
+
+    assert thresholds.shape == scores.shape
+    # check that the thresholds are probabilities with extreme values close to 0 and 1.
+    # they are not exactly 0 and 1 because they are the extremum of the
+    # `estimator.predict_proba(X)` values.
+    assert 0 <= thresholds.min() <= 0.01
+    assert 0.99 <= thresholds.max() <= 1
+    # balanced accuracy should be between 0.5 and 1 when it is not adjusted
+    assert 0.5 <= scores.min() <= 1
+
+    # check that passing kwargs to the scorer works
+    curve_scorer = _CurveScorer(
+        balanced_accuracy_score,
+        sign=1,
+        response_method="predict_proba",
+        thresholds=10,
+        kwargs={"adjusted": True},
+    )
+    scores, thresholds = curve_scorer(estimator, X, y)
+
+    # balanced accuracy should be between 0.5 and 1 when it is not adjusted
+    assert 0 <= scores.min() <= 0.5
+
+    # check that we can inverse the sign of the score when dealing with `neg_*` scorer
+    curve_scorer = _CurveScorer(
+        balanced_accuracy_score,
+        sign=-1,
+        response_method="predict_proba",
+        thresholds=10,
+        kwargs={"adjusted": True},
+    )
+    scores, thresholds = curve_scorer(estimator, X, y)
+
+    assert all(scores <= 0)
+
+
+def test_curve_scorer_pos_label(global_random_seed):
+    """Check that we propagate properly the `pos_label` parameter to the scorer."""
+    n_samples = 30
+    X, y = make_classification(
+        n_samples=n_samples, weights=[0.9, 0.1], random_state=global_random_seed
+    )
+    estimator = LogisticRegression().fit(X, y)
+
+    curve_scorer = _CurveScorer(
+        recall_score,
+        sign=1,
+        response_method="predict_proba",
+        thresholds=10,
+        kwargs={"pos_label": 1},
+    )
+    scores_pos_label_1, thresholds_pos_label_1 = curve_scorer(estimator, X, y)
+
+    curve_scorer = _CurveScorer(
+        recall_score,
+        sign=1,
+        response_method="predict_proba",
+        thresholds=10,
+        kwargs={"pos_label": 0},
+    )
+    scores_pos_label_0, thresholds_pos_label_0 = curve_scorer(estimator, X, y)
+
+    # Since `pos_label` is forwarded to the curve_scorer, the thresholds are not equal.
+    assert not (thresholds_pos_label_1 == thresholds_pos_label_0).all()
+    # The min-max range for the thresholds is defined by the probabilities of the
+    # `pos_label` class (the column of `predict_proba`).
+    y_pred = estimator.predict_proba(X)
+    assert thresholds_pos_label_0.min() == pytest.approx(y_pred.min(axis=0)[0])
+    assert thresholds_pos_label_0.max() == pytest.approx(y_pred.max(axis=0)[0])
+    assert thresholds_pos_label_1.min() == pytest.approx(y_pred.min(axis=0)[1])
+    assert thresholds_pos_label_1.max() == pytest.approx(y_pred.max(axis=0)[1])
+
+    # The recall cannot be negative and `pos_label=1` should have a higher recall
+    # since there is less samples to be considered.
+    assert 0.0 < scores_pos_label_0.min() < scores_pos_label_1.min()
+    assert scores_pos_label_0.max() == pytest.approx(1.0)
+    assert scores_pos_label_1.max() == pytest.approx(1.0)
+
+
+def test_fit_and_score_over_thresholds_curve_scorers():
+    """Check that `_fit_and_score_over_thresholds` returns thresholds in ascending order
+    for the different accepted curve scorers."""
+    X, y = make_classification(n_samples=100, random_state=0)
+    train_idx, val_idx = np.arange(50), np.arange(50, 100)
+    classifier = LogisticRegression()
+
+    curve_scorer = _CurveScorer(
+        score_func=balanced_accuracy_score,
+        sign=1,
+        response_method="predict_proba",
+        thresholds=10,
+        kwargs={},
+    )
+    scores, thresholds = _fit_and_score_over_thresholds(
+        classifier,
+        X,
+        y,
+        fit_params={},
+        train_idx=train_idx,
+        val_idx=val_idx,
+        curve_scorer=curve_scorer,
+        score_params={},
+    )
+
+    assert np.all(thresholds[:-1] <= thresholds[1:])
+    assert isinstance(scores, np.ndarray)
+    assert np.logical_and(scores >= 0, scores <= 1).all()
+
+
+def test_fit_and_score_over_thresholds_prefit():
+    """Check the behaviour with a prefit classifier."""
+    X, y = make_classification(n_samples=100, random_state=0)
+
+    # `train_idx is None` to indicate that the classifier is prefit
+    train_idx, val_idx = None, np.arange(50, 100)
+    classifier = DecisionTreeClassifier(random_state=0).fit(X, y)
+    # make sure that the classifier memorized the full dataset such that
+    # we get perfect predictions and thus match the expected score
+    assert classifier.score(X[val_idx], y[val_idx]) == pytest.approx(1.0)
+
+    curve_scorer = _CurveScorer(
+        score_func=balanced_accuracy_score,
+        sign=1,
+        response_method="predict_proba",
+        thresholds=2,
+        kwargs={},
+    )
+    scores, thresholds = _fit_and_score_over_thresholds(
+        classifier,
+        X,
+        y,
+        fit_params={},
+        train_idx=train_idx,
+        val_idx=val_idx,
+        curve_scorer=curve_scorer,
+        score_params={},
+    )
+    assert np.all(thresholds[:-1] <= thresholds[1:])
+    assert_allclose(scores, [0.5, 1.0])
+
+
+@pytest.mark.usefixtures("enable_slep006")
+def test_fit_and_score_over_thresholds_sample_weight():
+    """Check that we dispatch the sample-weight to fit and score the classifier."""
+    X, y = load_iris(return_X_y=True)
+    X, y = X[:100], y[:100]  # only 2 classes
+
+    # create a dataset and repeat twice the sample of class #0
+    X_repeated, y_repeated = np.vstack([X, X[y == 0]]), np.hstack([y, y[y == 0]])
+    # create a sample weight vector that is equivalent to the repeated dataset
+    sample_weight = np.ones_like(y)
+    sample_weight[:50] *= 2
+
+    classifier = LogisticRegression()
+    train_repeated_idx = np.arange(X_repeated.shape[0])
+    val_repeated_idx = np.arange(X_repeated.shape[0])
+    curve_scorer = _CurveScorer(
+        score_func=balanced_accuracy_score,
+        sign=1,
+        response_method="predict_proba",
+        thresholds=10,
+        kwargs={},
+    )
+    scores_repeated, thresholds_repeated = _fit_and_score_over_thresholds(
+        classifier,
+        X_repeated,
+        y_repeated,
+        fit_params={},
+        train_idx=train_repeated_idx,
+        val_idx=val_repeated_idx,
+        curve_scorer=curve_scorer,
+        score_params={},
+    )
+
+    train_idx, val_idx = np.arange(X.shape[0]), np.arange(X.shape[0])
+    scores, thresholds = _fit_and_score_over_thresholds(
+        classifier.set_fit_request(sample_weight=True),
+        X,
+        y,
+        fit_params={"sample_weight": sample_weight},
+        train_idx=train_idx,
+        val_idx=val_idx,
+        curve_scorer=curve_scorer.set_score_request(sample_weight=True),
+        score_params={"sample_weight": sample_weight},
+    )
+
+    assert_allclose(thresholds_repeated, thresholds)
+    assert_allclose(scores_repeated, scores)
+
+
+@pytest.mark.usefixtures("enable_slep006")
+@pytest.mark.parametrize("fit_params_type", ["list", "array"])
+def test_fit_and_score_over_thresholds_fit_params(fit_params_type):
+    """Check that we pass `fit_params` to the classifier when calling `fit`."""
+    X, y = make_classification(n_samples=100, random_state=0)
+    fit_params = {
+        "a": _convert_container(y, fit_params_type),
+        "b": _convert_container(y, fit_params_type),
+    }
+
+    classifier = CheckingClassifier(expected_fit_params=["a", "b"], random_state=0)
+    classifier.set_fit_request(a=True, b=True)
+    train_idx, val_idx = np.arange(50), np.arange(50, 100)
+
+    curve_scorer = _CurveScorer(
+        score_func=balanced_accuracy_score,
+        sign=1,
+        response_method="predict_proba",
+        thresholds=10,
+        kwargs={},
+    )
+    _fit_and_score_over_thresholds(
+        classifier,
+        X,
+        y,
+        fit_params=fit_params,
+        train_idx=train_idx,
+        val_idx=val_idx,
+        curve_scorer=curve_scorer,
+        score_params={},
+    )
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        make_classification(n_classes=3, n_clusters_per_class=1, random_state=0),
+        make_multilabel_classification(random_state=0),
+    ],
+)
+def test_tuned_threshold_classifier_no_binary(data):
+    """Check that we raise an informative error message for non-binary problem."""
+    err_msg = "Only binary classification is supported."
+    with pytest.raises(ValueError, match=err_msg):
+        TunedThresholdClassifierCV(LogisticRegression()).fit(*data)
+
+
+@pytest.mark.parametrize(
+    "params, err_type, err_msg",
+    [
+        (
+            {"cv": "prefit", "refit": True},
+            ValueError,
+            "When cv='prefit', refit cannot be True.",
+        ),
+        (
+            {"cv": 10, "refit": False},
+            ValueError,
+            "When cv has several folds, refit cannot be False.",
+        ),
+        (
+            {"cv": "prefit", "refit": False},
+            NotFittedError,
+            "`estimator` must be fitted.",
+        ),
+    ],
+)
+def test_tuned_threshold_classifier_conflict_cv_refit(params, err_type, err_msg):
+    """Check that we raise an informative error message when `cv` and `refit`
+    cannot be used together.
+    """
+    X, y = make_classification(n_samples=100, random_state=0)
+    with pytest.raises(err_type, match=err_msg):
+        TunedThresholdClassifierCV(LogisticRegression(), **params).fit(X, y)
+
+
+@pytest.mark.parametrize(
+    "estimator",
+    [LogisticRegression(), SVC(), GradientBoostingClassifier(n_estimators=4)],
+)
+@pytest.mark.parametrize(
+    "response_method", ["predict_proba", "predict_log_proba", "decision_function"]
+)
+@pytest.mark.parametrize(
+    "ThresholdClassifier", [FixedThresholdClassifier, TunedThresholdClassifierCV]
+)
+def test_threshold_classifier_estimator_response_methods(
+    ThresholdClassifier, estimator, response_method
+):
+    """Check that `TunedThresholdClassifierCV` exposes the same response methods as the
+    underlying estimator.
+    """
+    X, y = make_classification(n_samples=100, random_state=0)
+
+    model = ThresholdClassifier(estimator=estimator)
+    assert hasattr(model, response_method) == hasattr(estimator, response_method)
+
+    model.fit(X, y)
+    assert hasattr(model, response_method) == hasattr(estimator, response_method)
+
+    if hasattr(model, response_method):
+        y_pred_cutoff = getattr(model, response_method)(X)
+        y_pred_underlying_estimator = getattr(model.estimator_, response_method)(X)
+
+        assert_allclose(y_pred_cutoff, y_pred_underlying_estimator)
+
+
+@pytest.mark.parametrize(
+    "response_method", ["auto", "decision_function", "predict_proba"]
+)
+def test_tuned_threshold_classifier_without_constraint_value(response_method):
+    """Check that `TunedThresholdClassifierCV` is optimizing a given objective
+    metric."""
+    X, y = load_breast_cancer(return_X_y=True)
+    # remove feature to degrade performances
+    X = X[:, :5]
+
+    # make the problem completely imbalanced such that the balanced accuracy is low
+    indices_pos = np.flatnonzero(y == 1)
+    indices_pos = indices_pos[: indices_pos.size // 50]
+    indices_neg = np.flatnonzero(y == 0)
+
+    X = np.vstack([X[indices_neg], X[indices_pos]])
+    y = np.hstack([y[indices_neg], y[indices_pos]])
+
+    lr = make_pipeline(StandardScaler(), LogisticRegression()).fit(X, y)
+    thresholds = 100
+    model = TunedThresholdClassifierCV(
+        estimator=lr,
+        scoring="balanced_accuracy",
+        response_method=response_method,
+        thresholds=thresholds,
+        store_cv_results=True,
+    )
+    score_optimized = balanced_accuracy_score(y, model.fit(X, y).predict(X))
+    score_baseline = balanced_accuracy_score(y, lr.predict(X))
+    assert score_optimized > score_baseline
+    assert model.cv_results_["thresholds"].shape == (thresholds,)
+    assert model.cv_results_["scores"].shape == (thresholds,)
+
+
+def test_tuned_threshold_classifier_metric_with_parameter():
+    """Check that we can pass a metric with a parameter in addition check that
+    `f_beta` with `beta=1` is equivalent to `f1` and different from `f_beta` with
+    `beta=2`.
+    """
+    X, y = load_breast_cancer(return_X_y=True)
+    lr = make_pipeline(StandardScaler(), LogisticRegression()).fit(X, y)
+    model_fbeta_1 = TunedThresholdClassifierCV(
+        estimator=lr, scoring=make_scorer(fbeta_score, beta=1)
+    ).fit(X, y)
+    model_fbeta_2 = TunedThresholdClassifierCV(
+        estimator=lr, scoring=make_scorer(fbeta_score, beta=2)
+    ).fit(X, y)
+    model_f1 = TunedThresholdClassifierCV(
+        estimator=lr, scoring=make_scorer(f1_score)
+    ).fit(X, y)
+
+    assert model_fbeta_1.best_threshold_ == pytest.approx(model_f1.best_threshold_)
+    assert model_fbeta_1.best_threshold_ != pytest.approx(model_fbeta_2.best_threshold_)
+
+
+@pytest.mark.parametrize(
+    "response_method", ["auto", "decision_function", "predict_proba"]
+)
+@pytest.mark.parametrize(
+    "metric",
+    [
+        make_scorer(balanced_accuracy_score),
+        make_scorer(f1_score, pos_label="cancer"),
+    ],
+)
+def test_tuned_threshold_classifier_with_string_targets(response_method, metric):
+    """Check that targets represented by str are properly managed.
+    Also, check with several metrics to be sure that `pos_label` is properly
+    dispatched.
+    """
+    X, y = load_breast_cancer(return_X_y=True)
+    # Encode numeric targets by meaningful strings. We purposely designed the class
+    # names such that the `pos_label` is the first alphabetically sorted class and thus
+    # encoded as 0.
+    classes = np.array(["cancer", "healthy"], dtype=object)
+    y = classes[y]
+    model = TunedThresholdClassifierCV(
+        estimator=make_pipeline(StandardScaler(), LogisticRegression()),
+        scoring=metric,
+        response_method=response_method,
+        thresholds=100,
+    ).fit(X, y)
+    assert_array_equal(model.classes_, np.sort(classes))
+    y_pred = model.predict(X)
+    assert_array_equal(np.unique(y_pred), np.sort(classes))
+
+
+@pytest.mark.usefixtures("enable_slep006")
+@pytest.mark.parametrize("with_sample_weight", [True, False])
+def test_tuned_threshold_classifier_refit(with_sample_weight, global_random_seed):
+    """Check the behaviour of the `refit` parameter."""
+    rng = np.random.RandomState(global_random_seed)
+    X, y = make_classification(n_samples=100, random_state=0)
+    if with_sample_weight:
+        sample_weight = rng.randn(X.shape[0])
+        sample_weight = np.abs(sample_weight, out=sample_weight)
+    else:
+        sample_weight = None
+
+    # check that `estimator_` if fitted on the full dataset when `refit=True`
+    estimator = LogisticRegression().set_fit_request(sample_weight=True)
+    model = TunedThresholdClassifierCV(estimator, refit=True).fit(
+        X, y, sample_weight=sample_weight
+    )
+
+    assert model.estimator_ is not estimator
+    estimator.fit(X, y, sample_weight=sample_weight)
+    assert_allclose(model.estimator_.coef_, estimator.coef_)
+    assert_allclose(model.estimator_.intercept_, estimator.intercept_)
+
+    # check that `estimator_` was not altered when `refit=False` and `cv="prefit"`
+    estimator = LogisticRegression().set_fit_request(sample_weight=True)
+    estimator.fit(X, y, sample_weight=sample_weight)
+    coef = estimator.coef_.copy()
+    model = TunedThresholdClassifierCV(estimator, cv="prefit", refit=False).fit(
+        X, y, sample_weight=sample_weight
+    )
+
+    assert model.estimator_ is estimator
+    assert_allclose(model.estimator_.coef_, coef)
+
+    # check that we train `estimator_` on the training split of a given cross-validation
+    estimator = LogisticRegression().set_fit_request(sample_weight=True)
+    cv = [
+        (np.arange(50), np.arange(50, 100)),
+    ]  # single split
+    model = TunedThresholdClassifierCV(estimator, cv=cv, refit=False).fit(
+        X, y, sample_weight=sample_weight
+    )
+
+    assert model.estimator_ is not estimator
+    if with_sample_weight:
+        sw_train = sample_weight[cv[0][0]]
+    else:
+        sw_train = None
+    estimator.fit(X[cv[0][0]], y[cv[0][0]], sample_weight=sw_train)
+    assert_allclose(model.estimator_.coef_, estimator.coef_)
+
+
+@pytest.mark.usefixtures("enable_slep006")
+@pytest.mark.parametrize("fit_params_type", ["list", "array"])
+def test_tuned_threshold_classifier_fit_params(fit_params_type):
+    """Check that we pass `fit_params` to the classifier when calling `fit`."""
+    X, y = make_classification(n_samples=100, random_state=0)
+    fit_params = {
+        "a": _convert_container(y, fit_params_type),
+        "b": _convert_container(y, fit_params_type),
+    }
+
+    classifier = CheckingClassifier(expected_fit_params=["a", "b"], random_state=0)
+    classifier.set_fit_request(a=True, b=True)
+    model = TunedThresholdClassifierCV(classifier)
+    model.fit(X, y, **fit_params)
+
+
+@pytest.mark.usefixtures("enable_slep006")
+def test_tuned_threshold_classifier_cv_zeros_sample_weights_equivalence():
+    """Check that passing removing some sample from the dataset `X` is
+    equivalent to passing a `sample_weight` with a factor 0."""
+    X, y = load_iris(return_X_y=True)
+    # Scale the data to avoid any convergence issue
+    X = StandardScaler().fit_transform(X)
+    # Only use 2 classes and select samples such that 2-fold cross-validation
+    # split will lead to an equivalence with a `sample_weight` of 0
+    X = np.vstack((X[:40], X[50:90]))
+    y = np.hstack((y[:40], y[50:90]))
+    sample_weight = np.zeros_like(y)
+    sample_weight[::2] = 1
+
+    estimator = LogisticRegression().set_fit_request(sample_weight=True)
+    model_without_weights = TunedThresholdClassifierCV(estimator, cv=2)
+    model_with_weights = clone(model_without_weights)
+
+    model_with_weights.fit(X, y, sample_weight=sample_weight)
+    model_without_weights.fit(X[::2], y[::2])
+
+    assert_allclose(
+        model_with_weights.estimator_.coef_, model_without_weights.estimator_.coef_
+    )
+
+    y_pred_with_weights = model_with_weights.predict_proba(X)
+    y_pred_without_weights = model_without_weights.predict_proba(X)
+    assert_allclose(y_pred_with_weights, y_pred_without_weights)
+
+
+def test_tuned_threshold_classifier_thresholds_array():
+    """Check that we can pass an array to `thresholds` and it is used as candidate
+    threshold internally."""
+    X, y = make_classification(random_state=0)
+    estimator = LogisticRegression()
+    thresholds = np.linspace(0, 1, 11)
+    tuned_model = TunedThresholdClassifierCV(
+        estimator,
+        thresholds=thresholds,
+        response_method="predict_proba",
+        store_cv_results=True,
+    ).fit(X, y)
+    assert_allclose(tuned_model.cv_results_["thresholds"], thresholds)
+
+
+@pytest.mark.parametrize("store_cv_results", [True, False])
+def test_tuned_threshold_classifier_store_cv_results(store_cv_results):
+    """Check that if `cv_results_` exists depending on `store_cv_results`."""
+    X, y = make_classification(random_state=0)
+    estimator = LogisticRegression()
+    tuned_model = TunedThresholdClassifierCV(
+        estimator, store_cv_results=store_cv_results
+    ).fit(X, y)
+    if store_cv_results:
+        assert hasattr(tuned_model, "cv_results_")
+    else:
+        assert not hasattr(tuned_model, "cv_results_")
+
+
+def test_tuned_threshold_classifier_cv_float():
+    """Check the behaviour when `cv` is set to a float."""
+    X, y = make_classification(random_state=0)
+
+    # case where `refit=False` and cv is a float: the underlying estimator will be fit
+    # on the training set given by a ShuffleSplit. We check that we get the same model
+    # coefficients.
+    test_size = 0.3
+    estimator = LogisticRegression()
+    tuned_model = TunedThresholdClassifierCV(
+        estimator, cv=test_size, refit=False, random_state=0
+    ).fit(X, y)
+    tuned_model.fit(X, y)
+
+    cv = StratifiedShuffleSplit(n_splits=1, test_size=test_size, random_state=0)
+    train_idx, val_idx = next(cv.split(X, y))
+    cloned_estimator = clone(estimator).fit(X[train_idx], y[train_idx])
+
+    assert_allclose(tuned_model.estimator_.coef_, cloned_estimator.coef_)
+
+    # case where `refit=True`, then the underlying estimator is fitted on the full
+    # dataset.
+    tuned_model.set_params(refit=True).fit(X, y)
+    cloned_estimator = clone(estimator).fit(X, y)
+
+    assert_allclose(tuned_model.estimator_.coef_, cloned_estimator.coef_)
+
+
+def test_tuned_threshold_classifier_error_constant_predictor():
+    """Check that we raise a ValueError if the underlying classifier returns constant
+    probabilities such that we cannot find any threshold.
+    """
+    X, y = make_classification(random_state=0)
+    estimator = DummyClassifier(strategy="constant", constant=1)
+    tuned_model = TunedThresholdClassifierCV(estimator, response_method="predict_proba")
+    err_msg = "The provided estimator makes constant predictions"
+    with pytest.raises(ValueError, match=err_msg):
+        tuned_model.fit(X, y)
+
+
+@pytest.mark.parametrize(
+    "response_method", ["auto", "predict_proba", "decision_function"]
+)
+def test_fixed_threshold_classifier_equivalence_default(response_method):
+    """Check that `FixedThresholdClassifier` has the same behaviour as the vanilla
+    classifier.
+    """
+    X, y = make_classification(random_state=0)
+    classifier = LogisticRegression().fit(X, y)
+    classifier_default_threshold = FixedThresholdClassifier(
+        estimator=clone(classifier), response_method=response_method
+    )
+    classifier_default_threshold.fit(X, y)
+
+    # emulate the response method that should take into account the `pos_label`
+    if response_method in ("auto", "predict_proba"):
+        y_score = classifier_default_threshold.predict_proba(X)[:, 1]
+        threshold = 0.5
+    else:  # response_method == "decision_function"
+        y_score = classifier_default_threshold.decision_function(X)
+        threshold = 0.0
+
+    y_pred_lr = (y_score >= threshold).astype(int)
+    assert_allclose(classifier_default_threshold.predict(X), y_pred_lr)
+
+
+@pytest.mark.parametrize(
+    "response_method, threshold", [("predict_proba", 0.7), ("decision_function", 2.0)]
+)
+@pytest.mark.parametrize("pos_label", [0, 1])
+def test_fixed_threshold_classifier(response_method, threshold, pos_label):
+    """Check that applying `predict` lead to the same prediction as applying the
+    threshold to the output of the response method.
+    """
+    X, y = make_classification(n_samples=50, random_state=0)
+    logistic_regression = LogisticRegression().fit(X, y)
+    model = FixedThresholdClassifier(
+        estimator=clone(logistic_regression),
+        threshold=threshold,
+        response_method=response_method,
+        pos_label=pos_label,
+    ).fit(X, y)
+
+    # check that the underlying estimator is the same
+    assert_allclose(model.estimator_.coef_, logistic_regression.coef_)
+
+    # emulate the response method that should take into account the `pos_label`
+    if response_method == "predict_proba":
+        y_score = model.predict_proba(X)[:, pos_label]
+    else:  # response_method == "decision_function"
+        y_score = model.decision_function(X)
+        y_score = y_score if pos_label == 1 else -y_score
+
+    # create a mapping from boolean values to class labels
+    map_to_label = np.array([0, 1]) if pos_label == 1 else np.array([1, 0])
+    y_pred_lr = map_to_label[(y_score >= threshold).astype(int)]
+    assert_allclose(model.predict(X), y_pred_lr)
+
+    for method in ("predict_proba", "predict_log_proba", "decision_function"):
+        assert_allclose(
+            getattr(model, method)(X), getattr(logistic_regression, method)(X)
+        )
+        assert_allclose(
+            getattr(model.estimator_, method)(X),
+            getattr(logistic_regression, method)(X),
+        )
+
+
+@pytest.mark.usefixtures("enable_slep006")
+def test_fixed_threshold_classifier_metadata_routing():
+    """Check that everything works with metadata routing."""
+    X, y = make_classification(random_state=0)
+    sample_weight = np.ones_like(y)
+    sample_weight[::2] = 2
+    classifier = LogisticRegression().set_fit_request(sample_weight=True)
+    classifier.fit(X, y, sample_weight=sample_weight)
+    classifier_default_threshold = FixedThresholdClassifier(estimator=clone(classifier))
+    classifier_default_threshold.fit(X, y, sample_weight=sample_weight)
+    assert_allclose(classifier_default_threshold.estimator_.coef_, classifier.coef_)
+
+
+class ClassifierLoggingFit(ClassifierMixin, BaseEstimator):
+    """Classifier that logs the number of `fit` calls."""
+
+    def __init__(self, fit_calls=0):
+        self.fit_calls = fit_calls
+
+    def fit(self, X, y, **fit_params):
+        self.fit_calls += 1
+        self.is_fitted_ = True
+        return self
+
+    def predict_proba(self, X):
+        return np.ones((X.shape[0], 2), np.float64)  # pragma: nocover
+
+
+def test_fixed_threshold_classifier_prefit():
+    """Check the behaviour of the `FixedThresholdClassifier` with the `prefit`
+    parameter."""
+    X, y = make_classification(random_state=0)
+
+    estimator = ClassifierLoggingFit()
+    model = FixedThresholdClassifier(estimator=estimator, prefit=True)
+    with pytest.raises(NotFittedError):
+        model.fit(X, y)
+
+    # check that we don't clone the classifier when `prefit=True`.
+    estimator.fit(X, y)
+    model.fit(X, y)
+    assert estimator.fit_calls == 1
+    assert model.estimator_ is estimator
+
+    # check that we clone the classifier when `prefit=False`.
+    estimator = ClassifierLoggingFit()
+    model = FixedThresholdClassifier(estimator=estimator, prefit=False)
+    model.fit(X, y)
+    assert estimator.fit_calls == 0
+    assert model.estimator_.fit_calls == 1
+    assert model.estimator_ is not estimator
diff --git a/sklearn/model_selection/tests/test_search.py b/sklearn/model_selection/tests/test_search.py
index 9eb647df887c0..cb4af646aee39 100644
--- a/sklearn/model_selection/tests/test_search.py
+++ b/sklearn/model_selection/tests/test_search.py
@@ -3,6 +3,7 @@
 import pickle
 import re
 import sys
+import warnings
 from collections.abc import Iterable, Sized
 from functools import partial
 from io import StringIO
@@ -2553,6 +2554,28 @@ def test_search_html_repr():
         assert "<pre>LogisticRegression()</pre>" in repr_html
 
