DOC Ensures that fetch_lfw_people passes numpydoc validation (#24161)

fcharras · web-flow · commit 5f604b711e15 · 2022-08-10T09:32:12.000-04:00
Co-authored-by: Franck Charras &lt;franck.charras@inria.fr&gt;
diff --git a/sklearn/datasets/_lfw.py b/sklearn/datasets/_lfw.py
@@ -278,7 +278,7 @@ def fetch_lfw_people(
     slice_ : tuple of slice, default=(slice(70, 195), slice(78, 172))
         Provide a custom 2D slice (height, width) to extract the
         'interesting' part of the jpeg files and avoid use statistical
-        correlation from the background
+        correlation from the background.
 
     download_if_missing : bool, default=True
         If False, raise a IOError if the data is not locally available
@@ -315,6 +315,10 @@ def fetch_lfw_people(
             Description of the Labeled Faces in the Wild (LFW) dataset.
 
     (data, target) : tuple if ``return_X_y`` is True
+        A tuple of two ndarray. The first containing a 2D array of
+        shape (n_samples, n_features) with each row representing one
+        sample and each column representing the features. The second
+        ndarray of shape (n_samples,) containing the target samples.
 
         .. versionadded:: 0.20
     """
diff --git a/sklearn/tests/test_docstrings.py b/sklearn/tests/test_docstrings.py
@@ -12,7 +12,6 @@
 numpydoc_validation = pytest.importorskip("numpydoc.validate")
 
 FUNCTION_DOCSTRING_IGNORE_LIST = [
-    "sklearn.datasets._lfw.fetch_lfw_people",
     "sklearn.datasets._svmlight_format_io.load_svmlight_file",
     "sklearn.datasets._svmlight_format_io.load_svmlight_files",
     "sklearn.decomposition._dict_learning.dict_learning",
