DOC Ensures that sklearn.metrics._classification.hamming_loss passes …

…numpydoc validation (scikit-learn#21449)
norbusan · Oct 25, 2021 · 66df3ac · 66df3ac
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diff --git a/maint_tools/test_docstrings.py b/maint_tools/test_docstrings.py
@@ -100,7 +100,6 @@
     "sklearn.metrics._classification.confusion_matrix",
     "sklearn.metrics._classification.f1_score",
     "sklearn.metrics._classification.fbeta_score",
-    "sklearn.metrics._classification.hamming_loss",
     "sklearn.metrics._classification.hinge_loss",
     "sklearn.metrics._classification.jaccard_score",
     "sklearn.metrics._classification.log_loss",

diff --git a/sklearn/metrics/_classification.py b/sklearn/metrics/_classification.py
@@ -2212,7 +2212,12 @@ def hamming_loss(y_true, y_pred, *, sample_weight=None):
 
     See Also
     --------
-    accuracy_score, jaccard_score, zero_one_loss
+    accuracy_score : Compute the accuracy score. By default, the function will
+        return the fraction of correct predictions divided by the total number
+        of predictions.
+    jaccard_score : Compute the Jaccard similarity coefficient score.
+    zero_one_loss : Compute the Zero-one classification loss. By default, the
+        function will return the percentage of imperfectly predicted subsets.
 
     Notes
     -----