StackedEnsembleClassifier.
__init__
Stacked ensemble classifier.
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 classifier used to combine the base estimators. If None, uses LogisticRegressionClassifier.
cv (int, cross-validation generator or an iterable) – Determines the cross-validation splitting strategy used to train final_estimator. For int/None inputs, if the estimator is a classifier and y is either binary or multiclass, StratifiedKFold 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, np.random.RandomState) – seed for the random number generator