From 93eebe86643aadac55a86e13c11cee697a6706f5 Mon Sep 17 00:00:00 2001 From: Jiaming Yuan Date: Tue, 15 Feb 2022 14:07:34 +0800 Subject: [PATCH] [doc] Fix broken link. [skip ci] (#7655) --- doc/tutorials/multioutput.rst | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/doc/tutorials/multioutput.rst b/doc/tutorials/multioutput.rst index 0be27ced0..280fb106f 100644 --- a/doc/tutorials/multioutput.rst +++ b/doc/tutorials/multioutput.rst @@ -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 `_. +terminologies related to different multi-output models please refer to the +:doc:`scikit-learn user guide `. 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