 
+# TODO(1.7): remove this test
+@pytest.mark.parametrize("SearchCV", [GridSearchCV, RandomizedSearchCV])
+def test_inverse_transform_Xt_deprecation(SearchCV):
+    clf = MockClassifier()
+    search = SearchCV(clf, {"foo_param": [1, 2, 3]}, cv=3, verbose=3)
+
+    X2 = search.fit(X, y).transform(X)
+
+    with pytest.raises(TypeError, match="Missing required positional argument"):
+        search.inverse_transform()
+
+    with pytest.raises(TypeError, match="Cannot use both X and Xt. Use X only"):
+        search.inverse_transform(X=X2, Xt=X2)
+
+    with warnings.catch_warnings(record=True):
+        warnings.simplefilter("error")
+        search.inverse_transform(X2)
+
+    with pytest.warns(FutureWarning, match="Xt was renamed X in version 1.5"):
+        search.inverse_transform(Xt=X2)
+
+
 # Metadata Routing Tests
 # ======================
 
@@ -2618,3 +2641,48 @@ def test_score_rejects_params_with_no_routing_enabled(SearchCV, param_search):
 
 # End of Metadata Routing Tests
 # =============================
+
+
+def test_cv_results_dtype_issue_29074():
+    """Non-regression test for https://github.com/scikit-learn/scikit-learn/issues/29074"""
+
+    class MetaEstimator(BaseEstimator, ClassifierMixin):
+        def __init__(
+            self,
+            base_clf,
+            parameter1=None,
+            parameter2=None,
+            parameter3=None,
+            parameter4=None,
+        ):
+            self.base_clf = base_clf
+            self.parameter1 = parameter1
+            self.parameter2 = parameter2
+            self.parameter3 = parameter3
+            self.parameter4 = parameter4
+
+        def fit(self, X, y=None):
+            self.base_clf.fit(X, y)
+            return self
+
+        def score(self, X, y):
+            return self.base_clf.score(X, y)
+
+    # Values of param_grid are such that np.result_type gives slightly
+    # different errors, in particular ValueError and TypeError
+    param_grid = {
+        "parameter1": [None, {"option": "A"}, {"option": "B"}],
+        "parameter2": [None, [1, 2]],
+        "parameter3": [{"a": 1}],
+        "parameter4": ["str1", "str2"],
+    }
+    grid_search = GridSearchCV(
+        estimator=MetaEstimator(LogisticRegression()),
+        param_grid=param_grid,
+        cv=3,
+    )
+
+    X, y = make_blobs(random_state=0)
+    grid_search.fit(X, y)
+    for param in param_grid:
+        assert grid_search.cv_results_[f"param_{param}"].dtype == object
diff --git a/sklearn/model_selection/tests/test_validation.py b/sklearn/model_selection/tests/test_validation.py
index a1a860b243249..679c0052e3956 100644
--- a/sklearn/model_selection/tests/test_validation.py
+++ b/sklearn/model_selection/tests/test_validation.py
@@ -1535,7 +1535,7 @@ def test_learning_curve_with_shuffle():
     )
 
 
-def test_learning_curve_fit_params():
+def test_learning_curve_params():
     X = np.arange(100).reshape(10, 10)
     y = np.array([0] * 5 + [1] * 5)
     clf = CheckingClassifier(expected_sample_weight=True)
@@ -1547,14 +1547,14 @@ def test_learning_curve_fit_params():
     err_msg = r"sample_weight.shape == \(1,\), expected \(2,\)!"
     with pytest.raises(ValueError, match=err_msg):
         learning_curve(
-            clf, X, y, error_score="raise", fit_params={"sample_weight": np.ones(1)}
+            clf, X, y, error_score="raise", params={"sample_weight": np.ones(1)}
         )
     learning_curve(
-        clf, X, y, error_score="raise", fit_params={"sample_weight": np.ones(10)}
+        clf, X, y, error_score="raise", params={"sample_weight": np.ones(10)}
     )
 
 
-def test_learning_curve_incremental_learning_fit_params():
+def test_learning_curve_incremental_learning_params():
     X, y = make_classification(
         n_samples=30,
         n_features=1,
@@ -1587,7 +1587,7 @@ def test_learning_curve_incremental_learning_fit_params():
             exploit_incremental_learning=True,
             train_sizes=np.linspace(0.1, 1.0, 10),
             error_score="raise",
-            fit_params={"sample_weight": np.ones(3)},
+            params={"sample_weight": np.ones(3)},
         )
 
     learning_curve(
@@ -1598,7 +1598,7 @@ def test_learning_curve_incremental_learning_fit_params():
         exploit_incremental_learning=True,
         train_sizes=np.linspace(0.1, 1.0, 10),
         error_score="raise",
-        fit_params={"sample_weight": np.ones(2)},
+        params={"sample_weight": np.ones(2)},
     )
 
 
@@ -2481,34 +2481,34 @@ def test_cross_validate_return_indices(global_random_seed):
         assert_array_equal(test_indices[split_idx], expected_test_idx)
 
 
-# Tests for metadata routing in cross_val*
-# ========================================
+# Tests for metadata routing in cross_val* and learning_curve
+# ===========================================================
 
 
-# TODO(1.6): remove this test in 1.6
-def test_cross_validate_fit_param_deprecation():
+# TODO(1.6): remove `cross_validate` and `cross_val_predict` from this test in 1.6 and
+# `learning_curve` in 1.8
+@pytest.mark.parametrize("func", [cross_validate, cross_val_predict, learning_curve])
+def test_fit_param_deprecation(func):
     """Check that we warn about deprecating `fit_params`."""
     with pytest.warns(FutureWarning, match="`fit_params` is deprecated"):
-        cross_validate(estimator=ConsumingClassifier(), X=X, y=y, cv=2, fit_params={})
+        func(estimator=ConsumingClassifier(), X=X, y=y, cv=2, fit_params={})
 
     with pytest.raises(
         ValueError, match="`params` and `fit_params` cannot both be provided"
     ):
-        cross_validate(
-            estimator=ConsumingClassifier(), X=X, y=y, fit_params={}, params={}
-        )
+        func(estimator=ConsumingClassifier(), X=X, y=y, fit_params={}, params={})
 
 
 @pytest.mark.usefixtures("enable_slep006")
 @pytest.mark.parametrize(
-    "cv_method", [cross_validate, cross_val_score, cross_val_predict]
+    "func", [cross_validate, cross_val_score, cross_val_predict, learning_curve]
 )
-def test_groups_with_routing_validation(cv_method):
+def test_groups_with_routing_validation(func):
     """Check that we raise an error if `groups` are passed to the cv method instead
     of `params` when metadata routing is enabled.
     """
     with pytest.raises(ValueError, match="`groups` can only be passed if"):
-        cv_method(
+        func(
             estimator=ConsumingClassifier(),
             X=X,
             y=y,
@@ -2518,14 +2518,14 @@ def test_groups_with_routing_validation(cv_method):
 
 @pytest.mark.usefixtures("enable_slep006")
 @pytest.mark.parametrize(
-    "cv_method", [cross_validate, cross_val_score, cross_val_predict]
+    "func", [cross_validate, cross_val_score, cross_val_predict, learning_curve]
 )
-def test_passed_unrequested_metadata(cv_method):
+def test_passed_unrequested_metadata(func):
     """Check that we raise an error when passing metadata that is not
     requested."""
     err_msg = re.escape("but are not explicitly set as requested or not requested")
     with pytest.raises(ValueError, match=err_msg):
-        cv_method(
+        func(
             estimator=ConsumingClassifier(),
             X=X,
             y=y,
@@ -2535,9 +2535,9 @@ def test_passed_unrequested_metadata(cv_method):
 
 @pytest.mark.usefixtures("enable_slep006")
 @pytest.mark.parametrize(
-    "cv_method", [cross_validate, cross_val_score, cross_val_predict]
+    "func", [cross_validate, cross_val_score, cross_val_predict, learning_curve]
 )
-def test_cross_validate_routing(cv_method):
+def test_validation_functions_routing(func):
     """Check that the respective cv method is properly dispatching the metadata
     to the consumer."""
     scorer_registry = _Registry()
@@ -2552,6 +2552,7 @@ def test_cross_validate_routing(cv_method):
     estimator = ConsumingClassifier(registry=estimator_registry).set_fit_request(
         sample_weight="fit_sample_weight", metadata="fit_metadata"
     )
+
     n_samples = _num_samples(X)
     rng = np.random.RandomState(0)
     score_weights = rng.rand(n_samples)
@@ -2563,8 +2564,9 @@ def test_cross_validate_routing(cv_method):
 
     extra_params = {
         cross_validate: dict(scoring=dict(my_scorer=scorer, accuracy="accuracy")),
-        # cross_val_score doesn't support multiple scorers
+        # cross_val_score and learning_curve don't support multiple scorers:
         cross_val_score: dict(scoring=scorer),
+        learning_curve: dict(scoring=scorer),
         # cross_val_predict doesn't need a scorer
         cross_val_predict: dict(),
     }
@@ -2576,22 +2578,22 @@ def test_cross_validate_routing(cv_method):
         fit_metadata=fit_metadata,
     )
 
-    if cv_method is not cross_val_predict:
+    if func is not cross_val_predict:
         params.update(
             score_weights=score_weights,
             score_metadata=score_metadata,
         )
 
-    cv_method(
+    func(
         estimator,
         X=X,
         y=y,
         cv=splitter,
-        **extra_params[cv_method],
+        **extra_params[func],
         params=params,
     )
 
-    if cv_method is not cross_val_predict:
+    if func is not cross_val_predict:
         # cross_val_predict doesn't need a scorer
         assert len(scorer_registry)
     for _scorer in scorer_registry:
@@ -2623,5 +2625,42 @@ def test_cross_validate_routing(cv_method):
         )
 
 
+@pytest.mark.usefixtures("enable_slep006")
+def test_learning_curve_exploit_incremental_learning_routing():
+    """Test that learning_curve routes metadata to the estimator correctly while
+    partial_fitting it with `exploit_incremental_learning=True`."""
+
+    n_samples = _num_samples(X)
+    rng = np.random.RandomState(0)
+    fit_sample_weight = rng.rand(n_samples)
+    fit_metadata = rng.rand(n_samples)
+
+    estimator_registry = _Registry()
+    estimator = ConsumingClassifier(
+        registry=estimator_registry
+    ).set_partial_fit_request(
+        sample_weight="fit_sample_weight", metadata="fit_metadata"
+    )
+
+    learning_curve(
+        estimator,
+        X=X,
+        y=y,
+        cv=ConsumingSplitter(),
+        exploit_incremental_learning=True,
+        params=dict(fit_sample_weight=fit_sample_weight, fit_metadata=fit_metadata),
+    )
+
+    assert len(estimator_registry)
+    for _estimator in estimator_registry:
+        check_recorded_metadata(
+            obj=_estimator,
+            method="partial_fit",
+            split_params=("sample_weight", "metadata"),
+            sample_weight=fit_sample_weight,
+            metadata=fit_metadata,
+        )
+
+
 # End of metadata routing tests
 # =============================
diff --git a/sklearn/multiclass.py b/sklearn/multiclass.py
index 075095ad4146e..5883f4a05f9a0 100644
--- a/sklearn/multiclass.py
+++ b/sklearn/multiclass.py
@@ -1,11 +1,8 @@
-"""
-Multiclass classification strategies
-====================================
+"""Multiclass learning algorithms.
 
-This module implements multiclass learning algorithms:
-    - one-vs-the-rest / one-vs-all
-    - one-vs-one
-    - error correcting output codes
+- one-vs-the-rest / one-vs-all
+- one-vs-one
+- error correcting output codes
 
 The estimators provided in this module are meta-estimators: they require a base
 estimator to be provided in their constructor. For example, it is possible to
@@ -619,8 +616,8 @@ def get_metadata_routing(self):
             .add(
                 estimator=self.estimator,
                 method_mapping=MethodMapping()
-                .add(callee="fit", caller="fit")
-                .add(callee="partial_fit", caller="partial_fit"),
+                .add(caller="fit", callee="fit")
+                .add(caller="partial_fit", callee="partial_fit"),
             )
         )
         return router
@@ -1018,8 +1015,8 @@ def get_metadata_routing(self):
             .add(
                 estimator=self.estimator,
                 method_mapping=MethodMapping()
-                .add(callee="fit", caller="fit")
-                .add(callee="partial_fit", caller="partial_fit"),
+                .add(caller="fit", callee="fit")
+                .add(caller="partial_fit", callee="partial_fit"),
             )
         )
         return router
@@ -1264,6 +1261,6 @@ def get_metadata_routing(self):
 
         router = MetadataRouter(owner=self.__class__.__name__).add(
             estimator=self.estimator,
-            method_mapping=MethodMapping().add(callee="fit", caller="fit"),
+            method_mapping=MethodMapping().add(caller="fit", callee="fit"),
         )
         return router
diff --git a/sklearn/multioutput.py b/sklearn/multioutput.py
index e0da38357e792..3851bc58aa151 100644
--- a/sklearn/multioutput.py
+++ b/sklearn/multioutput.py
@@ -1,5 +1,4 @@
-"""
-This module implements multioutput regression and classification.
+"""Multioutput regression and classification.
 
 The estimators provided in this module are meta-estimators: they require
 a base estimator to be provided in their constructor. The meta-estimator
@@ -334,8 +333,8 @@ def get_metadata_routing(self):
         router = MetadataRouter(owner=self.__class__.__name__).add(
             estimator=self.estimator,
             method_mapping=MethodMapping()
-            .add(callee="partial_fit", caller="partial_fit")
-            .add(callee="fit", caller="fit"),
+            .add(caller="partial_fit", callee="partial_fit")
+            .add(caller="fit", callee="fit"),
         )
         return router
 
@@ -1096,7 +1095,7 @@ def get_metadata_routing(self):
         """
         router = MetadataRouter(owner=self.__class__.__name__).add(
             estimator=self.base_estimator,
-            method_mapping=MethodMapping().add(callee="fit", caller="fit"),
+            method_mapping=MethodMapping().add(caller="fit", callee="fit"),
         )
         return router
 
@@ -1191,7 +1190,7 @@ class RegressorChain(MetaEstimatorMixin, RegressorMixin, _BaseChain):
     --------
     >>> from sklearn.multioutput import RegressorChain
     >>> from sklearn.linear_model import LogisticRegression
-    >>> logreg = LogisticRegression(solver='lbfgs',multi_class='multinomial')
+    >>> logreg = LogisticRegression(solver='lbfgs')
     >>> X, Y = [[1, 0], [0, 1], [1, 1]], [[0, 2], [1, 1], [2, 0]]
     >>> chain = RegressorChain(base_estimator=logreg, order=[0, 1]).fit(X, Y)
     >>> chain.predict(X)
@@ -1245,7 +1244,7 @@ def get_metadata_routing(self):
         """
         router = MetadataRouter(owner=self.__class__.__name__).add(
             estimator=self.base_estimator,
-            method_mapping=MethodMapping().add(callee="fit", caller="fit"),
+            method_mapping=MethodMapping().add(caller="fit", callee="fit"),
         )
         return router
 
diff --git a/sklearn/naive_bayes.py b/sklearn/naive_bayes.py
index c5a129779dd89..1c9b92ddd0fc9 100644
--- a/sklearn/naive_bayes.py
+++ b/sklearn/naive_bayes.py
@@ -1,6 +1,6 @@
-"""
-The :mod:`sklearn.naive_bayes` module implements Naive Bayes algorithms. These
-are supervised learning methods based on applying Bayes' theorem with strong
+"""Naive Bayes algorithms.
+
+These are supervised learning methods based on applying Bayes' theorem with strong
 (naive) feature independence assumptions.
 """
 
diff --git a/sklearn/neighbors/__init__.py b/sklearn/neighbors/__init__.py
index ce697656b4c2e..e2c1a50632564 100644
--- a/sklearn/neighbors/__init__.py
+++ b/sklearn/neighbors/__init__.py
@@ -1,7 +1,4 @@
-"""
-The :mod:`sklearn.neighbors` module implements the k-nearest neighbors
-algorithm.
-"""
+"""The k-nearest neighbors algorithms."""
 
 from ._ball_tree import BallTree
 from ._base import VALID_METRICS, VALID_METRICS_SPARSE, sort_graph_by_row_values
diff --git a/sklearn/neighbors/_binary_tree.pxi.tp b/sklearn/neighbors/_binary_tree.pxi.tp
index 5cf7b0ad99990..c25740c0d6f6c 100644
--- a/sklearn/neighbors/_binary_tree.pxi.tp
+++ b/sklearn/neighbors/_binary_tree.pxi.tp
@@ -230,10 +230,7 @@ NodeData = np.asarray(<NodeData_t[:1]>(&nd_tmp)).dtype
 ######################################################################
 # Define doc strings, substituting the appropriate class name using
 # the DOC_DICT variable defined in the pyx files.
-CLASS_DOC = """
-{BinaryTree}(X, leaf_size=40, metric='minkowski', **kwargs)
-
-{BinaryTree} for fast generalized N-point problems
+CLASS_DOC = """{BinaryTree} for fast generalized N-point problems
 
 Read more in the :ref:`User Guide <unsupervised_neighbors>`.
 
diff --git a/sklearn/neighbors/_lof.py b/sklearn/neighbors/_lof.py
index fcf1c1ce990bd..c120908c51e80 100644
--- a/sklearn/neighbors/_lof.py
+++ b/sklearn/neighbors/_lof.py
@@ -317,6 +317,14 @@ def fit(self, X, y=None):
                 self.negative_outlier_factor_, 100.0 * self.contamination
             )
 
+        # Verify if negative_outlier_factor_ values are within acceptable range.
+        # Novelty must also be false to detect outliers
+        if np.min(self.negative_outlier_factor_) < -1e7 and not self.novelty:
+            warnings.warn(
+                "Duplicate values are leading to incorrect results. "
+                "Increase the number of neighbors for more accurate results."
+            )
+
         return self
 
     def _check_novelty_predict(self):
diff --git a/sklearn/neighbors/_nearest_centroid.py b/sklearn/neighbors/_nearest_centroid.py
index 75086ee25448e..c9c99aeeaadb2 100644
--- a/sklearn/neighbors/_nearest_centroid.py
+++ b/sklearn/neighbors/_nearest_centroid.py
@@ -7,14 +7,11 @@
 #
 # License: BSD 3 clause
 
-import warnings
 from numbers import Real
 
 import numpy as np
 from scipy import sparse as sp
 
-from sklearn.metrics.pairwise import _VALID_METRICS
-
 from ..base import BaseEstimator, ClassifierMixin, _fit_context
 from ..metrics.pairwise import pairwise_distances_argmin
 from ..preprocessing import LabelEncoder
@@ -34,25 +31,17 @@ class NearestCentroid(ClassifierMixin, BaseEstimator):
 
     Parameters
     ----------
-    metric : str or callable, default="euclidean"
-        Metric to use for distance computation. See the documentation of
-        `scipy.spatial.distance
-        <https://docs.scipy.org/doc/scipy/reference/spatial.distance.html>`_ and
-        the metrics listed in
-        :class:`~sklearn.metrics.pairwise.distance_metrics` for valid metric
-        values. Note that "wminkowski", "seuclidean" and "mahalanobis" are not
-        supported.
-
-        The centroids for the samples corresponding to each class is
-        the point from which the sum of the distances (according to the metric)
-        of all samples that belong to that particular class are minimized.
-        If the `"manhattan"` metric is provided, this centroid is the median
-        and for all other metrics, the centroid is now set to be the mean.
-
-        .. deprecated:: 1.3
-            Support for metrics other than `euclidean` and `manhattan` and for
-            callables was deprecated in version 1.3 and will be removed in
-            version 1.5.
+    metric : {"euclidean", "manhattan"}, default="euclidean"
+        Metric to use for distance computation.
+
+        If `metric="euclidean"`, the centroid for the samples corresponding to each
+        class is the arithmetic mean, which minimizes the sum of squared L1 distances.
+        If `metric="manhattan"`, the centroid is the feature-wise median, which
+        minimizes the sum of L1 distances.
+
+        .. versionchanged:: 1.5
+            All metrics but `"euclidean"` and `"manhattan"` were deprecated and
+            now raise an error.
 
