evalml.pipelines.components.LinearRegressor

class evalml.pipelines.components.LinearRegressor(fit_intercept=True, normalize=False, n_jobs=- 1, random_state=None, random_seed=0, **kwargs)

Linear Regressor.

name = 'Linear Regressor'

model_family = 'linear_model'

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

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

default_parameters = {'fit_intercept': True, 'n_jobs': -1, '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.ElasticNetRegressor.save evalml.pipelines.components.LinearRegressor.__init__