evalml.pipelines.components.XGBoostRegressor

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

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

evalml.pipelines.components.RandomForestRegressor.save evalml.pipelines.components.XGBoostRegressor.__init__