文章基本信息

标题：bnclassify: Learning Bayesian Network Classifiers
本地全文：下载
作者：Bojan Mihaljević ; Concha Bielza ; Pedro Larrañaga 等
期刊名称：R News
印刷版ISSN：1609-3631
出版年度：2018
卷号：10
期号：2
页码：455-468
语种：English
出版社：The R Foundation for Statistical Computing
摘要：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.