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[doc] Reference enable_categorical doc in sklearn. (#9884)

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Jiaming Yuan 2023-12-14 23:29:19 +08:00 committed by GitHub
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2 changed files with 5 additions and 10 deletions
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7
python-package/xgboost/core.py
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@ -824,9 +824,10 @@ class DMatrix: # pylint: disable=too-many-instance-attributes,too-many-public-m
Experimental support of specializing for categorical features. Experimental support of specializing for categorical features.
If passing 'True' and 'data' is a data frame (from supported libraries If passing 'True' and 'data' is a data frame (from supported libraries such
such as Pandas or Modin), columns of categorical types will automatically as Pandas, Modin or cuDF), columns of categorical types will automatically
be set to be of categorical type (feature_type='c') in the resulting DMatrix. be set to be of categorical type (feature_type='c') in the resulting
DMatrix.
If passing 'False' and 'data' is a data frame with categorical columns, If passing 'False' and 'data' is a data frame with categorical columns,
it will result in an error being thrown. it will result in an error being thrown.

8
python-package/xgboost/sklearn.py
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@ -276,13 +276,7 @@ __model_doc = f"""
enable_categorical : bool enable_categorical : bool
.. versionadded:: 1.5.0 See the same parameter of :py:class:`DMatrix` for details.
.. note:: This parameter is experimental
Experimental support for categorical data. When enabled, cudf/pandas.DataFrame
should be used to specify categorical data type. Also, JSON/UBJSON
serialization format is required.
feature_types : Optional[FeatureTypes] feature_types : Optional[FeatureTypes]