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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:
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
from typing import Dict

19
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

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
: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
**********************************
@ -49,7 +54,7 @@ Training with Vector Leaf
.. 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
of targets when the tree method `hist` is used. The behavior can be controlled by the