evalml.pipelines.components.RFRegressorSelectFromModel

class evalml.pipelines.components.RFRegressorSelectFromModel(number_features=None, n_estimators=10, max_depth=None, percent_features=0.5, threshold=-inf, n_jobs=-1, random_state=0, **kwargs)

Selects top features based on importance weights using a Random Forest regressor.

name = 'RF Regressor Select From Model'

model_family = 'none'

hyperparameter_ranges = {'percent_features': Real(low=0.01, high=1, prior='uniform', transform='identity'), 'threshold': ['mean', -inf]}

default_parameters = {'max_depth': None, 'n_estimators': 10, 'n_jobs': -1, 'number_features': None, 'percent_features': 0.5, 'threshold': -inf}

Instance attributes

parameters

Returns the parameters which were used to initialize the component

Methods:

__init__	Initialize self.
clone	Constructs a new component with the same parameters
describe	Describe a component and its parameters
fit	Fits component to data
fit_transform	Fits feature selector on data X then transforms X by selecting features
get_names	Get names of selected features.
transform	Transforms data X by selecting features

evalml.pipelines.components.StandardScaler.transform evalml.pipelines.components.RFRegressorSelectFromModel.__init__