         .. versionchanged:: 0.19
             `metric='precomputed'` was deprecated and now raises an error
@@ -108,15 +97,8 @@ class NearestCentroid(ClassifierMixin, BaseEstimator):
     [1]
     """
 
-    _valid_metrics = set(_VALID_METRICS) - {"mahalanobis", "seuclidean", "wminkowski"}
-
     _parameter_constraints: dict = {
-        "metric": [
-            StrOptions(
-                _valid_metrics, deprecated=_valid_metrics - {"manhattan", "euclidean"}
-            ),
-            callable,
-        ],
+        "metric": [StrOptions({"manhattan", "euclidean"})],
         "shrink_threshold": [Interval(Real, 0, None, closed="neither"), None],
     }
 
@@ -143,19 +125,6 @@ def fit(self, X, y):
         self : object
             Fitted estimator.
         """
-        if isinstance(self.metric, str) and self.metric not in (
-            "manhattan",
-            "euclidean",
-        ):
-            warnings.warn(
-                (
-                    "Support for distance metrics other than euclidean and "
-                    "manhattan and for callables was deprecated in version "
-                    "1.3 and will be removed in version 1.5."
-                ),
-                FutureWarning,
-            )
-
         # If X is sparse and the metric is "manhattan", store it in a csc
         # format is easier to calculate the median.
         if self.metric == "manhattan":
@@ -195,14 +164,7 @@ def fit(self, X, y):
                     self.centroids_[cur_class] = np.median(X[center_mask], axis=0)
                 else:
                     self.centroids_[cur_class] = csc_median_axis_0(X[center_mask])
-            else:
-                # TODO(1.5) remove warning when metric is only manhattan or euclidean
-                if self.metric != "euclidean":
-                    warnings.warn(
-                        "Averaging for metrics other than "
-                        "euclidean and manhattan not supported. "
-                        "The average is set to be the mean."
-                    )
+            else:  # metric == "euclidean"
                 self.centroids_[cur_class] = X[center_mask].mean(axis=0)
 
         if self.shrink_threshold:
@@ -231,7 +193,6 @@ def fit(self, X, y):
             self.centroids_ = dataset_centroid_[np.newaxis, :] + msd
         return self
 
-    # TODO(1.5) remove note about precomputed metric
     def predict(self, X):
         """Perform classification on an array of test vectors `X`.
 
@@ -246,12 +207,6 @@ def predict(self, X):
         -------
         C : ndarray of shape (n_samples,)
             The predicted classes.
-
-        Notes
-        -----
-        If the metric constructor parameter is `"precomputed"`, `X` is assumed
-        to be the distance matrix between the data to be predicted and
-        `self.centroids_`.
         """
         check_is_fitted(self)
 
diff --git a/sklearn/neighbors/_quad_tree.pyx b/sklearn/neighbors/_quad_tree.pyx
index e481e41ca65e4..f1ef4e64f30fe 100644
--- a/sklearn/neighbors/_quad_tree.pyx
+++ b/sklearn/neighbors/_quad_tree.pyx
@@ -4,6 +4,7 @@
 
 from cpython cimport Py_INCREF, PyObject, PyTypeObject
 
+from libc.math cimport fabsf
 from libc.stdlib cimport free
 from libc.string cimport memcpy
 from libc.stdio cimport printf
@@ -15,9 +16,6 @@ import numpy as np
 cimport numpy as cnp
 cnp.import_array()
 
-cdef extern from "math.h":
-    float fabsf(float x) nogil
-
 cdef extern from "numpy/arrayobject.h":
     object PyArray_NewFromDescr(PyTypeObject* subtype, cnp.dtype descr,
                                 int nd, cnp.npy_intp* dims,
diff --git a/sklearn/neighbors/meson.build b/sklearn/neighbors/meson.build
index b85188cab98be..7e361be25052e 100644
--- a/sklearn/neighbors/meson.build
+++ b/sklearn/neighbors/meson.build
@@ -24,7 +24,7 @@ foreach name: name_list
   )
   py.extension_module(
     name,
-    [pyx, neighbors_cython_tree, utils_cython_tree],
+    [pyx, neighbors_cython_tree, utils_cython_tree, metrics_cython_tree],
     dependencies: [np_dep],
     cython_args: cython_args,
     subdir: 'sklearn/neighbors',
diff --git a/sklearn/neighbors/tests/test_lof.py b/sklearn/neighbors/tests/test_lof.py
index 3f5c1e161b7e8..9b87b8ceeec40 100644
--- a/sklearn/neighbors/tests/test_lof.py
+++ b/sklearn/neighbors/tests/test_lof.py
@@ -359,3 +359,37 @@ def test_lof_dtype_equivalence(algorithm, novelty, contamination):
             y_pred_32 = getattr(lof_32, method)(X_32)
             y_pred_64 = getattr(lof_64, method)(X_64)
             assert_allclose(y_pred_32, y_pred_64, atol=0.0002)
+
+
+def test_lof_duplicate_samples():
+    """
+    Check that LocalOutlierFactor raises a warning when duplicate values
+    in the training data cause inaccurate results.
+
+    Non-regression test for:
+    https://github.com/scikit-learn/scikit-learn/issues/27839
+    """
+
+    rng = np.random.default_rng(0)
+
+    x = rng.permutation(
+        np.hstack(
+            [
+                [0.1] * 1000,  # constant values
+                np.linspace(0.1, 0.3, num=3000),
+                rng.random(500) * 100,  # the clear outliers
+            ]
+        )
+    )
+    X = x.reshape(-1, 1)
+
+    error_msg = (
+        "Duplicate values are leading to incorrect results. "
+        "Increase the number of neighbors for more accurate results."
+    )
+
+    lof = neighbors.LocalOutlierFactor(n_neighbors=5, contamination=0.1)
+
+    # Catch the warning
+    with pytest.warns(UserWarning, match=re.escape(error_msg)):
+        lof.fit_predict(X)
diff --git a/sklearn/neighbors/tests/test_nearest_centroid.py b/sklearn/neighbors/tests/test_nearest_centroid.py
index 09c2501818fd3..5ce792ac29d56 100644
--- a/sklearn/neighbors/tests/test_nearest_centroid.py
+++ b/sklearn/neighbors/tests/test_nearest_centroid.py
@@ -56,21 +56,17 @@ def test_classification_toy(csr_container):
     assert_array_equal(clf.predict(T_csr.tolil()), true_result)
 
 
-# TODO(1.5): Remove filterwarnings when support for some metrics is removed
-@pytest.mark.filterwarnings("ignore:Support for distance metrics:FutureWarning:sklearn")
 def test_iris():
     # Check consistency on dataset iris.
-    for metric in ("euclidean", "cosine"):
+    for metric in ("euclidean", "manhattan"):
         clf = NearestCentroid(metric=metric).fit(iris.data, iris.target)
         score = np.mean(clf.predict(iris.data) == iris.target)
         assert score > 0.9, "Failed with score = " + str(score)
 
 
-# TODO(1.5): Remove filterwarnings when support for some metrics is removed
-@pytest.mark.filterwarnings("ignore:Support for distance metrics:FutureWarning:sklearn")
 def test_iris_shrinkage():
     # Check consistency on dataset iris, when using shrinkage.
-    for metric in ("euclidean", "cosine"):
+    for metric in ("euclidean", "manhattan"):
         for shrink_threshold in [None, 0.1, 0.5]:
             clf = NearestCentroid(metric=metric, shrink_threshold=shrink_threshold)
             clf = clf.fit(iris.data, iris.target)
@@ -151,20 +147,6 @@ def test_manhattan_metric(csr_container):
     assert_array_equal(dense_centroid, [[-1, -1], [1, 1]])
 
 
-# TODO(1.5): remove this test
-@pytest.mark.parametrize(
-    "metric", sorted(list(NearestCentroid._valid_metrics - {"manhattan", "euclidean"}))
-)
-def test_deprecated_distance_metric_supports(metric):
-    # Check that a warning is raised for all deprecated distance metric supports
-    clf = NearestCentroid(metric=metric)
-    with pytest.warns(
-        FutureWarning,
-        match="Support for distance metrics other than euclidean and manhattan",
-    ):
-        clf.fit(X, y)
-
-
 def test_features_zero_var():
     # Test that features with 0 variance throw error
 
diff --git a/sklearn/neighbors/tests/test_neighbors.py b/sklearn/neighbors/tests/test_neighbors.py
index d3fc71478e6f5..3aac121f6b06b 100644
--- a/sklearn/neighbors/tests/test_neighbors.py
+++ b/sklearn/neighbors/tests/test_neighbors.py
@@ -1644,8 +1644,16 @@ def test_nearest_neighbors_validate_params():
     + DISTANCE_METRIC_OBJS,
 )
 def test_neighbors_metrics(
-    global_dtype, metric, n_samples=20, n_features=3, n_query_pts=2, n_neighbors=5
+    global_dtype,
+    global_random_seed,
+    metric,
+    n_samples=20,
+    n_features=3,
+    n_query_pts=2,
+    n_neighbors=5,
 ):
+    rng = np.random.RandomState(global_random_seed)
+
     metric = _parse_metric(metric, global_dtype)
 
     # Test computing the neighbors for various metrics
@@ -1697,15 +1705,19 @@ def test_neighbors_metrics(
         brute_dst, brute_idx = results["brute"]
         ball_tree_dst, ball_tree_idx = results["ball_tree"]
 
-        assert_allclose(brute_dst, ball_tree_dst)
+        # The returned distances are always in float64 regardless of the input dtype
+        # We need to adjust the tolerance w.r.t the input dtype
+        rtol = 1e-7 if global_dtype == np.float64 else 1e-4
+
+        assert_allclose(brute_dst, ball_tree_dst, rtol=rtol)
         assert_array_equal(brute_idx, ball_tree_idx)
 
         if not exclude_kd_tree:
             kd_tree_dst, kd_tree_idx = results["kd_tree"]
-            assert_allclose(brute_dst, kd_tree_dst)
+            assert_allclose(brute_dst, kd_tree_dst, rtol=rtol)
             assert_array_equal(brute_idx, kd_tree_idx)
 
-            assert_allclose(ball_tree_dst, kd_tree_dst)
+            assert_allclose(ball_tree_dst, kd_tree_dst, rtol=rtol)
             assert_array_equal(ball_tree_idx, kd_tree_idx)
 
 
diff --git a/sklearn/neural_network/__init__.py b/sklearn/neural_network/__init__.py
index 0b321b605de0b..0b349b05f9a0c 100644
--- a/sklearn/neural_network/__init__.py
+++ b/sklearn/neural_network/__init__.py
@@ -1,7 +1,4 @@
-"""
-The :mod:`sklearn.neural_network` module includes models based on neural
-networks.
-"""
+"""Models based on neural networks."""
 
 # License: BSD 3 clause
 
diff --git a/sklearn/pipeline.py b/sklearn/pipeline.py
index 1b17599068d7a..4d8e268163940 100644
--- a/sklearn/pipeline.py
+++ b/sklearn/pipeline.py
@@ -1,7 +1,4 @@
-"""
-The :mod:`sklearn.pipeline` module implements utilities to build a composite
-estimator, as a chain of transforms and estimators.
-"""
+"""Utilities to build a composite estimator as a chain of transforms and estimators."""
 
 # Author: Edouard Duchesnay
 #         Gael Varoquaux
@@ -19,7 +16,7 @@
 from .base import TransformerMixin, _fit_context, clone
 from .exceptions import NotFittedError
 from .preprocessing import FunctionTransformer
-from .utils import Bunch
+from .utils import Bunch, _safe_indexing
 from .utils._estimator_html_repr import _VisualBlock
 from .utils._metadata_requests import METHODS
 from .utils._param_validation import HasMethods, Hidden
@@ -29,6 +26,7 @@
 )
 from .utils._tags import _safe_tags
 from .utils._user_interface import _print_elapsed_time
+from .utils.deprecation import _deprecate_Xt_in_inverse_transform
 from .utils.metadata_routing import (
     MetadataRouter,
     MethodMapping,
@@ -178,7 +176,7 @@ def set_output(self, *, transform=None):
 
         Parameters
         ----------
-        transform : {"default", "pandas"}, default=None
+        transform : {"default", "pandas", "polars"}, default=None
             Configure output of `transform` and `fit_transform`.
 
             - `"default"`: Default output format of a transformer
@@ -909,19 +907,28 @@ def _can_inverse_transform(self):
         return all(hasattr(t, "inverse_transform") for _, _, t in self._iter())
 
     @available_if(_can_inverse_transform)
-    def inverse_transform(self, Xt, **params):
+    def inverse_transform(self, X=None, *, Xt=None, **params):
         """Apply `inverse_transform` for each step in a reverse order.
 
         All estimators in the pipeline must support `inverse_transform`.
 
         Parameters
         ----------
+        X : array-like of shape (n_samples, n_transformed_features)
+            Data samples, where ``n_samples`` is the number of samples and
+            ``n_features`` is the number of features. Must fulfill
+            input requirements of last step of pipeline's
+            ``inverse_transform`` method.
+
         Xt : array-like of shape (n_samples, n_transformed_features)
             Data samples, where ``n_samples`` is the number of samples and
             ``n_features`` is the number of features. Must fulfill
             input requirements of last step of pipeline's
             ``inverse_transform`` method.
 
+            .. deprecated:: 1.5
+                `Xt` was deprecated in 1.5 and will be removed in 1.7. Use `X` instead.
+
         **params : dict of str -> object
             Parameters requested and accepted by steps. Each step must have
             requested certain metadata for these parameters to be forwarded to
@@ -940,15 +947,15 @@ def inverse_transform(self, Xt, **params):
         """
         _raise_for_params(params, self, "inverse_transform")
 
+        X = _deprecate_Xt_in_inverse_transform(X, Xt)
+
         # we don't have to branch here, since params is only non-empty if
         # enable_metadata_routing=True.
         routed_params = process_routing(self, "inverse_transform", **params)
         reverse_iter = reversed(list(self._iter()))
         for _, name, transform in reverse_iter:
-            Xt = transform.inverse_transform(
-                Xt, **routed_params[name].inverse_transform
-            )
-        return Xt
+            X = transform.inverse_transform(X, **routed_params[name].inverse_transform)
+        return X
 
     @available_if(_final_estimator_has("score"))
     def score(self, X, y=None, sample_weight=None, **params):
@@ -1258,7 +1265,7 @@ def make_pipeline(*steps, memory=None, verbose=False):
     return Pipeline(_name_estimators(steps), memory=memory, verbose=verbose)
 
 
-def _transform_one(transformer, X, y, weight, params):
+def _transform_one(transformer, X, y, weight, columns=None, params=None):
     """Call transform and apply weight to output.
 
     Parameters
@@ -1275,11 +1282,17 @@ def _transform_one(transformer, X, y, weight, params):
     weight : float
         Weight to be applied to the output of the transformation.
 
+    columns : str, array-like of str, int, array-like of int, array-like of bool, slice
+        Columns to select before transforming.
+
     params : dict
         Parameters to be passed to the transformer's ``transform`` method.
 
         This should be of the form ``process_routing()["step_name"]``.
     """
+    if columns is not None:
+        X = _safe_indexing(X, columns, axis=1)
+
     res = transformer.transform(X, **params.transform)
     # if we have a weight for this transformer, multiply output
     if weight is None:
@@ -1288,7 +1301,14 @@ def _transform_one(transformer, X, y, weight, params):
 
 
 def _fit_transform_one(
-    transformer, X, y, weight, message_clsname="", message=None, params=None
+    transformer,
+    X,
+    y,
+    weight,
+    columns=None,
+    message_clsname="",
+    message=None,
+    params=None,
 ):
     """
     Fits ``transformer`` to ``X`` and ``y``. The transformed result is returned
@@ -1297,6 +1317,9 @@ def _fit_transform_one(
 
     ``params`` needs to be of the form ``process_routing()["step_name"]``.
     """
+    if columns is not None:
+        X = _safe_indexing(X, columns, axis=1)
+
     params = params or {}
     with _print_elapsed_time(message_clsname, message):
         if hasattr(transformer, "fit_transform"):
@@ -1447,11 +1470,12 @@ def set_output(self, *, transform=None):
 
         Parameters
         ----------
-        transform : {"default", "pandas"}, default=None
+        transform : {"default", "pandas", "polars"}, default=None
             Configure output of `transform` and `fit_transform`.
 
             - `"default"`: Default output format of a transformer
             - `"pandas"`: DataFrame output
+            - `"polars"`: Polars output
             - `None`: Transform configuration is unchanged
 
         Returns
@@ -1792,7 +1816,7 @@ def transform(self, X, **params):
                 routed_params[name] = Bunch(transform={})
 
         Xs = Parallel(n_jobs=self.n_jobs)(
-            delayed(_transform_one)(trans, X, None, weight, routed_params[name])
+            delayed(_transform_one)(trans, X, None, weight, params=routed_params[name])
             for name, trans, weight in self._iter()
         )
         if not Xs:
diff --git a/sklearn/preprocessing/__init__.py b/sklearn/preprocessing/__init__.py
index c730a71260808..9e49379c9122a 100644
--- a/sklearn/preprocessing/__init__.py
+++ b/sklearn/preprocessing/__init__.py
@@ -1,7 +1,4 @@
-"""
-The :mod:`sklearn.preprocessing` module includes scaling, centering,
-normalization, binarization methods.
-"""
+"""Methods for scaling, centering, normalization, binarization, and more."""
 
 from ._data import (
     Binarizer,
diff --git a/sklearn/preprocessing/_discretization.py b/sklearn/preprocessing/_discretization.py
index 02d144b87f798..ee8a336a75453 100644
--- a/sklearn/preprocessing/_discretization.py
+++ b/sklearn/preprocessing/_discretization.py
@@ -12,6 +12,7 @@
 from ..base import BaseEstimator, TransformerMixin, _fit_context
 from ..utils import resample
 from ..utils._param_validation import Interval, Options, StrOptions
+from ..utils.deprecation import _deprecate_Xt_in_inverse_transform
 from ..utils.stats import _weighted_percentile
 from ..utils.validation import (
     _check_feature_names_in,
@@ -389,7 +390,7 @@ def transform(self, X):
             self._encoder.dtype = dtype_init
         return Xt_enc
 
-    def inverse_transform(self, Xt):
+    def inverse_transform(self, X=None, *, Xt=None):
         """
         Transform discretized data back to original feature space.
 
@@ -398,20 +399,28 @@ def inverse_transform(self, Xt):
 
         Parameters
         ----------
+        X : array-like of shape (n_samples, n_features)
+            Transformed data in the binned space.
+
         Xt : array-like of shape (n_samples, n_features)
             Transformed data in the binned space.
 
+            .. deprecated:: 1.5
+                `Xt` was deprecated in 1.5 and will be removed in 1.7. Use `X` instead.
+
         Returns
         -------
         Xinv : ndarray, dtype={np.float32, np.float64}
             Data in the original feature space.
         """
+        X = _deprecate_Xt_in_inverse_transform(X, Xt)
+
         check_is_fitted(self)
 
         if "onehot" in self.encode:
-            Xt = self._encoder.inverse_transform(Xt)
+            X = self._encoder.inverse_transform(X)
 
-        Xinv = check_array(Xt, copy=True, dtype=(np.float64, np.float32))
+        Xinv = check_array(X, copy=True, dtype=(np.float64, np.float32))
         n_features = self.n_bins_.shape[0]
         if Xinv.shape[1] != n_features:
             raise ValueError(
diff --git a/sklearn/preprocessing/_function_transformer.py b/sklearn/preprocessing/_function_transformer.py
index 0442e75346fed..c49684d0ebfbc 100644
--- a/sklearn/preprocessing/_function_transformer.py
+++ b/sklearn/preprocessing/_function_transformer.py
@@ -393,7 +393,7 @@ def set_output(self, *, transform=None):
 
         Parameters
         ----------
-        transform : {"default", "pandas"}, default=None
+        transform : {"default", "pandas", "polars"}, default=None
             Configure output of `transform` and `fit_transform`.
 
             - `"default"`: Default output format of a transformer
diff --git a/sklearn/preprocessing/_label.py b/sklearn/preprocessing/_label.py
index 301dc19bb1985..ecf0c400a2c2f 100644
--- a/sklearn/preprocessing/_label.py
+++ b/sklearn/preprocessing/_label.py
@@ -17,6 +17,7 @@
 
 from ..base import BaseEstimator, TransformerMixin, _fit_context
 from ..utils import column_or_1d
+from ..utils._array_api import _setdiff1d, device, get_namespace
 from ..utils._encode import _encode, _unique
 from ..utils._param_validation import Interval, validate_params
 from ..utils.multiclass import type_of_target, unique_labels
@@ -129,10 +130,11 @@ def transform(self, y):
             Labels as normalized encodings.
         """
         check_is_fitted(self)
+        xp, _ = get_namespace(y)
         y = column_or_1d(y, dtype=self.classes_.dtype, warn=True)
         # transform of empty array is empty array
         if _num_samples(y) == 0:
-            return np.array([])
+            return xp.asarray([])
 
         return _encode(y, uniques=self.classes_)
 
@@ -141,7 +143,7 @@ def inverse_transform(self, y):
 
         Parameters
         ----------
-        y : ndarray of shape (n_samples,)
+        y : array-like of shape (n_samples,)
             Target values.
 
