evalml.pipelines.components.RandomForestRegressor

class evalml.pipelines.components.RandomForestRegressor(n_estimators=100, max_depth=6, n_jobs=- 1, random_seed=0, **kwargs)

Random Forest Regressor.

name = 'Random Forest Regressor'

model_family = 'random_forest'

supported_problem_types = [<ProblemTypes.REGRESSION: 'regression'>, <ProblemTypes.TIME_SERIES_REGRESSION: 'time series 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')}

default_parameters = {'max_depth': 6, 'n_estimators': 100, 'n_jobs': -1}

predict_uses_y = False

Instance attributes

feature_importance	Returns importance associated with each feature.
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.ExtraTreesRegressor.save evalml.pipelines.components.RandomForestRegressor.__init__