from sklearn.linear_model import LinearRegression as SKLinearRegression
from evalml.model_types import ModelTypes
from evalml.pipelines.components import ComponentTypes
from evalml.pipelines.components.estimators import Estimator
from evalml.problem_types import ProblemTypes
[docs]class LinearRegressor(Estimator):
"""Linear Regressor"""
name = "Linear Regressor"
component_type = ComponentTypes.REGRESSOR
_needs_fitting = True
hyperparameter_ranges = {
'fit_intercept': [True, False],
'normalize': [True, False]
}
model_type = ModelTypes.LINEAR_MODEL
problem_types = [ProblemTypes.REGRESSION]
[docs] def __init__(self, fit_intercept=True, normalize=False, n_jobs=-1):
parameters = {
'fit_intercept': fit_intercept,
'normalize': normalize
}
linear_regressor = SKLinearRegression(fit_intercept=fit_intercept,
normalize=normalize,
n_jobs=n_jobs)
super().__init__(parameters=parameters,
component_obj=linear_regressor,
random_state=0)
@property
def feature_importances(self):
return self._component_obj.coef_