Bayesian network Classifiers

Description

Structure learning algorithms for Bayesian network classifiers.

Details

The algorithms are aimed at classification, and favour predictive power over the ability to recover the correct network structure. The implementation in bnlearn assumes that all variables, including the classifiers, are discrete.

• Naive Bayes (naive.bayes): a very simple algorithm that assumes all classifiers are independent and uses the target variable's posterior probability for classification.

• Tree-Augmented Naive Bayes (tree.bayes): improves over Naive Bayes by using the Chow-Liu algorithm to approximate the dependence structure of the classifiers.

  Friedman N, Geiger D, Goldszmit M (1997). "Bayesian Network Classifiers." Machine Learning, 29:131–163.