         Returns
@@ -150,19 +152,24 @@ def inverse_transform(self, y):
             Original encoding.
         """
         check_is_fitted(self)
+        xp, _ = get_namespace(y)
         y = column_or_1d(y, warn=True)
         # inverse transform of empty array is empty array
         if _num_samples(y) == 0:
-            return np.array([])
+            return xp.asarray([])
 
-        diff = np.setdiff1d(y, np.arange(len(self.classes_)))
-        if len(diff):
+        diff = _setdiff1d(
+            ar1=y,
+            ar2=xp.arange(self.classes_.shape[0], device=device(y)),
+            xp=xp,
+        )
+        if diff.shape[0]:
             raise ValueError("y contains previously unseen labels: %s" % str(diff))
-        y = np.asarray(y)
-        return self.classes_[y]
+        y = xp.asarray(y)
+        return xp.take(self.classes_, y, axis=0)
 
     def _more_tags(self):
-        return {"X_types": ["1dlabels"]}
+        return {"X_types": ["1dlabels"], "array_api_support": True}
 
 
 class LabelBinarizer(TransformerMixin, BaseEstimator, auto_wrap_output_keys=None):
diff --git a/sklearn/preprocessing/tests/test_data.py b/sklearn/preprocessing/tests/test_data.py
index b7e8e4e40686e..3810e485ae301 100644
--- a/sklearn/preprocessing/tests/test_data.py
+++ b/sklearn/preprocessing/tests/test_data.py
@@ -595,6 +595,10 @@ def test_standard_scaler_partial_fit_numerical_stability(sparse_container):
     scaler_incr = StandardScaler(with_mean=False)
 
     for chunk in X:
+        if chunk.ndim == 1:
+            # Sparse arrays can be 1D (in scipy 1.14 and later) while old
+            # sparse matrix instances are always 2D.
+            chunk = chunk.reshape(1, -1)
         scaler_incr = scaler_incr.partial_fit(chunk)
 
     # Regardless of magnitude, they must not differ more than of 6 digits
diff --git a/sklearn/preprocessing/tests/test_discretization.py b/sklearn/preprocessing/tests/test_discretization.py
index 19aaa5bdba850..fd16a3db3efac 100644
--- a/sklearn/preprocessing/tests/test_discretization.py
+++ b/sklearn/preprocessing/tests/test_discretization.py
@@ -478,3 +478,23 @@ def test_kbinsdiscretizer_subsample(strategy, global_random_seed):
     assert_allclose(
         kbd_subsampling.bin_edges_[0], kbd_no_subsampling.bin_edges_[0], rtol=1e-2
     )
+
+
+# TODO(1.7): remove this test
+def test_KBD_inverse_transform_Xt_deprecation():
+    X = np.arange(10)[:, None]
+    kbd = KBinsDiscretizer()
+    X = kbd.fit_transform(X)
+
+    with pytest.raises(TypeError, match="Missing required positional argument"):
+        kbd.inverse_transform()
+
+    with pytest.raises(TypeError, match="Cannot use both X and Xt. Use X only"):
+        kbd.inverse_transform(X=X, Xt=X)
+
+    with warnings.catch_warnings(record=True):
+        warnings.simplefilter("error")
+        kbd.inverse_transform(X)
+
+    with pytest.warns(FutureWarning, match="Xt was renamed X in version 1.5"):
+        kbd.inverse_transform(Xt=X)
diff --git a/sklearn/preprocessing/tests/test_label.py b/sklearn/preprocessing/tests/test_label.py
index e438805df1254..90e3aa210eebb 100644
--- a/sklearn/preprocessing/tests/test_label.py
+++ b/sklearn/preprocessing/tests/test_label.py
@@ -2,7 +2,7 @@
 import pytest
 from scipy.sparse import issparse
 
-from sklearn import datasets
+from sklearn import config_context, datasets
 from sklearn.preprocessing._label import (
     LabelBinarizer,
     LabelEncoder,
@@ -11,7 +11,16 @@
     _inverse_binarize_thresholding,
     label_binarize,
 )
-from sklearn.utils._testing import assert_array_equal, ignore_warnings
+from sklearn.utils._array_api import (
+    _convert_to_numpy,
+    get_namespace,
+    yield_namespace_device_dtype_combinations,
+)
+from sklearn.utils._testing import (
+    _array_api_for_tests,
+    assert_array_equal,
+    ignore_warnings,
+)
 from sklearn.utils.fixes import (
     COO_CONTAINERS,
     CSC_CONTAINERS,
@@ -697,3 +706,42 @@ def test_label_encoders_do_not_have_set_output(encoder):
     y_encoded_with_kwarg = encoder.fit_transform(y=["a", "b", "c"])
     y_encoded_positional = encoder.fit_transform(["a", "b", "c"])
     assert_array_equal(y_encoded_with_kwarg, y_encoded_positional)
+
+
+@pytest.mark.parametrize(
+    "array_namespace, device, dtype", yield_namespace_device_dtype_combinations()
+)
+@pytest.mark.parametrize(
+    "y",
+    [
+        np.array([2, 1, 3, 1, 3]),
+        np.array([1, 1, 4, 5, -1, 0]),
+        np.array([3, 5, 9, 5, 9, 3]),
+    ],
+)
+def test_label_encoder_array_api_compliance(y, array_namespace, device, dtype):
+    xp = _array_api_for_tests(array_namespace, device)
+    xp_y = xp.asarray(y, device=device)
+    with config_context(array_api_dispatch=True):
+        xp_label = LabelEncoder()
+        np_label = LabelEncoder()
+        xp_label = xp_label.fit(xp_y)
+        xp_transformed = xp_label.transform(xp_y)
+        xp_inv_transformed = xp_label.inverse_transform(xp_transformed)
+        np_label = np_label.fit(y)
+        np_transformed = np_label.transform(y)
+        assert get_namespace(xp_transformed)[0].__name__ == xp.__name__
+        assert get_namespace(xp_inv_transformed)[0].__name__ == xp.__name__
+        assert get_namespace(xp_label.classes_)[0].__name__ == xp.__name__
+        assert_array_equal(_convert_to_numpy(xp_transformed, xp), np_transformed)
+        assert_array_equal(_convert_to_numpy(xp_inv_transformed, xp), y)
+        assert_array_equal(_convert_to_numpy(xp_label.classes_, xp), np_label.classes_)
+
+        xp_label = LabelEncoder()
+        np_label = LabelEncoder()
+        xp_transformed = xp_label.fit_transform(xp_y)
+        np_transformed = np_label.fit_transform(y)
+        assert get_namespace(xp_transformed)[0].__name__ == xp.__name__
+        assert get_namespace(xp_label.classes_)[0].__name__ == xp.__name__
+        assert_array_equal(_convert_to_numpy(xp_transformed, xp), np_transformed)
+        assert_array_equal(_convert_to_numpy(xp_label.classes_, xp), np_label.classes_)
diff --git a/sklearn/random_projection.py b/sklearn/random_projection.py
index 886a805960d52..c6f7057396d4a 100644
--- a/sklearn/random_projection.py
+++ b/sklearn/random_projection.py
@@ -1,11 +1,11 @@
-"""Random Projection transformers.
+"""Random projection transformers.
 
-Random Projections are a simple and computationally efficient way to
+Random projections are a simple and computationally efficient way to
 reduce the dimensionality of the data by trading a controlled amount
 of accuracy (as additional variance) for faster processing times and
 smaller model sizes.
 
-The dimensions and distribution of Random Projections matrices are
+The dimensions and distribution of random projections matrices are
 controlled so as to preserve the pairwise distances between any two
 samples of the dataset.
 
@@ -20,7 +20,6 @@
   much lower dimension in such a way that distances between the points are
   nearly preserved. The map used for the embedding is at least Lipschitz,
   and can even be taken to be an orthogonal projection.
-
 """
 
 # Authors: Olivier Grisel <olivier.grisel@ensta.org>,
diff --git a/sklearn/semi_supervised/__init__.py b/sklearn/semi_supervised/__init__.py
index 126906cdde1d7..055c3f431b2c1 100644
--- a/sklearn/semi_supervised/__init__.py
+++ b/sklearn/semi_supervised/__init__.py
@@ -1,8 +1,7 @@
-"""
-The :mod:`sklearn.semi_supervised` module implements semi-supervised learning
-algorithms. These algorithms utilize small amounts of labeled data and large
-amounts of unlabeled data for classification tasks. This module includes Label
-Propagation.
+"""Semi-supervised learning algorithms.
+
+These algorithms utilize small amounts of labeled data and large amounts of unlabeled
+data for classification tasks.
 """
 
 from ._label_propagation import LabelPropagation, LabelSpreading
diff --git a/sklearn/svm/__init__.py b/sklearn/svm/__init__.py
index 0d64ce24cdd63..660427198991a 100644
--- a/sklearn/svm/__init__.py
+++ b/sklearn/svm/__init__.py
@@ -1,6 +1,4 @@
-"""
-The :mod:`sklearn.svm` module includes Support Vector Machine algorithms.
-"""
+"""Support vector machine algorithms."""
 
 # See http://scikit-learn.sourceforge.net/modules/svm.html for complete
 # documentation.
diff --git a/sklearn/tests/metadata_routing_common.py b/sklearn/tests/metadata_routing_common.py
index 3df47d3f8dd4e..6fba2f037fd15 100644
--- a/sklearn/tests/metadata_routing_common.py
+++ b/sklearn/tests/metadata_routing_common.py
@@ -19,6 +19,7 @@
 )
 from sklearn.utils.metadata_routing import (
     MetadataRouter,
+    MethodMapping,
     process_routing,
 )
 from sklearn.utils.multiclass import _check_partial_fit_first_call
@@ -193,7 +194,10 @@ def decision_function(self, X):
         return self.predict(X)
 
     def predict(self, X):
-        return np.ones(len(X))
+        y_pred = np.empty(shape=(len(X),))
+        y_pred[: len(X) // 2] = 0
+        y_pred[len(X) // 2 :] = 1
+        return y_pred
 
 
 class NonConsumingRegressor(RegressorMixin, BaseEstimator):
@@ -256,16 +260,19 @@ def predict(self, X, sample_weight="default", metadata="default"):
         record_metadata_not_default(
             self, "predict", sample_weight=sample_weight, metadata=metadata
         )
-        return np.zeros(shape=(len(X),))
+        y_score = np.empty(shape=(len(X),), dtype="int8")
+        y_score[len(X) // 2 :] = 0
+        y_score[: len(X) // 2] = 1
+        return y_score
 
     def predict_proba(self, X, sample_weight="default", metadata="default"):
-        pass  # pragma: no cover
-
-        # uncomment when needed
-        # record_metadata_not_default(
-        #     self, "predict_proba", sample_weight=sample_weight, metadata=metadata
-        # )
-        # return np.asarray([[0.0, 1.0]] * len(X))
+        record_metadata_not_default(
+            self, "predict_proba", sample_weight=sample_weight, metadata=metadata
+        )
+        y_proba = np.empty(shape=(len(X), 2))
+        y_proba[: len(X) // 2, :] = np.asarray([1.0, 0.0])
+        y_proba[len(X) // 2 :, :] = np.asarray([0.0, 1.0])
+        return y_proba
 
     def predict_log_proba(self, X, sample_weight="default", metadata="default"):
         pass  # pragma: no cover
@@ -280,7 +287,10 @@ def decision_function(self, X, sample_weight="default", metadata="default"):
         record_metadata_not_default(
             self, "predict_proba", sample_weight=sample_weight, metadata=metadata
         )
-        return np.zeros(shape=(len(X),))
+        y_score = np.empty(shape=(len(X),))
+        y_score[len(X) // 2 :] = 0
+        y_score[: len(X) // 2] = 1
+        return y_score
 
     # uncomment when needed
     # def score(self, X, y, sample_weight="default", metadata="default"):
@@ -418,7 +428,8 @@ def fit(self, X, y, **fit_params):
 
     def get_metadata_routing(self):
         router = MetadataRouter(owner=self.__class__.__name__).add(
-            estimator=self.estimator, method_mapping="one-to-one"
+            estimator=self.estimator,
+            method_mapping=MethodMapping().add(caller="fit", callee="fit"),
         )
         return router
 
@@ -447,7 +458,12 @@ def get_metadata_routing(self):
         router = (
             MetadataRouter(owner=self.__class__.__name__)
             .add_self_request(self)
-            .add(estimator=self.estimator, method_mapping="one-to-one")
+            .add(
+                estimator=self.estimator,
+                method_mapping=MethodMapping()
+                .add(caller="fit", callee="fit")
+                .add(caller="predict", callee="predict"),
+            )
         )
         return router
 
@@ -472,7 +488,10 @@ def get_metadata_routing(self):
         router = (
             MetadataRouter(owner=self.__class__.__name__)
             .add_self_request(self)
-            .add(estimator=self.estimator, method_mapping="fit")
+            .add(
+                estimator=self.estimator,
+                method_mapping=MethodMapping().add(caller="fit", callee="fit"),
+            )
         )
         return router
 
@@ -494,5 +513,8 @@ def transform(self, X, y=None, **transform_params):
 
     def get_metadata_routing(self):
         return MetadataRouter(owner=self.__class__.__name__).add(
-            transformer=self.transformer, method_mapping="one-to-one"
+            transformer=self.transformer,
+            method_mapping=MethodMapping()
+            .add(caller="fit", callee="fit")
+            .add(caller="transform", callee="transform"),
         )
diff --git a/sklearn/tests/test_base.py b/sklearn/tests/test_base.py
index 3bbc236e703df..917da863ece3b 100644
--- a/sklearn/tests/test_base.py
+++ b/sklearn/tests/test_base.py
@@ -18,13 +18,16 @@
     TransformerMixin,
     clone,
     is_classifier,
+    is_clusterer,
+    is_regressor,
 )
+from sklearn.cluster import KMeans
 from sklearn.decomposition import PCA
 from sklearn.exceptions import InconsistentVersionWarning
 from sklearn.model_selection import GridSearchCV
 from sklearn.pipeline import Pipeline
 from sklearn.preprocessing import StandardScaler
-from sklearn.svm import SVC
+from sklearn.svm import SVC, SVR
 from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
 from sklearn.utils._mocking import MockDataFrame
 from sklearn.utils._set_output import _get_output_config
@@ -259,12 +262,55 @@ def test_get_params():
         test.set_params(a__a=2)
 
 
-def test_is_classifier():
-    svc = SVC()
-    assert is_classifier(svc)
-    assert is_classifier(GridSearchCV(svc, {"C": [0.1, 1]}))
-    assert is_classifier(Pipeline([("svc", svc)]))
-    assert is_classifier(Pipeline([("svc_cv", GridSearchCV(svc, {"C": [0.1, 1]}))]))
+@pytest.mark.parametrize(
+    "estimator, expected_result",
+    [
+        (SVC(), True),
+        (GridSearchCV(SVC(), {"C": [0.1, 1]}), True),
+        (Pipeline([("svc", SVC())]), True),
+        (Pipeline([("svc_cv", GridSearchCV(SVC(), {"C": [0.1, 1]}))]), True),
+        (SVR(), False),
+        (GridSearchCV(SVR(), {"C": [0.1, 1]}), False),
+        (Pipeline([("svr", SVR())]), False),
+        (Pipeline([("svr_cv", GridSearchCV(SVR(), {"C": [0.1, 1]}))]), False),
+    ],
+)
+def test_is_classifier(estimator, expected_result):
+    assert is_classifier(estimator) == expected_result
+
+
+@pytest.mark.parametrize(
+    "estimator, expected_result",
+    [
+        (SVR(), True),
+        (GridSearchCV(SVR(), {"C": [0.1, 1]}), True),
+        (Pipeline([("svr", SVR())]), True),
+        (Pipeline([("svr_cv", GridSearchCV(SVR(), {"C": [0.1, 1]}))]), True),
+        (SVC(), False),
+        (GridSearchCV(SVC(), {"C": [0.1, 1]}), False),
+        (Pipeline([("svc", SVC())]), False),
+        (Pipeline([("svc_cv", GridSearchCV(SVC(), {"C": [0.1, 1]}))]), False),
+    ],
+)
+def test_is_regressor(estimator, expected_result):
+    assert is_regressor(estimator) == expected_result
+
+
+@pytest.mark.parametrize(
+    "estimator, expected_result",
+    [
+        (KMeans(), True),
+        (GridSearchCV(KMeans(), {"n_clusters": [3, 8]}), True),
+        (Pipeline([("km", KMeans())]), True),
+        (Pipeline([("km_cv", GridSearchCV(KMeans(), {"n_clusters": [3, 8]}))]), True),
+        (SVC(), False),
+        (GridSearchCV(SVC(), {"C": [0.1, 1]}), False),
+        (Pipeline([("svc", SVC())]), False),
+        (Pipeline([("svc_cv", GridSearchCV(SVC(), {"C": [0.1, 1]}))]), False),
+    ],
+)
+def test_is_clusterer(estimator, expected_result):
+    assert is_clusterer(estimator) == expected_result
 
 
 def test_set_params():
@@ -834,7 +880,7 @@ class Estimator(BaseEstimator, WithSlots):
     [
         ("dataframe", "1.5.0"),
         ("pyarrow", "12.0.0"),
-        ("polars", "0.19.12"),
+        ("polars", "0.20.23"),
     ],
 )
 def test_dataframe_protocol(constructor_name, minversion):
diff --git a/sklearn/tests/test_common.py b/sklearn/tests/test_common.py
index 4564eddee410c..1b3b5b0128057 100644
--- a/sklearn/tests/test_common.py
+++ b/sklearn/tests/test_common.py
@@ -20,6 +20,7 @@
 import pytest
 
 import sklearn
+from sklearn.base import BaseEstimator
 from sklearn.cluster import (
     OPTICS,
     AffinityPropagation,
@@ -103,6 +104,16 @@ def _sample_func(x, y=1):
     pass
 
 
+class CallableEstimator(BaseEstimator):
+    """Dummy development stub for an estimator.
+
+    This is to make sure a callable estimator passes common tests.
+    """
+
+    def __call__(self):
+        pass  # pragma: nocover
+
+
 @pytest.mark.parametrize(
     "val, expected",
     [
@@ -122,6 +133,7 @@ def _sample_func(x, y=1):
                 "solver='newton-cg',warm_start=True)"
             ),
         ),
+        (CallableEstimator(), "CallableEstimator()"),
     ],
 )
 def test_get_check_estimator_ids(val, expected):
@@ -245,13 +257,6 @@ def test_root_import_all_completeness():
         assert modname in sklearn.__all__
 
 
-@pytest.mark.skipif(
-    sklearn._BUILT_WITH_MESON,
-    reason=(
-        "This test fails with Meson editable installs see"
-        " https://github.com/mesonbuild/meson-python/issues/557 for more details"
-    ),
-)
 def test_all_tests_are_importable():
     # Ensure that for each contentful subpackage, there is a test directory
     # within it that is also a subpackage (i.e. a directory with __init__.py)
diff --git a/sklearn/tests/test_isotonic.py b/sklearn/tests/test_isotonic.py
index 93df0221236b8..90598b48f6434 100644
--- a/sklearn/tests/test_isotonic.py
+++ b/sklearn/tests/test_isotonic.py
@@ -227,7 +227,13 @@ def test_isotonic_regression_with_ties_in_differently_sized_groups():
 
 def test_isotonic_regression_reversed():
     y = np.array([10, 9, 10, 7, 6, 6.1, 5])
+    y_result = np.array([10, 9.5, 9.5, 7, 6.05, 6.05, 5])
+
+    y_iso = isotonic_regression(y, increasing=False)
+    assert_allclose(y_iso, y_result)
+
     y_ = IsotonicRegression(increasing=False).fit_transform(np.arange(len(y)), y)
+    assert_allclose(y_, y_result)
     assert_array_equal(np.ones(y_[:-1].shape), ((y_[:-1] - y_[1:]) >= 0))
 
 
@@ -502,25 +508,25 @@ def test_isotonic_copy_before_fit():
     copy.copy(ir)
 
 
-def test_isotonic_dtype():
+@pytest.mark.parametrize("dtype", [np.int32, np.int64, np.float32, np.float64])
+def test_isotonic_dtype(dtype):
     y = [2, 1, 4, 3, 5]
     weights = np.array([0.9, 0.9, 0.9, 0.9, 0.9], dtype=np.float64)
     reg = IsotonicRegression()
 
-    for dtype in (np.int32, np.int64, np.float32, np.float64):
-        for sample_weight in (None, weights.astype(np.float32), weights):
-            y_np = np.array(y, dtype=dtype)
-            expected_dtype = check_array(
-                y_np, dtype=[np.float64, np.float32], ensure_2d=False
-            ).dtype
+    for sample_weight in (None, weights.astype(np.float32), weights):
+        y_np = np.array(y, dtype=dtype)
+        expected_dtype = check_array(
+            y_np, dtype=[np.float64, np.float32], ensure_2d=False
+        ).dtype
 
-            res = isotonic_regression(y_np, sample_weight=sample_weight)
-            assert res.dtype == expected_dtype
+        res = isotonic_regression(y_np, sample_weight=sample_weight)
+        assert res.dtype == expected_dtype
 
-            X = np.arange(len(y)).astype(dtype)
-            reg.fit(X, y_np, sample_weight=sample_weight)
-            res = reg.predict(X)
-            assert res.dtype == expected_dtype
+        X = np.arange(len(y)).astype(dtype)
+        reg.fit(X, y_np, sample_weight=sample_weight)
+        res = reg.predict(X)
+        assert res.dtype == expected_dtype
 
 
 @pytest.mark.parametrize("y_dtype", [np.int32, np.int64, np.float32, np.float64])
diff --git a/sklearn/tests/test_kernel_approximation.py b/sklearn/tests/test_kernel_approximation.py
index 170113fd0d768..a25baa45823ae 100644
--- a/sklearn/tests/test_kernel_approximation.py
+++ b/sklearn/tests/test_kernel_approximation.py
@@ -144,19 +144,6 @@ def test_additive_chi2_sampler_sample_steps(method, sample_steps):
     assert transformer.sample_interval == sample_interval
 
 
-# TODO(1.5): remove
-def test_additive_chi2_sampler_future_warnings():
-    """Check that we raise a FutureWarning when accessing to `sample_interval_`."""
-    transformer = AdditiveChi2Sampler()
-    transformer.fit(X)
-    msg = re.escape(
-        "The ``sample_interval_`` attribute was deprecated in version 1.3 and "
-        "will be removed 1.5."
-    )
-    with pytest.warns(FutureWarning, match=msg):
-        assert transformer.sample_interval_ is not None
-
-
 @pytest.mark.parametrize("method", ["fit", "fit_transform", "transform"])
 def test_additive_chi2_sampler_wrong_sample_steps(method):
     """Check that we raise a ValueError on invalid sample_steps"""
diff --git a/sklearn/tests/test_metadata_routing.py b/sklearn/tests/test_metadata_routing.py
index 110452870d682..109c730bf0718 100644
--- a/sklearn/tests/test_metadata_routing.py
+++ b/sklearn/tests/test_metadata_routing.py
@@ -114,11 +114,16 @@ def get_metadata_routing(self):
             router.add(
                 **{f"step_{i}": step},
                 method_mapping=MethodMapping()
-                .add(callee="fit", caller="fit")
-                .add(callee="transform", caller="fit")
-                .add(callee="transform", caller="predict"),
+                .add(caller="fit", callee="fit")
+                .add(caller="fit", callee="transform")
+                .add(caller="predict", callee="transform"),
             )
-        router.add(predictor=self.steps[-1], method_mapping="one-to-one")
+        router.add(
+            predictor=self.steps[-1],
+            method_mapping=MethodMapping()
+            .add(caller="fit", callee="fit")
+            .add(caller="predict", callee="predict"),
+        )
         return router
 
 
@@ -150,7 +155,10 @@ def test_assert_request_is_empty():
     assert_request_is_empty(
         MetadataRouter(owner="test")
         .add_self_request(WeightedMetaRegressor(estimator=None))
-        .add(method_mapping="fit", estimator=ConsumingRegressor())
+        .add(
+            estimator=ConsumingRegressor(),
+            method_mapping=MethodMapping().add(caller="fit", callee="fit"),
+        )
     )
 
 
@@ -677,13 +685,13 @@ class ConsumingRegressorWarn(ConsumingRegressor):
             MetadataRequest(owner="test"),
             "{}",
         ),
-        (MethodMapping.from_str("score"), "[{'callee': 'score', 'caller': 'score'}]"),
         (
             MetadataRouter(owner="test").add(
-                method_mapping="predict", estimator=ConsumingRegressor()
+                estimator=ConsumingRegressor(),
+                method_mapping=MethodMapping().add(caller="predict", callee="predict"),
             ),
             (
-                "{'estimator': {'mapping': [{'callee': 'predict', 'caller':"
+                "{'estimator': {'mapping': [{'caller': 'predict', 'callee':"
                 " 'predict'}], 'router': {'fit': {'sample_weight': None, 'metadata':"
                 " None}, 'partial_fit': {'sample_weight': None, 'metadata': None},"
                 " 'predict': {'sample_weight': None, 'metadata': None}, 'score':"
@@ -702,24 +710,17 @@ def test_string_representations(obj, string):
         (
             MethodMapping(),
             "add",
-            {"callee": "invalid", "caller": "fit"},
+            {"caller": "fit", "callee": "invalid"},
             ValueError,
             "Given callee",
         ),
         (
             MethodMapping(),
             "add",
-            {"callee": "fit", "caller": "invalid"},
+            {"caller": "invalid", "callee": "fit"},
             ValueError,
             "Given caller",
         ),
-        (
-            MethodMapping,
-            "from_str",
-            {"route": "invalid"},
-            ValueError,
-            "route should be 'one-to-one' or a single method!",
-        ),
         (
             MetadataRouter(owner="test"),
             "add_self_request",
@@ -749,16 +750,17 @@ def test_methodmapping():
     )
 
     mm_list = list(mm)
-    assert mm_list[0] == ("transform", "fit")
+    assert mm_list[0] == ("fit", "transform")
     assert mm_list[1] == ("fit", "fit")
 
-    mm = MethodMapping.from_str("one-to-one")
+    mm = MethodMapping()
     for method in METHODS:
+        mm.add(caller=method, callee=method)
         assert MethodPair(method, method) in mm._routes
     assert len(mm._routes) == len(METHODS)
 
