diff --git a/doc/modules/ensemble.rst b/doc/modules/ensemble.rst
index 334e00e35a848..ee8cac3e715c2 100644
--- a/doc/modules/ensemble.rst
+++ b/doc/modules/ensemble.rst
@@ -286,10 +286,12 @@ model.
 For a predictor :math:`F` with two features:
 
 - a **monotonic increase constraint** is a constraint of the form:
+
   .. math::
       x_1 \leq x_1' \implies F(x_1, x_2) \leq F(x_1', x_2)
 
 - a **monotonic decrease constraint** is a constraint of the form:
+
   .. math::
       x_1 \leq x_1' \implies F(x_1, x_2) \geq F(x_1', x_2)
 
diff --git a/examples/release_highlights/plot_release_highlights_1_4_0.py b/examples/release_highlights/plot_release_highlights_1_4_0.py
index d8112699e04ed..0d2924d9e8bb4 100644
--- a/examples/release_highlights/plot_release_highlights_1_4_0.py
+++ b/examples/release_highlights/plot_release_highlights_1_4_0.py
@@ -155,7 +155,7 @@
 # ------------------------
 # Many meta-estimators and cross-validation routines now support metadata
 # routing, which are listed in the :ref:`user guide
-# <_metadata_routing_models>`. For instance, this is how you can do a nested
+# <metadata_routing_models>`. For instance, this is how you can do a nested
 # cross-validation with sample weights and :class:`~model_selection.GroupKFold`:
 import sklearn
 from sklearn.metrics import get_scorer