Source code for evalml.pipelines.regression.linear_regression

from evalml.model_types import ModelTypes
from evalml.pipelines import PipelineBase
from evalml.pipelines.components import (
    LinearRegressor,
    OneHotEncoder,
    SimpleImputer,
    StandardScaler
)
from evalml.problem_types import ProblemTypes


[docs]class LinearRegressionPipeline(PipelineBase): """Linear Regression Pipeline for regression problems""" name = "Linear Regressor w/ One Hot Encoder + Simple Imputer + Standard Scaler" model_type = ModelTypes.LINEAR_MODEL problem_types = [ProblemTypes.REGRESSION] hyperparameters = { 'impute_strategy': ['most_frequent', 'mean', 'median'], 'normalize': [False, True], 'fit_intercept': [False, True] }
[docs] def __init__(self, objective, number_features, impute_strategy, normalize=False, fit_intercept=True, random_state=0, n_jobs=-1): imputer = SimpleImputer(impute_strategy=impute_strategy) enc = OneHotEncoder() scaler = StandardScaler() estimator = LinearRegressor(normalize=normalize, fit_intercept=fit_intercept, n_jobs=-1) super().__init__(objective=objective, component_list=[enc, imputer, scaler, estimator], n_jobs=n_jobs, random_state=random_state)