evalml.pipelines.components.LinearRegressor

class evalml.pipelines.components.LinearRegressor(fit_intercept=True, normalize=False, n_jobs=-1, random_state=0, **kwargs)

Linear Regressor.

name = 'Linear Regressor'

model_family = 'linear_model'

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

hyperparameter_ranges = {'fit_intercept': [True, False], 'normalize': [True, False]}

default_parameters = {'fit_intercept': True, 'n_jobs': -1, 'normalize': False}

Instance attributes

feature_importance	Returns importance associated with each feature.
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
predict	Make predictions using selected features.
predict_proba	Make probability estimates for labels.

evalml.pipelines.components.ElasticNetRegressor.predict_proba evalml.pipelines.components.LinearRegressor.__init__