Hendrik/xgboost
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

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

Browse Source
This commit is contained in:
Jiaming Yuan 2022-02-15 14:07:34 +08:00 committed by GitHub
parent 0da7d872ef
commit 93eebe8664
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
1 changed files with 2 additions and 2 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

4
doc/tutorials/multioutput.rst
View File

@ -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
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
terminologies related to different multi-output models please refer to the `scikit-learn
user guide <https://scikit-learn.org/stable/modules/multiclass.HTML>`_.
terminologies related to different multi-output models please refer to the
:doc:`scikit-learn user guide <sklearn:modules/multiclass>`.
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