Update document for multi output and categorical. (#7574)

* Group together categorical related parameters.
* Update documents about multioutput and categorical.

demo/guide-python/custom_rmsle.py

@@ -7,7 +7,7 @@ weight is not used in following example. In this script, we implement the Square
 Error (SLE) objective and RMSLE metric as customized functions, then compare it with
 native implementation in XGBoost.
 
-See doc/tutorials/custom_metric_obj.rst for a step by step walkthrough, with other
+See :doc:`/tutorials/custom_metric_obj` for a step by step walkthrough, with other
 details.
 
 The `SLE` objective reduces impact of outliers in training dataset, hence here we also

demo/guide-python/multioutput_regression.py

@@ -5,6 +5,8 @@ A demo for multi-output regression
 The demo is adopted from scikit-learn:
 
 https://scikit-learn.org/stable/auto_examples/ensemble/plot_random_forest_regression_multioutput.html#sphx-glr-auto-examples-ensemble-plot-random-forest-regression-multioutput-py
+
+See :doc:`/tutorials/multioutput` for more information.
 """
 import numpy as np
 import xgboost as xgb