v0.22.rst

.. currentmodule:: sklearn

Version 0.22.0

November 29 2019

For a short description of the main highlights of the release, please refer to :ref:`sphx_glr_auto_examples_release_highlights_plot_release_highlights_0_22_0.py`.

Website update

Our website was revamped and given a fresh new look. :pr:`14849` by `Thomas Fan`_.

Clear definition of the public API

Scikit-learn has a public API, and a private API.

We do our best not to break the public API, and to only introduce backward-compatible changes that do not require any user action. However, in cases where that's not possible, any change to the public API is subject to a deprecation cycle of two minor versions. The private API isn't publicly documented and isn't subject to any deprecation cycle, so users should not rely on its stability.

A function or object is public if it is documented in the API Reference and if it can be imported with an import path without leading underscores. For example sklearn.pipeline.make_pipeline is public, while sklearn.pipeline._name_estimators is private. sklearn.ensemble._gb.BaseEnsemble is private too because the whole _gb module is private.

Up to 0.22, some tools were de-facto public (no leading underscore), while they should have been private in the first place. In version 0.22, these tools have been made properly private, and the public API space has been cleaned. In addition, importing from most sub-modules is now deprecated: you should for example use from sklearn.cluster import Birch instead of from sklearn.cluster.birch import Birch (in practice, birch.py has been moved to _birch.py).

Note

All the tools in the public API should be documented in the API Reference. If you find a public tool (without leading underscore) that isn't in the API reference, that means it should either be private or documented. Please let us know by opening an issue!

This work was tracked in issue 9250 and issue 12927.

Deprecations: using FutureWarning from now on

When deprecating a feature, previous versions of scikit-learn used to raise a DeprecationWarning. Since the DeprecationWarnings aren't shown by default by Python, scikit-learn needed to resort to a custom warning filter to always show the warnings. That filter would sometimes interfere with users custom warning filters.

Starting from version 0.22, scikit-learn will show FutureWarnings for deprecations, as recommended by the Python documentation. FutureWarnings are always shown by default by Python, so the custom filter has been removed and scikit-learn no longer hinders with user filters. :pr:`15080` by `Nicolas Hug`_.

Changed models

The following estimators and functions, when fit with the same data and parameters, may produce different models from the previous version. This often occurs due to changes in the modelling logic (bug fixes or enhancements), or in random sampling procedures.

• :class:`cluster.KMeans` when n_jobs=1. |Fix|
• :class:`decomposition.SparseCoder`, :class:`decomposition.DictionaryLearning`, and :class:`decomposition.MiniBatchDictionaryLearning` |Fix|
• :class:`decomposition.SparseCoder` with algorithm='lasso_lars' |Fix|
• :class:`decomposition.SparsePCA` where normalize_components has no effect due to deprecation.
• :class:`ensemble.HistGradientBoostingClassifier` and :class:`ensemble.HistGradientBoostingRegressor` |Fix|, |Feature|, |Enhancement|.
• :class:`impute.IterativeImputer` when X has features with no missing values. |Feature|
• :class:`linear_model.Ridge` when X is sparse. |Fix|
• :class:`model_selection.StratifiedKFold` and any use of cv=int with a classifier. |Fix|
• :class:`cross_decomposition.CCA` when using scipy >= 1.3 |Fix|

Details are listed in the changelog below.

(While we are trying to better inform users by providing this information, we cannot assure that this list is complete.)

Changelog

:mod:`sklearn.base`

• |API| From version 0.24 :meth:`base.BaseEstimator.get_params` will raise an AttributeError rather than return None for parameters that are in the estimator's constructor but not stored as attributes on the instance. :pr:`14464` by `Joel Nothman`_.

:mod:`sklearn.calibration`

• |Fix| Fixed a bug that made :class:`calibration.CalibratedClassifierCV` fail when given a sample_weight parameter of type list (in the case where sample_weights are not supported by the wrapped estimator). :pr:`13575` by :user:`William de Vazelhes <wdevazelhes>`.

:mod:`sklearn.cluster`

• |Feature| :class:`cluster.SpectralClustering` now accepts precomputed sparse neighbors graph as input. :issue:`10482` by `Tom Dupre la Tour`_ and :user:`Kumar Ashutosh <thechargedneutron>`.
• |Enhancement| :class:`cluster.SpectralClustering` now accepts a n_components parameter. This parameter extends SpectralClustering class functionality to match :meth:`cluster.spectral_clustering`. :pr:`13726` by :user:`Shuzhe Xiao <fdas3213>`.
• |Fix| Fixed a bug where :class:`cluster.KMeans` produced inconsistent results between n_jobs=1 and n_jobs>1 due to the handling of the random state. :pr:`9288` by :user:`Bryan Yang <bryanyang0528>`.
• |Fix| Fixed a bug where elkan algorithm in :class:`cluster.KMeans` was producing Segmentation Fault on large arrays due to integer index overflow. :pr:`15057` by :user:`Vladimir Korolev <balodja>`.
• |Fix| :class:`~cluster.MeanShift` now accepts a :term:`max_iter` with a default value of 300 instead of always using the default 300. It also now exposes an n_iter_ indicating the maximum number of iterations performed on each seed. :pr:`15120` by `Adrin Jalali`_.
• |Fix| :class:`cluster.AgglomerativeClustering` and :class:`cluster.FeatureAgglomeration` now raise an error if affinity='cosine' and X has samples that are all-zeros. :pr:`7943` by :user:`mthorrell`.
• |Enhancement| :class:`cluster.AgglomerativeClustering` has a faster and more more memory efficient implementation of single linkage clustering. :pr:`11514` by :user:`Leland McInnes <lmcinnes>`.

