evalml.pipelines.components.StackedEnsembleRegressor

class evalml.pipelines.components.StackedEnsembleRegressor(input_pipelines=None, final_estimator=None, cv=None, n_jobs=- 1, random_seed=0, **kwargs)

Stacked Ensemble Regressor.

name = 'Stacked Ensemble Regressor'

model_family = 'ensemble'

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

hyperparameter_ranges = {}

default_parameters = {'cv': None, 'final_estimator': None, 'n_jobs': -1}

predict_uses_y = False

Instance attributes

feature_importance	Not implemented for StackedEnsembleClassifier and StackedEnsembleRegressor
needs_fitting
parameters	Returns the parameters which were used to initialize the component

Methods:

__init__	Stacked ensemble regressor.
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.TimeSeriesBaselineEstimator.save evalml.pipelines.components.StackedEnsembleRegressor.__init__