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## weka.classifiers.bayes
Class NaiveBayesUpdateable

    java.lang.Object
      weka.classifiers.AbstractClassifier
          weka.classifiers.bayes.NaiveBayes
              weka.classifiers.bayes.NaiveBayesUpdateable

All Implemented Interfaces:
  java.io.Serializable, java.lang.Cloneable, Classifier, UpdateableClassifier, CapabilitiesHandler, OptionHandler, RevisionHandler, TechnicalInformationHandler, WeightedInstancesHandler

    public class NaiveBayesUpdateable

extends NaiveBayes
implements UpdateableClassifier

Class for a Naive Bayes classifier using estimator classes. This is the updateable version of NaiveBayes.
This classifier will use a default precision of 0.1 for numeric attributes when buildClassifier is called with zero training instances.
For more information on Naive Bayes classifiers, see
George H. John, Pat Langley: Estimating Continuous Distributions in Bayesian Classifiers. In: Eleventh Conference on Uncertainty in Artificial Intelligence, San Mateo, 338-345, 1995.

BibTeX:

     @inproceedings{John1995,
        address = {San Mateo},
        author = {George H. John and Pat Langley},
        booktitle = {Eleventh Conference on Uncertainty in Artificial Intelligence},
        pages = {338-345},
        publisher = {Morgan Kaufmann},
        title = {Estimating Continuous Distributions in Bayesian Classifiers},
        year = {1995}
     }

Valid options are:

     -K
      Use kernel density estimator rather than normal
      distribution for numeric attributes

     -D
      Use supervised discretization to process numeric attributes

     -O
      Display model in old format (good when there are many classes)

Version:
  $Revision: 1.11 $
Author:
  Len Trigg (trigg@cs.waikato.ac.nz), Eibe Frank (eibe@cs.waikato.ac.nz)
See Also:
  Serialized Form

Constructor Summary
`NaiveBayesUpdateable()`

Method Summary
`java.lang.String` | `getRevision()` Returns the revision string.
`TechnicalInformation` | `getTechnicalInformation()` Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
`java.lang.String` | `globalInfo()` Returns a string describing this classifier
`static void` | `main(java.lang.String[] argv)` Main method for testing this class.
`void` | `setUseSupervisedDiscretization(boolean newblah)` Set whether supervised discretization is to be used.

Methods inherited from class weka.classifiers.bayes.NaiveBayes
`buildClassifier, displayModelInOldFormatTipText, distributionForInstance, getCapabilities, getDisplayModelInOldFormat, getOptions, getUseKernelEstimator, getUseSupervisedDiscretization, listOptions, setDisplayModelInOldFormat, setOptions, setUseKernelEstimator, toString, updateClassifier, useKernelEstimatorTipText, useSupervisedDiscretizationTipText`

Methods inherited from class weka.classifiers.AbstractClassifier
`classifyInstance, debugTipText, forName, getDebug, makeCopies, makeCopy, setDebug`

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

Methods inherited from interface weka.classifiers.UpdateableClassifier
`updateClassifier`

Constructor Detail

### NaiveBayesUpdateable

    public NaiveBayesUpdateable()

Method Detail

### globalInfo

    public java.lang.String globalInfo()

  Returns a string describing this classifier

  Overrides:
    `globalInfo` in class `NaiveBayes`
  Returns:
    a description of the classifier suitable for displaying in the explorer/experimenter gui

### getTechnicalInformation

    public TechnicalInformation getTechnicalInformation()

  Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.

  Specified by:
    `getTechnicalInformation` in interface `TechnicalInformationHandler`
  Overrides:
    `getTechnicalInformation` in class `NaiveBayes`
  Returns:
    the technical information about this class

### setUseSupervisedDiscretization

    public void setUseSupervisedDiscretization(boolean newblah)

  Set whether supervised discretization is to be used.

  Overrides:
    `setUseSupervisedDiscretization` in class `NaiveBayes`
  Parameters:
    `newblah` - true if supervised discretization is to be used.

### getRevision

    public java.lang.String getRevision()

  Returns the revision string.

  Specified by:
    `getRevision` in interface `RevisionHandler`
  Overrides:
    `getRevision` in class `NaiveBayes`
  Returns:
    the revision

### main

    public static void main(java.lang.String[] argv)

  Main method for testing this class.

  Parameters:
    `argv` - the options

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