Package org.tribuo.regression.rtree.impl

Class JointRegressorTrainingNode

java.lang.Object

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

org.tribuo.regression.rtree.impl.JointRegressorTrainingNode

All Implemented Interfaces:

Serializable, Node<Regressor>

public class JointRegressorTrainingNode
extends AbstractTrainingNode<Regressor>

A decision tree node used at training time. Contains a list of the example indices currently found in this node, the current impurity and a bunch of other statistics.

See Also:

• Serialized Form

Nested Class Summary

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

AbstractTrainingNode.LeafDeterminer

Constructor Summary

Constructors

Constructor	Description
JointRegressorTrainingNode(RegressorImpurity impurity, org.tribuo.Dataset<Regressor> examples, boolean normalize, AbstractTrainingNode.LeafDeterminer leafDeterminer)	Constructor which creates the inverted file.

Method Summary

Modifier and Type	Method	Description
List<AbstractTrainingNode<Regressor>>	buildTree(int[] featureIDs, SplittableRandom rng, boolean useRandomSplitPoints)	Builds a tree according to CART (as it does not do multi-way splits on categorical values like C4.5).
Node<Regressor>	convertTree()	Generates a test time tree (made of SplitNode and LeafNode) from the tree rooted at this node.
double	getImpurity()
float	getWeightSum()

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

copy, createSplitNode, getDepth, getNextNode, getNumExamples, isLeaf, shouldMakeLeaf

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Details

JointRegressorTrainingNode

public JointRegressorTrainingNode(RegressorImpurity impurity,
 org.tribuo.Dataset<Regressor> examples,
 boolean normalize,
 AbstractTrainingNode.LeafDeterminer leafDeterminer)

Constructor which creates the inverted file.

Parameters:

impurity - The impurity function to use.

examples - The training data.

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

leafDeterminer - Contains parameters needed to determine whether a node is a leaf.

Method Details

getImpurity

public double getImpurity()

getWeightSum

public float getWeightSum()

Specified by:

getWeightSum in class AbstractTrainingNode<Regressor>

buildTree

public List<AbstractTrainingNode<Regressor>> buildTree(int[] featureIDs,
 SplittableRandom rng,
 boolean useRandomSplitPoints)

Builds a tree according to CART (as it does not do multi-way splits on categorical values like C4.5).

Specified by:

buildTree in class AbstractTrainingNode<Regressor>

Parameters:

featureIDs - Indices of the features available in this split.

rng - Splittable random number generator.

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

Returns:

A possibly empty list of TrainingNodes.

convertTree

public Node<Regressor> convertTree()

Generates a test time tree (made of SplitNode and LeafNode) from the tree rooted at this node.

Specified by:

convertTree in class AbstractTrainingNode<Regressor>

Returns:

A subtree using the SplitNode and LeafNode classes.