-    mm = MethodMapping.from_str("score")
-    assert repr(mm) == "[{'callee': 'score', 'caller': 'score'}]"
+    mm = MethodMapping().add(caller="score", callee="score")
+    assert repr(mm) == "[{'caller': 'score', 'callee': 'score'}]"
 
 
 def test_metadatarouter_add_self_request():
@@ -793,12 +795,12 @@ def test_metadatarouter_add_self_request():
 def test_metadata_routing_add():
     # adding one with a string `method_mapping`
     router = MetadataRouter(owner="test").add(
-        method_mapping="fit",
         est=ConsumingRegressor().set_fit_request(sample_weight="weights"),
+        method_mapping=MethodMapping().add(caller="fit", callee="fit"),
     )
     assert (
         str(router)
-        == "{'est': {'mapping': [{'callee': 'fit', 'caller': 'fit'}], 'router': {'fit':"
+        == "{'est': {'mapping': [{'caller': 'fit', 'callee': 'fit'}], 'router': {'fit':"
         " {'sample_weight': 'weights', 'metadata': None}, 'partial_fit':"
         " {'sample_weight': None, 'metadata': None}, 'predict': {'sample_weight':"
         " None, 'metadata': None}, 'score': {'sample_weight': None, 'metadata':"
@@ -807,12 +809,12 @@ def test_metadata_routing_add():
 
     # adding one with an instance of MethodMapping
     router = MetadataRouter(owner="test").add(
-        method_mapping=MethodMapping().add(callee="score", caller="fit"),
+        method_mapping=MethodMapping().add(caller="fit", callee="score"),
         est=ConsumingRegressor().set_score_request(sample_weight=True),
     )
     assert (
         str(router)
-        == "{'est': {'mapping': [{'callee': 'score', 'caller': 'fit'}], 'router':"
+        == "{'est': {'mapping': [{'caller': 'fit', 'callee': 'score'}], 'router':"
         " {'fit': {'sample_weight': None, 'metadata': None}, 'partial_fit':"
         " {'sample_weight': None, 'metadata': None}, 'predict': {'sample_weight':"
         " None, 'metadata': None}, 'score': {'sample_weight': True, 'metadata':"
@@ -829,17 +831,17 @@ def test_metadata_routing_get_param_names():
             )
         )
         .add(
-            method_mapping="fit",
             trs=ConsumingTransformer().set_fit_request(
                 sample_weight="transform_weights"
             ),
+            method_mapping=MethodMapping().add(caller="fit", callee="fit"),
         )
     )
 
     assert (
         str(router)
         == "{'$self_request': {'fit': {'sample_weight': 'self_weights'}, 'score':"
-        " {'sample_weight': None}}, 'trs': {'mapping': [{'callee': 'fit', 'caller':"
+        " {'sample_weight': None}}, 'trs': {'mapping': [{'caller': 'fit', 'callee':"
         " 'fit'}], 'router': {'fit': {'sample_weight': 'transform_weights',"
         " 'metadata': None}, 'transform': {'sample_weight': None, 'metadata': None},"
         " 'inverse_transform': {'sample_weight': None, 'metadata': None}}}}"
diff --git a/sklearn/tests/test_metaestimators_metadata_routing.py b/sklearn/tests/test_metaestimators_metadata_routing.py
index aa6af5bd09aac..8bfb7b0663c18 100644
--- a/sklearn/tests/test_metaestimators_metadata_routing.py
+++ b/sklearn/tests/test_metaestimators_metadata_routing.py
@@ -14,8 +14,6 @@
     AdaBoostRegressor,
     BaggingClassifier,
     BaggingRegressor,
-    StackingClassifier,
-    StackingRegressor,
 )
 from sklearn.exceptions import UnsetMetadataPassedError
 from sklearn.experimental import (
@@ -43,10 +41,12 @@
     RidgeCV,
 )
 from sklearn.model_selection import (
+    FixedThresholdClassifier,
     GridSearchCV,
     HalvingGridSearchCV,
     HalvingRandomSearchCV,
     RandomizedSearchCV,
+    TunedThresholdClassifierCV,
 )
 from sklearn.multiclass import (
     OneVsOneClassifier,
@@ -77,6 +77,7 @@
 N, M = 100, 4
 X = rng.rand(N, M)
 y = rng.randint(0, 3, size=N)
+y_binary = (y >= 1).astype(int)
 classes = np.unique(y)
 y_multi = rng.randint(0, 3, size=(N, 3))
 classes_multi = [np.unique(y_multi[:, i]) for i in range(y_multi.shape[1])]
@@ -200,6 +201,24 @@ def enable_slep006():
         "cv_name": "cv",
         "cv_routing_methods": ["fit"],
     },
+    {
+        "metaestimator": FixedThresholdClassifier,
+        "estimator_name": "estimator",
+        "estimator": "classifier",
+        "X": X,
+        "y": y_binary,
+        "estimator_routing_methods": ["fit"],
+        "preserves_metadata": "subset",
+    },
+    {
+        "metaestimator": TunedThresholdClassifierCV,
+        "estimator_name": "estimator",
+        "estimator": "classifier",
+        "X": X,
+        "y": y_binary,
+        "estimator_routing_methods": ["fit"],
+        "preserves_metadata": "subset",
+    },
     {
         "metaestimator": OneVsRestClassifier,
         "estimator_name": "estimator",
@@ -408,8 +427,6 @@ def enable_slep006():
     RFECV(ConsumingClassifier()),
     SelfTrainingClassifier(ConsumingClassifier()),
     SequentialFeatureSelector(ConsumingClassifier()),
-    StackingClassifier(ConsumingClassifier()),
-    StackingRegressor(ConsumingRegressor()),
     TransformedTargetRegressor(),
 ]
 
diff --git a/sklearn/tests/test_pipeline.py b/sklearn/tests/test_pipeline.py
index 1d4cfb3dd6e2b..c7f0afe642a65 100644
--- a/sklearn/tests/test_pipeline.py
+++ b/sklearn/tests/test_pipeline.py
@@ -6,6 +6,7 @@
 import re
 import shutil
 import time
+import warnings
 from tempfile import mkdtemp
 
 import joblib
@@ -1792,6 +1793,26 @@ def test_feature_union_feature_names_in_():
     assert not hasattr(union, "feature_names_in_")
 
 
+# TODO(1.7): remove this test
+def test_pipeline_inverse_transform_Xt_deprecation():
+    X = np.random.RandomState(0).normal(size=(10, 5))
+    pipe = Pipeline([("pca", PCA(n_components=2))])
+    X = pipe.fit_transform(X)
+
+    with pytest.raises(TypeError, match="Missing required positional argument"):
+        pipe.inverse_transform()
+
+    with pytest.raises(TypeError, match="Cannot use both X and Xt. Use X only"):
+        pipe.inverse_transform(X=X, Xt=X)
+
+    with warnings.catch_warnings(record=True):
+        warnings.simplefilter("error")
+        pipe.inverse_transform(X)
+
+    with pytest.warns(FutureWarning, match="Xt was renamed X in version 1.5"):
+        pipe.inverse_transform(Xt=X)
+
+
 # Test that metadata is routed correctly for pipelines and FeatureUnion
 # =====================================================================
 
diff --git a/sklearn/tests/test_public_functions.py b/sklearn/tests/test_public_functions.py
index 3b8c6fdac4d00..1ed852396b08b 100644
--- a/sklearn/tests/test_public_functions.py
+++ b/sklearn/tests/test_public_functions.py
@@ -235,6 +235,7 @@ def _check_function_param_validation(
     "sklearn.metrics.consensus_score",
     "sklearn.metrics.coverage_error",
     "sklearn.metrics.d2_absolute_error_score",
+    "sklearn.metrics.d2_log_loss_score",
     "sklearn.metrics.d2_pinball_score",
     "sklearn.metrics.d2_tweedie_score",
     "sklearn.metrics.davies_bouldin_score",
diff --git a/sklearn/tree/__init__.py b/sklearn/tree/__init__.py
index 8cfb42c73e118..23ab17aa0bbbe 100644
--- a/sklearn/tree/__init__.py
+++ b/sklearn/tree/__init__.py
@@ -1,7 +1,4 @@
-"""
-The :mod:`sklearn.tree` module includes decision tree-based models for
-classification and regression.
-"""
+"""Decision tree based models for classification and regression."""
 
 from ._classes import (
     BaseDecisionTree,
diff --git a/sklearn/utils/__init__.py b/sklearn/utils/__init__.py
index af02393966cc2..1e38b807973b2 100644
--- a/sklearn/utils/__init__.py
+++ b/sklearn/utils/__init__.py
@@ -1,6 +1,4 @@
-"""
-The :mod:`sklearn.utils` module includes various utilities.
-"""
+"""Various utilities to help with development."""
 
 import warnings
 from collections.abc import Sequence
diff --git a/sklearn/utils/_array_api.py b/sklearn/utils/_array_api.py
index 70e210e7e913e..7bf9183c80772 100644
--- a/sklearn/utils/_array_api.py
+++ b/sklearn/utils/_array_api.py
@@ -13,10 +13,10 @@
 _NUMPY_NAMESPACE_NAMES = {"numpy", "array_api_compat.numpy"}
 
 
-def yield_namespace_device_dtype_combinations(include_numpy_namespaces=True):
-    """Yield supported namespace, device, dtype tuples for testing.
+def yield_namespaces(include_numpy_namespaces=True):
+    """Yield supported namespace.
 
-    Use this to test that an estimator works with all combinations.
+    This is meant to be used for testing purposes only.
 
     Parameters
     ----------
@@ -27,14 +27,6 @@ def yield_namespace_device_dtype_combinations(include_numpy_namespaces=True):
     -------
     array_namespace : str
         The name of the Array API namespace.
-
-    device : str
-        The name of the device on which to allocate the arrays. Can be None to
-        indicate that the default value should be used.
-
-    dtype_name : str
-        The name of the data type to use for arrays. Can be None to indicate
-        that the default value should be used.
     """
     for array_namespace in [
         # The following is used to test the array_api_compat wrapper when
@@ -50,6 +42,35 @@ def yield_namespace_device_dtype_combinations(include_numpy_namespaces=True):
     ]:
         if not include_numpy_namespaces and array_namespace in _NUMPY_NAMESPACE_NAMES:
             continue
+        yield array_namespace
+
+
+def yield_namespace_device_dtype_combinations(include_numpy_namespaces=True):
+    """Yield supported namespace, device, dtype tuples for testing.
+
+    Use this to test that an estimator works with all combinations.
+
+    Parameters
+    ----------
+    include_numpy_namespaces : bool, default=True
+        If True, also yield numpy namespaces.
+
+    Returns
+    -------
+    array_namespace : str
+        The name of the Array API namespace.
+
+    device : str
+        The name of the device on which to allocate the arrays. Can be None to
+        indicate that the default value should be used.
+
+    dtype_name : str
+        The name of the data type to use for arrays. Can be None to indicate
+        that the default value should be used.
+    """
+    for array_namespace in yield_namespaces(
+        include_numpy_namespaces=include_numpy_namespaces
+    ):
         if array_namespace == "torch":
             for device, dtype in itertools.product(
                 ("cpu", "cuda"), ("float64", "float32")
@@ -385,6 +406,11 @@ def unique_counts(self, x):
     def unique_values(self, x):
         return numpy.unique(x)
 
+    def unique_all(self, x):
+        return numpy.unique(
+            x, return_index=True, return_inverse=True, return_counts=True
+        )
+
     def concat(self, arrays, *, axis=None):
         return numpy.concatenate(arrays, axis=axis)
 
@@ -406,6 +432,9 @@ def reshape(self, x, shape, *, copy=None):
     def isdtype(self, dtype, kind):
         return isdtype(dtype, kind, xp=self)
 
+    def pow(self, x1, x2):
+        return numpy.power(x1, x2)
+
 
 _NUMPY_API_WRAPPER_INSTANCE = _NumPyAPIWrapper()
 
@@ -531,6 +560,25 @@ def get_namespace(*arrays, remove_none=True, remove_types=(str,), xp=None):
     return namespace, is_array_api_compliant
 
 
+def get_namespace_and_device(*array_list, remove_none=True, remove_types=(str,)):
+    """Combination into one single function of `get_namespace` and `device`."""
+    array_list = _remove_non_arrays(
+        *array_list, remove_none=remove_none, remove_types=remove_types
+    )
+
+    skip_remove_kwargs = dict(remove_none=False, remove_types=[])
+
+    xp, is_array_api = get_namespace(*array_list, **skip_remove_kwargs)
+    if is_array_api:
+        return (
+            xp,
+            is_array_api,
+            device(*array_list, **skip_remove_kwargs),
+        )
+    else:
+        return xp, False, None
+
+
 def _expit(X, xp=None):
     xp, _ = get_namespace(X, xp=xp)
     if _is_numpy_namespace(xp):
@@ -588,10 +636,7 @@ def _average(a, axis=None, weights=None, normalize=True, xp=None):
     https://numpy.org/doc/stable/reference/generated/numpy.average.html but
     only for the common cases needed in scikit-learn.
     """
-    input_arrays = [a, weights]
-    xp, _ = get_namespace(*input_arrays, xp=xp)
-
-    device_ = device(*input_arrays)
+    xp, _, device_ = get_namespace_and_device(a, weights)
 
     if _is_numpy_namespace(xp):
         if normalize:
@@ -690,7 +735,9 @@ def _nanmax(X, axis=None, xp=None):
         return X
 
 
-def _asarray_with_order(array, dtype=None, order=None, copy=None, *, xp=None):
+def _asarray_with_order(
+    array, dtype=None, order=None, copy=None, *, xp=None, device=None
+):
     """Helper to support the order kwarg only for NumPy-backed arrays
 
     Memory layout parameter `order` is not exposed in the Array API standard,
@@ -715,7 +762,21 @@ def _asarray_with_order(array, dtype=None, order=None, copy=None, *, xp=None):
         # container that is consistent with the input's namespace.
         return xp.asarray(array)
     else:
-        return xp.asarray(array, dtype=dtype, copy=copy)
+        return xp.asarray(array, dtype=dtype, copy=copy, device=device)
+
+
+def _ravel(array, xp=None):
+    """Array API compliant version of np.ravel.
+
+    For non numpy namespaces, it just returns a flattened array, that might
+    be or not be a copy.
+    """
+    xp, _ = get_namespace(array, xp=xp)
+    if _is_numpy_namespace(xp):
+        array = numpy.asarray(array)
+        return xp.asarray(numpy.ravel(array, order="C"))
+
+    return xp.reshape(array, shape=(-1,))
 
 
 def _convert_to_numpy(array, xp):
@@ -788,3 +849,121 @@ def indexing_dtype(xp):
     # TODO: once sufficiently adopted, we might want to instead rely on the
     # newer inspection API: https://github.com/data-apis/array-api/issues/640
     return xp.asarray(0).dtype
+
+
+def _searchsorted(xp, a, v, *, side="left", sorter=None):
+    # Temporary workaround needed as long as searchsorted is not widely
+    # adopted by implementers of the Array API spec. This is a quite
+    # recent addition to the spec:
+    # https://data-apis.org/array-api/latest/API_specification/generated/array_api.searchsorted.html # noqa
+    if hasattr(xp, "searchsorted"):
+        return xp.searchsorted(a, v, side=side, sorter=sorter)
+
+    a_np = _convert_to_numpy(a, xp=xp)
+    v_np = _convert_to_numpy(v, xp=xp)
+    indices = numpy.searchsorted(a_np, v_np, side=side, sorter=sorter)
+    return xp.asarray(indices, device=device(a))
+
+
+def _setdiff1d(ar1, ar2, xp, assume_unique=False):
+    """Find the set difference of two arrays.
+
+    Return the unique values in `ar1` that are not in `ar2`.
+    """
+    if _is_numpy_namespace(xp):
+        return xp.asarray(
+            numpy.setdiff1d(
+                ar1=ar1,
+                ar2=ar2,
+                assume_unique=assume_unique,
+            )
+        )
+
+    if assume_unique:
+        ar1 = xp.reshape(ar1, (-1,))
+    else:
+        ar1 = xp.unique_values(ar1)
+        ar2 = xp.unique_values(ar2)
+    return ar1[_in1d(ar1=ar1, ar2=ar2, xp=xp, assume_unique=True, invert=True)]
+
+
+def _isin(element, test_elements, xp, assume_unique=False, invert=False):
+    """Calculates ``element in test_elements``, broadcasting over `element`
+    only.
+
+    Returns a boolean array of the same shape as `element` that is True
+    where an element of `element` is in `test_elements` and False otherwise.
+    """
+    if _is_numpy_namespace(xp):
+        return xp.asarray(
+            numpy.isin(
+                element=element,
+                test_elements=test_elements,
+                assume_unique=assume_unique,
+                invert=invert,
+            )
+        )
+
+    original_element_shape = element.shape
+    element = xp.reshape(element, (-1,))
+    test_elements = xp.reshape(test_elements, (-1,))
+    return xp.reshape(
+        _in1d(
+            ar1=element,
+            ar2=test_elements,
+            xp=xp,
+            assume_unique=assume_unique,
+            invert=invert,
+        ),
+        original_element_shape,
+    )
+
+
+# Note: This is a helper for the functions `_isin` and
+# `_setdiff1d`. It is not meant to be called directly.
+def _in1d(ar1, ar2, xp, assume_unique=False, invert=False):
+    """Checks whether each element of an array is also present in a
+    second array.
+
+    Returns a boolean array the same length as `ar1` that is True
+    where an element of `ar1` is in `ar2` and False otherwise.
+
+    This function has been adapted using the original implementation
+    present in numpy:
+    https://github.com/numpy/numpy/blob/v1.26.0/numpy/lib/arraysetops.py#L524-L758
+    """
+    xp, _ = get_namespace(ar1, ar2, xp=xp)
+
+    # This code is run to make the code significantly faster
+    if ar2.shape[0] < 10 * ar1.shape[0] ** 0.145:
+        if invert:
+            mask = xp.ones(ar1.shape[0], dtype=xp.bool, device=device(ar1))
+            for a in ar2:
+                mask &= ar1 != a
+        else:
+            mask = xp.zeros(ar1.shape[0], dtype=xp.bool, device=device(ar1))
+            for a in ar2:
+                mask |= ar1 == a
+        return mask
+
+    if not assume_unique:
+        ar1, rev_idx = xp.unique_inverse(ar1)
+        ar2 = xp.unique_values(ar2)
+
+    ar = xp.concat((ar1, ar2))
+    device_ = device(ar)
+    # We need this to be a stable sort.
+    order = xp.argsort(ar, stable=True)
+    reverse_order = xp.argsort(order, stable=True)
+    sar = xp.take(ar, order, axis=0)
+    if invert:
+        bool_ar = sar[1:] != sar[:-1]
+    else:
+        bool_ar = sar[1:] == sar[:-1]
+    flag = xp.concat((bool_ar, xp.asarray([invert], device=device_)))
+    ret = xp.take(flag, reverse_order, axis=0)
+
+    if assume_unique:
+        return ret[: ar1.shape[0]]
+    else:
+        return xp.take(ret, rev_idx, axis=0)
diff --git a/sklearn/utils/_encode.py b/sklearn/utils/_encode.py
index a468af43f857d..3fd4d45f522e6 100644
--- a/sklearn/utils/_encode.py
+++ b/sklearn/utils/_encode.py
@@ -4,6 +4,13 @@
 
 import numpy as np
 
+from ._array_api import (
+    _isin,
+    _searchsorted,
+    _setdiff1d,
+    device,
+    get_namespace,
+)
 from ._missing import is_scalar_nan
 
 
@@ -51,31 +58,29 @@ def _unique(values, *, return_inverse=False, return_counts=False):
 def _unique_np(values, return_inverse=False, return_counts=False):
     """Helper function to find unique values for numpy arrays that correctly
     accounts for nans. See `_unique` documentation for details."""
-    uniques = np.unique(
-        values, return_inverse=return_inverse, return_counts=return_counts
-    )
+    xp, _ = get_namespace(values)
 
     inverse, counts = None, None
 
-    if return_counts:
-        *uniques, counts = uniques
-
-    if return_inverse:
-        *uniques, inverse = uniques
-
-    if return_counts or return_inverse:
-        uniques = uniques[0]
+    if return_inverse and return_counts:
+        uniques, _, inverse, counts = xp.unique_all(values)
+    elif return_inverse:
+        uniques, inverse = xp.unique_inverse(values)
+    elif return_counts:
+        uniques, counts = xp.unique_counts(values)
+    else:
+        uniques = xp.unique_values(values)
 
     # np.unique will have duplicate missing values at the end of `uniques`
     # here we clip the nans and remove it from uniques
     if uniques.size and is_scalar_nan(uniques[-1]):
-        nan_idx = np.searchsorted(uniques, np.nan)
+        nan_idx = _searchsorted(xp, uniques, xp.nan)
         uniques = uniques[: nan_idx + 1]
         if return_inverse:
             inverse[inverse > nan_idx] = nan_idx
 
         if return_counts:
-            counts[nan_idx] = np.sum(counts[nan_idx:])
+            counts[nan_idx] = xp.sum(counts[nan_idx:])
             counts = counts[: nan_idx + 1]
 
     ret = (uniques,)
@@ -161,8 +166,9 @@ def __missing__(self, key):
 
 def _map_to_integer(values, uniques):
     """Map values based on its position in uniques."""
+    xp, _ = get_namespace(values, uniques)
     table = _nandict({val: i for i, val in enumerate(uniques)})
-    return np.array([table[v] for v in values])
+    return xp.asarray([table[v] for v in values], device=device(values))
 
 
 def _unique_python(values, *, return_inverse, return_counts):
@@ -220,7 +226,8 @@ def _encode(values, *, uniques, check_unknown=True):
     encoded : ndarray
         Encoded values
     """
-    if values.dtype.kind in "OUS":
+    xp, _ = get_namespace(values, uniques)
+    if not xp.isdtype(values.dtype, "numeric"):
         try:
             return _map_to_integer(values, uniques)
         except KeyError as e:
@@ -230,7 +237,7 @@ def _encode(values, *, uniques, check_unknown=True):
             diff = _check_unknown(values, uniques)
             if diff:
                 raise ValueError(f"y contains previously unseen labels: {str(diff)}")
-        return np.searchsorted(uniques, values)
+        return _searchsorted(xp, uniques, values)
 
 
 def _check_unknown(values, known_values, return_mask=False):
@@ -258,9 +265,10 @@ def _check_unknown(values, known_values, return_mask=False):
         Additionally returned if ``return_mask=True``.
 
