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)[source]

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

Inheritance diagram of ElasticNetRegressor