Link to example from docstring

scikit-learn · Nov 11, 2022 · 1925f0b · 1925f0b
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diff --git a/examples/ensemble/plot_monotonic_constraints.py b/examples/ensemble/plot_monotonic_constraints.py
@@ -88,6 +88,11 @@
 # trend and ignores the local variations.
 
 # %%
+# .. monotonic_cst_features_names:
+#
+# Using feature names to specify monotonic constraints
+# ----------------------------------------------------
+#
 # Note that if the training data has feature names, it's possible to specifiy the
 # monotonic constraints by passing a dictionary:
 import pandas as pd

diff --git a/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py b/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py
@@ -1238,7 +1238,8 @@ class HistGradientBoostingRegressor(RegressorMixin, BaseHistGradientBoosting):
         - -1: monotonic decrease
 
         If a dict with str keys, map feature to monotonic constraints by name.
-        If an array, the feature are mapped to constraints by position.
+        If an array, the feature are mapped to constraints by position. See
+        example usage :ref:`monotonic_cst_features_names`.
 
         The constraints are only valid for binary classifications and hold
         over the probability of the positive class.
@@ -1580,7 +1581,8 @@ class HistGradientBoostingClassifier(ClassifierMixin, BaseHistGradientBoosting):
         - -1: monotonic decrease
 
         If a dict with str keys, map feature to monotonic constraints by name.
-        If an array, the feature are mapped to constraints by position.
+        If an array, the feature are mapped to constraints by position. See
+        example usage in :ref:`monotonic_cst_features_names`.
 
         The constraints are only valid for binary classifications and hold
         over the probability of the positive class.