     """
+    xp, _ = get_namespace(values, known_values)
     valid_mask = None
 
-    if values.dtype.kind in "OUS":
+    if not xp.isdtype(values.dtype, "numeric"):
         values_set = set(values)
         values_set, missing_in_values = _extract_missing(values_set)
 
@@ -282,9 +290,9 @@ def is_valid(value):
 
         if return_mask:
             if diff or nan_in_diff or none_in_diff:
-                valid_mask = np.array([is_valid(value) for value in values])
+                valid_mask = xp.array([is_valid(value) for value in values])
             else:
-                valid_mask = np.ones(len(values), dtype=bool)
+                valid_mask = xp.ones(len(values), dtype=xp.bool)
 
         diff = list(diff)
         if none_in_diff:
@@ -292,21 +300,21 @@ def is_valid(value):
         if nan_in_diff:
             diff.append(np.nan)
     else:
-        unique_values = np.unique(values)
-        diff = np.setdiff1d(unique_values, known_values, assume_unique=True)
+        unique_values = xp.unique_values(values)
+        diff = _setdiff1d(unique_values, known_values, xp, assume_unique=True)
         if return_mask:
             if diff.size:
-                valid_mask = np.isin(values, known_values)
+                valid_mask = _isin(values, known_values, xp)
             else:
-                valid_mask = np.ones(len(values), dtype=bool)
+                valid_mask = xp.ones(len(values), dtype=xp.bool)
 
         # check for nans in the known_values
-        if np.isnan(known_values).any():
-            diff_is_nan = np.isnan(diff)
-            if diff_is_nan.any():
+        if xp.any(xp.isnan(known_values)):
+            diff_is_nan = xp.isnan(diff)
+            if xp.any(diff_is_nan):
                 # removes nan from valid_mask
                 if diff.size and return_mask:
-                    is_nan = np.isnan(values)
+                    is_nan = xp.isnan(values)
                     valid_mask[is_nan] = 1
 
                 # remove nan from diff
diff --git a/sklearn/utils/_indexing.py b/sklearn/utils/_indexing.py
index 49e04a451dcb0..ca2327f2bb109 100644
--- a/sklearn/utils/_indexing.py
+++ b/sklearn/utils/_indexing.py
@@ -1,6 +1,7 @@
 import numbers
 import sys
 import warnings
+from collections import UserList
 from itertools import compress, islice
 
 import numpy as np
@@ -142,7 +143,9 @@ def _determine_key_type(key, accept_slice=True):
         if key_start_type is not None:
             return key_start_type
         return key_stop_type
-    if isinstance(key, (list, tuple)):
+    # TODO(1.9) remove UserList when the force_int_remainder_cols param
+    # of ColumnTransformer is removed
+    if isinstance(key, (list, tuple, UserList)):
         unique_key = set(key)
         key_type = {_determine_key_type(elt) for elt in unique_key}
         if not key_type:
diff --git a/sklearn/utils/_metadata_requests.py b/sklearn/utils/_metadata_requests.py
index 075fe196ae01f..02a79bb8a6f20 100644
--- a/sklearn/utils/_metadata_requests.py
+++ b/sklearn/utils/_metadata_requests.py
@@ -399,8 +399,10 @@ def _check_warnings(self, *, params):
             warn(
                 f"Support for {param} has recently been added to this class. "
                 "To maintain backward compatibility, it is ignored now. "
-                "You can set the request value to False to silence this "
-                "warning, or to True to consume and use the metadata."
+                f"Using `set_{self.method}_request({param}={{True, False}})` "
+                "on this method of the class, you can set the request value "
+                "to False to silence this warning, or to True to consume and "
+                "use the metadata."
             )
 
     def _route_params(self, params, parent, caller):
@@ -691,19 +693,18 @@ def __str__(self):
 
 # A namedtuple storing a single method route. A collection of these namedtuples
 # is stored in a MetadataRouter.
-MethodPair = namedtuple("MethodPair", ["callee", "caller"])
+MethodPair = namedtuple("MethodPair", ["caller", "callee"])
 
 
 class MethodMapping:
-    """Stores the mapping between callee and caller methods for a router.
+    """Stores the mapping between caller and callee methods for a router.
 
     This class is primarily used in a ``get_metadata_routing()`` of a router
     object when defining the mapping between a sub-object (a sub-estimator or a
-    scorer) to the router's methods. It stores a collection of ``Route``
-    namedtuples.
+    scorer) to the router's methods. It stores a collection of namedtuples.
 
     Iterating through an instance of this class will yield named
-    ``MethodPair(callee, caller)`` tuples.
+    ``MethodPair(caller, callee)`` tuples.
 
     .. versionadded:: 1.3
     """
@@ -714,33 +715,34 @@ def __init__(self):
     def __iter__(self):
         return iter(self._routes)
 
-    def add(self, *, callee, caller):
+    def add(self, *, caller, callee):
         """Add a method mapping.
 
         Parameters
         ----------
-        callee : str
-            Child object's method name. This method is called in ``caller``.
 
         caller : str
             Parent estimator's method name in which the ``callee`` is called.
 
+        callee : str
+            Child object's method name. This method is called in ``caller``.
+
         Returns
         -------
         self : MethodMapping
             Returns self.
         """
-        if callee not in METHODS:
+        if caller not in METHODS:
             raise ValueError(
-                f"Given callee:{callee} is not a valid method. Valid methods are:"
+                f"Given caller:{caller} is not a valid method. Valid methods are:"
                 f" {METHODS}"
             )
-        if caller not in METHODS:
+        if callee not in METHODS:
             raise ValueError(
-                f"Given caller:{caller} is not a valid method. Valid methods are:"
+                f"Given callee:{callee} is not a valid method. Valid methods are:"
                 f" {METHODS}"
             )
-        self._routes.append(MethodPair(callee=callee, caller=caller))
+        self._routes.append(MethodPair(caller=caller, callee=callee))
         return self
 
     def _serialize(self):
@@ -753,38 +755,9 @@ def _serialize(self):
         """
         result = list()
         for route in self._routes:
-            result.append({"callee": route.callee, "caller": route.caller})
+            result.append({"caller": route.caller, "callee": route.callee})
         return result
 
-    @classmethod
-    def from_str(cls, route):
-        """Construct an instance from a string.
-
-        Parameters
-        ----------
-        route : str
-            A string representing the mapping, it can be:
-
-              - `"one-to-one"`: a one to one mapping for all methods.
-              - `"method"`: the name of a single method, such as ``fit``,
-                ``transform``, ``score``, etc.
-
-        Returns
-        -------
-        obj : MethodMapping
-            A :class:`~sklearn.utils.metadata_routing.MethodMapping` instance
-            constructed from the given string.
-        """
-        routing = cls()
-        if route == "one-to-one":
-            for method in METHODS:
-                routing.add(callee=method, caller=method)
-        elif route in METHODS:
-            routing.add(callee=route, caller=route)
-        else:
-            raise ValueError("route should be 'one-to-one' or a single method!")
-        return routing
-
     def __repr__(self):
         return str(self._serialize())
 
@@ -866,10 +839,8 @@ def add(self, *, method_mapping, **objs):
 
         Parameters
         ----------
-        method_mapping : MethodMapping or str
-            The mapping between the child and the parent's methods. If str, the
-            output of :func:`~sklearn.utils.metadata_routing.MethodMapping.from_str`
-            is used.
+        method_mapping : MethodMapping
+            The mapping between the child and the parent's methods.
 
         **objs : dict
             A dictionary of objects from which metadata is extracted by calling
@@ -880,10 +851,7 @@ def add(self, *, method_mapping, **objs):
         self : MetadataRouter
             Returns `self`.
         """
-        if isinstance(method_mapping, str):
-            method_mapping = MethodMapping.from_str(method_mapping)
-        else:
-            method_mapping = deepcopy(method_mapping)
+        method_mapping = deepcopy(method_mapping)
 
         for name, obj in objs.items():
             self._route_mappings[name] = RouterMappingPair(
@@ -914,7 +882,7 @@ def consumes(self, method, params):
             res = res | self._self_request.consumes(method=method, params=params)
 
         for _, route_mapping in self._route_mappings.items():
-            for callee, caller in route_mapping.mapping:
+            for caller, callee in route_mapping.mapping:
                 if caller == method:
                     res = res | route_mapping.router.consumes(
                         method=callee, params=params
@@ -957,7 +925,7 @@ def _get_param_names(self, *, method, return_alias, ignore_self_request):
             )
 
         for name, route_mapping in self._route_mappings.items():
-            for callee, caller in route_mapping.mapping:
+            for caller, callee in route_mapping.mapping:
                 if caller == method:
                     res = res.union(
                         route_mapping.router._get_param_names(
@@ -1031,9 +999,9 @@ def _route_params(self, *, params, method, parent, caller):
     def route_params(self, *, caller, params):
         """Return the input parameters requested by child objects.
 
-        The output of this method is a bunch, which includes the inputs for all
-        methods of each child object that are used in the router's `caller`
-        method.
+        The output of this method is a :class:`~sklearn.utils.Bunch`, which includes the
+        metadata for all methods of each child object that is used in the router's
+        `caller` method.
 
         If the router is also a consumer, it also checks for warnings of
         `self`'s/consumer's requested metadata.
@@ -1052,7 +1020,7 @@ def route_params(self, *, caller, params):
         -------
         params : Bunch
             A :class:`~sklearn.utils.Bunch` of the form
-            ``{"object_name": {"method_name": {prop: value}}}`` which can be
+            ``{"object_name": {"method_name": {params: value}}}`` which can be
             used to pass the required metadata to corresponding methods or
             corresponding child objects.
         """
@@ -1064,7 +1032,7 @@ def route_params(self, *, caller, params):
             router, mapping = route_mapping.router, route_mapping.mapping
 
             res[name] = Bunch()
-            for _callee, _caller in mapping:
+            for _caller, _callee in mapping:
                 if _caller == caller:
                     res[name][_callee] = router._route_params(
                         params=params,
@@ -1126,12 +1094,11 @@ def _serialize(self):
 
     def __iter__(self):
         if self._self_request:
-            yield (
-                "$self_request",
-                RouterMappingPair(
-                    mapping=MethodMapping.from_str("one-to-one"),
-                    router=self._self_request,
-                ),
+            method_mapping = MethodMapping()
+            for method in METHODS:
+                method_mapping.add(caller=method, callee=method)
+            yield "$self_request", RouterMappingPair(
+                mapping=method_mapping, router=self._self_request
             )
         for name, route_mapping in self._route_mappings.items():
             yield (name, route_mapping)
@@ -1159,12 +1126,12 @@ def get_routing_for_object(obj=None):
     Parameters
     ----------
     obj : object
+        - If the object provides a `get_metadata_routing` method, return a copy
+            of the output of that method.
         - If the object is already a
             :class:`~sklearn.utils.metadata_routing.MetadataRequest` or a
             :class:`~sklearn.utils.metadata_routing.MetadataRouter`, return a copy
             of that.
-        - If the object provides a `get_metadata_routing` method, return a copy
-            of the output of that method.
         - Returns an empty :class:`~sklearn.utils.metadata_routing.MetadataRequest`
             otherwise.
 
@@ -1557,9 +1524,10 @@ def process_routing(_obj, _method, /, **kwargs):
     This function is used inside a router's method, e.g. :term:`fit`,
     to validate the metadata and handle the routing.
 
-    Assuming this signature: ``fit(self, X, y, sample_weight=None, **fit_params)``,
+    Assuming this signature of a router's fit method:
+    ``fit(self, X, y, sample_weight=None, **fit_params)``,
     a call to this function would be:
-    ``process_routing(self, sample_weight=sample_weight, **fit_params)``.
+    ``process_routing(self, "fit", sample_weight=sample_weight, **fit_params)``.
 
     Note that if routing is not enabled and ``kwargs`` is empty, then it
     returns an empty routing where ``process_routing(...).ANYTHING.ANY_METHOD``
@@ -1582,8 +1550,10 @@ def process_routing(_obj, _method, /, **kwargs):
     Returns
     -------
     routed_params : Bunch
+        A :class:`~utils.Bunch` of the form ``{"object_name": {"method_name":
+        {params: value}}}`` which can be used to pass the required metadata to
         A :class:`~sklearn.utils.Bunch` of the form ``{"object_name": {"method_name":
-        {prop: value}}}`` which can be used to pass the required metadata to
+        {params: value}}}`` which can be used to pass the required metadata to
         corresponding methods or corresponding child objects. The object names
         are those defined in `obj.get_metadata_routing()`.
     """
diff --git a/sklearn/utils/_mocking.py b/sklearn/utils/_mocking.py
index 16acabf03755b..0afed8c08cfaa 100644
--- a/sklearn/utils/_mocking.py
+++ b/sklearn/utils/_mocking.py
@@ -3,7 +3,13 @@
 from ..base import BaseEstimator, ClassifierMixin
 from ..utils._metadata_requests import RequestMethod
 from .metaestimators import available_if
-from .validation import _check_sample_weight, _num_samples, check_array, check_is_fitted
+from .validation import (
+    _check_sample_weight,
+    _num_samples,
+    check_array,
+    check_is_fitted,
+    check_random_state,
+)
 
 
 class ArraySlicingWrapper:
@@ -133,6 +139,7 @@ def __init__(
         foo_param=0,
         expected_sample_weight=None,
         expected_fit_params=None,
+        random_state=None,
     ):
         self.check_y = check_y
         self.check_y_params = check_y_params
@@ -142,6 +149,7 @@ def __init__(
         self.foo_param = foo_param
         self.expected_sample_weight = expected_sample_weight
         self.expected_fit_params = expected_fit_params
+        self.random_state = random_state
 
     def _check_X_y(self, X, y=None, should_be_fitted=True):
         """Validate X and y and make extra check.
@@ -243,7 +251,8 @@ def predict(self, X):
         """
         if self.methods_to_check == "all" or "predict" in self.methods_to_check:
             X, y = self._check_X_y(X)
-        return self.classes_[np.zeros(_num_samples(X), dtype=int)]
+        rng = check_random_state(self.random_state)
+        return rng.choice(self.classes_, size=_num_samples(X))
 
     def predict_proba(self, X):
         """Predict probabilities for each class.
@@ -263,8 +272,10 @@ def predict_proba(self, X):
         """
         if self.methods_to_check == "all" or "predict_proba" in self.methods_to_check:
             X, y = self._check_X_y(X)
-        proba = np.zeros((_num_samples(X), len(self.classes_)))
-        proba[:, 0] = 1
+        rng = check_random_state(self.random_state)
+        proba = rng.randn(_num_samples(X), len(self.classes_))
+        proba = np.abs(proba, out=proba)
+        proba /= np.sum(proba, axis=1)[:, np.newaxis]
         return proba
 
     def decision_function(self, X):
@@ -286,14 +297,13 @@ def decision_function(self, X):
             or "decision_function" in self.methods_to_check
         ):
             X, y = self._check_X_y(X)
+        rng = check_random_state(self.random_state)
         if len(self.classes_) == 2:
             # for binary classifier, the confidence score is related to
             # classes_[1] and therefore should be null.
-            return np.zeros(_num_samples(X))
+            return rng.randn(_num_samples(X))
         else:
-            decision = np.zeros((_num_samples(X), len(self.classes_)))
-            decision[:, 0] = 1
-            return decision
+            return rng.randn(_num_samples(X), len(self.classes_))
 
     def score(self, X=None, Y=None):
         """Fake score.
diff --git a/sklearn/utils/_pprint.py b/sklearn/utils/_pprint.py
index a0eb31685f37c..9b33cd617a5fc 100644
--- a/sklearn/utils/_pprint.py
+++ b/sklearn/utils/_pprint.py
@@ -65,7 +65,6 @@
 
 import inspect
 import pprint
-from collections import OrderedDict
 
 from .._config import get_config
 from ..base import BaseEstimator
@@ -200,10 +199,8 @@ def _pprint_estimator(self, object, stream, indent, allowance, context, level):
         else:
             params = object.get_params(deep=False)
 
-        params = OrderedDict((name, val) for (name, val) in sorted(params.items()))
-
         self._format_params(
-            params.items(), stream, indent, allowance + 1, context, level
+            sorted(params.items()), stream, indent, allowance + 1, context, level
         )
         stream.write(")")
 
diff --git a/sklearn/utils/_response.py b/sklearn/utils/_response.py
index 0207cc1205120..0381c872a94b0 100644
--- a/sklearn/utils/_response.py
+++ b/sklearn/utils/_response.py
@@ -243,7 +243,9 @@ def _get_response_values(
     return y_pred, pos_label
 
 
-def _get_response_values_binary(estimator, X, response_method, pos_label=None):
+def _get_response_values_binary(
+    estimator, X, response_method, pos_label=None, return_response_method_used=False
+):
     """Compute the response values of a binary classifier.
 
     Parameters
@@ -266,6 +268,12 @@ def _get_response_values_binary(estimator, X, response_method, pos_label=None):
         the metrics. By default, `estimators.classes_[1]` is
         considered as the positive class.
 
+    return_response_method_used : bool, default=False
+        Whether to return the response method used to compute the response
+        values.
+
+        .. versionadded:: 1.5
+
     Returns
     -------
     y_pred : ndarray of shape (n_samples,)
@@ -275,6 +283,12 @@ def _get_response_values_binary(estimator, X, response_method, pos_label=None):
     pos_label : int, float, bool or str
         The class considered as the positive class when computing
         the metrics.
+
+    response_method_used : str
+        The response method used to compute the response values. Only returned
+        if `return_response_method_used` is `True`.
+
+        .. versionadded:: 1.5
     """
     classification_error = "Expected 'estimator' to be a binary classifier."
 
@@ -296,4 +310,5 @@ def _get_response_values_binary(estimator, X, response_method, pos_label=None):
         X,
         response_method,
         pos_label=pos_label,
+        return_response_method_used=return_response_method_used,
     )
diff --git a/sklearn/utils/_set_output.py b/sklearn/utils/_set_output.py
index d5c23a4c7c6f9..42757dbb00fae 100644
--- a/sklearn/utils/_set_output.py
+++ b/sklearn/utils/_set_output.py
@@ -392,7 +392,7 @@ def set_output(self, *, transform=None):
 
         Parameters
         ----------
-        transform : {"default", "pandas"}, default=None
+        transform : {"default", "pandas", "polars"}, default=None
             Configure output of `transform` and `fit_transform`.
 
             - `"default"`: Default output format of a transformer
@@ -428,7 +428,7 @@ def _safe_set_output(estimator, *, transform=None):
     estimator : estimator instance
         Estimator instance.
 
-    transform : {"default", "pandas"}, default=None
+    transform : {"default", "pandas", "polars"}, default=None
         Configure output of the following estimator's methods:
 
         - `"transform"`
diff --git a/sklearn/utils/_show_versions.py b/sklearn/utils/_show_versions.py
index 1431108477263..cc17b71b23799 100644
--- a/sklearn/utils/_show_versions.py
+++ b/sklearn/utils/_show_versions.py
@@ -9,8 +9,9 @@
 import platform
 import sys
 
+from threadpoolctl import threadpool_info
+
 from .. import __version__
-from ..utils.fixes import threadpool_info
 from ._openmp_helpers import _openmp_parallelism_enabled
 
 
diff --git a/sklearn/utils/arrayfuncs.pyx b/sklearn/utils/arrayfuncs.pyx
index 346531d325ca5..2cf98e0f5cc3e 100644
--- a/sklearn/utils/arrayfuncs.pyx
+++ b/sklearn/utils/arrayfuncs.pyx
@@ -1,7 +1,4 @@
-"""
-The :mod:`sklearn.utils.arrayfuncs` module includes a small collection of auxiliary
-functions that operate on arrays.
-"""
+"""A small collection of auxiliary functions that operate on arrays."""
 
 from cython cimport floating
 from cython.parallel cimport prange
@@ -16,6 +13,7 @@ ctypedef fused real_numeric:
     short
     int
     long
+    long long
     float
     double
 
diff --git a/sklearn/utils/class_weight.py b/sklearn/utils/class_weight.py
index 55802f780ed41..b3c0aaaaf4411 100644
--- a/sklearn/utils/class_weight.py
+++ b/sklearn/utils/class_weight.py
@@ -1,7 +1,4 @@
-"""
-The :mod:`sklearn.utils.class_weight` module includes utilities for handling
-weights based on class labels.
-"""
+"""Utilities for handling weights based on class labels."""
 
 # Authors: Andreas Mueller
 #          Manoj Kumar
diff --git a/sklearn/utils/deprecation.py b/sklearn/utils/deprecation.py
index c46149d943431..a3225597701c7 100644
--- a/sklearn/utils/deprecation.py
+++ b/sklearn/utils/deprecation.py
@@ -114,3 +114,22 @@ def _is_deprecated(func):
         [c.cell_contents for c in closures if isinstance(c.cell_contents, str)]
     )
     return is_deprecated
+
+
+# TODO: remove in 1.7
+def _deprecate_Xt_in_inverse_transform(X, Xt):
+    """Helper to deprecate the `Xt` argument in favor of `X` in inverse_transform."""
+    if X is not None and Xt is not None:
+        raise TypeError("Cannot use both X and Xt. Use X only.")
+
+    if X is None and Xt is None:
+        raise TypeError("Missing required positional argument: X.")
+
+    if Xt is not None:
+        warnings.warn(
+            "Xt was renamed X in version 1.5 and will be removed in 1.7.",
+            FutureWarning,
+        )
+        return Xt
+
+    return X
diff --git a/sklearn/utils/discovery.py b/sklearn/utils/discovery.py
index 1b31a843ffd8a..c7bebe5df0093 100644
--- a/sklearn/utils/discovery.py
+++ b/sklearn/utils/discovery.py
@@ -1,7 +1,4 @@
-"""
-The :mod:`sklearn.utils.discovery` module includes utilities to discover
-objects (i.e. estimators, displays, functions) from the `sklearn` package.
-"""
+"""Utilities to discover scikit-learn objects."""
 
 import inspect
 import pkgutil
diff --git a/sklearn/utils/estimator_checks.py b/sklearn/utils/estimator_checks.py
index fb60f206bea66..6c5e78acb14cb 100644
--- a/sklearn/utils/estimator_checks.py
+++ b/sklearn/utils/estimator_checks.py
@@ -1,7 +1,4 @@
-"""
-The :mod:`sklearn.utils.estimator_checks` module includes various utilities to
-check the compatibility of estimators with the scikit-learn API.
-"""
+"""Various utilities to check the compatibility of estimators with scikit-learn API."""
 
 import pickle
 import re
@@ -9,7 +6,7 @@
 from contextlib import nullcontext
 from copy import deepcopy
 from functools import partial, wraps
-from inspect import signature
+from inspect import isfunction, signature
 from numbers import Integral, Real
 
 import joblib
@@ -405,13 +402,11 @@ def _get_check_estimator_ids(obj):
     --------
     check_estimator
     """
-    if callable(obj):
-        if not isinstance(obj, partial):
-            return obj.__name__
-
+    if isfunction(obj):
+        return obj.__name__
+    if isinstance(obj, partial):
         if not obj.keywords:
             return obj.func.__name__
-
         kwstring = ",".join(["{}={}".format(k, v) for k, v in obj.keywords.items()])
         return "{}({})".format(obj.func.__name__, kwstring)
     if hasattr(obj, "get_params"):
@@ -810,7 +805,7 @@ class _NotAnArray:
     def __init__(self, data):
         self.data = np.asarray(data)
 
-    def __array__(self, dtype=None):
+    def __array__(self, dtype=None, copy=None):
         return self.data
 
     def __array_function__(self, func, types, args, kwargs):
diff --git a/sklearn/utils/extmath.py b/sklearn/utils/extmath.py
index 44f70deaa3f18..638423e1ebc54 100644
--- a/sklearn/utils/extmath.py
+++ b/sklearn/utils/extmath.py
@@ -1,7 +1,4 @@
-"""
-The :mod:`sklearn.utils.extmath` module includes utilities to perform
-optimal mathematical operations in scikit-learn that are not available in SciPy.
-"""
+"""Utilities to perform optimal mathematical operations in scikit-learn."""
 
 # Authors: Gael Varoquaux
 #          Alexandre Gramfort
diff --git a/sklearn/utils/fixes.py b/sklearn/utils/fixes.py
index e33519a3154d8..21e62150b0356 100644
--- a/sklearn/utils/fixes.py
+++ b/sklearn/utils/fixes.py
@@ -18,7 +18,6 @@
 import scipy
 import scipy.sparse.linalg
 import scipy.stats
-import threadpoolctl
 
 import sklearn
 
@@ -97,42 +96,6 @@ def _percentile(a, q, *, method="linear", **kwargs):
     from numpy import percentile  # type: ignore  # noqa
 
 
-# compatibility fix for threadpoolctl >= 3.0.0
-# since version 3 it's possible to setup a global threadpool controller to avoid
-# looping through all loaded shared libraries each time.
-# the global controller is created during the first call to threadpoolctl.
-def _get_threadpool_controller():
-    if not hasattr(threadpoolctl, "ThreadpoolController"):
-        return None
-
-    if not hasattr(sklearn, "_sklearn_threadpool_controller"):
-        sklearn._sklearn_threadpool_controller = threadpoolctl.ThreadpoolController()
-
-    return sklearn._sklearn_threadpool_controller
-
-
-def threadpool_limits(limits=None, user_api=None):
-    controller = _get_threadpool_controller()
-    if controller is not None:
-        return controller.limit(limits=limits, user_api=user_api)
-    else:
-        return threadpoolctl.threadpool_limits(limits=limits, user_api=user_api)
-
-
-threadpool_limits.__doc__ = threadpoolctl.threadpool_limits.__doc__
-
-
-def threadpool_info():
-    controller = _get_threadpool_controller()
-    if controller is not None:
-        return controller.info()
-    else:
-        return threadpoolctl.threadpool_info()
-
-
-threadpool_info.__doc__ = threadpoolctl.threadpool_info.__doc__
-
-
 # TODO: Remove when SciPy 1.11 is the minimum supported version
 def _mode(a, axis=0):
     if sp_version >= parse_version("1.9.0"):
@@ -428,7 +391,7 @@ def _in_unstable_openblas_configuration():
     import numpy  # noqa
     import scipy  # noqa
 
-    modules_info = threadpool_info()
+    modules_info = sklearn._threadpool_controller.info()
 
     open_blas_used = any(info["internal_api"] == "openblas" for info in modules_info)
     if not open_blas_used:
diff --git a/sklearn/utils/graph.py b/sklearn/utils/graph.py
index 06b2e152101a9..fd06f1df5f6c9 100644
--- a/sklearn/utils/graph.py
+++ b/sklearn/utils/graph.py
@@ -1,6 +1,4 @@
-"""
-The :mod:`sklearn.utils.graph` module includes graph utilities and algorithms.
-"""
+"""Graph utilities and algorithms."""
 
