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[doc] Document the current status of some features. (#9469)

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Jiaming Yuan
2023-08-13 23:42:27 +08:00
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doc/tutorials/categorical.rst
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@@ -4,16 +4,17 @@ Categorical Data
.. note::
As of XGBoost 1.6, the feature is experimental and has limited features
As of XGBoost 1.6, the feature is experimental and has limited features. Only the
Python package is fully supported.
Starting from version 1.5, XGBoost has experimental support for categorical data available
for public testing. For numerical data, the split condition is defined as :math:`value <
threshold`, while for categorical data the split is defined depending on whether
partitioning or onehot encoding is used. For partition-based splits, the splits are
specified as :math:`value \in categories`, where ``categories`` is the set of categories
in one feature. If onehot encoding is used instead, then the split is defined as
:math:`value == category`. More advanced categorical split strategy is planned for future
releases and this tutorial details how to inform XGBoost about the data type.
Starting from version 1.5, the XGBoost Python package has experimental support for
categorical data available for public testing. For numerical data, the split condition is
defined as :math:`value < threshold`, while for categorical data the split is defined
depending on whether partitioning or onehot encoding is used. For partition-based splits,
the splits are specified as :math:`value \in categories`, where ``categories`` is the set
of categories in one feature. If onehot encoding is used instead, then the split is
defined as :math:`value == category`. More advanced categorical split strategy is planned
for future releases and this tutorial details how to inform XGBoost about the data type.
************************************
Training with scikit-learn Interface