Ensemble mixers together in order to generate predictions

class ensemble.BaseEnsemble(target, mixers, data)[source]

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:
  1. __init__() should prepare all mixers and internal ensemble logic.

  2. __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)[source]

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)[source]
class ensemble.ModeEnsemble(target, mixers, data, dtype_dict, accuracy_functions, args, ts_analysis=None)[source]
class ensemble.WeightedMeanEnsemble(target, mixers, data, args, dtype_dict, accuracy_functions, ts_analysis=None)[source]