 # Authors: Aric Hagberg <hagberg@lanl.gov>
 #          Gael Varoquaux <gael.varoquaux@normalesup.org>
diff --git a/sklearn/utils/metadata_routing.py b/sklearn/utils/metadata_routing.py
index bb98d2f08b93e..b50c54b86b7a1 100644
--- a/sklearn/utils/metadata_routing.py
+++ b/sklearn/utils/metadata_routing.py
@@ -1,7 +1,4 @@
-"""
-The :mod:`sklearn.utils.metadata_routing` module includes utilities to route
-metadata within scikit-learn estimators.
-"""
+"""Utilities to route metadata within scikit-learn estimators."""
 
 # This module is not a separate sub-folder since that would result in a circular
 # import issue.
diff --git a/sklearn/utils/metaestimators.py b/sklearn/utils/metaestimators.py
index 639e000dd77a7..36edf4d166083 100644
--- a/sklearn/utils/metaestimators.py
+++ b/sklearn/utils/metaestimators.py
@@ -1,6 +1,4 @@
-"""
-The :mod:`sklearn.utils.metaestimators` module includes utilities for meta-estimators.
-"""
+"""Utilities for meta-estimators."""
 
 # Author: Joel Nothman
 #         Andreas Mueller
diff --git a/sklearn/utils/multiclass.py b/sklearn/utils/multiclass.py
index 2d87bfb77839e..1abc7832f4407 100644
--- a/sklearn/utils/multiclass.py
+++ b/sklearn/utils/multiclass.py
@@ -1,11 +1,9 @@
-"""
-The :mod:`sklearn.utils.multiclass` module includes utilities to handle
-multiclass/multioutput target in classifiers.
-"""
+"""Utilities to handle multiclass/multioutput target in classifiers."""
 
 # Author: Arnaud Joly, Joel Nothman, Hamzeh Alsalhi
 #
 # License: BSD 3 clause
+
 import warnings
 from collections.abc import Sequence
 from itertools import chain
diff --git a/sklearn/utils/parallel.py b/sklearn/utils/parallel.py
index d0dc2ec2be030..f6b3d8c4c4a6e 100644
--- a/sklearn/utils/parallel.py
+++ b/sklearn/utils/parallel.py
@@ -1,6 +1,4 @@
-"""
-The :mod:`sklearn.utils.parallel` customizes `joblib` tools for scikit-learn usage.
-"""
+"""Customizations of :mod:`joblib` tools for scikit-learn usage."""
 
 import functools
 import warnings
diff --git a/sklearn/utils/random.py b/sklearn/utils/random.py
index 1dfe8d83a94b3..9121f8425b58b 100644
--- a/sklearn/utils/random.py
+++ b/sklearn/utils/random.py
@@ -1,6 +1,4 @@
-"""
-The mod:`sklearn.utils.random` module includes utilities for random sampling.
-"""
+"""Utilities for random sampling."""
 
 # Author: Hamzeh Alsalhi <ha258@cornell.edu>
 #
diff --git a/sklearn/utils/sparsefuncs.py b/sklearn/utils/sparsefuncs.py
index a46e9e4d9ed93..29fb8fab8b75f 100644
--- a/sklearn/utils/sparsefuncs.py
+++ b/sklearn/utils/sparsefuncs.py
@@ -1,7 +1,4 @@
-"""
-The :mod:`sklearn.utils.sparsefuncs` module includes a collection of utilities to
-work with sparse matrices and arrays.
-"""
+"""A collection of utilities to work with sparse matrices and arrays."""
 
 # Authors: Manoj Kumar
 #          Thomas Unterthiner
diff --git a/sklearn/utils/sparsefuncs_fast.pyx b/sklearn/utils/sparsefuncs_fast.pyx
index c3bd0370d8b96..a6fdcd8c94463 100644
--- a/sklearn/utils/sparsefuncs_fast.pyx
+++ b/sklearn/utils/sparsefuncs_fast.pyx
@@ -1,7 +1,4 @@
-"""
-The :mod:`sklearn.utils.sparsefuncs_fast` module includes a collection of utilities to
-work with sparse matrices and arrays written in Cython.
-"""
+"""Utilities to work with sparse matrices and arrays written in Cython."""
 
 # Authors: Mathieu Blondel
 #          Olivier Grisel
diff --git a/sklearn/utils/tests/test_array_api.py b/sklearn/utils/tests/test_array_api.py
index ec4a487f0b1df..25913e7f54846 100644
--- a/sklearn/utils/tests/test_array_api.py
+++ b/sklearn/utils/tests/test_array_api.py
@@ -14,17 +14,22 @@
     _average,
     _convert_to_numpy,
     _estimator_with_converted_arrays,
+    _is_numpy_namespace,
+    _isin,
     _nanmax,
     _nanmin,
     _NumPyAPIWrapper,
+    _ravel,
     device,
     get_namespace,
+    get_namespace_and_device,
     indexing_dtype,
     supported_float_dtypes,
     yield_namespace_device_dtype_combinations,
 )
 from sklearn.utils._testing import (
     _array_api_for_tests,
+    assert_array_equal,
     skip_if_array_api_compat_not_configured,
 )
 from sklearn.utils.fixes import _IS_32BIT
@@ -348,6 +353,26 @@ def test_nan_reductions(library, X, reduction, expected):
     assert_allclose(result, expected)
 
 
+@pytest.mark.parametrize(
+    "namespace, _device, _dtype", yield_namespace_device_dtype_combinations()
+)
+def test_ravel(namespace, _device, _dtype):
+    xp = _array_api_for_tests(namespace, _device)
+
+    array = [[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]]
+    array_xp = xp.asarray(array, device=_device)
+    with config_context(array_api_dispatch=True):
+        result = _ravel(array_xp)
+
+    result = _convert_to_numpy(result, xp)
+    expected = numpy.ravel(array, order="C")
+
+    assert_allclose(expected, result)
+
+    if _is_numpy_namespace(xp):
+        assert numpy.asarray(result).flags["C_CONTIGUOUS"]
+
+
 @skip_if_array_api_compat_not_configured
 @pytest.mark.parametrize("library", ["cupy", "torch", "cupy.array_api"])
 def test_convert_to_numpy_gpu(library):  # pragma: nocover
@@ -482,3 +507,62 @@ def test_indexing_dtype(namespace, _device, _dtype):
         assert indexing_dtype(xp) == xp.int32
     else:
         assert indexing_dtype(xp) == xp.int64
+
+
+@pytest.mark.parametrize(
+    "array_namespace, device, _", yield_namespace_device_dtype_combinations()
+)
+@pytest.mark.parametrize("invert", [True, False])
+@pytest.mark.parametrize("assume_unique", [True, False])
+@pytest.mark.parametrize("element_size", [6, 10, 14])
+@pytest.mark.parametrize("int_dtype", ["int16", "int32", "int64", "uint8"])
+def test_isin(
+    array_namespace, device, _, invert, assume_unique, element_size, int_dtype
+):
+    xp = _array_api_for_tests(array_namespace, device)
+    r = element_size // 2
+    element = 2 * numpy.arange(element_size).reshape((r, 2)).astype(int_dtype)
+    test_elements = numpy.array(numpy.arange(14), dtype=int_dtype)
+    element_xp = xp.asarray(element, device=device)
+    test_elements_xp = xp.asarray(test_elements, device=device)
+    expected = numpy.isin(
+        element=element,
+        test_elements=test_elements,
+        assume_unique=assume_unique,
+        invert=invert,
+    )
+    with config_context(array_api_dispatch=True):
+        result = _isin(
+            element=element_xp,
+            test_elements=test_elements_xp,
+            xp=xp,
+            assume_unique=assume_unique,
+            invert=invert,
+        )
+
+    assert_array_equal(_convert_to_numpy(result, xp=xp), expected)
+
+
+def test_get_namespace_and_device():
+    # Use torch as a library with custom Device objects:
+    torch = pytest.importorskip("torch")
+    xp_torch = pytest.importorskip("array_api_compat.torch")
+    some_torch_tensor = torch.arange(3, device="cpu")
+    some_numpy_array = numpy.arange(3)
+
+    # When dispatch is disabled, get_namespace_and_device should return the
+    # default NumPy wrapper namespace and no device. Our code will handle such
+    # inputs via the usual __array__ interface without attempting to dispatch
+    # via the array API.
+    namespace, is_array_api, device = get_namespace_and_device(some_torch_tensor)
+    assert namespace is get_namespace(some_numpy_array)[0]
+    assert not is_array_api
+    assert device is None
+
+    # Otherwise, expose the torch namespace and device via array API compat
+    # wrapper.
+    with config_context(array_api_dispatch=True):
+        namespace, is_array_api, device = get_namespace_and_device(some_torch_tensor)
+        assert namespace is xp_torch
+        assert is_array_api
+        assert device == some_torch_tensor.device
diff --git a/sklearn/utils/tests/test_arrayfuncs.py b/sklearn/utils/tests/test_arrayfuncs.py
index 4a80a4c1edefd..a5c99427cbd00 100644
--- a/sklearn/utils/tests/test_arrayfuncs.py
+++ b/sklearn/utils/tests/test_arrayfuncs.py
@@ -26,7 +26,9 @@ def test_min_pos_no_positive(dtype):
     assert min_pos(X) == np.finfo(dtype).max
 
 
-@pytest.mark.parametrize("dtype", [np.int16, np.int32, np.float32, np.float64])
+@pytest.mark.parametrize(
+    "dtype", [np.int16, np.int32, np.int64, np.float32, np.float64]
+)
 @pytest.mark.parametrize("value", [0, 1.5, -1])
 def test_all_with_any_reduction_axis_1(dtype, value):
     # Check that return value is False when there is no row equal to `value`
diff --git a/sklearn/utils/tests/test_mask.py b/sklearn/utils/tests/test_mask.py
new file mode 100644
index 0000000000000..0eb88e71771f8
--- /dev/null
+++ b/sklearn/utils/tests/test_mask.py
@@ -0,0 +1,19 @@
+import pytest
+
+from sklearn.utils._mask import safe_mask
+from sklearn.utils.fixes import CSR_CONTAINERS
+from sklearn.utils.validation import check_random_state
+
+
+@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
+def test_safe_mask(csr_container):
+    random_state = check_random_state(0)
+    X = random_state.rand(5, 4)
+    X_csr = csr_container(X)
+    mask = [False, False, True, True, True]
+
+    mask = safe_mask(X, mask)
+    assert X[mask].shape[0] == 3
+
+    mask = safe_mask(X_csr, mask)
+    assert X_csr[mask].shape[0] == 3
diff --git a/sklearn/utils/tests/test_missing.py b/sklearn/utils/tests/test_missing.py
new file mode 100644
index 0000000000000..830e327f06a11
--- /dev/null
+++ b/sklearn/utils/tests/test_missing.py
@@ -0,0 +1,27 @@
+import numpy as np
+import pytest
+
+from sklearn.utils._missing import is_scalar_nan
+
+
+@pytest.mark.parametrize(
+    "value, result",
+    [
+        (float("nan"), True),
+        (np.nan, True),
+        (float(np.nan), True),
+        (np.float32(np.nan), True),
+        (np.float64(np.nan), True),
+        (0, False),
+        (0.0, False),
+        (None, False),
+        ("", False),
+        ("nan", False),
+        ([np.nan], False),
+        (9867966753463435747313673, False),  # Python int that overflows with C type
+    ],
+)
+def test_is_scalar_nan(value, result):
+    assert is_scalar_nan(value) is result
+    # make sure that we are returning a Python bool
+    assert isinstance(is_scalar_nan(value), bool)
diff --git a/sklearn/utils/tests/test_mocking.py b/sklearn/utils/tests/test_mocking.py
index 9c66d1345bb6d..bd143855e6dcd 100644
--- a/sklearn/utils/tests/test_mocking.py
+++ b/sklearn/utils/tests/test_mocking.py
@@ -1,6 +1,6 @@
 import numpy as np
 import pytest
-from numpy.testing import assert_allclose, assert_array_equal
+from numpy.testing import assert_array_equal
 from scipy import sparse
 
 from sklearn.datasets import load_iris
@@ -90,7 +90,7 @@ def test_checking_classifier(iris, input_type):
     assert clf.n_features_in_ == 4
 
     y_pred = clf.predict(X)
-    assert_array_equal(y_pred, np.zeros(y_pred.size, dtype=int))
+    assert all(pred in clf.classes_ for pred in y_pred)
 
     assert clf.score(X) == pytest.approx(0)
     clf.set_params(foo_param=10)
@@ -98,13 +98,10 @@ def test_checking_classifier(iris, input_type):
 
     y_proba = clf.predict_proba(X)
     assert y_proba.shape == (150, 3)
-    assert_allclose(y_proba[:, 0], 1)
-    assert_allclose(y_proba[:, 1:], 0)
+    assert np.logical_and(y_proba >= 0, y_proba <= 1).all()
 
     y_decision = clf.decision_function(X)
     assert y_decision.shape == (150, 3)
-    assert_allclose(y_decision[:, 0], 1)
-    assert_allclose(y_decision[:, 1:], 0)
 
     # check the shape in case of binary classification
     first_2_classes = np.logical_or(y == 0, y == 1)
@@ -114,12 +111,10 @@ def test_checking_classifier(iris, input_type):
 
     y_proba = clf.predict_proba(X)
     assert y_proba.shape == (100, 2)
-    assert_allclose(y_proba[:, 0], 1)
-    assert_allclose(y_proba[:, 1], 0)
+    assert np.logical_and(y_proba >= 0, y_proba <= 1).all()
 
     y_decision = clf.decision_function(X)
     assert y_decision.shape == (100,)
-    assert_allclose(y_decision, 0)
 
 
 @pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
diff --git a/sklearn/utils/tests/test_multiclass.py b/sklearn/utils/tests/test_multiclass.py
index 40a13526ab009..95a1ea0bb0806 100644
--- a/sklearn/utils/tests/test_multiclass.py
+++ b/sklearn/utils/tests/test_multiclass.py
@@ -598,7 +598,7 @@ def test_ovr_decision_function():
     assert_allclose(dec_values, dec_values_one, atol=1e-6)
 
 
-# TODO(1.6): Change to ValueError when byte labels is deprecated.
+# TODO(1.7): Change to ValueError when byte labels is deprecated.
 @pytest.mark.parametrize("input_type", ["list", "array"])
 def test_labels_in_bytes_format(input_type):
     # check that we raise an error with bytes encoded labels
diff --git a/sklearn/utils/tests/test_response.py b/sklearn/utils/tests/test_response.py
index c84bf6030336a..858c16cca4df1 100644
--- a/sklearn/utils/tests/test_response.py
+++ b/sklearn/utils/tests/test_response.py
@@ -240,36 +240,60 @@ def test_get_response_error(estimator, X, y, err_msg, params):
         _get_response_values_binary(estimator, X, **params)
 
 
-def test_get_response_predict_proba():
+@pytest.mark.parametrize("return_response_method_used", [True, False])
+def test_get_response_predict_proba(return_response_method_used):
     """Check the behaviour of `_get_response_values_binary` using `predict_proba`."""
     classifier = DecisionTreeClassifier().fit(X_binary, y_binary)
-    y_proba, pos_label = _get_response_values_binary(
-        classifier, X_binary, response_method="predict_proba"
+    results = _get_response_values_binary(
+        classifier,
+        X_binary,
+        response_method="predict_proba",
+        return_response_method_used=return_response_method_used,
     )
-    assert_allclose(y_proba, classifier.predict_proba(X_binary)[:, 1])
-    assert pos_label == 1
+    assert_allclose(results[0], classifier.predict_proba(X_binary)[:, 1])
+    assert results[1] == 1
+    if return_response_method_used:
+        assert results[2] == "predict_proba"
 
-    y_proba, pos_label = _get_response_values_binary(
-        classifier, X_binary, response_method="predict_proba", pos_label=0
+    results = _get_response_values_binary(
+        classifier,
+        X_binary,
+        response_method="predict_proba",
+        pos_label=0,
+        return_response_method_used=return_response_method_used,
     )
-    assert_allclose(y_proba, classifier.predict_proba(X_binary)[:, 0])
-    assert pos_label == 0
+    assert_allclose(results[0], classifier.predict_proba(X_binary)[:, 0])
+    assert results[1] == 0
+    if return_response_method_used:
+        assert results[2] == "predict_proba"
 
 
-def test_get_response_decision_function():
+@pytest.mark.parametrize("return_response_method_used", [True, False])
+def test_get_response_decision_function(return_response_method_used):
     """Check the behaviour of `_get_response_values_binary` using decision_function."""
     classifier = LogisticRegression().fit(X_binary, y_binary)
-    y_score, pos_label = _get_response_values_binary(
-        classifier, X_binary, response_method="decision_function"
+    results = _get_response_values_binary(
+        classifier,
+        X_binary,
+        response_method="decision_function",
+        return_response_method_used=return_response_method_used,
     )
-    assert_allclose(y_score, classifier.decision_function(X_binary))
-    assert pos_label == 1
+    assert_allclose(results[0], classifier.decision_function(X_binary))
+    assert results[1] == 1
+    if return_response_method_used:
+        assert results[2] == "decision_function"
 
-    y_score, pos_label = _get_response_values_binary(
-        classifier, X_binary, response_method="decision_function", pos_label=0
+    results = _get_response_values_binary(
+        classifier,
+        X_binary,
+        response_method="decision_function",
+        pos_label=0,
+        return_response_method_used=return_response_method_used,
     )
-    assert_allclose(y_score, classifier.decision_function(X_binary) * -1)
-    assert pos_label == 0
+    assert_allclose(results[0], classifier.decision_function(X_binary) * -1)
+    assert results[1] == 0
+    if return_response_method_used:
+        assert results[2] == "decision_function"
 
 
 @pytest.mark.parametrize(
diff --git a/sklearn/utils/tests/test_show_versions.py b/sklearn/utils/tests/test_show_versions.py
index bd166dfd8e522..aade231e46f56 100644
--- a/sklearn/utils/tests/test_show_versions.py
+++ b/sklearn/utils/tests/test_show_versions.py
@@ -1,6 +1,7 @@
+from threadpoolctl import threadpool_info
+
 from sklearn.utils._show_versions import _get_deps_info, _get_sys_info, show_versions
 from sklearn.utils._testing import ignore_warnings
-from sklearn.utils.fixes import threadpool_info
 
 
 def test_get_sys_info():
diff --git a/sklearn/utils/tests/test_utils.py b/sklearn/utils/tests/test_utils.py
index 8d9af93d216ea..4d71bf8860c81 100644
--- a/sklearn/utils/tests/test_utils.py
+++ b/sklearn/utils/tests/test_utils.py
@@ -1,153 +1,7 @@
-import warnings
-
 import joblib
-import numpy as np
 import pytest
 
-from sklearn.utils import (
-    check_random_state,
-    column_or_1d,
-    deprecated,
-    parallel_backend,
-    register_parallel_backend,
-    safe_mask,
-    tosequence,
-)
-from sklearn.utils._missing import is_scalar_nan
-from sklearn.utils._testing import assert_array_equal
-from sklearn.utils.fixes import CSR_CONTAINERS
-from sklearn.utils.validation import _is_polars_df
-
-
-def test_make_rng():
-    # Check the check_random_state utility function behavior
-    assert check_random_state(None) is np.random.mtrand._rand
-    assert check_random_state(np.random) is np.random.mtrand._rand
-
-    rng_42 = np.random.RandomState(42)
-    assert check_random_state(42).randint(100) == rng_42.randint(100)
-
-    rng_42 = np.random.RandomState(42)
-    assert check_random_state(rng_42) is rng_42
-
-    rng_42 = np.random.RandomState(42)
-    assert check_random_state(43).randint(100) != rng_42.randint(100)
-
-    with pytest.raises(ValueError):
-        check_random_state("some invalid seed")
-
-
-def test_deprecated():
-    # Test whether the deprecated decorator issues appropriate warnings
-    # Copied almost verbatim from https://docs.python.org/library/warnings.html
-
-    # First a function...
-    with warnings.catch_warnings(record=True) as w:
-        warnings.simplefilter("always")
-
-        @deprecated()
-        def ham():
-            return "spam"
-
-        spam = ham()
-
-        assert spam == "spam"  # function must remain usable
-
-        assert len(w) == 1
-        assert issubclass(w[0].category, FutureWarning)
-        assert "deprecated" in str(w[0].message).lower()
-
-    # ... then a class.
-    with warnings.catch_warnings(record=True) as w:
-        warnings.simplefilter("always")
-
-        @deprecated("don't use this")
-        class Ham:
-            SPAM = 1
-
-        ham = Ham()
-
-        assert hasattr(ham, "SPAM")
-
-        assert len(w) == 1
-        assert issubclass(w[0].category, FutureWarning)
-        assert "deprecated" in str(w[0].message).lower()
-
-
-@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
-def test_safe_mask(csr_container):
-    random_state = check_random_state(0)
-    X = random_state.rand(5, 4)
-    X_csr = csr_container(X)
-    mask = [False, False, True, True, True]
-
-    mask = safe_mask(X, mask)
-    assert X[mask].shape[0] == 3
-
-    mask = safe_mask(X_csr, mask)
-    assert X_csr[mask].shape[0] == 3
-
-
-def test_column_or_1d():
-    EXAMPLES = [
-        ("binary", ["spam", "egg", "spam"]),
-        ("binary", [0, 1, 0, 1]),
-        ("continuous", np.arange(10) / 20.0),
-        ("multiclass", [1, 2, 3]),
-        ("multiclass", [0, 1, 2, 2, 0]),
-        ("multiclass", [[1], [2], [3]]),
-        ("multilabel-indicator", [[0, 1, 0], [0, 0, 1]]),
-        ("multiclass-multioutput", [[1, 2, 3]]),
-        ("multiclass-multioutput", [[1, 1], [2, 2], [3, 1]]),
-        ("multiclass-multioutput", [[5, 1], [4, 2], [3, 1]]),
-        ("multiclass-multioutput", [[1, 2, 3]]),
-        ("continuous-multioutput", np.arange(30).reshape((-1, 3))),
-    ]
-
-    for y_type, y in EXAMPLES:
-        if y_type in ["binary", "multiclass", "continuous"]:
-            assert_array_equal(column_or_1d(y), np.ravel(y))
-        else:
-            with pytest.raises(ValueError):
-                column_or_1d(y)
-
-
-@pytest.mark.parametrize(
-    "value, result",
-    [
-        (float("nan"), True),
-        (np.nan, True),
-        (float(np.nan), True),
-        (np.float32(np.nan), True),
-        (np.float64(np.nan), True),
-        (0, False),
-        (0.0, False),
-        (None, False),
-        ("", False),
-        ("nan", False),
-        ([np.nan], False),
-        (9867966753463435747313673, False),  # Python int that overflows with C type
-    ],
-)
-def test_is_scalar_nan(value, result):
-    assert is_scalar_nan(value) is result
-    # make sure that we are returning a Python bool
-    assert isinstance(is_scalar_nan(value), bool)
-
-
-def dummy_func():
-    pass
-
-
-def test__is_polars_df():
-    """Check that _is_polars_df return False for non-dataframe objects."""
-
-    class LooksLikePolars:
-        def __init__(self):
-            self.columns = ["a", "b"]
-            self.schema = ["a", "b"]
-
-    assert not _is_polars_df(LooksLikePolars())
+from sklearn.utils import parallel_backend, register_parallel_backend, tosequence
 
