Class ExtraTreesTrainer<T extends Output<T>>

java.lang.Object
org.tribuo.ensemble.BaggingTrainer<T>
org.tribuo.common.tree.ExtraTreesTrainer<T>
All Implemented Interfaces:
com.oracle.labs.mlrg.olcut.config.Configurable, com.oracle.labs.mlrg.olcut.provenance.Provenancable<TrainerProvenance>, Trainer<T>

public class ExtraTreesTrainer<T extends Output<T>> extends BaggingTrainer<T>
A trainer which produces an Extremely Randomized Tree Ensemble.

Extremely Randomized Trees are similar to Random Forests, but they add an extra element of randomness in that the split points for features are also chosen randomly. As with Random Forests, feature subsampling is available at each of the nodes. An exception will be thrown if the inner trainer is not a decision tree trainer or if random splitting is turned off.

See:

 P. Geurts, D. Ernst, L. Wehenkel.
 "Extremely Randomized Trees"
 March 2006. PDF
 
  • Constructor Details

    • ExtraTreesTrainer

      public ExtraTreesTrainer(DecisionTreeTrainer<T> trainer, EnsembleCombiner<T> combiner, int numMembers)
      Constructs an ExtraTreesTrainer with the default seed Trainer.DEFAULT_SEED.

      Throws PropertyException if the trainer is not set to use random split points.

      Parameters:
      trainer - The tree trainer.
      combiner - The combining function for the ensemble.
      numMembers - The number of ensemble members to train.
    • ExtraTreesTrainer

      public ExtraTreesTrainer(DecisionTreeTrainer<T> trainer, EnsembleCombiner<T> combiner, int numMembers, long seed)
      Constructs an ExtraTreesTrainer with the supplied seed, trainer, combining function and number of members.

      Throws PropertyException if the trainer is not set to use random split points.

      Parameters:
      trainer - The tree trainer.
      combiner - The combining function for the ensemble.
      numMembers - The number of ensemble members to train.
      seed - The RNG seed.
  • Method Details