:mod:`sklearn.compose`

• |Feature| Adds :func:`compose.make_column_selector` which is used with :class:`compose.ColumnTransformer` to select DataFrame columns on the basis of name and dtype. :pr:`12303` by `Thomas Fan`_.
• |Fix| Fixed a bug in :class:`compose.ColumnTransformer` which failed to select the proper columns when using a boolean list, with NumPy older than 1.12. :pr:`14510` by `Guillaume Lemaitre`_.
• |Fix| Fixed a bug in :class:`compose.TransformedTargetRegressor` which did not pass **fit_params to the underlying regressor. :pr:`14890` by :user:`Miguel Cabrera <mfcabrera>`.
• |Fix| The :class:`compose.ColumnTransformer` now requires the number of features to be consistent between fit and transform. A FutureWarning is raised now, and this will raise an error in 0.24. If the number of features isn't consistent and negative indexing is used, an error is raised. :pr:`14544` by `Adrin Jalali`_.

:mod:`sklearn.cross_decomposition`

• |Feature| :class:`cross_decomposition.PLSCanonical` and :class:`cross_decomposition.PLSRegression` have a new function inverse_transform to transform data to the original space. :pr:`15304` by :user:`Jaime Ferrando Huertas <jiwidi>`.
• |Enhancement| :class:`decomposition.KernelPCA` now properly checks the eigenvalues found by the solver for numerical or conditioning issues. This ensures consistency of results across solvers (different choices for eigen_solver), including approximate solvers such as 'randomized' and 'lobpcg' (see :issue:`12068`). :pr:`12145` by :user:`Sylvain Marié <smarie>`
• |Fix| Fixed a bug where :class:`cross_decomposition.PLSCanonical` and :class:`cross_decomposition.PLSRegression` were raising an error when fitted with a target matrix Y in which the first column was constant. :issue:`13609` by :user:`Camila Williamson <camilaagw>`.
• |Fix| :class:`cross_decomposition.CCA` now produces the same results with scipy 1.3 and previous scipy versions. :pr:`15661` by `Thomas Fan`_.

:mod:`sklearn.datasets`

• |Feature| :func:`datasets.fetch_openml` now supports heterogeneous data using pandas by setting as_frame=True. :pr:`13902` by `Thomas Fan`_.
• |Feature| :func:`datasets.fetch_openml` now includes the target_names in the returned Bunch. :pr:`15160` by `Thomas Fan`_.
• |Enhancement| The parameter return_X_y was added to :func:`datasets.fetch_20newsgroups` and :func:`datasets.fetch_olivetti_faces` . :pr:`14259` by :user:`Sourav Singh <souravsingh>`.
• |Enhancement| :func:`datasets.make_classification` now accepts array-like weights parameter, i.e. list or numpy.array, instead of list only. :pr:`14764` by :user:`Cat Chenal <CatChenal>`.

• |Enhancement| The parameter normalize was added to

  :func:`datasets.fetch_20newsgroups_vectorized`. :pr:`14740` by :user:`Stéphan Tulkens <stephantul>`

• |Fix| Fixed a bug in :func:`datasets.fetch_openml`, which failed to load an OpenML dataset that contains an ignored feature. :pr:`14623` by :user:`Sarra Habchi <HabchiSarra>`.

:mod:`sklearn.decomposition`

• |Efficiency| :class:`decomposition.NMF(solver='mu')` fitted on sparse input matrices now uses batching to avoid briefly allocating an array with size (#non-zero elements, n_components). :pr:`15257` by Mart Willocx.
• |Enhancement| :func:`decomposition.dict_learning()` and :func:`decomposition.dict_learning_online()` now accept method_max_iter and pass it to :meth:`decomposition.sparse_encode`. :issue:`12650` by `Adrin Jalali`_.
• |Enhancement| :class:`decomposition.SparseCoder`, :class:`decomposition.DictionaryLearning`, and :class:`decomposition.MiniBatchDictionaryLearning` now take a transform_max_iter parameter and pass it to either :func:`decomposition.dict_learning()` or :func:`decomposition.sparse_encode()`. :issue:`12650` by `Adrin Jalali`_.
• |Enhancement| :class:`decomposition.IncrementalPCA` now accepts sparse matrices as input, converting them to dense in batches thereby avoiding the need to store the entire dense matrix at once. :pr:`13960` by :user:`Scott Gigante <scottgigante>`.
• |Fix| :func:`decomposition.sparse_encode()` now passes the max_iter to the underlying :class:`linear_model.LassoLars` when algorithm='lasso_lars'. :issue:`12650` by `Adrin Jalali`_.

