Ensemble mixers together in order to generate predictions
- class ensemble.BaseEnsemble(target, mixers, data)¶
Base class for all ensembles.
Ensembles wrap sets of Lightwood mixers, with the objective of generating better predictions based on the output of each mixer.
- There are two important methods for any ensemble to work:
__init__() should prepare all mixers and internal ensemble logic.
__call__() applies any aggregation rules to generate final predictions based on the output of each mixer.
Class Attributes: - mixers: List of mixers the ensemble will use. - supports_proba: For classification tasks, whether the ensemble supports yielding per-class scores rather than only returning the predicted label.
- class ensemble.BestOf(target, mixers, data, accuracy_functions, args, ts_analysis=None)¶
This ensemble acts as a mixer selector. After evaluating accuracy for all internal mixers with the validation data, it sets the best mixer as the underlying model.
- class ensemble.MeanEnsemble(target, mixers, data, dtype_dict)¶
- class ensemble.ModeEnsemble(target, mixers, data, dtype_dict, accuracy_functions, args, ts_analysis=None)¶
- class ensemble.WeightedMeanEnsemble(target, mixers, data, args, dtype_dict, accuracy_functions, ts_analysis=None)¶