evalml.pipelines.components.ElasticNetRegressor

class evalml.pipelines.components.ElasticNetRegressor(alpha=0.5, l1_ratio=0.5, max_iter=1000, normalize=False, random_state=0, **kwargs)

Elastic Net Regressor.

name = 'Elastic Net Regressor'

model_family = 'linear_model'

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

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

default_parameters = {'alpha': 0.5, 'l1_ratio': 0.5, 'max_iter': 1000, 'normalize': False}

predict_uses_y = False

Instance attributes

feature_importance	Return an attribute of instance, which is of type owner.
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.CatBoostRegressor.save evalml.pipelines.components.ElasticNetRegressor.__init__