dask_ml.xgboost.XGBRegressor
dask_ml.xgboost.XGBRegressor¶
- class dask_ml.xgboost.XGBRegressor(*, objective: Optional[Union[str, Callable[[numpy.ndarray, numpy.ndarray], Tuple[numpy.ndarray, numpy.ndarray]]]] = 'reg:squarederror', **kwargs: Any)¶
- Attributes
- best_iteration
- best_ntree_limit
- best_score
coef_Coefficients property
feature_importances_Feature importances property, return depends on importance_type parameter.
intercept_Intercept (bias) property
- n_features_in_
Methods
apply(X[, ntree_limit, 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_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 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_params(**params)Set the parameters of this estimator.