Class BaggingTrainer<T extends Output<T>>

java.lang.Object
org.tribuo.ensemble.BaggingTrainer<T>
All Implemented Interfaces:
com.oracle.labs.mlrg.olcut.config.Configurable, com.oracle.labs.mlrg.olcut.provenance.Provenancable<TrainerProvenance>, Trainer<T>
Direct Known Subclasses:
RandomForestTrainer
public class BaggingTrainer<T extends Output<T>> extends Object implements Trainer<T>
A Trainer that wraps another trainer and produces a bagged ensemble.

A bagged ensemble is a set of models each of which was trained on a bootstrap sample of the original dataset, combined with an unweighted majority vote.

See: 

 J. Friedman, T. Hastie, & R. Tibshirani.
 "The Elements of Statistical Learning"
 Springer 2001. PDF

  • Field Details

    • innerTrainer

      @Config(mandatory=true, description="The trainer to use for each ensemble member.") protected Trainer<T extends Output<T>> innerTrainer

    • numMembers

      @Config(mandatory=true, description="The number of ensemble members to train.") protected int numMembers

    • seed

      @Config(mandatory=true, description="The seed for the RNG.") protected long seed

    • combiner

      @Config(mandatory=true, description="The combination function to aggregate each ensemble member's outputs.") protected EnsembleCombiner<T extends Output<T>> combiner

    • rng

      protected SplittableRandom rng

    • trainInvocationCounter

      protected int trainInvocationCounter

  • Constructor Details

  • Method Details

    • postConfig

      public void postConfig()
      Specified by:
      postConfig in interface com.oracle.labs.mlrg.olcut.config.Configurable

    • ensembleName

      protected String ensembleName()

    • toString

      public String toString()
      Overrides:
      toString in class Object

    • train

      public Model<T> train(Dataset<T> examples, Map<String, com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance)
      Description copied from interface: Trainer
      Trains a predictive model using the examples in the given data set.
      Specified by:
      train in interface Trainer<T extends Output<T>>
      Parameters:
      examples - the data set containing the examples.
      runProvenance - Training run specific provenance (e.g., fold number).
      Returns:
      a predictive model that can be used to generate predictions for new examples.

    • trainSingleModel

      protected Model<T> trainSingleModel(Dataset<T> examples, ImmutableFeatureMap featureIDs, ImmutableOutputInfo<T> labelIDs, SplittableRandom localRNG, Map<String, com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance)

    • getInvocationCount

      public int getInvocationCount()
      Description copied from interface: Trainer
      The number of times this trainer instance has had it's train method invoked.

      This is used to determine how many times the trainer's RNG has been accessed to ensure replicability in the random number stream.

      Specified by:
      getInvocationCount in interface Trainer<T extends Output<T>>
      Returns:
      The number of train invocations.

    • getProvenance

      public TrainerProvenance getProvenance()
      Specified by:
      getProvenance in interface com.oracle.labs.mlrg.olcut.provenance.Provenancable<T extends Output<T>>