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## weka.classifiers.trees.lmt
Class ResidualSplit

    java.lang.Object
      weka.classifiers.trees.j48.ClassifierSplitModel
          weka.classifiers.trees.lmt.ResidualSplit

All Implemented Interfaces:
  java.io.Serializable, java.lang.Cloneable

    public class ResidualSplit

extends ClassifierSplitModel

Helper class for logistic model trees (weka.classifiers.trees.lmt.LMT) to implement the splitting criterion based on residuals of the LogitBoost algorithm.

Version:
  $Revision: 1.1 $
Author:
  Niels Landwehr
See Also:
  Serialized Form

Constructor Summary
`ResidualSplit(int attIndex)` Creates a split object

Method Summary
`void` | `buildClassifier(Instances data)` Method not in use
`void` | `buildClassifier(Instances data, double[][] dataZs, double[][] dataWs)` Builds the split.
`boolean` | `checkModel(int minNumInstances)` Checks if there are at least 2 subsets that contain >= minNumInstances.
`double` | `entropyGain()` Computes entropy gain for current split.
`java.lang.String` | `leftSide(Instances data)` Returns name of splitting attribute (left side of condition).
`java.lang.String` | `rightSide(int index, Instances data)` Prints the condition satisfied by instances in a subset.
`java.lang.String` | `sourceExpression(int index, Instances data)` Method not in use
`double[]` | `weights(Instance instance)` Method not in use
`int` | `whichSubset(Instance instance)` Returns index of subset instance is assigned to.

Methods inherited from class weka.classifiers.trees.j48.ClassifierSplitModel
`checkModel, classifyInstance, classProb, classProbLaplace, clone, codingCost, distribution, dumpLabel, dumpModel, numSubsets, resetDistribution, sourceClass, split`

Methods inherited from class java.lang.Object
`equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait`

Constructor Detail

### ResidualSplit

    public ResidualSplit(int attIndex)

  Creates a split object

  Parameters:
    `attIndex` - the index of the attribute to split on

Method Detail

### buildClassifier

    public void buildClassifier(Instances data,
                                double[][] dataZs,
                                double[][] dataWs)
                         throws java.lang.Exception

  Builds the split. Needs the Z/W values of LogitBoost for the set of instances.

  Throws:
    `java.lang.Exception`

### entropyGain

    public double entropyGain()
                       throws java.lang.Exception

  Computes entropy gain for current split.

  Throws:
    `java.lang.Exception`

### checkModel

    public boolean checkModel(int minNumInstances)

  Checks if there are at least 2 subsets that contain >= minNumInstances.

### leftSide

    public final java.lang.String leftSide(Instances data)

  Returns name of splitting attribute (left side of condition).

  Specified by:
    `leftSide` in class `ClassifierSplitModel`
  Parameters:
    `data` - the data.

### rightSide

    public final java.lang.String rightSide(int index,
                                            Instances data)

  Prints the condition satisfied by instances in a subset.

  Specified by:
    `rightSide` in class `ClassifierSplitModel`

### whichSubset

    public final int whichSubset(Instance instance)
                          throws java.lang.Exception

  Description copied from class: `ClassifierSplitModel`
  Returns index of subset instance is assigned to. Returns -1 if instance is assigned to more than one subset.

  Specified by:
    `whichSubset` in class `ClassifierSplitModel`
  Throws:
    `java.lang.Exception` - if something goes wrong

### buildClassifier

    public void buildClassifier(Instances data)

  Method not in use

  Specified by:
    `buildClassifier` in class `ClassifierSplitModel`

### weights

    public final double[] weights(Instance instance)

  Method not in use

  Specified by:
    `weights` in class `ClassifierSplitModel`

### sourceExpression

    public final java.lang.String sourceExpression(int index,
                                                   Instances data)

  Method not in use

  Specified by:
    `sourceExpression` in class `ClassifierSplitModel`

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