From c646f95fef7dd6e18238a3c4a2ba5ac70f70dc37 Mon Sep 17 00:00:00 2001
From: Dimitri Papadopoulos
 <3234522+DimitriPapadopoulos@users.noreply.github.com>
Date: Tue, 18 Jul 2023 09:24:08 +0200
Subject: [PATCH 1/4] DOC fix broken links

---
 doc/modules/ensemble.rst | 7 ++++---
 doc/whats_new/v0.22.rst  | 2 +-
 2 files changed, 5 insertions(+), 4 deletions(-)

diff --git a/doc/modules/ensemble.rst b/doc/modules/ensemble.rst
index c3ea63bc6e944..94054c1b520c5 100644
--- a/doc/modules/ensemble.rst
+++ b/doc/modules/ensemble.rst
@@ -11,12 +11,13 @@ 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>`_.
+<gradient-boosting>`_ and
+`random forests <random-forests-and-other-randomized-tree-ensembles>`_.
 
 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
-boosting, as :ref:`AdaBoost <adaboost>`.
+`model stacking <stacked-generalization>`_, or `Voting <voting-classifier>`_,
+or in boosting, as :ref:`AdaBoost <adaboost>`.
 
 .. contents::
     :local:
diff --git a/doc/whats_new/v0.22.rst b/doc/whats_new/v0.22.rst
index fea27b0c1c1a4..da2f5e8796db8 100644
--- 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

From f459144b3593bfb80a67ea1ddaf180468a5e82b4 Mon Sep 17 00:00:00 2001
From: Dimitri Papadopoulos Orfanos
 <3234522+DimitriPapadopoulos@users.noreply.github.com>
Date: Tue, 18 Jul 2023 17:15:42 +0200
Subject: [PATCH 2/4] Update doc/modules/ensemble.rst

Co-authored-by: Arturo Amor <86408019+ArturoAmorQ@users.noreply.github.com>
---
 doc/modules/ensemble.rst | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/doc/modules/ensemble.rst b/doc/modules/ensemble.rst
index 94054c1b520c5..917bd37c0bf7f 100644
--- a/doc/modules/ensemble.rst
+++ b/doc/modules/ensemble.rst
@@ -16,8 +16,8 @@ Two very famous examples of ensemble methods are `gradient-boosted trees
 
 More generally, ensemble models can be applied to any base learner beyond
 trees, in averaging methods such as :ref:`Bagging methods <bagging>`,
-`model stacking <stacked-generalization>`_, or `Voting <voting-classifier>`_,
-or in boosting, as :ref:`AdaBoost <adaboost>`.
+:ref:`model stacking <stacking>`, or :ref:`Voting <voting_classifier>`, or in
+boosting, as :ref:`AdaBoost <adaboost>`.
 
 .. contents::
     :local:

From 1811f4417c562c415b0a86c7122d1b420b8685b3 Mon Sep 17 00:00:00 2001
From: Dimitri Papadopoulos Orfanos
 <3234522+DimitriPapadopoulos@users.noreply.github.com>
Date: Wed, 19 Jul 2023 20:54:46 +0200
Subject: [PATCH 3/4] Update doc/modules/ensemble.rst

Co-authored-by: Arturo Amor <86408019+ArturoAmorQ@users.noreply.github.com>
---
 doc/modules/ensemble.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/modules/ensemble.rst b/doc/modules/ensemble.rst
index 917bd37c0bf7f..7e1d30e5bb67a 100644
--- a/doc/modules/ensemble.rst
+++ b/doc/modules/ensemble.rst
@@ -10,7 +10,7 @@ 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
+Two very famous examples of ensemble methods are :ref:`gradient-boosted trees
 <gradient-boosting>`_ and
 `random forests <random-forests-and-other-randomized-tree-ensembles>`_.
 

From bcae063b9ff15a4999f2b3c36768eea57f430c50 Mon Sep 17 00:00:00 2001
From: Dimitri Papadopoulos Orfanos
 <3234522+DimitriPapadopoulos@users.noreply.github.com>
Date: Wed, 19 Jul 2023 20:54:58 +0200
Subject: [PATCH 4/4] Update doc/modules/ensemble.rst

Co-authored-by: Arturo Amor <86408019+ArturoAmorQ@users.noreply.github.com>
---
 doc/modules/ensemble.rst | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/doc/modules/ensemble.rst b/doc/modules/ensemble.rst
index 7e1d30e5bb67a..36eed98da0f6b 100644
--- a/doc/modules/ensemble.rst
+++ b/doc/modules/ensemble.rst
@@ -11,8 +11,7 @@ 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 :ref:`gradient-boosted trees
-<gradient-boosting>`_ and
-`random forests <random-forests-and-other-randomized-tree-ensembles>`_.
+<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>`,