scikit-learn · thomasjpfan · Jul 21, 2023 · Jul 18, 2023 · Jul 18, 2023 · Jul 19, 2023
diff --git a/doc/modules/ensemble.rst b/doc/modules/ensemble.rst
@@ -10,12 +10,12 @@ Ensembles: Gradient boosting, random forests, bagging, voting, stacking
 base estimators built with a given learning algorithm in order to improve
 generalizability / robustness over a single estimator.
 
-Two very famous examples of ensemble methods are `gradient-boosted trees
-<gradient_boosting>`_ and `random forests <forest>`_.
+Two very famous examples of ensemble methods are :ref:`gradient-boosted trees
+<gradient_boosting>` and :ref:`random forests <forest>`.
 
 More generally, ensemble models can be applied to any base learner beyond
 trees, in averaging methods such as :ref:`Bagging methods <bagging>`,
-`model stacking <stacking>`_, or `Voting <voting_classifier>`_, or in
+:ref:`model stacking <stacking>`, or :ref:`Voting <voting_classifier>`, or in
 boosting, as :ref:`AdaBoost <adaboost>`.
 
 .. contents::

diff --git a/doc/whats_new/v0.22.rst b/doc/whats_new/v0.22.rst
@@ -392,7 +392,7 @@ Changelog
 
 - |Efficiency| :class:`decomposition.NMF` with `solver="mu"` fitted on sparse input
   matrices now uses batching to avoid briefly allocating an array with size
-  (#non-zero elements, n_components). :pr:`15257` by `Mart Willocx <Maocx>`_.
+  (#non-zero elements, n_components). :pr:`15257` by :user:`Mart Willocx <Maocx>`.
 
 - |Enhancement| :func:`decomposition.dict_learning` and
   :func:`decomposition.dict_learning_online` now accept `method_max_iter` and