DOC Ensures that l1_min_c passes numpydoc validation#24134
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jeremiedbb merged 3 commits intoscikit-learn:mainfrom Sep 6, 2022
Merged
DOC Ensures that l1_min_c passes numpydoc validation#24134jeremiedbb merged 3 commits intoscikit-learn:mainfrom
jeremiedbb merged 3 commits intoscikit-learn:mainfrom
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lucyleeow
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Aug 7, 2022
sklearn/svm/_bounds.py
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| linear_model.LogisticRegression with penalty='l1'. | ||
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| This value is valid if class_weight parameter in fit() is not set. | ||
| Return the lowest bound for C such that for C in (l1_min_C, infinity) the model is guaranteed not to be empty. This applies to l1 penalized classifiers, such as LinearSVC with penalty='l1' and linear_model.LogisticRegression with penalty='l1'. This value is valid if class_weight parameter in fit() is not set. |
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Thanks @kshitijkapadni , any reason for changing the docstring to be one line? We generally like to keep lines <88 characters
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I get this SS06: Summary should fit in a single line. If I keep it multiple lines in the docstring validation. Can you guide me on this?
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It just means that the short summary should be less than 88 characters.
You can then use a long summary. The following will solve the problem
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| Return the lowest bound for C such that for C in (l1_min_C, infinity) the model is guaranteed not to be empty. This applies to l1 penalized classifiers, such as LinearSVC with penalty='l1' and linear_model.LogisticRegression with penalty='l1'. This value is valid if class_weight parameter in fit() is not set. | |
| Return the lowest bound for C. | |
| The lower bound C is computed such that for C in (l1_min_C, infinity) | |
| the model is guaranteed not to be empty. This applies to l1 penalized | |
| classifiers, such as LinearSVC with penalty='l1' and | |
| linear_model.LogisticRegression with penalty='l1'. |
jeremiedbb
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Thanks for the PR @kshitijkapadni. I directly pushed to apply the last review comment. LGTM
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Reference Issues/PRs
Addresses #21350
What does this implement/fix? Explain your changes.
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