evalml.pipelines.components.ExtraTreesRegressor

class evalml.pipelines.components.ExtraTreesRegressor(n_estimators=100, max_features='auto', max_depth=6, min_samples_split=2, min_weight_fraction_leaf=0.0, n_jobs=-1, random_state=0)

Extra Trees Regressor

name = 'Extra Trees Regressor'

model_family = 'extra_trees'

supported_problem_types = [<ProblemTypes.REGRESSION: 'regression'>]

hyperparameter_ranges = {'max_depth': Integer(low=4, high=10, prior='uniform', transform='identity'), 'max_features': ['auto', 'sqrt', 'log2'], 'n_estimators': Integer(low=10, high=1000, prior='uniform', transform='identity')}

Instance attributes

feature_importances

Returns feature importances.

Methods:

__init__	Initialize self.
describe	Describe a component and its parameters
fit	Fits component to data
predict	Make predictions using selected features.
predict_proba	Make probability estimates for labels.