DOC Ensures that sklearn.metrics._classification.accuracy_score passes numpydoc validation #21441
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glemaitre
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sklearn/metrics/_classification.py
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| hamming_loss : Compute the average Hamming loss. | ||
| zero_one_loss : Zero-one classification loss. |
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Instead of the losses, I am thinking that it could be better to add the balanced_accuracy_score
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I think hamming_loss and zero_one_loss are directly related to accuracy so this is fine to put them in the See also section for accuracy score. Maybe we could be slightly more explicit on how they are related.
+1 for adding balanced_accuracy_score.
glemaitre
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Oct 24, 2021
Co-authored-by: Guillaume Lemaitre <[email protected]>
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Thanks @embandera |
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…s numpydoc validation (scikit-learn#21441) Co-authored-by: Guillaume Lemaitre <[email protected]>
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…s numpydoc validation (scikit-learn#21441) Co-authored-by: Guillaume Lemaitre <[email protected]>
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…s numpydoc validation (scikit-learn#21441) Co-authored-by: Guillaume Lemaitre <[email protected]>
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…s numpydoc validation (#21441) Co-authored-by: Guillaume Lemaitre <[email protected]>
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Reference Issues/PRs
Addresses #21350
#DataUmbrella
What does this implement/fix? Explain your changes.
This PR ensures sklearn.metrics._classification.accuracy_score is compatible with numpydoc: