evalml.pipelines.components.XGBoostRegressor

class evalml.pipelines.components.XGBoostRegressor(eta=0.1, max_depth=6, min_child_weight=1, n_estimators=100, random_seed=0, **kwargs)[source]

XGBoost Regressor.

name = 'XGBoost Regressor'
model_family = 'xgboost'
supported_problem_types = [<ProblemTypes.REGRESSION: 'regression'>, <ProblemTypes.TIME_SERIES_REGRESSION: 'time series regression'>]
hyperparameter_ranges = {'eta': Real(low=1e-06, high=1, prior='uniform', transform='identity'), 'max_depth': Integer(low=1, high=20, prior='uniform', transform='identity'), 'min_child_weight': Real(low=1, high=10, prior='uniform', transform='identity'), 'n_estimators': Integer(low=1, high=1000, prior='uniform', transform='identity')}
default_parameters = {'eta': 0.1, 'max_depth': 6, 'min_child_weight': 1, 'n_estimators': 100}
predict_uses_y = False

Instance attributes

SEED_MAX

SEED_MIN

feature_importance

Return an attribute of instance, which is of type owner.

needs_fitting

parameters

Returns the parameters which were used to initialize the component

Methods:

__init__

Initialize self.

clone

Constructs a new component with the same parameters and random state.

describe

Describe a component and its parameters

fit

Fits component to data

load

Loads component at file path

predict

Make predictions using selected features.

predict_proba

Make probability estimates for labels.

save

Saves component at file path

Class Inheritance

Inheritance diagram of XGBoostRegressor