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## weka.classifiers.bayes
Class NaiveBayesSimple

    java.lang.Object
      weka.classifiers.AbstractClassifier
          weka.classifiers.bayes.NaiveBayesSimple

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

    public class NaiveBayesSimple

extends AbstractClassifier
implements TechnicalInformationHandler

Class for building and using a simple Naive Bayes classifier.Numeric attributes are modelled by a normal distribution.
For more information, see
Richard Duda, Peter Hart (1973). Pattern Classification and Scene Analysis. Wiley, New York.

BibTeX:

     @book{Duda1973,
        address = {New York},
        author = {Richard Duda and Peter Hart},
        publisher = {Wiley},
        title = {Pattern Classification and Scene Analysis},
        year = {1973}
     }

Valid options are:

     -D
      If set, classifier is run in debug mode and
      may output additional info to the console

Version:
  $Revision: 5928 $
Author:
  Eibe Frank (eibe@cs.waikato.ac.nz)
See Also:
  Serialized Form

Constructor Summary
`NaiveBayesSimple()`

Method Summary
`void` | `buildClassifier(Instances instances)` Generates the classifier.
`double[]` | `distributionForInstance(Instance instance)` Calculates the class membership probabilities for the given test instance.
`Capabilities` | `getCapabilities()` Returns default capabilities of the classifier.
`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.
`java.lang.String` | `toString()` Returns a description of the classifier.

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

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

Constructor Detail

### NaiveBayesSimple

    public NaiveBayesSimple()

Method Detail

### globalInfo

    public java.lang.String globalInfo()

  Returns a string describing this classifier

  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`
  Returns:
    the technical information about this class

### getCapabilities

    public Capabilities getCapabilities()

  Returns default capabilities of the classifier.

  Specified by:
    `getCapabilities` in interface `Classifier`
  Specified by:
    `getCapabilities` in interface `CapabilitiesHandler`
  Overrides:
    `getCapabilities` in class `AbstractClassifier`
  Returns:
    the capabilities of this classifier
  See Also:
    `Capabilities`

### buildClassifier

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

  Generates the classifier.

  Specified by:
    `buildClassifier` in interface `Classifier`
  Parameters:
    `instances` - set of instances serving as training data
  Throws:
    `java.lang.Exception` - if the classifier has not been generated successfully

### distributionForInstance

    public double[] distributionForInstance(Instance instance)
                                     throws java.lang.Exception

  Calculates the class membership probabilities for the given test instance.

  Specified by:
    `distributionForInstance` in interface `Classifier`
  Overrides:
    `distributionForInstance` in class `AbstractClassifier`
  Parameters:
    `instance` - the instance to be classified
  Returns:
    predicted class probability distribution
  Throws:
    `java.lang.Exception` - if distribution can't be computed

### toString

    public java.lang.String toString()

  Returns a description of the classifier.

  Overrides:
    `toString` in class `java.lang.Object`
  Returns:
    a description of the classifier as a string.

### getRevision

    public java.lang.String getRevision()

  Returns the revision string.

  Specified by:
    `getRevision` in interface `RevisionHandler`
  Overrides:
    `getRevision` in class `AbstractClassifier`
  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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