DOC Explaining integer+float behavior in HistGradientingBoosting (sci…

…kit-learn#26529)

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>`.