evalml.pipelines.components.ElasticNetRegressor

class evalml.pipelines.components.ElasticNetRegressor(alpha=0.5, l1_ratio=0.5, random_state=0, normalize=False, max_iter=1000, n_jobs=-1)

Elastic Net Regressor

name = 'Elastic Net Regressor'

model_family = 'linear_model'

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

hyperparameter_ranges = {'alpha': Real(low=0, high=1, prior='uniform', transform='identity'), 'l1_ratio': Real(low=0, high=1, 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.