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documentation of attributes of HistGradientBoostingClassifier except … #16283

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11 changes: 11 additions & 0 deletions sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py
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Expand Up @@ -923,13 +923,24 @@ class HistGradientBoostingClassifier(BaseHistGradientBoosting,

Attributes
----------
bin_mapper_ : BinMapper object
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IMO, this could be private.

Transformer that maps a dataset into integer-valued bins.
do_early_stopping_ : bool
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This could be public since it is derived from an input parameter.

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Not very useful, also obsolete once #14516 is merged

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agreed this is obsolete now :)

Specifies if early stopping is used during the training.
loss_ : callable
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This could be public since it is derived from an input parameter.

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I disagree, the losses are all private objects

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The effective loss name could be stored as a public attribute.

Loss function used by the algorithm.
n_features : int
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This could be private

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Private for now and replaced by n_features_in_ later.

The number of selected features.
n_iter_ : int
The number of estimators as selected by early stopping (if
n_iter_no_change is not None). Otherwise it corresponds to max_iter.
n_trees_per_iteration_ : int
The number of tree that are built at each iteration. This is equal to 1
for binary classification, and to ``n_classes`` for multiclass
classification.
scorer_ : callable
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This could be public

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I don't think this is a very useful public attribute. What are the benefit of exposing it?

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Indeed, we could also keep it private. The only other classes that have a public scorer_ attribute are the SearchCV classes and for those it's actually a list of scorer objects to deal with multi-metrics things.

Let's keep it private.

Scoring parameter to use for early stopping alternatively to the loss.
None if the loss is used.
train_score_ : ndarray, shape (n_iter_+1,)
The scores at each iteration on the training data. The first entry
is the score of the ensemble before the first iteration. Scores are
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