:mod:`sklearn.dummy`

• |Fix| :class:`dummy.DummyClassifier` now handles checking the existence of the provided constant in multiouput cases. :pr:`14908` by :user:`Martina G. Vilas <martinagvilas>`.
• |API| The default value of the strategy parameter in :class:`dummy.DummyClassifier` will change from 'stratified' in version 0.22 to 'prior' in 0.24. A FutureWarning is raised when the default value is used. :pr:`15382` by `Thomas Fan`_.
• |API| The outputs_2d_ attribute is deprecated in :class:`dummy.DummyClassifier` and :class:`dummy.DummyRegressor`. It is equivalent to n_outputs > 1. :pr:`14933` by `Nicolas Hug`_

:mod:`sklearn.ensemble`

• |MajorFeature| Added :class:`ensemble.StackingClassifier` and :class:`ensemble.StackingRegressor` to stack predictors using a final classifier or regressor. :pr:`11047` by :user:`Guillaume Lemaitre <glemaitre>` and :user:`Caio Oliveira <caioaao>` and :pr:`15138` by :user:`Jon Cusick <jcusick13>`..

• Many improvements were made to :class:`ensemble.HistGradientBoostingClassifier` and :class:`ensemble.HistGradientBoostingRegressor`:

  • |MajorFeature| Estimators now natively support dense data with missing values both for training and predicting. They also support infinite values. :pr:`13911` and :pr:`14406` by `Nicolas Hug`_, `Adrin Jalali`_ and `Olivier Grisel`_.
  • |Feature| Estimators now have an additional warm_start parameter that enables warm starting. :pr:`14012` by :user:`Johann Faouzi <johannfaouzi>`.
  • |Feature| :func:`inspection.partial_dependence` and :func:`inspection.plot_partial_dependence` now support the fast 'recursion' method for both estimators. :pr:`13769` by `Nicolas Hug`_.
  • |Enhancement| for :class:`ensemble.HistGradientBoostingClassifier` the training loss or score is now monitored on a class-wise stratified subsample to preserve the class balance of the original training set. :pr:`14194` by :user:`Johann Faouzi <johannfaouzi>`.
  • |Enhancement| :class:`ensemble.HistGradientBoostingRegressor` now supports the 'least_absolute_deviation' loss. :pr:`13896` by `Nicolas Hug`_.
  • |Fix| Estimators now bin the training and validation data separately to avoid any data leak. :pr:`13933` by `Nicolas Hug`_.
  • |Fix| Fixed a bug where early stopping would break with string targets. :pr:`14710` by `Guillaume Lemaitre`_.
  • |Fix| :class:`ensemble.HistGradientBoostingClassifier` now raises an error if categorical_crossentropy loss is given for a binary classification problem. :pr:`14869` by `Adrin Jalali`_.

  Note that pickles from 0.21 will not work in 0.22.

• |Enhancement| Addition of max_samples argument allows limiting size of bootstrap samples to be less than size of dataset. Added to :class:`ensemble.ForestClassifier`, :class:`ensemble.ForestRegressor`, :class:`ensemble.RandomForestClassifier`, :class:`ensemble.RandomForestRegressor`, :class:`ensemble.ExtraTreesClassifier`, :class:`ensemble.ExtraTreesRegressor`, :class:`ensemble.RandomTreesEmbedding`. :pr:`14682` by :user:`Matt Hancock <notmatthancock>` and :pr:`5963` by :user:`Pablo Duboue <DrDub>`.

• |Fix| :func:`ensemble.VotingClassifier.predict_proba` will no longer be present when voting='hard'. :pr:`14287` by `Thomas Fan`_.

• |Fix| The named_estimators_ attribute in :class:`ensemble.VotingClassifier` and :class:`ensemble.VotingRegressor` now correctly maps to dropped estimators. Previously, the named_estimators_ mapping was incorrect whenever one of the estimators was dropped. :pr:`15375` by `Thomas Fan`_.

• |Fix| Run by default :func:`utils.estimator_checks.check_estimator` on both :class:`ensemble.VotingClassifier` and :class:`ensemble.VotingRegressor`. It leads to solve issues regarding shape consistency during predict which was failing when the underlying estimators were not outputting consistent array dimensions. Note that it should be replaced by refactoring the common tests in the future. :pr:`14305` by `Guillaume Lemaitre`_.

• |Fix| :class:`ensemble.AdaBoostClassifier` computes probabilities based on the decision function as in the literature. Thus, predict and predict_proba give consistent results. :pr:`14114` by `Guillaume Lemaitre`_.

• |Fix| Stacking and Voting estimators now ensure that their underlying estimators are either all classifiers or all regressors. :class:`ensemble.StackingClassifier`, :class:`ensemble.StackingRegressor`, and :class:`ensemble.VotingClassifier` and :class:`VotingRegressor` now raise consistent error messages. :pr:`15084` by `Guillaume Lemaitre`_.

• |Fix| :class:`ensemble.AdaBoostRegressor` where the loss should be normalized by the max of the samples with non-null weights only. :pr:`14294` by `Guillaume Lemaitre`_.

• |API| presort is now deprecated in :class:`ensemble.GradientBoostingClassifier` and :class:`ensemble.GradientBoostingRegressor`, and the parameter has no effect. Users are recommended to use :class:`ensemble.HistGradientBoostingClassifier` and :class:`ensemble.HistGradientBoostingRegressor` instead. :pr:`14907` by `Adrin Jalali`_.

