evalml.pipelines.components.StackedEnsembleRegressor.init¶

StackedEnsembleRegressor.__init__(input_pipelines=None, final_estimator=None, cv=None, n_jobs=- 1, random_state=0, **kwargs)[source]¶

Stacked ensemble regressor.

Parameters

input_pipelines (list(PipelineBase or subclass obj)) – List of pipeline instances to use as the base estimators. This must not be None or an empty list or else EnsembleMissingPipelinesError will be raised.
final_estimator (Estimator or subclass) – The regressor used to combine the base estimators. If None, uses LinearRegressor.
cv (int, cross-validation generator or an iterable) –
Determines the cross-validation splitting strategy used to train final_estimator. For int/None inputs, KFold is used. Defaults to None. Possible inputs for cv are:
- None: 3-fold cross validation
- int: the number of folds in a (Stratified) KFold
- An scikit-learn cross-validation generator object
- An iterable yielding (train, test) splits
n_jobs (int or None) – Non-negative integer describing level of parallelism used for pipelines. None and 1 are equivalent. If set to -1, all CPUs are used. For n_jobs below -1, (n_cpus + 1 + n_jobs) are used. Defaults to None. - Note: there could be some multi-process errors thrown for values of n_jobs != 1. If this is the case, please use n_jobs = 1.
random_state (int) – Seed for the random number generator. Defaults to 0.

evalml.pipelines.components.StackedEnsembleRegressor.__init__¶