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## weka.classifiers.trees.j48
Class NBTreeSplit

    java.lang.Object
      weka.classifiers.trees.j48.ClassifierSplitModel
          weka.classifiers.trees.j48.NBTreeSplit

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

    public class NBTreeSplit

extends ClassifierSplitModel

Class implementing a NBTree split on an attribute.

Version:
  $Revision: 1.2 $
Author:
  Mark Hall (mhall@cs.waikato.ac.nz)
See Also:
  Serialized Form

Constructor Summary
`NBTreeSplit(int attIndex, int minNoObj, double sumOfWeights)` Initializes the split model.

Method Summary
`int` | `attIndex()` Returns index of attribute for which split was generated.
`void` | `buildClassifier(Instances trainInstances)` Creates a NBTree-type split on the given data.
`double` | `classProb(int classIndex, Instance instance, int theSubset)` Return the probability for a class value
`double` | `getErrors()` Return the errors made by the naive bayes models arising from this split.
`NBTreeNoSplit` | `getGlobalModel()` Return the global naive bayes model for this node
`java.lang.String` | `leftSide(Instances data)` Prints left side of condition..
`java.lang.String` | `rightSide(int index, Instances data)` Prints the condition satisfied by instances in a subset.
`void` | `setGlobalModel(NBTreeNoSplit global)` Set the global naive bayes model for this node
`java.lang.String` | `sourceExpression(int index, Instances data)` Returns a string containing java source code equivalent to the test made at this node.
`double[]` | `weights(Instance instance)` Returns weights if instance is assigned to more than one subset.
`int` | `whichSubset(Instance instance)` Returns index of subset instance is assigned to.

Methods inherited from class weka.classifiers.trees.j48.ClassifierSplitModel
`checkModel, classifyInstance, 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

### NBTreeSplit

    public NBTreeSplit(int attIndex,
                       int minNoObj,
                       double sumOfWeights)

  Initializes the split model.

Method Detail

### buildClassifier

    public void buildClassifier(Instances trainInstances)
                         throws java.lang.Exception

  Creates a NBTree-type split on the given data. Assumes that none of the class values is missing.

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

### attIndex

    public final int attIndex()

  Returns index of attribute for which split was generated.

### whichSubset

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

  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

### weights

    public final double[] weights(Instance instance)

  Returns weights if instance is assigned to more than one subset. Returns null if instance is only assigned to one subset.

  Specified by:
    `weights` in class `ClassifierSplitModel`

### sourceExpression

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

  Returns a string containing java source code equivalent to the test made at this node. The instance being tested is called "i".

  Specified by:
    `sourceExpression` in class `ClassifierSplitModel`
  Parameters:
    `index` - index of the nominal value tested
    `data` - the data containing instance structure info
  Returns:
    a value of type 'String'

### 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`
  Parameters:
    `index` - of subset
    `data` - training set.

### leftSide

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

  Prints left side of condition..

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

### classProb

    public double classProb(int classIndex,
                            Instance instance,
                            int theSubset)
                     throws java.lang.Exception

  Return the probability for a class value

  Overrides:
    `classProb` in class `ClassifierSplitModel`
  Parameters:
    `classIndex` - the index of the class value
    `instance` - the instance to generate a probability for
    `theSubset` - the subset to consider
  Returns:
    a probability
  Throws:
    `java.lang.Exception` - if an error occurs

### getGlobalModel

    public NBTreeNoSplit getGlobalModel()

  Return the global naive bayes model for this node

  Returns:
    a `NBTreeNoSplit` value

### setGlobalModel

    public void setGlobalModel(NBTreeNoSplit global)

  Set the global naive bayes model for this node

  Parameters:
    `global` - a `NBTreeNoSplit` value

### getErrors

    public double getErrors()

  Return the errors made by the naive bayes models arising from this split.

  Returns:
    a `double` value

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