Update parameter for categorical feature.

doc/parameter.rst

@@ -235,7 +235,7 @@ These parameters are only used for training with categorical data. See
 
 * ``max_cat_to_onehot``
 
-  .. versionadded:: 1.6
+  .. versionadded:: 1.6.0
 
   .. note:: This parameter is experimental. ``exact`` tree method is not yet supported.
 

doc/tutorials/categorical.rst

@@ -84,7 +84,7 @@ values are categories, and the measure is the output leaf value.  Intuitively, w
 group the categories that output similar leaf values. During split finding, we first sort
 the gradient histogram to prepare the contiguous partitions then enumerate the splits
 according to these sorted values. One of the related parameters for XGBoost is
-``max_cat_to_one_hot``, which controls whether one-hot encoding or partitioning should be
+``max_cat_to_onehot``, which controls whether one-hot encoding or partitioning should be
 used for each feature, see :ref:`cat-param` for details.
 
 

python-package/xgboost/sklearn.py

@@ -249,8 +249,20 @@ def inner(y_score: np.ndarray, dmatrix: DMatrix) -> Tuple[str, float]:
         A threshold for deciding whether XGBoost should use one-hot encoding based split
         for categorical data.  When number of categories is lesser than the threshold
         then one-hot encoding is chosen, otherwise the categories will be partitioned
-        into children nodes.  Only relevant for regression and binary classification.
-        See :doc:`Categorical Data </tutorials/categorical>` for details.
+        into children nodes. Also, `enable_categorical` needs to be set to have
+        categorical feature support. See :doc:`Categorical Data
+        </tutorials/categorical>` and :ref:`cat-param` for details.
+
+    max_cat_threshold : Optional[int]
+
+        .. versionadded:: 1.7.0
+
+        .. note:: This parameter is experimental
+
+        Maximum number of categories considered for each split. Used only by
+        partition-based splits for preventing over-fitting. Also, `enable_categorical`
+        needs to be set to have categorical feature support. See :doc:`Categorical Data
+        </tutorials/categorical>` and :ref:`cat-param` for details.
 
     eval_metric : Optional[Union[str, List[str], Callable]]
 
@@ -562,6 +574,7 @@ def __init__(
         enable_categorical: bool = False,
         feature_types: FeatureTypes = None,
         max_cat_to_onehot: Optional[int] = None,
+        max_cat_threshold: Optional[int] = None,
         eval_metric: Optional[Union[str, List[str], Callable]] = None,
         early_stopping_rounds: Optional[int] = None,
         callbacks: Optional[List[TrainingCallback]] = None,
@@ -607,6 +620,7 @@ def __init__(
         self.enable_categorical = enable_categorical
         self.feature_types = feature_types
         self.max_cat_to_onehot = max_cat_to_onehot
+        self.max_cat_threshold = max_cat_threshold
         self.eval_metric = eval_metric
         self.early_stopping_rounds = early_stopping_rounds
         self.callbacks = callbacks