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DOC Ensures that roc_auc_score passes numpydoc validation #23505

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Jun 1, 2022
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DOC Ensures that roc_auc_score passes numpydoc validation #23505

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DOC Ensures that roc_auc_score passes numpydoc validation
May 31, 2022
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May 31, 2022
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Merge branch 'main' into ranking-roc-auc-score
adrinjalali Jun 1, 2022
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21 changes: 11 additions & 10 deletions sklearn/metrics/_ranking.py
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Original file line number Diff line number Diff line change
Expand Up @@ -372,7 +372,7 @@ def roc_auc_score(
multi_class="raise",
labels=None,
):
"""Compute Area Under the Receiver Operating Characteristic Curve (ROC AUC)
"""Compute Area Under the Receiver Operating Characteristic Curve (ROC AUC) \
from prediction scores.

Note: this implementation can be used with binary, multiclass and
Expand Down Expand Up @@ -471,6 +471,16 @@ class scores must correspond to the order of ``labels``,
Returns
-------
auc : float
Area Under the Curve score.

See Also
--------
average_precision_score : Area under the precision-recall curve.
roc_curve : Compute Receiver operating characteristic (ROC) curve.
RocCurveDisplay.from_estimator : Plot Receiver Operating Characteristic
(ROC) curve given an estimator and some data.
RocCurveDisplay.from_predictions : Plot Receiver Operating Characteristic
(ROC) curve given the true and predicted values.

References
----------
Expand All @@ -493,15 +503,6 @@ class scores must correspond to the order of ``labels``,
Machine Learning, 45(2), 171-186.
<http://link.springer.com/article/10.1023/A:1010920819831>`_

See Also
--------
average_precision_score : Area under the precision-recall curve.
roc_curve : Compute Receiver operating characteristic (ROC) curve.
RocCurveDisplay.from_estimator : Plot Receiver Operating Characteristic
(ROC) curve given an estimator and some data.
RocCurveDisplay.from_predictions : Plot Receiver Operating Characteristic
(ROC) curve given the true and predicted values.

Examples
--------
Binary case:
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1 change: 0 additions & 1 deletion sklearn/tests/test_docstrings.py
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Expand Up @@ -40,7 +40,6 @@
"sklearn.metrics._plot.precision_recall_curve.plot_precision_recall_curve",
"sklearn.metrics._ranking.coverage_error",
"sklearn.metrics._ranking.dcg_score",
"sklearn.metrics._ranking.roc_auc_score",
"sklearn.metrics._ranking.roc_curve",
"sklearn.metrics._ranking.top_k_accuracy_score",
"sklearn.metrics._regression.mean_pinball_loss",
Expand Down