evalml.pipelines.LinearRegressionPipeline

class evalml.pipelines.LinearRegressionPipeline(parameters, objective, random_state=0)

Linear Regression Pipeline for regression problems

name = 'Linear Regression Pipeline'

summary = 'Linear Regressor w/ One Hot Encoder + Simple Imputer + Standard Scaler'

component_graph = ['One Hot Encoder', 'Simple Imputer', 'Standard Scaler', <evalml.pipelines.components.estimators.regressors.linear_regressor.LinearRegressor object>]

supported_problem_types = ['regression']

model_family = 'linear_model'

hyperparameters = {'fit_intercept': [True, False], 'impute_strategy': ['mean', 'median', 'most_frequent'], 'normalize': [True, False]}

custom_hyperparameters = None

Instance attributes

feature_importances	Return feature importances.
parameters	Returns parameter dictionary for this pipeline

Methods:

__init__	Machine learning pipeline made out of transformers and a estimator.
describe	Outputs pipeline details including component parameters
feature_importance_graph	Generate a bar graph of the pipeline’s feature importances
fit	Build a model
get_component	Returns component by name
graph	Generate an image representing the pipeline graph
load	Loads pipeline at file path
predict	Make predictions using selected features.
predict_proba	Make probability estimates for labels.
save	Saves pipeline at file path
score	Evaluate model performance on current and additional objectives