:mod:`sklearn.feature_extraction`

• |Enhancement| A warning will now be raised if a parameter choice means that another parameter will be unused on calling the fit() method for :class:`feature_extraction.text.HashingVectorizer`, :class:`feature_extraction.text.CountVectorizer` and :class:`feature_extraction.text.TfidfVectorizer`. :pr:`14602` by :user:`Gaurav Chawla <getgaurav2>`.
• |Fix| Functions created by build_preprocessor and build_analyzer of :class:`feature_extraction.text.VectorizerMixin` can now be pickled. :pr:`14430` by :user:`Dillon Niederhut <deniederhut>`.
• |Fix| :func:`feature_extraction.text.strip_accents_unicode` now correctly removes accents from strings that are in NFKD normalized form. :pr:`15100` by :user:`Daniel Grady <DGrady>`.
• |Fix| Fixed a bug that caused :class:`feature_extraction.DictVectorizer` to raise an OverflowError during the transform operation when producing a scipy.sparse matrix on large input data. :pr:`15463` by :user:`Norvan Sahiner <norvan>`.
• |API| Deprecated unused copy param for :meth:`feature_extraction.text.TfidfVectorizer.transform` it will be removed in v0.24. :pr:`14520` by :user:`Guillem G. Subies <guillemgsubies>`.

:mod:`sklearn.feature_selection`

• |Enhancement| Updated the following :mod:`feature_selection` estimators to allow NaN/Inf values in transform and fit: :class:`feature_selection.RFE`, :class:`feature_selection.RFECV`, :class:`feature_selection.SelectFromModel`, and :class:`feature_selection.VarianceThreshold`. Note that if the underlying estimator of the feature selector does not allow NaN/Inf then it will still error, but the feature selectors themselves no longer enforce this restriction unnecessarily. :issue:`11635` by :user:`Alec Peters <adpeters>`.
• |Fix| Fixed a bug where :class:`feature_selection.VarianceThreshold` with threshold=0 did not remove constant features due to numerical instability, by using range rather than variance in this case. :pr:`13704` by :user:`Roddy MacSween <rlms>`.

:mod:`sklearn.gaussian_process`

• |Feature| :func:`gaussian_process.GaussianProcessClassifier.log_marginal_likelihood` and :func:`gaussian_process.GaussianProcessRegressor.log_marginal_likelihood` now accept a clone_kernel=True keyword argument. When set to False, the kernel attribute is modified, but may result in a performance improvement. :pr:`14378` by :user:`Masashi Shibata <c-bata>`.
• |Feature| Gaussian process models on structured data: :class:`gaussian_process.GaussianProcessRegressor` and :class:`gaussian_process.GaussianProcessClassifier` can now accept a list of generic objects (e.g. strings, trees, graphs, etc.) as the X argument to their training/prediction methods. A user-defined kernel should be provided for computing the kernel matrix among the generic objects, and should inherit from :class:`gaussian_process.kernels.GenericKernelMixin` to notify the GPR/GPC model that it handles non-vectorial samples. :pr:`15557` by :user:`Yu-Hang Tang <yhtang>`.
• |API| From version 0.24 :meth:`gaussian_process.kernels.Kernel.get_params` will raise an AttributeError rather than return None for parameters that are in the estimator's constructor but not stored as attributes on the instance. :pr:`14464` by `Joel Nothman`_.

:mod:`sklearn.impute`

• |MajorFeature| Added :class:`impute.KNNImputer`, to impute missing values using k-Nearest Neighbors. :issue:`12852` by :user:`Ashim Bhattarai <ashimb9>` and `Thomas Fan`_ and :pr:`15010` by `Guillaume Lemaitre`_.
• |Feature| :class:`impute.IterativeImputer` has new skip_compute flag that is False by default, which, when True, will skip computation on features that have no missing values during the fit phase. :issue:`13773` by :user:`Sergey Feldman <sergeyf>`.
• |Efficiency| :meth:`impute.MissingIndicator.fit_transform` avoid repeated computation of the masked matrix. :pr:`14356` by :user:`Harsh Soni <harsh020>`.
• |Fix| :class:`impute.IterativeImputer` now works when there is only one feature. By :user:`Sergey Feldman <sergeyf>`.
• |Fix| Fixed a bug in :class:`impute.IterativeImputer` where features where imputed in the reverse desired order with imputation_order either "ascending" or "descending". :pr:`15393` by :user:`Venkatachalam N <venkyyuvy>`.

:mod:`sklearn.inspection`

• |MajorFeature| :func:`inspection.permutation_importance` has been added to measure the importance of each feature in an arbitrary trained model with respect to a given scoring function. :issue:`13146` by `Thomas Fan`_.
• |Feature| :func:`inspection.partial_dependence` and :func:`inspection.plot_partial_dependence` now support the fast 'recursion' method for :class:`ensemble.HistGradientBoostingClassifier` and :class:`ensemble.HistGradientBoostingRegressor`. :pr:`13769` by `Nicolas Hug`_.
• |Enhancement| :func:`inspection.plot_partial_dependence` has been extended to now support the new visualization API described in the :ref:`User Guide <visualizations>`. :pr:`14646` by `Thomas Fan`_.
• |Enhancement| :func:`inspection.partial_dependence` accepts pandas DataFrame and :class:`pipeline.Pipeline` containing :class:`compose.ColumnTransformer`. In addition :func:`inspection.plot_partial_dependence` will use the column names by default when a dataframe is passed. :pr:`14028` and :pr:`15429` by `Guillaume Lemaitre`_.

:mod:`sklearn.kernel_approximation`

• |Fix| Fixed a bug where :class:`kernel_approximation.Nystroem` raised a KeyError when using kernel="precomputed". :pr:`14706` by :user:`Venkatachalam N <venkyyuvy>`.

