DOC added intervals for parameters for 4 glm classes #22076
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@ogrisel Did you want to review this PR as well? It's (relatively) simpler than the other one on SGDClassifier. |
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glemaitre
reviewed
Jan 10, 2022
sklearn/linear_model/_glm/glm.py
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| verbose : int, default=0 | ||
| For the lbfgs solver set verbose to any positive number for verbosity. | ||
| Values should be in the range `[1, inf)`. |
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Suggested change
| Values should be in the range `[1, inf)`. | |
| Values should be in the range `[0, inf)`. |
sklearn/linear_model/_glm/glm.py
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| verbose : int, default=0 | ||
| For the lbfgs solver set verbose to any positive number for verbosity. | ||
| Values should be in the range `[1, inf)`. |
There was a problem hiding this comment.
Suggested change
| Values should be in the range `[1, inf)`. | |
| Values should be in the range `[0, inf)`. |
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Reminder to me:
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glemaitre
reviewed
Jan 10, 2022
sklearn/linear_model/_glm/glm.py
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| regularization strength. ``alpha = 0`` is equivalent to unpenalized | ||
| GLMs. In this case, the design matrix `X` must have full column rank | ||
| (no collinearities). | ||
| Values should be in the range `[0.0, inf)`. |
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To be in line with #21946
Suggested change
| Values should be in the range `[0.0, inf)`. | |
| Values must be in the range `[0.0, inf)`. |
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Reference Issues/PRs
References #21927 and #21946
What does this implement/fix? Explain your changes.
class GeneralizedLinearRegressor(RegressorMixin, BaseEstimator)class PoissonRegressor(GeneralizedLinearRegressor)class GammaRegressor(GeneralizedLinearRegressor)class TweedieRegressor(GeneralizedLinearRegressor)alpha,max_iter,tolandverboseAny other comments?
#DataUmbrella #postsprint