Package org.tribuo.regression.rtree

Class CARTJointRegressionTrainer

java.lang.Object

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

org.tribuo.regression.rtree.CARTJointRegressionTrainer

All Implemented Interfaces:

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

public class CARTJointRegressionTrainer
extends AbstractCARTTrainer<Regressor>

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

Builds a single tree for all the regression dimensions.

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, INCREMENT_INVOCATION_COUNT

Constructor Summary

Constructors

Constructor	Description
CARTJointRegressionTrainer()	Creates a CART Trainer.
CARTJointRegressionTrainer(int maxDepth)	Creates a CART Trainer.
CARTJointRegressionTrainer(int maxDepth, boolean normalize)	Creates a CART Trainer.
CARTJointRegressionTrainer(int maxDepth, float minChildWeight, float minImpurityDecrease, float fractionFeaturesInSplit, boolean useRandomSplitPoints, RegressorImpurity impurity, boolean normalize, long seed)	Creates a CART Trainer.
CARTJointRegressionTrainer(int maxDepth, float minChildWeight, float minImpurityDecrease, float fractionFeaturesInSplit, RegressorImpurity impurity, boolean normalize, long seed)	Creates a CART Trainer.

Method Summary

Modifier and Type	Method	Description
TrainerProvenance	getProvenance()
protected AbstractTrainingNode<Regressor>	mkTrainingNode(Dataset<Regressor> examples, AbstractTrainingNode.LeafDeterminer leafDeterminer)	Makes the initial training node.
String	toString()

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

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

Methods inherited from class java.lang.Object

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

Constructor Details

CARTJointRegressionTrainer

public CARTJointRegressionTrainer(int maxDepth,
 float minChildWeight,
 float minImpurityDecrease,
 float fractionFeaturesInSplit,
 boolean useRandomSplitPoints,
 RegressorImpurity impurity,
 boolean normalize,
 long seed)

Creates a CART Trainer.

Parameters:

maxDepth - maxDepth The maximum depth of the tree.

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

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

fractionFeaturesInSplit - fractionFeaturesInSplit The fraction of features available in each split.

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

impurity - impurity The impurity function to use to determine split quality.

normalize - Normalize the leaves so each output sums to one.

seed - The seed to use for the RNG.

CARTJointRegressionTrainer

public CARTJointRegressionTrainer(int maxDepth,
 float minChildWeight,
 float minImpurityDecrease,
 float fractionFeaturesInSplit,
 RegressorImpurity impurity,
 boolean normalize,
 long seed)

Creates a CART Trainer.

Computes the exact split point.

Parameters:

maxDepth - maxDepth The maximum depth of the tree.

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

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

fractionFeaturesInSplit - fractionFeaturesInSplit The fraction of features available in each split.

impurity - impurity The impurity function to use to determine split quality.

normalize - Normalize the leaves so each output sums to one.

seed - The seed to use for the RNG.

CARTJointRegressionTrainer

public CARTJointRegressionTrainer()

Creates a CART Trainer.

Sets the impurity to the MeanSquaredError, computes an arbitrary depth tree with exact split points using all the features, and does not normalize the outputs.

CARTJointRegressionTrainer

public CARTJointRegressionTrainer(int maxDepth)

Creates a CART Trainer.

Sets the impurity to the MeanSquaredError, computes the exact split points using all the features, and does not normalize the outputs.

Parameters:

maxDepth - The maximum depth of the tree.

CARTJointRegressionTrainer

public CARTJointRegressionTrainer(int maxDepth,
 boolean normalize)

Creates a CART Trainer. Sets the impurity to the MeanSquaredError.

Parameters:

maxDepth - The maximum depth of the tree.

normalize - Normalises the leaves so each leaf has a distribution which sums to 1.0.

Method Details

mkTrainingNode

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

Description copied from class: AbstractCARTTrainer

Makes the initial training node.

Specified by:

mkTrainingNode in class AbstractCARTTrainer<Regressor>

Parameters:

examples - The dataset to use.

leafDeterminer - The leaf determination function.

Returns:

The initial training node.

toString

public String toString()

Overrides:

toString in class Object

getProvenance

public TrainerProvenance getProvenance()