EnsembleModel

class progpy.EnsembleModel(models: list, **kwargs)

Bases: progpy.prognostics_model.PrognosticsModel

New in version 1.5.0.

An Ensemble Model is a collection of models which run together. The results of each model are aggregated using the aggregation_method function. This is generally done to improve the accuracy of prediction when you have multiple models that each represent part of the behavior, or represent a distribution of different behaviors.

Ensemble Models are constructed from a set of other models (e.g., m = EnsembleModel((m1, m2, m3))). The models then operate functionally as one prognostic model.

See example examples.ensemble

Parameters

models (list[PrognosticsModel]) – List of at least 2 models that form the ensemble

Keyword Arguments

aggregation_method (function) – Function that aggregates the outputs of the models in the ensemble. Default is np.mean