Uses of Interface
org.tribuo.ensemble.EnsembleCombiner

Packages that use EnsembleCombiner

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.

Uses of EnsembleCombiner in org.tribuo.ensemble

Fields in org.tribuo.ensemble declared as EnsembleCombiner

Modifier and Type	Field	Description
protected EnsembleCombiner<T>	BaggingTrainer.combiner
protected final EnsembleCombiner<T>	WeightedEnsembleModel.combiner	The ensemble combination function.

Methods in org.tribuo.ensemble that return EnsembleCombiner

Modifier and Type	Method	Description
static EnsembleCombiner<?>	EnsembleCombiner.deserialize(org.tribuo.protos.core.EnsembleCombinerProto proto)	Deserialization helper for EnsembleCombiner.

Methods in org.tribuo.ensemble with parameters of type EnsembleCombiner

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.

Constructors in org.tribuo.ensemble with parameters of type EnsembleCombiner

Modifier	Constructor	Description
	BaggingTrainer(Trainer<T> trainer, EnsembleCombiner<T> combiner, int numMembers)	Constructs a bagging trainer with the supplied parameters using Trainer.DEFAULT_SEED as the RNG seed.
	BaggingTrainer(Trainer<T> trainer, EnsembleCombiner<T> combiner, int numMembers, long seed)	Constructs a bagging trainer with the supplied parameters.
	WeightedEnsembleModel(String name, EnsembleModelProvenance provenance, ImmutableFeatureMap featureIDMap, ImmutableOutputInfo<T> outputIDInfo, List<Model<T>> newModels, EnsembleCombiner<T> combiner)	Unless you are implementing a Trainer you should not use this constructor directly.
	WeightedEnsembleModel(String name, EnsembleModelProvenance provenance, ImmutableFeatureMap featureIDMap, ImmutableOutputInfo<T> outputIDInfo, List<Model<T>> newModels, EnsembleCombiner<T> combiner, float[] weights)	Unless you are implementing a Trainer you should not use this constructor directly.