Class RandomForestTrainer<T extends Output<T>>

java.lang.Object

org.tribuo.ensemble.BaggingTrainer<T>

org.tribuo.common.tree.RandomForestTrainer<T>

All Implemented Interfaces:

com.oracle.labs.mlrg.olcut.config.Configurable, com.oracle.labs.mlrg.olcut.provenance.Provenancable<TrainerProvenance>, Trainer<T>

public class RandomForestTrainer<T extends Output<T>>
extends BaggingTrainer<T>

A trainer which produces a random forest.

Random Forests are basically bagged trees, with feature subsampling at each of the nodes. It's up to the user to supply a decision tree trainer which has feature subsampling turned on by checking DecisionTreeTrainer.getFractionFeaturesInSplit().

See:

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

Field Summary

Fields inherited from class org.tribuo.ensemble.BaggingTrainer

combiner, innerTrainer, numMembers, rng, seed, trainInvocationCounter

Fields inherited from interface org.tribuo.Trainer

DEFAULT_SEED

Constructor Summary

Constructors

Constructor	Description
RandomForestTrainer(DecisionTreeTrainer<T> trainer, EnsembleCombiner<T> combiner, int numMembers)	Constructs a RandomForestTrainer with the default seed Trainer.DEFAULT_SEED.
RandomForestTrainer(DecisionTreeTrainer<T> trainer, EnsembleCombiner<T> combiner, int numMembers, long seed)	Constructs a RandomForestTrainer with the supplied seed, trainer, combining function and number of members.

Method Summary

Modifier and Type	Method	Description
protected String	ensembleName()
void	postConfig()	Used by the OLCUT configuration system, and should not be called by external code.
String	toString()

Methods inherited from class org.tribuo.ensemble.BaggingTrainer

getInvocationCount, getProvenance, train, trainSingleModel

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Methods inherited from interface org.tribuo.Trainer

train

Constructor Details

RandomForestTrainer

public RandomForestTrainer(DecisionTreeTrainer<T> trainer,
 EnsembleCombiner<T> combiner,
 int numMembers)

Constructs a RandomForestTrainer with the default seed Trainer.DEFAULT_SEED.

Throws PropertyException if the trainer is not set to subsample the features.

Parameters:

trainer - The tree trainer.

combiner - The combining function for the ensemble.

numMembers - The number of ensemble members to train.

RandomForestTrainer

public RandomForestTrainer(DecisionTreeTrainer<T> trainer,
 EnsembleCombiner<T> combiner,
 int numMembers,
 long seed)

Constructs a RandomForestTrainer with the supplied seed, trainer, combining function and number of members.

Throws PropertyException if the trainer is not set to subsample the features.

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

postConfig

public void postConfig()

Used by the OLCUT configuration system, and should not be called by external code.

Specified by:

postConfig in interface com.oracle.labs.mlrg.olcut.config.Configurable

Overrides:

postConfig in class BaggingTrainer<T extends Output<T>>

ensembleName

protected String ensembleName()

Overrides:

ensembleName in class BaggingTrainer<T extends Output<T>>

toString

public String toString()

Overrides:

toString in class BaggingTrainer<T extends Output<T>>