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: The best iteration obtained by early stopping.
best_ntree_limit
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[, 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.

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

dask_ml.xgboost.XGBClassifier

dask_ml.xgboost.train

dask_ml.xgboost.XGBRegressor

dask_ml.xgboost.XGBRegressor¶

`dask_ml.xgboost`.XGBRegressor¶