:mod:`sklearn.linear_model`

• |Efficiency| The 'liblinear' logistic regression solver is now faster and requires less memory. :pr:`14108`, :pr:`14170`, :pr:`14296` by :user:`Alex Henrie <alexhenrie>`.
• |Enhancement| :class:`linear_model.BayesianRidge` now accepts hyperparameters alpha_init and lambda_init which can be used to set the initial value of the maximization procedure in :term:`fit`. :pr:`13618` by :user:`Yoshihiro Uchida <c56pony>`.
• |Fix| :class:`linear_model.Ridge` now correctly fits an intercept when X is sparse, solver="auto" and fit_intercept=True, because the default solver in this configuration has changed to sparse_cg, which can fit an intercept with sparse data. :pr:`13995` by :user:`Jérôme Dockès <jeromedockes>`.
• |Fix| :class:`linear_model.Ridge` with solver='sag' now accepts F-ordered and non-contiguous arrays and makes a conversion instead of failing. :pr:`14458` by `Guillaume Lemaitre`_.
• |Fix| :class:`linear_model.LassoCV` no longer forces precompute=False when fitting the final model. :pr:`14591` by `Andreas Müller`_.
• |Fix| :class:`linear_model.RidgeCV` and :class:`linear_model.RidgeClassifierCV` now correctly scores when cv=None. :pr:`14864` by :user:`Venkatachalam N <venkyyuvy>`.
• |Fix| Fixed a bug in :class:`linear_model.LogisticRegressionCV` where the scores_, n_iter_ and coefs_paths_ attribute would have a wrong ordering with penalty='elastic-net'. :pr:`15044` by `Nicolas Hug`_
• |Fix| :class:`linear_model.MultiTaskLassoCV` and :class:`linear_model.MultiTaskElasticNetCV` with X of dtype int and fit_intercept=True. :pr:`15086` by :user:`Alex Gramfort <agramfort>`.
• |Fix| The liblinear solver now supports sample_weight. :pr:`15038` by `Guillaume Lemaitre`_.

:mod:`sklearn.manifold`

• |Feature| :class:`manifold.Isomap`, :class:`manifold.TSNE`, and :class:`manifold.SpectralEmbedding` now accept precomputed sparse neighbors graph as input. :issue:`10482` by `Tom Dupre la Tour`_ and :user:`Kumar Ashutosh <thechargedneutron>`.
• |Feature| Exposed the n_jobs parameter in :class:`manifold.TSNE` for multi-core calculation of the neighbors graph. This parameter has no impact when metric="precomputed" or (metric="euclidean" and method="exact"). :issue:`15082` by `Roman Yurchak`_.
• |Efficiency| Improved efficiency of :class:`manifold.TSNE` when method="barnes-hut" by computing the gradient in parallel. :pr:`13213` by :user:`Thomas Moreau <tommoral>`
• |Fix| Fixed a bug where :func:`manifold.spectral_embedding` (and therefore :class:`manifold.SpectralEmbedding` and :class:`cluster.SpectralClustering`) computed wrong eigenvalues with eigen_solver='amg' when n_samples < 5 * n_components. :pr:`14647` by `Andreas Müller`_.
• |Fix| Fixed a bug in :func:`manifold.spectral_embedding` used in :class:`manifold.SpectralEmbedding` and :class:`cluster.SpectralClustering` where eigen_solver="amg" would sometimes result in a LinAlgError. :issue:`13393` by :user:`Andrew Knyazev <lobpcg>` :pr:`13707` by :user:`Scott White <whitews>`
• |API| Deprecate training_data_ unused attribute in :class:`manifold.Isomap`. :issue:`10482` by `Tom Dupre la Tour`_.

