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[doc] Fix broken link. [skip ci] (#7655)

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doc/tutorials/multioutput.rst
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@ -8,8 +8,8 @@ Starting from version 1.6, XGBoost has experimental support for multi-output reg
and multi-label classification with Python package. Multi-label classification usually and multi-label classification with Python package. Multi-label classification usually
refers to targets that have multiple non-exclusive class labels. For instance, a movie refers to targets that have multiple non-exclusive class labels. For instance, a movie
can be simultaneously classified as both sci-fi and comedy. For detailed explanation of can be simultaneously classified as both sci-fi and comedy. For detailed explanation of
terminologies related to different multi-output models please refer to the `scikit-learn terminologies related to different multi-output models please refer to the
user guide <https://scikit-learn.org/stable/modules/multiclass.HTML>`_. :doc:`scikit-learn user guide <sklearn:modules/multiclass>`.
Internally, XGBoost builds one model for each target similar to sklearn meta estimators, Internally, XGBoost builds one model for each target similar to sklearn meta estimators,
with the added benefit of reusing data and other integrated features like SHAP. For a with the added benefit of reusing data and other integrated features like SHAP. For a