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Update categorical docs to remove experimental label #11690

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Merged
merged 3 commits into from
Sep 20, 2025
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Update categorical docs to remove experimental label #11690

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DOC Update categorical docs
dantegd Sep 17, 2025
79bb3e2
Merge branch 'master' into doc-categorical-nonexp
dantegd Sep 17, 2025
a527db8
Note for the R package.
trivialfis Sep 19, 2025
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27 changes: 11 additions & 16 deletions doc/tutorials/categorical.rst
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Expand Up @@ -2,23 +2,15 @@
Categorical Data
################

.. note::
Since version 1.5, XGBoost has support for categorical data. 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.

As of XGBoost 1.6, the feature is experimental and has limited features. Only the
Python package is fully supported.

.. versionadded:: 3.0

Support for the R package using ``factor``.
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Could you please help keep this?


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
Expand Down Expand Up @@ -69,6 +61,9 @@ for a worked example of using categorical data with ``scikit-learn`` interface w
one-hot encoding. A comparison between using one-hot encoded data and XGBoost's
categorical data support can be found :ref:`sphx_glr_python_examples_cat_in_the_dat.py`.

.. versionadded:: 3.0

Support for the R package using ``factor``.

********************
Optimal Partitioning
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