DOC Ensure polynomial_kernel passes numpydoc validation (#23953)

Co-authored-by: Meekail Zain <34613774+Micky774@users.noreply.github.com>
scikit-learn · Aug 5, 2022 · 5b60f4d · 5b60f4d
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diff --git a/sklearn/metrics/pairwise.py b/sklearn/metrics/pairwise.py
@@ -1190,28 +1190,33 @@ def linear_kernel(X, Y=None, dense_output=True):
 
 def polynomial_kernel(X, Y=None, degree=3, gamma=None, coef0=1):
     """
-    Compute the polynomial kernel between X and Y::
+    Compute the polynomial kernel between X and Y.
 
-        K(X, Y) = (gamma <X, Y> + coef0)^degree
+    :math:`K(X, Y) = (gamma <X, Y> + coef0)^degree`
 
     Read more in the :ref:`User Guide <polynomial_kernel>`.
 
     Parameters
     ----------
     X : ndarray of shape (n_samples_X, n_features)
+        A feature array.
 
     Y : ndarray of shape (n_samples_Y, n_features), default=None
+        An optional second feature array. If `None`, uses `Y=X`.
 
     degree : int, default=3
+        Kernel degree.
 
     gamma : float, default=None
-        If None, defaults to 1.0 / n_features.
+        Coefficient of the vector inner product. If None, defaults to 1.0 / n_features.
 
     coef0 : float, default=1
+        Constant offset added to scaled inner product.
 
     Returns
     -------
     Gram matrix : ndarray of shape (n_samples_X, n_samples_Y)
+        The polynomial kernel.
     """
     X, Y = check_pairwise_arrays(X, Y)
     if gamma is None:

diff --git a/sklearn/tests/test_docstrings.py b/sklearn/tests/test_docstrings.py
@@ -48,7 +48,6 @@
     "sklearn.metrics.cluster._supervised.rand_score",
     "sklearn.metrics.cluster._supervised.v_measure_score",
     "sklearn.metrics.pairwise.pairwise_distances_chunked",
-    "sklearn.metrics.pairwise.polynomial_kernel",
     "sklearn.preprocessing._data.maxabs_scale",
     "sklearn.preprocessing._data.scale",
     "sklearn.preprocessing._label.label_binarize",