:mod:`sklearn.metrics`

• |MajorFeature| :func:`metrics.plot_roc_curve` has been added to plot roc curves. This function introduces the visualization API described in the :ref:`User Guide <visualizations>`. :pr:`14357` by `Thomas Fan`_.
• |Feature| Added a new parameter zero_division to multiple classification metrics: :func:`precision_score`, :func:`recall_score`, :func:`f1_score`, :func:`fbeta_score`, :func:`precision_recall_fscore_support`, :func:`classification_report`. This allows to set returned value for ill-defined metrics. :pr:`14900` by :user:`Marc Torrellas Socastro <marctorrellas>`.
• |Feature| Added the :func:`metrics.pairwise.nan_euclidean_distances` metric, which calculates euclidean distances in the presence of missing values. :issue:`12852` by :user:`Ashim Bhattarai <ashimb9>` and `Thomas Fan`_.
• |Feature| New ranking metrics :func:`metrics.ndcg_score` and :func:`metrics.dcg_score` have been added to compute Discounted Cumulative Gain and Normalized Discounted Cumulative Gain. :pr:`9951` by :user:`Jérôme Dockès <jeromedockes>`.
• |Feature| :func:`metrics.plot_precision_recall_curve` has been added to plot precision recall curves. :pr:`14936` by `Thomas Fan`_.
• |Feature| :func:`metrics.plot_confusion_matrix` has been added to plot confusion matrices. :pr:`15083` by `Thomas Fan`_.
• |Feature| Added multiclass support to :func:`metrics.roc_auc_score` with corresponding scorers 'roc_auc_ovr', 'roc_auc_ovo', 'roc_auc_ovr_weighted', and 'roc_auc_ovo_weighted'. :pr:`12789` and :pr:`15274` by :user:`Kathy Chen <kathyxchen>`, :user:`Mohamed Maskani <maskani-moh>`, and `Thomas Fan`_.
• |Feature| Add :class:`metrics.mean_tweedie_deviance` measuring the Tweedie deviance for a given power parameter. Also add mean Poisson deviance :class:`metrics.mean_poisson_deviance` and mean Gamma deviance :class:`metrics.mean_gamma_deviance` that are special cases of the Tweedie deviance for power=1 and power=2 respectively. :pr:`13938` by :user:`Christian Lorentzen <lorentzenchr>` and `Roman Yurchak`_.
• |Efficiency| Improved performance of :func:`metrics.pairwise.manhattan_distances` in the case of sparse matrices. :pr:`15049` by Paolo Toccaceli <ptocca>.
• |Enhancement| The parameter beta in :func:`metrics.fbeta_score` is updated to accept the zero and float('+inf') value. :pr:`13231` by :user:`Dong-hee Na <corona10>`.
• |Enhancement| Added parameter squared in :func:`metrics.mean_squared_error` to return root mean squared error. :pr:`13467` by :user:`Urvang Patel <urvang96>`.
• |Enhancement| Allow computing averaged metrics in the case of no true positives. :pr:`14595` by `Andreas Müller`_.
• |Enhancement| Multilabel metrics now supports list of lists as input. :pr:`14865` :user:`Srivatsan Ramesh <srivatsan-ramesh>`, :user:`Herilalaina Rakotoarison <herilalaina>`, :user:`Léonard Binet <leonardbinet>`.
• |Enhancement| :func:`metrics.median_absolute_error` now supports multioutput parameter. :pr:`14732` by :user:`Agamemnon Krasoulis <agamemnonc>`.
• |Enhancement| 'roc_auc_ovr_weighted' and 'roc_auc_ovo_weighted' can now be used as the :term:`scoring` parameter of model-selection tools. :pr:`14417` by `Thomas Fan`_.
• |Enhancement| :func:`metrics.confusion_matrix` accepts a parameters normalize allowing to normalize the confusion matrix by column, rows, or overall. :pr:`15625` by Guillaume Lemaitre <glemaitre>.
• |Fix| Raise a ValueError in :func:`metrics.silhouette_score` when a precomputed distance matrix contains non-zero diagonal entries. :pr:`12258` by :user:`Stephen Tierney <sjtrny>`.
• |API| scoring="neg_brier_score" should be used instead of scoring="brier_score_loss" which is now deprecated. :pr:`14898` by :user:`Stefan Matcovici <stefan-matcovici>`.

:mod:`sklearn.model_selection`

• |Efficiency| Improved performance of multimetric scoring in :func:`model_selection.cross_validate`, :class:`model_selection.GridSearchCV`, and :class:`model_selection.RandomizedSearchCV`. :pr:`14593` by `Thomas Fan`_.
• |Enhancement| :class:`model_selection.learning_curve` now accepts parameter return_times which can be used to retrieve computation times in order to plot model scalability (see learning_curve example). :pr:`13938` by :user:`Hadrien Reboul <H4dr1en>`.
• |Enhancement| :class:`model_selection.RandomizedSearchCV` now accepts lists of parameter distributions. :pr:`14549` by `Andreas Müller`_.
• |Fix| Reimplemented :class:`model_selection.StratifiedKFold` to fix an issue where one test set could be n_classes larger than another. Test sets should now be near-equally sized. :pr:`14704` by `Joel Nothman`_.
• |Fix| The cv_results_ attribute of :class:`model_selection.GridSearchCV` and :class:`model_selection.RandomizedSearchCV` now only contains unfitted estimators. This potentially saves a lot of memory since the state of the estimators isn't stored. :pr:`#15096` by `Andreas Müller`_.
• |API| :class:`model_selection.KFold` and :class:`model_selection.StratifiedKFold` now raise a warning if random_state is set but shuffle is False. This will raise an error in 0.24.

:mod:`sklearn.multioutput`

• |Fix| :class:`multioutput.MultiOutputClassifier` now has attribute classes_. :pr:`14629` by :user:`Agamemnon Krasoulis <agamemnonc>`.
• |Fix| :class:`multioutput.MultiOutputClassifier` now has predict_proba as property and can be checked with hasattr. :issue:`15488` :pr:`15490` by :user:`Rebekah Kim <rebekahkim>`

:mod:`sklearn.naive_bayes`

• |MajorFeature| Added :class:`naive_bayes.CategoricalNB` that implements the Categorical Naive Bayes classifier. :pr:`12569` by :user:`Tim Bicker <timbicker>` and :user:`Florian Wilhelm <FlorianWilhelm>`.

