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

Author: trivialfis

demo/guide-python/quantile_regression.py

@@ -7,6 +7,11 @@
 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

doc/tutorials/categorical.rst

@@ -4,16 +4,17 @@ Categorical Data
 
 .. note::
 
-   As of XGBoost 1.6, the feature is experimental and has limited features
-
-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.
+   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, 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

doc/tutorials/multioutput.rst

@@ -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