Examples of weka.classifiers.bayes.NaiveBayes

weka.classifiers.bayes.NaiveBayes
Class for a Naive Bayes classifier using estimator classes. Numeric estimator precision values are chosen based on analysis of the training data. For this reason, the classifier is not an UpdateableClassifier (which in typical usage are initialized with zero training instances) -- if you need the UpdateableClassifier functionality, use the NaiveBayesUpdateable classifier. The NaiveBayesUpdateable 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) 
```
@author Len Trigg (trigg@cs.waikato.ac.nz) @author Eibe Frank (eibe@cs.waikato.ac.nz) @version $Revision: 5516 $

    }
    
    m_percentUsedByDT = (double)count / (m_theInstances.numAttributes() - 1);
    m_percentDeleted = numDeleted / (m_theInstances.numAttributes() -1);


    m_NB = new NaiveBayes();
    m_NB.buildClassifier(m_theInstances);


    m_dtInstances = new Instances(m_dtInstances, 0);
    m_theInstances = new Instances(m_theInstances, 0);
  }

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  }
     }
     
     if (m_NB == null) {
  // construct naive bayes for the first time
  m_NB = new NaiveBayes();
  m_NB.buildClassifier(m_theInstances);
     }
     return fc;
   }

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    }
    
    m_percentUsedByDT = (double)count / (m_theInstances.numAttributes() - 1);
    m_percentDeleted = numDeleted / (m_theInstances.numAttributes() -1);


    m_NB = new NaiveBayes();
    m_NB.buildClassifier(m_theInstances);


    m_dtInstances = new Instances(m_dtInstances, 0);
    m_theInstances = new Instances(m_theInstances, 0);
  }

View Full Code Here

  {
    try {
      switch (classifier)
      {
        case NAIVE_BAYES:
          return new NaiveBayes();
        case J48:
          J48 j48 = new J48();      
          j48.setOptions(new String[] { "-C", "0.25", "-M", "2" });
          return j48;
//        case SMO:

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  }
     }
     
     if (m_NB == null) {
  // construct naive bayes for the first time
  m_NB = new NaiveBayes();
  m_NB.buildClassifier(m_theInstances);
     }
     return fc;
   }

View Full Code Here

  {
    try {
      switch (classifier)
      {
        case NAIVE_BAYES:
          return new NaiveBayes();
        case J48:
          J48 j48 = new J48();      
          j48.setOptions(new String[] { "-C", "0.25", "-M", "2" });
          return j48;
        case SMO:

View Full Code Here

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