:mod:`sklearn.neighbors`

• |MajorFeature| Added :class:`neighbors.KNeighborsTransformer` and :class:`neighbors.RadiusNeighborsTransformer`, which transform input dataset into a sparse neighbors graph. They give finer control on nearest neighbors computations and enable easy pipeline caching for multiple use. :issue:`10482` by `Tom Dupre la Tour`_.
• |Feature| :class:`neighbors.KNeighborsClassifier`, :class:`neighbors.KNeighborsRegressor`, :class:`neighbors.RadiusNeighborsClassifier`, :class:`neighbors.RadiusNeighborsRegressor`, and :class:`neighbors.LocalOutlierFactor` now accept precomputed sparse neighbors graph as input. :issue:`10482` by `Tom Dupre la Tour`_ and :user:`Kumar Ashutosh <thechargedneutron>`.
• |Feature| :class:`neighbors.RadiusNeighborsClassifier` now supports predicting probabilities by using predict_proba and supports more outlier_label options: 'most_frequent', or different outlier_labels for multi-outputs. :pr:`9597` by :user:`Wenbo Zhao <webber26232>`.
• |Efficiency| Efficiency improvements for :func:`neighbors.RadiusNeighborsClassifier.predict`. :pr:`9597` by :user:`Wenbo Zhao <webber26232>`.
• |Fix| :class:`neighbors.KNeighborsRegressor` now throws error when metric='precomputed' and fit on non-square data. :pr:`14336` by :user:`Gregory Dexter <gdex1>`.

:mod:`sklearn.neural_network`

• |Feature| Add max_fun parameter in :class:`neural_network.BaseMultilayerPerceptron`, :class:`neural_network.MLPRegressor`, and :class:`neural_network.MLPClassifier` to give control over maximum number of function evaluation to not meet tol improvement. :issue:`9274` by :user:`Daniel Perry <daniel-perry>`.

:mod:`sklearn.pipeline`

• |Enhancement| :class:`pipeline.Pipeline` now supports :term:`score_samples` if the final estimator does. :pr:`13806` by :user:`Anaël Beaugnon <ab-anssi>`.
• |Fix| The fit in :class:`~pipeline.FeatureUnion` now accepts fit_params to pass to the underlying transformers. :pr:`15119` by `Adrin Jalali`_.
• |API| None as a transformer is now deprecated in :class:`pipeline.FeatureUnion`. Please use 'drop' instead. :pr:`15053` by `Thomas Fan`_.

:mod:`sklearn.preprocessing`

• |Efficiency| :class:`preprocessing.PolynomialFeatures` is now faster when the input data is dense. :pr:`13290` by :user:`Xavier Dupré <sdpython>`.
• |Enhancement| Avoid unnecessary data copy when fitting preprocessors :class:`preprocessing.StandardScaler`, :class:`preprocessing.MinMaxScaler`, :class:`preprocessing.MaxAbsScaler`, :class:`preprocessing.RobustScaler` and :class:`preprocessing.QuantileTransformer` which results in a slight performance improvement. :pr:`13987` by `Roman Yurchak`_.
• |Fix| KernelCenterer now throws error when fit on non-square :class:`preprocessing.KernelCenterer` :pr:`14336` by :user:`Gregory Dexter <gdex1>`.

:mod:`sklearn.model_selection`

• |Fix| :class:`model_selection.GridSearchCV` and model_selection.RandomizedSearchCV now supports the :term:`_pairwise` property, which prevents an error during cross-validation for estimators with pairwise inputs (such as :class:`neighbors.KNeighborsClassifier` when :term:`metric` is set to 'precomputed'). :pr:`13925` by :user:`Isaac S. Robson <isrobson>` and :pr:`15524` by :user:`Xun Tang <xun-tang>`.

:mod:`sklearn.svm`

• |Enhancement| :class:`svm.SVC` and :class:`svm.NuSVC` now accept a break_ties parameter. This parameter results in :term:`predict` breaking the ties according to the confidence values of :term:`decision_function`, if decision_function_shape='ovr', and the number of target classes > 2. :pr:`12557` by `Adrin Jalali`_.
• |Enhancement| SVM estimators now throw a more specific error when kernel='precomputed' and fit on non-square data. :pr:`14336` by :user:`Gregory Dexter <gdex1>`.
• |Fix| :class:`svm.SVC`, :class:`svm.SVR`, :class:`svm.NuSVR` and :class:`svm.OneClassSVM` when received values negative or zero for parameter sample_weight in method fit(), generated an invalid model. This behavior occurred only in some border scenarios. Now in these cases, fit() will fail with an Exception. :pr:`14286` by :user:`Alex Shacked <alexshacked>`.
• |Fix| The n_support_ attribute of :class:`svm.SVR` and :class:`svm.OneClassSVM` was previously non-initialized, and had size 2. It has now size 1 with the correct value. :pr:`15099` by `Nicolas Hug`_.
• |Fix| fixed a bug in :class:`BaseLibSVM._sparse_fit` where n_SV=0 raised a ZeroDivisionError. :pr:`14894` by :user:`Danna Naser <danna-naser>`.
• |Fix| The liblinear solver now supports sample_weight. :pr:`15038` by `Guillaume Lemaitre`_.

:mod:`sklearn.tree`

• |Feature| Adds minimal cost complexity pruning, controlled by ccp_alpha, to :class:`tree.DecisionTreeClassifier`, :class:`tree.DecisionTreeRegressor`, :class:`tree.ExtraTreeClassifier`, :class:`tree.ExtraTreeRegressor`, :class:`ensemble.RandomForestClassifier`, :class:`ensemble.RandomForestRegressor`, :class:`ensemble.ExtraTreesClassifier`, :class:`ensemble.ExtraTreesRegressor`, :class:`ensemble.GradientBoostingClassifier`, and :class:`ensemble.GradientBoostingRegressor`. :pr:`12887` by `Thomas Fan`_.
• |API| presort is now deprecated in :class:`tree.DecisionTreeClassifier` and :class:`tree.DecisionTreeRegressor`, and the parameter has no effect. :pr:`14907` by `Adrin Jalali`_.
• |API| The classes_ and n_classes_ attributes of :class:`tree.DecisionTreeRegressor` are now deprecated. :pr:`15028` by :user:`Mei Guan <meiguan>`, `Nicolas Hug`_, and `Adrin Jalali`_.

