NaiveBayes

Package

weka.classifiers.bayes

Synopsis

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.

Options

The table below describes the options available for NaiveBayes.

Option	Description
debug	If set to true, classifier may output additional info to the console.
displayModelInOldFormat	Use old format for model output. The old format is better when there are many class values. The new format is better when there are fewer classes and many attributes.
useKernelEstimator	Use a kernel estimator for numeric attributes rather than a normal distribution.
useSupervisedDiscretization	Use supervised discretization to convert numeric attributes to nominal ones.

Capabilities

The table below describes the capabilites of NaiveBayes.

Capability	Supported
Class	Binary class, Nominal class, Missing class values
Attributes	Empty nominal attributes, Binary attributes, Missing values, Numeric attributes, Nominal attributes, Unary attributes
Min # of instances	0