 
 # TODO(1.7): remove
diff --git a/sklearn/utils/tests/test_validation.py b/sklearn/utils/tests/test_validation.py
index 5e54443a84165..92fff950e875e 100644
--- a/sklearn/utils/tests/test_validation.py
+++ b/sklearn/utils/tests/test_validation.py
@@ -80,11 +80,31 @@
     check_is_fitted,
     check_memory,
     check_non_negative,
+    check_random_state,
     check_scalar,
+    column_or_1d,
     has_fit_parameter,
 )
 
 
+def test_make_rng():
+    # Check the check_random_state utility function behavior
+    assert check_random_state(None) is np.random.mtrand._rand
+    assert check_random_state(np.random) is np.random.mtrand._rand
+
+    rng_42 = np.random.RandomState(42)
+    assert check_random_state(42).randint(100) == rng_42.randint(100)
+
+    rng_42 = np.random.RandomState(42)
+    assert check_random_state(rng_42) is rng_42
+
+    rng_42 = np.random.RandomState(42)
+    assert check_random_state(43).randint(100) != rng_42.randint(100)
+
+    with pytest.raises(ValueError):
+        check_random_state("some invalid seed")
+
+
 def test_as_float_array():
     # Test function for as_float_array
     X = np.ones((3, 10), dtype=np.int32)
@@ -341,6 +361,14 @@ def test_check_array():
     with pytest.raises(ValueError, match="Expected 2D array, got scalar array instead"):
         check_array(10, ensure_2d=True)
 
+    # ensure_2d=True with 1d sparse array
+    if hasattr(sp, "csr_array"):
+        sparse_row = next(iter(sp.csr_array(X)))
+        if sparse_row.ndim == 1:
+            # In scipy 1.14 and later, sparse row is 1D while it was 2D before.
+            with pytest.raises(ValueError, match="Expected 2D input, got"):
+                check_array(sparse_row, accept_sparse=True, ensure_2d=True)
+
     # don't allow ndim > 3
     X_ndim = np.arange(8).reshape(2, 2, 2)
     with pytest.raises(ValueError):
@@ -2061,3 +2089,38 @@ def test_to_object_array(sequence):
     assert isinstance(out, np.ndarray)
     assert out.dtype.kind == "O"
     assert out.ndim == 1
+
+
+def test_column_or_1d():
+    EXAMPLES = [
+        ("binary", ["spam", "egg", "spam"]),
+        ("binary", [0, 1, 0, 1]),
+        ("continuous", np.arange(10) / 20.0),
+        ("multiclass", [1, 2, 3]),
+        ("multiclass", [0, 1, 2, 2, 0]),
+        ("multiclass", [[1], [2], [3]]),
+        ("multilabel-indicator", [[0, 1, 0], [0, 0, 1]]),
+        ("multiclass-multioutput", [[1, 2, 3]]),
+        ("multiclass-multioutput", [[1, 1], [2, 2], [3, 1]]),
+        ("multiclass-multioutput", [[5, 1], [4, 2], [3, 1]]),
+        ("multiclass-multioutput", [[1, 2, 3]]),
+        ("continuous-multioutput", np.arange(30).reshape((-1, 3))),
+    ]
+
+    for y_type, y in EXAMPLES:
+        if y_type in ["binary", "multiclass", "continuous"]:
+            assert_array_equal(column_or_1d(y), np.ravel(y))
+        else:
+            with pytest.raises(ValueError):
+                column_or_1d(y)
+
+
+def test__is_polars_df():
+    """Check that _is_polars_df return False for non-dataframe objects."""
+
+    class LooksLikePolars:
+        def __init__(self):
+            self.columns = ["a", "b"]
+            self.schema = ["a", "b"]
+
+    assert not _is_polars_df(LooksLikePolars())
diff --git a/sklearn/utils/validation.py b/sklearn/utils/validation.py
index d0a2fb098931f..a2d621e6a0dc6 100644
--- a/sklearn/utils/validation.py
+++ b/sklearn/utils/validation.py
@@ -1,7 +1,4 @@
-"""
-The :mod:`sklearn.utils.validation` module includes functions to validate
-input and parameters within scikit-learn estimators.
-"""
+"""Functions to validate input and parameters within scikit-learn estimators."""
 
 # Authors: Olivier Grisel
 #          Gael Varoquaux
@@ -98,7 +95,7 @@ def _assert_all_finite(
 ):
     """Like assert_all_finite, but only for ndarray."""
 
-    xp, _ = get_namespace(X)
+    xp, is_array_api = get_namespace(X)
 
     if _get_config()["assume_finite"]:
         return
@@ -106,7 +103,7 @@ def _assert_all_finite(
     X = xp.asarray(X)
 
     # for object dtype data, we only check for NaNs (GH-13254)
-    if X.dtype == np.dtype("object") and not allow_nan:
+    if not is_array_api and X.dtype == np.dtype("object") and not allow_nan:
         if _object_dtype_isnan(X).any():
             raise ValueError("Input contains NaN")
 
@@ -488,7 +485,7 @@ def indexable(*iterables):
 
     Checks consistent length, passes through None, and ensures that everything
     can be indexed by converting sparse matrices to csr and converting
-    non-interable objects to arrays.
+    non-iterable objects to arrays.
 
     Parameters
     ----------
@@ -973,6 +970,13 @@ def is_sparse(dtype):
             estimator_name=estimator_name,
             input_name=input_name,
         )
+        if ensure_2d and array.ndim < 2:
+            raise ValueError(
+                f"Expected 2D input, got input with shape {array.shape}.\n"
+                "Reshape your data either using array.reshape(-1, 1) if "
+                "your data has a single feature or array.reshape(1, -1) "
+                "if it contains a single sample."
+            )
     else:
         # If np.array(..) gives ComplexWarning, then we convert the warning
         # to an error. This is needed because specifying a non complex
@@ -1303,7 +1307,7 @@ def _check_y(y, multi_output=False, y_numeric=False, estimator=None):
         y = column_or_1d(y, warn=True)
         _assert_all_finite(y, input_name="y", estimator_name=estimator_name)
         _ensure_no_complex_data(y)
-    if y_numeric and y.dtype.kind == "O":
+    if y_numeric and hasattr(y.dtype, "kind") and y.dtype.kind == "O":
         y = y.astype(np.float64)
 
     return y

From 9f58f8af3b92ab582982f359bfe9d3d635c5abbd Mon Sep 17 00:00:00 2001
From: Michael Mayer <mayermichael79@gmail.com>
Date: Fri, 31 May 2024 15:41:54 +0200
Subject: [PATCH 60/66] Review of docu

Co-authored-by: Christian Lorentzen <lorentzen.ch@gmail.com>
---
 doc/modules/h_statistic.rst                    | 8 ++++----
 examples/inspection/plot_partial_dependence.py | 2 +-
 2 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/doc/modules/h_statistic.rst b/doc/modules/h_statistic.rst
index c1e5f7a138579..69f328e37066a 100644
--- a/doc/modules/h_statistic.rst
+++ b/doc/modules/h_statistic.rst
@@ -7,8 +7,8 @@ Friedman and Popescu's H-Statistic
 
 .. currentmodule:: sklearn.inspection
 
-What is the difference between a white box model and a black box model?
-It is the many and complicated interaction effects of the latter.
+What is the difference between a white box model (think linear model) and a black box model (e.g. boosted trees)?
+One main difference is the many and complicated interaction effects of the latter.
 
 Such interaction effects can be visualized by two-dimensional or stratified
 partial dependence plots (PDP). But how to figure out between *which feature pairs*
@@ -27,7 +27,7 @@ the bike sharing dataset, with a
    :align: center
    :scale: 70
 
-The statistics have been compured for the five most important features.
+The statistics have been computed for the five most important features.
 
 Mathematical definition
 =======================
@@ -86,7 +86,7 @@ Remarks
 1. Partial dependence functions and :math:`F` are centered to mean 0.
 2. Partial dependence functions and :math:`F` are evaluated over the data distribution.
    This is different to partial dependence plots, where one uses a fixed grid.
-3. Weighted versions follow by replacing all arithmetic means by corresponding weighted means.
+3. Weighted versions follow by replacing all arithmetic means by corresponding weighted averages.
 4. Multi-output prediction (e.g., probabilistic classification) is handled component-wise.
 5. Due to undesired extrapolation of partial dependence functions, values above 1 may occur.
 
diff --git a/examples/inspection/plot_partial_dependence.py b/examples/inspection/plot_partial_dependence.py
index f90c0ec8efbf1..b0e0d18d957bb 100644
--- a/examples/inspection/plot_partial_dependence.py
+++ b/examples/inspection/plot_partial_dependence.py
@@ -603,7 +603,7 @@
 print(f"done in {time() - tic:.3f}s")
 
 fig, axes = plt.subplots(1, 2, figsize=(8, 4))
-bar_labels = np.array([str(pair) for pair in H["feature_pairs"]])
+bar_labels = [str(pair) for pair in H["feature_pairs"]]
 stats = (H["h_squared_pairwise"], np.sqrt(H["numerator_pairwise"]))
 
 for ax, stat, name in zip(axes, stats, ("$H^2$", "Unnormalized $H$")):

From 00d0f638d79185ed34079d49d33f78466ed66922 Mon Sep 17 00:00:00 2001
From: Michael Mayer <mayermichael79@gmail.com>
Date: Fri, 31 May 2024 15:47:46 +0200
Subject: [PATCH 61/66] Fix findings of docu review

---
 doc/images/h_statistic.png  | Bin 30211 -> 0 bytes
 doc/modules/h_statistic.rst |   4 ++--
 2 files changed, 2 insertions(+), 2 deletions(-)
 delete mode 100644 doc/images/h_statistic.png

diff --git a/doc/images/h_statistic.png b/doc/images/h_statistic.png
deleted file mode 100644
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F^$!oB&IbSh

diff --git a/doc/modules/h_statistic.rst b/doc/modules/h_statistic.rst
index 69f328e37066a..5a1001de70875 100644
--- a/doc/modules/h_statistic.rst
+++ b/doc/modules/h_statistic.rst
@@ -83,8 +83,8 @@ and
 Remarks
 =======
 
-1. Partial dependence functions and :math:`F` are centered to mean 0.
-2. Partial dependence functions and :math:`F` are evaluated over the data distribution.
+1. Partial dependence functions are centered to mean 0.
+2. Partial dependence functions are evaluated over the data distribution.
    This is different to partial dependence plots, where one uses a fixed grid.
 3. Weighted versions follow by replacing all arithmetic means by corresponding weighted averages.
 4. Multi-output prediction (e.g., probabilistic classification) is handled component-wise.

From 275c889655c047078be3976aa2173f9e4a377677 Mon Sep 17 00:00:00 2001
From: Michael Mayer <mayermichael79@gmail.com>
Date: Fri, 31 May 2024 18:25:26 +0200
Subject: [PATCH 62/66] revert change in plot_partial_dependence.py

---
 examples/inspection/plot_partial_dependence.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/inspection/plot_partial_dependence.py b/examples/inspection/plot_partial_dependence.py
index b0e0d18d957bb..f90c0ec8efbf1 100644
--- a/examples/inspection/plot_partial_dependence.py
+++ b/examples/inspection/plot_partial_dependence.py
@@ -603,7 +603,7 @@
 print(f"done in {time() - tic:.3f}s")
 
 fig, axes = plt.subplots(1, 2, figsize=(8, 4))
-bar_labels = [str(pair) for pair in H["feature_pairs"]]
+bar_labels = np.array([str(pair) for pair in H["feature_pairs"]])
 stats = (H["h_squared_pairwise"], np.sqrt(H["numerator_pairwise"]))
 
 for ax, stat, name in zip(axes, stats, ("$H^2$", "Unnormalized $H$")):

From 365891296ad77e8ee4a4f53ac65f6980b2959ccf Mon Sep 17 00:00:00 2001
From: Michael Mayer <mayermichael79@gmail.com>
Date: Fri, 7 Jun 2024 11:09:12 +0200
Subject: [PATCH 63/66] More unit tests

---
 sklearn/inspection/_h_statistic.py           |  6 +-
 sklearn/inspection/tests/test_h_statistic.py | 76 +++++++++++++++++++-
 2 files changed, 78 insertions(+), 4 deletions(-)

diff --git a/sklearn/inspection/_h_statistic.py b/sklearn/inspection/_h_statistic.py
index 0435416002442..afdee0b9d1522 100644
--- a/sklearn/inspection/_h_statistic.py
+++ b/sklearn/inspection/_h_statistic.py
@@ -236,16 +236,16 @@ def h_statistic(
     if is_regressor(estimator):
         pred_fun = getattr(estimator, "predict", None)
         if pred_fun is None:
-            raise ValueError("The regressor has no predict() method.")
+            raise ValueError("The regressor has no predict method")
     elif is_classifier(estimator):
         if isinstance(estimator.classes_[0], np.ndarray):
             raise ValueError("Multiclass-multioutput estimators are not supported")
         reduce_binary = len(estimator.classes_) == 2
         pred_fun = getattr(estimator, "predict_proba", None)
         if pred_fun is None:
-            raise ValueError("The classifier has no predict_proba() method.")
+            raise ValueError("The classifier has no predict_proba method")
     else:
-        raise ValueError("'estimator' must be a regressor or classifier.")
+        raise ValueError("'estimator' must be a regressor or classifier")
 
     # Use check_array only on lists and other non-array-likes / sparse. Do not
     # convert DataFrame into a NumPy array.
diff --git a/sklearn/inspection/tests/test_h_statistic.py b/sklearn/inspection/tests/test_h_statistic.py
index b561bed8d2f8e..a76f5a18d2179 100644
--- a/sklearn/inspection/tests/test_h_statistic.py
+++ b/sklearn/inspection/tests/test_h_statistic.py
@@ -2,7 +2,9 @@
 import pytest
 from numpy.testing import assert_allclose
 
-from sklearn.base import RegressorMixin, clone
+import sklearn
+from sklearn.base import BaseEstimator, ClassifierMixin, RegressorMixin, clone
+from sklearn.cluster import KMeans
 from sklearn.datasets import make_classification, make_regression
 from sklearn.ensemble import (
     HistGradientBoostingRegressor,
@@ -21,6 +23,8 @@ def __init__(self, pred_fun):
         self.is_fitted_ = True
 
     def fit(self, *args, **kwargs):
+        # Simulate that something happens during fit()
+        self.coef_ = 0
         return self
 
     def predict(self, X):
@@ -216,3 +220,73 @@ def test_h_statistic_does_not_change_pandas_input(n_max, sample_weight):
     _ = h_statistic(model, X_df, sample_weight=w, n_max=n_max, random_state=1)
 
     pd.testing.assert_frame_equal(X_df, X_df_orig)
+
+
+# Slightly modified from test_partial_dependence.py
+@pytest.mark.parametrize(
+    "Estimator",
+    (
+        sklearn.tree.DecisionTreeClassifier,
+        sklearn.ensemble.RandomForestClassifier,
+    ),
+)
+def test_multiclass_multioutput(Estimator):
+    # Make sure error is raised for multiclass-multioutput classifiers
+
+    # make multiclass-multioutput dataset
+    X, y = make_classification(n_classes=3, n_clusters_per_class=1, random_state=0)
+    y = np.array([y, y]).T
+
+    est = Estimator()
+    est.fit(X, y)
+
+    with pytest.raises(
+        ValueError, match="Multiclass-multioutput estimators are not supported"
+    ):
+        h_statistic(est, X, [0, 1])
+
+
+class RegressorWithoutPredict(RegressorMixin, BaseEstimator):
+    def fit(self, X, y):
+        # Simulate that something happens during fit()
+        self.coef_ = 0
+        return self
+
+    def predict_proba(self, X):
+        return np.zeros(X.shape[0])
+
+
+class ClassifierWithoutPredictProba(ClassifierMixin, BaseEstimator):
+    def fit(self, X, y):
+        # Simulate that we have some classes
+        self.classes_ = [0, 1]
+        return self
+
+    def predict(self, X):
+        return np.zeros(X.shape[0])
+
+
+@pytest.mark.parametrize(
+    "estimator, err_msg",
+    [
+        (
+            RegressorWithoutPredict(),
+            "The regressor has no predict method",
+        ),
+        (
+            ClassifierWithoutPredictProba(),
+            "The classifier has no predict_proba method",
+        ),
+        (
+            KMeans(n_clusters=2, random_state=0, n_init="auto"),
+            "'estimator' must be a regressor or classifier",
+        ),
+    ],
+)
+def test_h_statistic_error(estimator, err_msg):
+    y = np.array([0, 0, 1, 1])
+    X = np.zeros((len(y), 2))
+    estimator.fit(X, y)
+
+    with pytest.raises(ValueError, match=err_msg):
+        h_statistic(estimator, X)

From d27e57b80657b9d884a117928be48ab0a7566e41 Mon Sep 17 00:00:00 2001
From: Michael Mayer <mayermichael79@gmail.com>
Date: Fri, 7 Jun 2024 12:01:06 +0200
Subject: [PATCH 64/66] Test that method works even if input is mixed type

---
 sklearn/inspection/tests/test_h_statistic.py | 35 ++++++++++++++++++++
 1 file changed, 35 insertions(+)

diff --git a/sklearn/inspection/tests/test_h_statistic.py b/sklearn/inspection/tests/test_h_statistic.py
index a76f5a18d2179..9af15b3ddb15b 100644
--- a/sklearn/inspection/tests/test_h_statistic.py
+++ b/sklearn/inspection/tests/test_h_statistic.py
@@ -222,6 +222,41 @@ def test_h_statistic_does_not_change_pandas_input(n_max, sample_weight):
     pd.testing.assert_frame_equal(X_df, X_df_orig)
 
 
+def test_h_statistic_works_with_mixed_type_pandas_data():
+    pd = pytest.importorskip("pandas")
+
+    from sklearn.compose import ColumnTransformer
+    from sklearn.pipeline import Pipeline
+    from sklearn.preprocessing import OrdinalEncoder
+
+    y = np.array([0, 1, 2, 3])
+    X = np.array([y, y]).T
+    X_mixed = pd.DataFrame({"x1": y, "x2": list("abcd")})
+
+    # Model on homogeneous numpy array
+    model = RandomForestRegressor(random_state=0).fit(X, y)
+
+    # Identical model on mixed-type pandas df
+    preprocessor = ColumnTransformer(
+        transformers=[
+            ("keep", "passthrough", ["x1"]),
+            ("ordinal", OrdinalEncoder(), ["x2"]),
+        ]
+    )
+    model_mixed = Pipeline(
+        steps=[
+            ("prep", preprocessor),
+            ("rf", RandomForestRegressor(random_state=0)),
+        ]
+    )
+    model_mixed.fit(X_mixed, y)
+
+    assert_allclose(
+        h_statistic(model, X).h_squared_pairwise,
+        h_statistic(model_mixed, X_mixed).h_squared_pairwise,
+    )
+
+
 # Slightly modified from test_partial_dependence.py
 @pytest.mark.parametrize(
     "Estimator",

From 4a10780c086856410a4423e88b861fecaa64408c Mon Sep 17 00:00:00 2001
From: Michael Mayer <mayermichael79@gmail.com>
Date: Fri, 7 Jun 2024 14:13:14 +0200
Subject: [PATCH 65/66] Apply suggestions from unit test review

Co-authored-by: Christian Lorentzen <lorentzen.ch@gmail.com>
---
 sklearn/inspection/tests/test_h_statistic.py | 10 ++++------
 1 file changed, 4 insertions(+), 6 deletions(-)

diff --git a/sklearn/inspection/tests/test_h_statistic.py b/sklearn/inspection/tests/test_h_statistic.py
index 9af15b3ddb15b..f2ae683843e52 100644
--- a/sklearn/inspection/tests/test_h_statistic.py
+++ b/sklearn/inspection/tests/test_h_statistic.py
@@ -265,15 +265,13 @@ def test_h_statistic_works_with_mixed_type_pandas_data():
         sklearn.ensemble.RandomForestClassifier,
     ),
 )
-def test_multiclass_multioutput(Estimator):
+def test_h_statistic_raises_for_multioutput(Estimator):
     # Make sure error is raised for multiclass-multioutput classifiers
 
     # make multiclass-multioutput dataset
     X, y = make_classification(n_classes=3, n_clusters_per_class=1, random_state=0)
     y = np.array([y, y]).T
-
-    est = Estimator()
-    est.fit(X, y)
+    est = Estimator().fit(X, y)
 
     with pytest.raises(
         ValueError, match="Multiclass-multioutput estimators are not supported"
@@ -288,7 +286,7 @@ def fit(self, X, y):
         return self
 
     def predict_proba(self, X):
-        return np.zeros(X.shape[0])
+        return np.zeros(X.shape[0])  # pragma: no cover
 
 
 class ClassifierWithoutPredictProba(ClassifierMixin, BaseEstimator):
@@ -298,7 +296,7 @@ def fit(self, X, y):
         return self
 
     def predict(self, X):
-        return np.zeros(X.shape[0])
+        return np.zeros(X.shape[0])  # pragma: no cover
 
 
 @pytest.mark.parametrize(

From 9bd7767bbab023512c2c2403b0360aacf05a28a6 Mon Sep 17 00:00:00 2001
From: Michael Mayer <mayermichael79@gmail.com>
Date: Fri, 7 Jun 2024 14:14:47 +0200
Subject: [PATCH 66/66] Changes from review of unit tests (I)

---
 sklearn/inspection/tests/test_h_statistic.py | 14 ++++++++------
 1 file changed, 8 insertions(+), 6 deletions(-)

diff --git a/sklearn/inspection/tests/test_h_statistic.py b/sklearn/inspection/tests/test_h_statistic.py
index 9af15b3ddb15b..3ba554584abdb 100644
--- a/sklearn/inspection/tests/test_h_statistic.py
+++ b/sklearn/inspection/tests/test_h_statistic.py
@@ -5,6 +5,7 @@
 import sklearn
 from sklearn.base import BaseEstimator, ClassifierMixin, RegressorMixin, clone
 from sklearn.cluster import KMeans
+from sklearn.compose import ColumnTransformer
 from sklearn.datasets import make_classification, make_regression
 from sklearn.ensemble import (
     HistGradientBoostingRegressor,
@@ -13,6 +14,8 @@
 )
 from sklearn.inspection import h_statistic
 from sklearn.linear_model import LinearRegression
+from sklearn.pipeline import Pipeline
+from sklearn.preprocessing import OrdinalEncoder
 
 
 class Deterministic(RegressorMixin):
@@ -223,12 +226,9 @@ def test_h_statistic_does_not_change_pandas_input(n_max, sample_weight):
 
 
 def test_h_statistic_works_with_mixed_type_pandas_data():
+    """Mixed-type input should work even when np.unique() fails."""
     pd = pytest.importorskip("pandas")
 
-    from sklearn.compose import ColumnTransformer
-    from sklearn.pipeline import Pipeline
-    from sklearn.preprocessing import OrdinalEncoder
-
     y = np.array([0, 1, 2, 3])
     X = np.array([y, y]).T
     X_mixed = pd.DataFrame({"x1": y, "x2": list("abcd")})
@@ -257,7 +257,6 @@ def test_h_statistic_works_with_mixed_type_pandas_data():
     )
 
 
-# Slightly modified from test_partial_dependence.py
 @pytest.mark.parametrize(
     "Estimator",
     (
@@ -266,7 +265,10 @@ def test_h_statistic_works_with_mixed_type_pandas_data():
     ),
 )
 def test_multiclass_multioutput(Estimator):
-    # Make sure error is raised for multiclass-multioutput classifiers
+    """Make sure error is raised for multiclass-multioutput classifiers
+
+    Slightly modified from test_partial_dependence.py
+    """
 
     # make multiclass-multioutput dataset
     X, y = make_classification(n_classes=3, n_clusters_per_class=1, random_state=0)