DOC Ensures that sklearn.metrics._classification.accuracy_score passes numpydoc validation (#21441)

embandera · glemaitre · web-flow · commit 9eb6f1105395 · 2021-10-25T10:17:43.000+02:00
Co-authored-by: Guillaume Lemaitre &lt;g.lemaitre58@gmail.com&gt;
diff --git a/maint_tools/test_docstrings.py b/maint_tools/test_docstrings.py
@@ -94,7 +94,6 @@
     "sklearn.linear_model._ridge.ridge_regression",
     "sklearn.manifold._locally_linear.locally_linear_embedding",
     "sklearn.manifold._t_sne.trustworthiness",
-    "sklearn.metrics._classification.accuracy_score",
     "sklearn.metrics._classification.balanced_accuracy_score",
     "sklearn.metrics._classification.brier_score_loss",
     "sklearn.metrics._classification.classification_report",
diff --git a/sklearn/metrics/_classification.py b/sklearn/metrics/_classification.py
@@ -177,7 +177,13 @@ def accuracy_score(y_true, y_pred, *, normalize=True, sample_weight=None):
 
     See Also
     --------
-    jaccard_score, hamming_loss, zero_one_loss
+    balanced_accuracy_score : Compute the balanced accuracy to deal with
+        imbalanced datasets.
+    jaccard_score : Compute the Jaccard similarity coefficient score.
+    hamming_loss : Compute the average Hamming loss or Hamming distance between
+        two sets of samples.
+    zero_one_loss : Compute the Zero-one classification loss. By default, the
+        function will return the percentage of imperfectly predicted subsets.
 
     Notes
     -----
