DOC Explaining integer+float behavior in HistGradientingBoosting (#26529)

vijethmoudgalya · web-flow · commit 20be4dfb50e0 · 2023-06-16T15:16:12.000+02:00
diff --git a/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py b/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py
@@ -1271,6 +1271,9 @@ class HistGradientBoostingRegressor(RegressorMixin, BaseHistGradientBoosting):
         For each categorical feature, there must be at most `max_bins` unique
         categories, and each categorical value must be less then `max_bins - 1`.
         Negative values for categorical features are treated as missing values.
+        All categorical values are converted to floating point numbers.
+        This means that categorical values of 1.0 and 1 are treated as
+        the same category.
 
         Read more in the :ref:`User Guide <categorical_support_gbdt>`.
 
@@ -1628,6 +1631,9 @@ class HistGradientBoostingClassifier(ClassifierMixin, BaseHistGradientBoosting):
         For each categorical feature, there must be at most `max_bins` unique
         categories, and each categorical value must be less then `max_bins - 1`.
         Negative values for categorical features are treated as missing values.
+        All categorical values are converted to floating point numbers.
+        This means that categorical values of 1.0 and 1 are treated as
+        the same category.
 
         Read more in the :ref:`User Guide <categorical_support_gbdt>`.
 
