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-Bayes specific methods based on discriminative score optimization and Bayesian model averaging. The implementation is efficient enough to allow for time-consuming discriminative scores on medium sized 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.
Supplementary materials are available in addition to this article. It can be downloaded at RJ-2018-073.zip
bnlearn, bnclassify, caret, mlr, gRain, deal
Bayesian, gR, HighPerformanceComputing, MachineLearning, Multivariate
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For attribution, please cite this work as
Mihaljević, et al., "bnclassify: Learning Bayesian Network Classifiers", The R Journal, 2018
BibTeX citation
@article{RJ-2018-073,
author = {Mihaljević, Bojan and Bielza, Concha and Larrañaga, Pedro},
title = {bnclassify: Learning Bayesian Network Classifiers},
journal = {The R Journal},
year = {2018},
note = {https://doi.org/10.32614/RJ-2018-073},
doi = {10.32614/RJ-2018-073},
volume = {10},
issue = {2},
issn = {2073-4859},
pages = {455-468}
}