DOC Ensures that zero_one_loss passes numpydoc validation (#21450)

embandera · web-flow · commit ae69f9de3e5a · 2021-10-25T10:47:21.000+02:00
diff --git a/maint_tools/test_docstrings.py b/maint_tools/test_docstrings.py
@@ -106,7 +106,6 @@
     "sklearn.metrics._classification.precision_recall_fscore_support",
     "sklearn.metrics._classification.precision_score",
     "sklearn.metrics._classification.recall_score",
-    "sklearn.metrics._classification.zero_one_loss",
     "sklearn.metrics._plot.confusion_matrix.plot_confusion_matrix",
     "sklearn.metrics._plot.det_curve.plot_det_curve",
     "sklearn.metrics._plot.precision_recall_curve.plot_precision_recall_curve",
diff --git a/sklearn/metrics/_classification.py b/sklearn/metrics/_classification.py
@@ -945,16 +945,21 @@ def zero_one_loss(y_true, y_pred, *, normalize=True, sample_weight=None):
         If ``normalize == True``, return the fraction of misclassifications
         (float), else it returns the number of misclassifications (int).
 
+    See Also
+    --------
+    accuracy_score : Compute the accuracy score. By default, the function will
+        return the fraction of correct predictions divided by the total number
+        of predictions.
+    hamming_loss : Compute the average Hamming loss or Hamming distance between
+        two sets of samples.
+    jaccard_score : Compute the Jaccard similarity coefficient score.
+
     Notes
     -----
     In multilabel classification, the zero_one_loss function corresponds to
     the subset zero-one loss: for each sample, the entire set of labels must be
     correctly predicted, otherwise the loss for that sample is equal to one.
 
-    See Also
-    --------
-    accuracy_score, hamming_loss, jaccard_score
-
     Examples
     --------
     >>> from sklearn.metrics import zero_one_loss
