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DOC Update hgbt docstrings on categorical_features default value #28485

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DOC Update hgbt docstrings on categorical_features default value #28485

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8 changes: 6 additions & 2 deletions sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1498,7 +1498,7 @@ class HistGradientBoostingRegressor(RegressorMixin, BaseHistGradientBoosting):
``max_bins`` bins. In addition to the ``max_bins`` bins, one more bin
is always reserved for missing values. Must be no larger than 255.
categorical_features : array-like of {bool, int, str} of shape (n_features) \
or shape (n_categorical_features,), default=None
or shape (n_categorical_features,), default="warn"
Indicates the categorical features.

- None : no feature will be considered categorical.
Expand All @@ -1511,6 +1511,8 @@ class HistGradientBoostingRegressor(RegressorMixin, BaseHistGradientBoosting):
considered to be categorical features. The input must be an object
exposing a ``__dataframe__`` method such as pandas or polars
DataFrames to use this feature.
- `"warn"`: issue a warning when the input dataframe has undeclared
categorical columns.

For each categorical feature, there must be at most `max_bins` unique
categories. Negative values for categorical features encoded as numeric
Expand Down Expand Up @@ -1874,7 +1876,7 @@ class HistGradientBoostingClassifier(ClassifierMixin, BaseHistGradientBoosting):
``max_bins`` bins. In addition to the ``max_bins`` bins, one more bin
is always reserved for missing values. Must be no larger than 255.
categorical_features : array-like of {bool, int, str} of shape (n_features) \
or shape (n_categorical_features,), default=None
or shape (n_categorical_features,), default="warn"
Indicates the categorical features.

- None : no feature will be considered categorical.
Expand All @@ -1887,6 +1889,8 @@ class HistGradientBoostingClassifier(ClassifierMixin, BaseHistGradientBoosting):
considered to be categorical features. The input must be an object
exposing a ``__dataframe__`` method such as pandas or polars
DataFrames to use this feature.
- `"warn"`: issue a warning when the input dataframe has undeclared
categorical columns.

For each categorical feature, there must be at most `max_bins` unique
categories. Negative values for categorical features encoded as numeric
Expand Down