Uses of Class
org.tribuo.ensemble.WeightedEnsembleModel

Packages that use WeightedEnsembleModel

Package	Description
org.tribuo.ensemble	Provides an interface for model prediction combinations, two base classes for ensemble models, a base class for ensemble excuses, and a Bagging implementation.
org.tribuo.multilabel.ensemble	Provides a multi-label ensemble combiner that performs a (possibly weighted) majority vote among each label independently, along with an implementation of classifier chain ensembles.

Uses of WeightedEnsembleModel in org.tribuo.ensemble

Methods in org.tribuo.ensemble that return WeightedEnsembleModel

Modifier and Type	Method	Description
static <T extends Output<T>> WeightedEnsembleModel<T>	WeightedEnsembleModel.createEnsembleFromExistingModels(String name, List<Model<T>> models, EnsembleCombiner<T> combiner)	Creates an ensemble from existing models.
static <T extends Output<T>> WeightedEnsembleModel<T>	WeightedEnsembleModel.createEnsembleFromExistingModels(String name, List<Model<T>> models, EnsembleCombiner<T> combiner, float[] weights)	Creates an ensemble from existing models.
static WeightedEnsembleModel<?>	WeightedEnsembleModel.deserializeFromProto(int version, String className, com.google.protobuf.Any message)	Deserialization factory.

Uses of WeightedEnsembleModel in org.tribuo.multilabel.ensemble

Methods in org.tribuo.multilabel.ensemble that return WeightedEnsembleModel

Modifier and Type	Method	Description
WeightedEnsembleModel<MultiLabel>	CCEnsembleTrainer.train(Dataset<MultiLabel> examples)
WeightedEnsembleModel<MultiLabel>	CCEnsembleTrainer.train(Dataset<MultiLabel> examples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance)
WeightedEnsembleModel<MultiLabel>	CCEnsembleTrainer.train(Dataset<MultiLabel> examples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance, int invocationCount)