Inheritance diagram for xgboost.sklearn.XGBRegressor:
Public Member Functions | |
| None | __init__ (self, *SklObjective objective="reg:squarederror", **Any kwargs) |
 Public Member Functions inherited from xgboost.sklearn.XGBModel | |
| bool | __sklearn_is_fitted__ (self) |
| Booster | get_booster (self) |
| "XGBModel" | set_params (self, **Any params) |
| Dict[str, Any] | get_params (self, bool deep=True) |
| Dict[str, Any] | get_xgb_params (self) |
| int | get_num_boosting_rounds (self) |
| None | save_model (self, Union[str, os.PathLike] fname) |
| None | load_model (self, ModelIn fname) |
| "XGBModel" | fit (self, ArrayLike X, ArrayLike y, *Optional[ArrayLike] sample_weight=None, Optional[ArrayLike] base_margin=None, Optional[Sequence[Tuple[ArrayLike, ArrayLike]]] eval_set=None, Optional[Union[str, Sequence[str], Metric]] eval_metric=None, Optional[int] early_stopping_rounds=None, Optional[Union[bool, int]] verbose=True, Optional[Union[Booster, str, "XGBModel"]] xgb_model=None, Optional[Sequence[ArrayLike]] sample_weight_eval_set=None, Optional[Sequence[ArrayLike]] base_margin_eval_set=None, Optional[ArrayLike] feature_weights=None, Optional[Sequence[TrainingCallback]] callbacks=None) |
| ArrayLike | predict (self, ArrayLike X, bool output_margin=False, bool validate_features=True, Optional[ArrayLike] base_margin=None, Optional[Tuple[int, int]] iteration_range=None) |
| np.ndarray | apply (self, ArrayLike X, Optional[Tuple[int, int]] iteration_range=None) |
| Dict[str, Dict[str, List[float]]] | evals_result (self) |
| int | n_features_in_ (self) |
| np.ndarray | feature_names_in_ (self) |
| float | best_score (self) |
| int | best_iteration (self) |
| np.ndarray | feature_importances_ (self) |
| np.ndarray | coef_ (self) |
| np.ndarray | intercept_ (self) |
Static Public Attributes | |
| str | extra_parameters |
Additional Inherited Members | |
| Data Fields inherited from xgboost.sklearn.XGBModel | |
| n_estimators | |
| objective | |
| max_depth | |
| max_leaves | |
| max_bin | |
| grow_policy | |
| learning_rate | |
| verbosity | |
| booster | |
| tree_method | |
| gamma | |
| min_child_weight | |
| max_delta_step | |
| subsample | |
| sampling_method | |
| colsample_bytree | |
| colsample_bylevel | |
| colsample_bynode | |
| reg_alpha | |
| reg_lambda | |
| scale_pos_weight | |
| base_score | |
| missing | |
| num_parallel_tree | |
| random_state | |
| n_jobs | |
| monotone_constraints | |
| interaction_constraints | |
| importance_type | |
| device | |
| validate_parameters | |
| enable_categorical | |
| feature_types | |
| max_cat_to_onehot | |
| max_cat_threshold | |
| multi_strategy | |
| eval_metric | |
| early_stopping_rounds | |
| callbacks | |
| kwargs | |
| n_classes_ | |
| evals_result_ | |
| Protected Member Functions inherited from xgboost.sklearn.XGBModel | |
| Dict[str, bool] | _more_tags (self) |
| str | _get_type (self) |
| None | _load_model_attributes (self, dict config) |
| Tuple[ Optional[Union[Booster, str, "XGBModel"]], Optional[Metric], Dict[str, Any], Optional[int], Optional[Sequence[TrainingCallback]],] | _configure_fit (self, Optional[Union[Booster, "XGBModel", str]] booster, Optional[Union[Callable, str, Sequence[str]]] eval_metric, Dict[str, Any] params, Optional[int] early_stopping_rounds, Optional[Sequence[TrainingCallback]] callbacks) |
| DMatrix | _create_dmatrix (self, Optional[DMatrix] ref, **Any kwargs) |
| None | _set_evaluation_result (self, TrainingCallback.EvalsLog evals_result) |
| bool | _can_use_inplace_predict (self) |
| Tuple[int, int] | _get_iteration_range (self, Optional[Tuple[int, int]] iteration_range) |
| Protected Attributes inherited from xgboost.sklearn.XGBModel | |
| _Booster | |
Constructor & Destructor Documentation
◆ __init__()
| None xgboost.sklearn.XGBRegressor.__init__ | ( | self, | |
| *SklObjective | objective = "reg:squarederror", |
||
| **Any | kwargs | ||
| ) |
Reimplemented from xgboost.sklearn.XGBModel.
Field Documentation
◆ extra_parameters
|
static |
Initial value:
= """
n_estimators : Optional[int]
Number of trees in random forest to fit.
""",
The documentation for this class was generated from the following file:
- External/xgboost/python-package/xgboost/sklearn.py
