Class CARTClassificationTrainer

java.lang.Object

org.tribuo.common.tree.AbstractCARTTrainer<Label>

org.tribuo.classification.dtree.CARTClassificationTrainer

All Implemented Interfaces:

com.oracle.labs.mlrg.olcut.config.Configurable, com.oracle.labs.mlrg.olcut.provenance.Provenancable<TrainerProvenance>, DecisionTreeTrainer<Label>, SparseTrainer<Label>, Trainer<Label>, WeightedExamples

public class CARTClassificationTrainer
extends AbstractCARTTrainer<Label>

A Trainer that uses an approximation of the CART algorithm to build a decision tree.

See:

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

Nested Class Summary

Nested classes/interfaces inherited from class org.tribuo.common.tree.AbstractCARTTrainer

AbstractCARTTrainer.AbstractCARTTrainerProvenance

Field Summary

Fields inherited from class org.tribuo.common.tree.AbstractCARTTrainer

fractionFeaturesInSplit, maxDepth, MIN_EXAMPLES, minChildWeight, minImpurityDecrease, rng, seed, trainInvocationCounter, useRandomSplitPoints

Fields inherited from interface org.tribuo.Trainer

DEFAULT_SEED

Constructor Summary

Constructors

Constructor	Description
CARTClassificationTrainer()	Creates a CART Trainer.
CARTClassificationTrainer(int maxDepth)	Creates a CART trainer.
CARTClassificationTrainer(int maxDepth, float fractionFeaturesInSplit, boolean useRandomSplitPoints, long seed)	Creates a CART Trainer.
CARTClassificationTrainer(int maxDepth, float minChildWeight, float minImpurityDecrease, float fractionFeaturesInSplit, boolean useRandomSplitPoints, LabelImpurity impurity, long seed)	Creates a CART Trainer.
CARTClassificationTrainer(int maxDepth, float minChildWeight, float minImpurityDecrease, float fractionFeaturesInSplit, LabelImpurity impurity, long seed)	Creates a CART Trainer.
CARTClassificationTrainer(int maxDepth, float fractionFeaturesInSplit, long seed)	Creates a CART Trainer.

Method Summary

Modifier and Type	Method	Description
TrainerProvenance	getProvenance()
protected AbstractTrainingNode<Label>	mkTrainingNode(Dataset<Label> examples, AbstractTrainingNode.LeafDeterminer leafDeterminer)
String	toString()

Methods inherited from class org.tribuo.common.tree.AbstractCARTTrainer

getFractionFeaturesInSplit, getInvocationCount, getMinImpurityDecrease, getUseRandomSplitPoints, postConfig, train, train

Methods inherited from class java.lang.Object

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

Constructor Details

CARTClassificationTrainer

public CARTClassificationTrainer(int maxDepth,
 float minChildWeight,
 float minImpurityDecrease,
 float fractionFeaturesInSplit,
 boolean useRandomSplitPoints,
 LabelImpurity impurity,
 long seed)

Creates a CART Trainer.

Parameters:

maxDepth - The maximum depth of the tree.

minChildWeight - The minimum node weight to consider it for a split.

minImpurityDecrease - The minimum decrease in impurity necessary to split a node.

fractionFeaturesInSplit - The fraction of features available in each split.

useRandomSplitPoints - Whether to choose split points for features at random.

impurity - Impurity measure to determine split quality. See LabelImpurity.

seed - The RNG seed.

CARTClassificationTrainer

public CARTClassificationTrainer()

Creates a CART Trainer.

Sets the impurity to the GiniIndex, uses all the features, builds an arbitrary depth tree, computes the exact split point and sets the minimum number of examples in a leaf to AbstractCARTTrainer.MIN_EXAMPLES.

CARTClassificationTrainer

public CARTClassificationTrainer(int maxDepth)

Creates a CART trainer.

Sets the impurity to the GiniIndex, uses all the features, computes the exact split point and sets the minimum number of examples in a leaf to AbstractCARTTrainer.MIN_EXAMPLES.

Parameters:

maxDepth - The maximum depth of the tree.

CARTClassificationTrainer

public CARTClassificationTrainer(int maxDepth,
 float fractionFeaturesInSplit,
 long seed)

Creates a CART Trainer.

Sets the impurity to the GiniIndex.

Parameters:

maxDepth - The maximum depth of the tree.

fractionFeaturesInSplit - The fraction of features available in each split.

seed - The seed for the RNG.

CARTClassificationTrainer

public CARTClassificationTrainer(int maxDepth,
 float fractionFeaturesInSplit,
 boolean useRandomSplitPoints,
 long seed)

Creates a CART Trainer.

Sets the impurity to the GiniIndex. Computes the exact split point.

Parameters:

maxDepth - The maximum depth of the tree.

fractionFeaturesInSplit - The fraction of features available in each split.

useRandomSplitPoints - Whether to choose split points for features at random.

seed - The seed for the RNG.

CARTClassificationTrainer

public CARTClassificationTrainer(int maxDepth,
 float minChildWeight,
 float minImpurityDecrease,
 float fractionFeaturesInSplit,
 LabelImpurity impurity,
 long seed)

Creates a CART Trainer.

Computes the exact split point.

Parameters:

maxDepth - The maximum depth of the tree.

minChildWeight - The minimum node weight to consider it for a split.

minImpurityDecrease - The minimum decrease in impurity necessary to split a node.

fractionFeaturesInSplit - The fraction of features available in each split.

impurity - Impurity measure to determine split quality. See LabelImpurity.

seed - The RNG seed.

Method Details

mkTrainingNode

protected AbstractTrainingNode<Label> mkTrainingNode(Dataset<Label> examples,
 AbstractTrainingNode.LeafDeterminer leafDeterminer)

Specified by:

mkTrainingNode in class AbstractCARTTrainer<Label>

toString

public String toString()

Overrides:

toString in class Object

getProvenance

public TrainerProvenance getProvenance()