DOC Ensures that SelectKBest passes numpydoc validation (scikit-learn#20983)

jmloyola · web-flow · commit 6568222fce23 · 2021-09-09T11:05:04.000+02:00
diff --git a/maint_tools/test_docstrings.py b/maint_tools/test_docstrings.py
@@ -64,7 +64,6 @@
     "SelectFpr",
     "SelectFromModel",
     "SelectFwe",
-    "SelectKBest",
     "SelfTrainingClassifier",
     "SequentialFeatureSelector",
     "SimpleImputer",
diff --git a/sklearn/feature_selection/_univariate_selection.py b/sklearn/feature_selection/_univariate_selection.py
@@ -563,22 +563,6 @@ class SelectKBest(_BaseFilter):
 
         .. versionadded:: 1.0
 
-    Examples
-    --------
-    >>> from sklearn.datasets import load_digits
-    >>> from sklearn.feature_selection import SelectKBest, chi2
-    >>> X, y = load_digits(return_X_y=True)
-    >>> X.shape
-    (1797, 64)
-    >>> X_new = SelectKBest(chi2, k=20).fit_transform(X, y)
-    >>> X_new.shape
-    (1797, 20)
-
-    Notes
-    -----
-    Ties between features with equal scores will be broken in an unspecified
-    way.
-
     See Also
     --------
     f_classif: ANOVA F-value between label/feature for classification tasks.
@@ -593,6 +577,22 @@ class SelectKBest(_BaseFilter):
     SelectFwe : Select features based on family-wise error rate.
     GenericUnivariateSelect : Univariate feature selector with configurable
         mode.
+
+    Notes
+    -----
+    Ties between features with equal scores will be broken in an unspecified
+    way.
+
+    Examples
+    --------
+    >>> from sklearn.datasets import load_digits
+    >>> from sklearn.feature_selection import SelectKBest, chi2
+    >>> X, y = load_digits(return_X_y=True)
+    >>> X.shape
+    (1797, 64)
+    >>> X_new = SelectKBest(chi2, k=20).fit_transform(X, y)
+    >>> X_new.shape
+    (1797, 20)
     """
 
     def __init__(self, score_func=f_classif, *, k=10):
