DOC Ensure sklearn.metrics._classification.jaccard_score passes numpydoc validation (#23910)

stefmolin · glemaitre · web-flow · commit c3cb7ffde438 · 2022-07-17T13:57:59.000+02:00
* DOC: Update sklearn.metrics._classification.jaccard_score to pass numpydoc validation.

* DOC Incorporate suggested changes to return type

Co-authored-by: Guillaume Lemaitre &lt;g.lemaitre58@gmail.com&gt;

* DOC Incorporate suggested changes to return description.

Co-authored-by: Guillaume Lemaitre &lt;g.lemaitre58@gmail.com&gt;

Co-authored-by: Guillaume Lemaitre &lt;g.lemaitre58@gmail.com&gt;
diff --git a/sklearn/metrics/_classification.py b/sklearn/metrics/_classification.py
@@ -738,12 +738,16 @@ def jaccard_score(
 
     Returns
     -------
-    score : float (if average is not None) or array of floats, shape =\
-            [n_unique_labels]
+    score : float or ndarray of shape (n_unique_labels,), dtype=np.float64
+        The Jaccard score. When `average` is not `None`, a single scalar is
+        returned.
 
     See Also
     --------
-    accuracy_score, f1_score, multilabel_confusion_matrix
+    accuracy_score : Function for calculating the accuracy score.
+    f1_score : Function for calculating the F1 score.
+    multilabel_confusion_matrix : Function for computing a confusion matrix\
+                                  for each class or sample.
 
     Notes
     -----
diff --git a/sklearn/tests/test_docstrings.py b/sklearn/tests/test_docstrings.py
@@ -30,7 +30,6 @@
     "sklearn.linear_model._omp.orthogonal_mp_gram",
     "sklearn.manifold._locally_linear.locally_linear_embedding",
     "sklearn.manifold._t_sne.trustworthiness",
-    "sklearn.metrics._classification.jaccard_score",
     "sklearn.metrics._plot.det_curve.plot_det_curve",
     "sklearn.metrics._plot.precision_recall_curve.plot_precision_recall_curve",
     "sklearn.metrics._ranking.coverage_error",
