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dask_ml.xgboost.XGBRegressor

class dask_ml.xgboost.XGBRegressor(*, objective: Optional[Union[str, xgboost.sklearn._SklObjWProto, Callable[[Any, Any], Tuple[numpy.ndarray, numpy.ndarray]]]] = 'reg:squarederror', **kwargs: Any)

Attributes

best_iteration

The best iteration obtained by early stopping.

best_score

The best score obtained by early stopping.

coef_

Coefficients property

feature_importances_

Feature importances property, return depends on importance_type parameter.

feature_names_in_

Names of features seen during fit().

intercept_

Intercept (bias) property

n_features_in_

Number of features seen during fit().

Methods

apply(X[, iteration_range])	Return the predicted leaf every tree for each sample.
evals_result()	Return the evaluation results.
fit(X[, y, eval_set, sample_weight, ...])	Fit the gradient boosting model
get_booster()	Get the underlying xgboost Booster of this model.
get_metadata_routing()	Get metadata routing of this object.
get_num_boosting_rounds()	Gets the number of xgboost boosting rounds.
get_params([deep])	Get parameters.
get_xgb_params()	Get xgboost specific parameters.
load_model(fname)	Load the model from a file or a bytearray.
predict(X)	Predict with X.
save_model(fname)	Save the model to a file.
score(X, y[, sample_weight])	Return the coefficient of determination of the prediction.
set_fit_request(*[, early_stopping_rounds, ...])	Request metadata passed to the fit method.
set_params(**params)	Set the parameters of this estimator.
set_predict_request(*[, base_margin, ...])	Request metadata passed to the predict method.
set_score_request(*[, sample_weight])	Request metadata passed to the score method.

__init__(*, objective: Optional[Union[str, xgboost.sklearn._SklObjWProto, Callable[[Any, Any], Tuple[numpy.ndarray, numpy.ndarray]]]] = 'reg:squarederror', **kwargs: Any) → None

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