evalml.pipelines.LinearRegressionPipeline

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

Linear Regression Pipeline for regression problems.

name = 'Linear Regression Pipeline'

custom_name = None

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

component_graph = ['One Hot Encoder', 'Simple Imputer', 'Standard Scaler', 'Linear Regressor']

problem_type = 'regression'

model_family = 'linear_model'

hyperparameters = {'Linear Regressor': {'fit_intercept': [True, False], 'normalize': [True, False]}, 'One Hot Encoder': {}, 'Simple Imputer': {'impute_strategy': ['mean', 'median', 'most_frequent']}, 'Standard Scaler': {}}

custom_hyperparameters = None

default_parameters = {'Linear Regressor': {'fit_intercept': True, 'n_jobs': -1, 'normalize': False}, 'One Hot Encoder': {'categories': None, 'drop': None, 'handle_missing': 'error', 'handle_unknown': 'ignore', 'top_n': 10}, 'Simple Imputer': {'fill_value': None, 'impute_strategy': 'most_frequent'}}

Instance attributes

feature_importance	Return importance associated with each feature.
parameters	Returns parameter dictionary for this pipeline

Methods:

__init__	Machine learning pipeline made out of transformers and a estimator.
clone	Constructs a new pipeline with the same parameters and components.
describe	Outputs pipeline details including component parameters
fit	Build a model
get_component	Returns component by name
graph	Generate an image representing the pipeline graph
graph_feature_importance	Generate a bar graph of the pipeline’s feature importance
load	Loads pipeline at file path
predict	Make predictions using selected features.
save	Saves pipeline at file path
score	Evaluate model performance on current and additional objectives