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Package

weka.classifiers.bayes

Synopsis

Bayes Network learning using various search algorithms and quality measures.
Base class for a Bayes Network classifier. Provides datastructures (network structure, conditional probability distributions, etc.) and facilities common to Bayes Network learning algorithms like K2 and B.

For more information see:

http://www.cs.waikato.ac.nz/~remco/weka.pdf

Options

The table below describes the options available for BayesNet.

Option

Description

BIFFile

Set the name of a file in BIF XML format. A Bayes network learned from data can be compared with the Bayes network represented by the BIF file. Statistics calculated are o.a. the number of missing and extra arcs.

debug

If set to true, classifier may output additional info to the console.

estimator

Select Estimator algorithm for finding the conditional probability tables of the Bayes Network.

searchAlgorithm

Select method used for searching network structures.

useADTree

When ADTree (the data structure for increasing speed on counts, not to be confused with the classifier under the same name) is used learning time goes down typically. However, because ADTrees are memory intensive, memory problems may occur. Switching this option off makes the structure learning algorithms slower, and run with less memory. By default, ADTrees are used.

Capabilities

The table below describes the capabilites of BayesNet.

Capability

Supported

Class

Binary class, Missing class values, Nominal class

Attributes

Nominal attributes, Binary attributes, Empty nominal attributes, Missing values, Unary attributes, Numeric attributes

Min # of instances

0

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