Name
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-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. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.32614/rj-2018-073 | R JOURNAL |
Field | DocType | Volume |
Econometrics,Computer science,Bayesian network,Artificial intelligence,Machine learning | Journal | 10 |
Issue | ISSN | Citations |
2 | 2073-4859 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bojan Mihaljevic | 1 | 8 | 2.91 |
| Concha Bielza | 2 | 909 | 72.11 |
| Pedro Larrañaga | 3 | 3882 | 208.54 |