DOC Ensures that SequentialFeatureSelector passes numpydoc validation (#21035)

genvalen · web-flow · commit 66f6fb3816ce · 2021-09-14T10:11:25.000+02:00
diff --git a/maint_tools/test_docstrings.py b/maint_tools/test_docstrings.py
@@ -51,7 +51,6 @@
     "SGDOneClassSVM",
     "SGDRegressor",
     "SelfTrainingClassifier",
-    "SequentialFeatureSelector",
     "SimpleImputer",
     "SparseRandomProjection",
     "SpectralBiclustering",
diff --git a/sklearn/feature_selection/_sequential.py b/sklearn/feature_selection/_sequential.py
@@ -97,6 +97,8 @@ class SequentialFeatureSelector(SelectorMixin, MetaEstimatorMixin, BaseEstimator
 
     See Also
     --------
+    GenericUnivariateSelect : Univariate feature selector with configurable
+        strategy.
     RFE : Recursive feature elimination based on importance weights.
     RFECV : Recursive feature elimination based on importance weights, with
         automatic selection of the number of features.
@@ -139,19 +141,22 @@ def __init__(
         self.n_jobs = n_jobs
 
     def fit(self, X, y=None):
-        """Learn the features to select.
+        """Learn the features to select from X.
 
         Parameters
         ----------
         X : array-like of shape (n_samples, n_features)
-            Training vectors.
+            Training vectors, where `n_samples` is the number of samples and
+            `n_features` is the number of predictors.
+
         y : array-like of shape (n_samples,), default=None
             Target values. This parameter may be ignored for
             unsupervised learning.
 
         Returns
         -------
         self : object
+            Returns the instance itself.
         """
         tags = self._get_tags()
         X = self._validate_data(
