scikit-learn
diff --git a/‎maint_tools/test_docstrings.py
Lines changed: 0 additions & 1 deletion b/‎maint_tools/test_docstrings.py
Lines changed: 0 additions & 1 deletion
diff --git a/‎sklearn/feature_extraction/image.py
Lines changed: 17 additions & 5 deletions b/‎sklearn/feature_extraction/image.py
Lines changed: 17 additions & 5 deletions
@@ -22,7 +22,6 @@
     "OrthogonalMatchingPursuitCV",
     "PassiveAggressiveClassifier",
     "PassiveAggressiveRegressor",
-    "PatchExtractor",
     "PolynomialFeatures",
     "QuadraticDiscriminantAnalysis",
     "SelfTrainingClassifier",
 
@@ -451,7 +451,7 @@ def reconstruct_from_patches_2d(patches, image_size):
 
 
 class PatchExtractor(BaseEstimator):
-    """Extracts patches from a collection of images
+    """Extracts patches from a collection of images.
 
     Read more in the :ref:`User Guide <image_feature_extraction>`.
 
@@ -463,16 +463,20 @@ class PatchExtractor(BaseEstimator):
         The dimensions of one patch.
 
     max_patches : int or float, default=None
-        The maximum number of patches per image to extract. If max_patches is a
-        float in (0, 1), it is taken to mean a proportion of the total number
+        The maximum number of patches per image to extract. If `max_patches` is
+        a float in (0, 1), it is taken to mean a proportion of the total number
         of patches.
 
     random_state : int, RandomState instance, default=None
         Determines the random number generator used for random sampling when
-        `max_patches` is not None. Use an int to make the randomness
+        `max_patches is not None`. Use an int to make the randomness
         deterministic.
         See :term:`Glossary <random_state>`.
 
+    See Also
+    --------
+    reconstruct_from_patches_2d : Reconstruct image from all of its patches.
+
     Examples
     --------
     >>> from sklearn.datasets import load_sample_images
@@ -503,11 +507,19 @@ def fit(self, X, y=None):
         ----------
         X : array-like of shape (n_samples, n_features)
             Training data.
+
+        y : Ignored
+            Not used, present for API consistency by convention.
+
+        Returns
+        -------
+        self : object
+            Returns the instance itself.
         """
         return self
 
     def transform(self, X):
-        """Transforms the image samples in X into a matrix of patch data.
+        """Transform the image samples in `X` into a matrix of patch data.
 
         Parameters
         ----------