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Update document for multi output and categorical. (#7574)

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* Group together categorical related parameters.
* Update documents about multioutput and categorical.
This commit is contained in:
Jiaming Yuan
2022-01-19 04:35:17 +08:00
committed by GitHub
parent dac9eb13bd
commit b4ec1682c6
5 changed files with 27 additions and 22 deletions
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26
python-package/xgboost/sklearn.py
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@@ -197,6 +197,18 @@ __model_doc = f'''
Experimental support for categorical data. Do not set to true unless you are
interested in development. Only valid when `gpu_hist` and dataframe are used.
max_cat_to_onehot : bool
.. versionadded:: 1.6.0
.. note:: This parameter is experimental
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 and
`approx` tree method.
eval_metric : Optional[Union[str, List[str], Callable]]
.. versionadded:: 1.6.0
@@ -267,16 +279,6 @@ __model_doc = f'''
callbacks = [xgb.callback.EarlyStopping(rounds=early_stopping_rounds,
save_best=True)]
max_cat_to_onehot : bool
.. versionadded:: 1.6.0
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 and
`approx` tree method.
kwargs : dict, optional
Keyword arguments for XGBoost Booster object. Full documentation of parameters
can be found :doc:`here </parameter>`.
@@ -490,10 +492,10 @@ class XGBModel(XGBModelBase):
validate_parameters: Optional[bool] = None,
predictor: Optional[str] = None,
enable_categorical: bool = False,
max_cat_to_onehot: Optional[int] = None,
eval_metric: Optional[Union[str, List[str], Callable]] = None,
early_stopping_rounds: Optional[int] = None,
callbacks: Optional[List[TrainingCallback]] = None,
max_cat_to_onehot: Optional[int] = None,
**kwargs: Any
) -> None:
if not SKLEARN_INSTALLED:
@@ -530,10 +532,10 @@ class XGBModel(XGBModelBase):
self.validate_parameters = validate_parameters
self.predictor = predictor
self.enable_categorical = enable_categorical
self.max_cat_to_onehot = max_cat_to_onehot
self.eval_metric = eval_metric
self.early_stopping_rounds = early_stopping_rounds
self.callbacks = callbacks
self.max_cat_to_onehot = max_cat_to_onehot
if kwargs:
self.kwargs = kwargs