:mod:`sklearn.utils`

• |Feature| :func:`~utils.estimator_checks.check_estimator` can now generate checks by setting generate_only=True. Previously, running :func:`~utils.estimator_checks.check_estimator` will stop when the first check fails. With generate_only=True, all checks can run independently and report the ones that are failing. Read more in :ref:`rolling_your_own_estimator`. :pr:`14381` by `Thomas Fan`_.
• |Feature| Added a pytest specific decorator, :func:`~utils.estimator_checks.parametrize_with_checks`, to parametrize estimator checks for a list of estimators. :pr:`14381` by `Thomas Fan`_.
• |Feature| A new random variable, :class:`utils.fixes.loguniform` implements a log-uniform random variable (e.g., for use in RandomizedSearchCV). For example, the outcomes 1, 10 and 100 are all equally likely for loguniform(1, 100). See :issue:`11232` by :user:`Scott Sievert <stsievert>` and :user:`Nathaniel Saul <sauln>`, and SciPy PR 10815 <scipy/scipy#10815>.
• |Enhancement| :func:`utils.safe_indexing` (now deprecated) accepts an axis parameter to index array-like across rows and columns. The column indexing can be done on NumPy array, SciPy sparse matrix, and Pandas DataFrame. An additional refactoring was done. :pr:`14035` and :pr:`14475` by `Guillaume Lemaitre`_.
• |Enhancement| :func:`utils.extmath.safe_sparse_dot` works between 3D+ ndarray and sparse matrix. :pr:`14538` by :user:`Jérémie du Boisberranger <jeremiedbb>`.
• |Fix| :func:`utils.check_array` is now raising an error instead of casting NaN to integer. :pr:`14872` by `Roman Yurchak`_.
• |Fix| :func:`utils.check_array` will now correctly detect numeric dtypes in pandas dataframes, fixing a bug where float32 was upcast to float64 unnecessarily. :pr:`15094` by `Andreas Müller`_.
• |API| The following utils have been deprecated and are now private:
  • choose_check_classifiers_labels
  • enforce_estimator_tags_y
  • mocking.MockDataFrame
  • mocking.CheckingClassifier
  • optimize.newton_cg
  • random.random_choice_csc
  • utils.choose_check_classifiers_labels
  • utils.enforce_estimator_tags_y
  • utils.optimize.newton_cg
  • utils.random.random_choice_csc
  • utils.safe_indexing
  • utils.mocking
  • utils.fast_dict
  • utils.seq_dataset
  • utils.weight_vector
  • utils.fixes.parallel_helper (removed)
  • All of utils.testing except for all_estimators which is now in utils.

:mod:`sklearn.isotonic`

• |Fix| Fixed a bug where :class:`isotonic.IsotonicRegression.fit` raised error when X.dtype == 'float32' and X.dtype != y.dtype. :pr:`14902` by :user:`Lucas <lostcoaster>`.

Miscellaneous

• |Fix| Port lobpcg from SciPy which implement some bug fixes but only available in 1.3+. :pr:`13609` and :pr:`14971` by `Guillaume Lemaitre`_.
• |API| Scikit-learn now converts any input data structure implementing a duck array to a numpy array (using __array__) to ensure consistent behavior instead of relying on __array_function__ (see NEP 18). :pr:`14702` by `Andreas Müller`_.
• |API| Replace manual checks with check_is_fitted. Errors thrown when using a non-fitted estimators are now more uniform. :pr:`13013` by :user:`Agamemnon Krasoulis <agamemnonc>`.

Changes to estimator checks

These changes mostly affect library developers.

• Estimators are now expected to raise a NotFittedError if predict or transform is called before fit; previously an AttributeError or ValueError was acceptable. :pr:`13013` by by :user:`Agamemnon Krasoulis <agamemnonc>`.
• Binary only classifiers are now supported in estimator checks. Such classifiers need to have the binary_only=True estimator tag. :pr:`13875` by `Trevor Stephens`_.
• Estimators are expected to convert input data (X, y, sample_weights) to :class:`numpy.ndarray` and never call __array_function__ on the original datatype that is passed (see NEP 18). :pr:`14702` by `Andreas Müller`_.
• requires_positive_X estimator tag (for models that require X to be non-negative) is now used by :meth:`utils.estimator_checks.check_estimator` to make sure a proper error message is raised if X contains some negative entries. :pr:`14680` by :user:`Alex Gramfort <agramfort>`.
• Added check that pairwise estimators raise error on non-square data :pr:`14336` by :user:`Gregory Dexter <gdex1>`.
• Added two common multioutput estimator tests :func:`~utils.estimator_checks.check_classifier_multioutput` and :func:`~utils.estimator_checks.check_regressor_multioutput`. :pr:`13392` by :user:`Rok Mihevc <rok>`.
• |Fix| Added check_transformer_data_not_an_array to checks where missing
• |Fix| The estimators tags resolution now follows the regular MRO. They used to be overridable only once. :pr:`14884` by `Andreas Müller`_.