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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 committed by GitHub
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5
demo/guide-python/quantile_regression.py
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@ -7,6 +7,11 @@ Quantile Regression
The script is inspired by this awesome example in sklearn: The script is inspired by this awesome example in sklearn:
https://scikit-learn.org/stable/auto_examples/ensemble/plot_gradient_boosting_quantile.html https://scikit-learn.org/stable/auto_examples/ensemble/plot_gradient_boosting_quantile.html
.. note::
The feature is only supported using the Python package. In addition, quantile
crossing can happen due to limitation in the algorithm.
""" """
import argparse import argparse
from typing import Dict from typing import Dict

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

7
doc/tutorials/multioutput.rst
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@ -11,6 +11,11 @@ can be simultaneously classified as both sci-fi and comedy. For detailed explan
terminologies related to different multi-output models please refer to the terminologies related to different multi-output models please refer to the
:doc:`scikit-learn user guide <sklearn:modules/multiclass>`. :doc:`scikit-learn user guide <sklearn:modules/multiclass>`.
.. note::
As of XGBoost 2.0, the feature is experimental and has limited features. Only the
Python package is tested.
********************************** **********************************
Training with One-Model-Per-Target Training with One-Model-Per-Target
********************************** **********************************
@ -49,7 +54,7 @@ Training with Vector Leaf
.. note:: .. note::
This is still working-in-progress, and many features are missing. This is still working-in-progress, and most features are missing.
XGBoost can optionally build multi-output trees with the size of leaf equals to the number XGBoost can optionally build multi-output trees with the size of leaf equals to the number
of targets when the tree method `hist` is used. The behavior can be controlled by the of targets when the tree method `hist` is used. The behavior can be controlled by the