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Should the meaning of default=None be specified?

See original GitHub issue

Maybe related with #15761.

Describe the issue linked to the documentation

I have noticed that when the default is None for some parameter or attribute, the meaning is included only in some cases.

For instance, for the fit method in the class sklearn.tree.DecisionTreeClassifier, sample_weight=None is documented as:

sample_weight : array-like of shape (n_samples,), default=None Sample weights. If None, then samples are equally weighted.

However, for the score method it is:

sample_weight : array-like of shape (n_samples,), default=None Sample weights.

It is okay like that or should be specified always?

Issue Analytics

State:
Created 3 years ago
Comments:17 (17 by maintainers)

Top GitHub Comments

1reaction

alceballosacommented, Oct 23, 2021

Hi, sklearn.decomposition.SparsePCA was solved in #21421, so you can update the list of sub-issues @ogrisel. PR #21428 will probably get merged soon and addresses MiniBatchSparsePCA and MiniBatchDictionaryLearning.

1reaction

MaggieChegecommented, Oct 23, 2021

@muokicaleb and I will work on sklearn.manifold.spectral_embedding (eigen_solver)

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