Package weka.classifiers.trees.ht

Class NBNodeAdaptive

java.lang.Object
weka.classifiers.trees.ht.HNode
weka.classifiers.trees.ht.LeafNode
weka.classifiers.trees.ht.ActiveHNode
weka.classifiers.trees.ht.NBNode
weka.classifiers.trees.ht.NBNodeAdaptive
All Implemented Interfaces:
Serializable, LearningNode
public class NBNodeAdaptive extends NBNode implements LearningNode, Serializable
Implements a LearningNode that chooses between using majority class or naive Bayes for prediction
Version:
$Revision: 9705 $
Author:
Richard Kirkby (rkirkby@cs.waikato.ac.nz), Mark Hall (mhall{[at]}pentaho{[dot]}com)
See Also:

  • Constructor Details

    • NBNodeAdaptive

      public NBNodeAdaptive(Instances header, double nbWeightThreshold) throws Exception
      Constructor
      Parameters:
      header - the structure of the instances we're training from
      nbWeightThreshold - the weight mass to see before allowing naive Bayes to predict
      Throws:
      Exception - if a problem occurs

  • Method Details

    • updateNode

      public void updateNode(Instance inst) throws Exception
      Description copied from class: HNode
      Update the node with the supplied instance
      Overrides:
      updateNode in class NBNode
      Parameters:
      inst - the instance to update with
      Throws:
      Exception - if a problem occurs

    • getDistribution

      public double[] getDistribution(Instance inst, Attribute classAtt) throws Exception
      Description copied from class: HNode
      Return a class probability distribution computed from the frequency counts at this node
      Overrides:
      getDistribution in class NBNode
      Parameters:
      inst - the instance to get a prediction for
      classAtt - the class attribute
      Returns:
      a class probability distribution
      Throws:
      Exception - if a problem occurs