evalml.pipelines.components.RandomForestRegressor

class evalml.pipelines.components.RandomForestRegressor(n_estimators=10, max_depth=None, n_jobs=-1, random_state=0)

Random Forest Regressor

name = 'Random Forest Regressor'

model_family = 'random_forest'

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

hyperparameter_ranges = {'max_depth': Integer(low=1, high=32, prior='uniform', transform='identity'), '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.