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- Title
bnclassify: Learning Bayesian Network Classifiers.
- Authors
Mihaljević, Bojan; Bielza, Concha; Larrañaga, Pedro
- Abstract
The bnclassify package provides state-of-the art algorithms for learning Bayesian network classifiers from data. For structure learning it provides variants of the greedy hill-climbing search, a well-known adaptation of the Chow-Liu algorithm and averaged one-dependence estimators. It provides Bayesian and maximum likelihood parameter estimation, as well as three naive-Bayesspecific methods based on discriminative score optimization and Bayesian model averaging. The implementation is efficient enough to allow for time-consuming discriminative scores on mediumsized data sets. The bnclassify package provides utilities for model evaluation, such as cross-validated accuracy and penalized log-likelihood scores, and analysis of the underlying networks, including network plotting via the Rgraphviz package. It is extensively tested, with over 200 automated tests that give a code coverage of 94%. Here we present the main functionalities, illustrate them with a number of data sets, and comment on related software.
- Subjects
BAYESIAN analysis; NAIVE Bayes classification
- Publication
R Journal, 2018, Vol 10, Issue 2, p455
- ISSN
2073-4859
- Publication type
Article