MLPUGS: Multi-Label Prediction Using Gibbs Sampling (and Classifier Chains)

An implementation of classifier chains (CC's) for multi-label prediction. Users can employ an external package (e.g. 'randomForest', 'C50'), or supply their own. The package can train a single set of CC's or train an ensemble of CC's – in parallel if running in a multi-core environment. New observations are classified using a Gibbs sampler since each unobserved label is conditioned on the others. The package includes methods for evaluating the predictions for accuracy and aggregating across iterations and models to produce binary or probabilistic classifications.

Version: 0.2.0
Depends: R (≥ 3.1.2)
Suggests: knitr, progress, C50, randomForest
Published: 2016-07-06
Author: Mikhail Popov [aut, cre] (@bearloga on Twitter)
Maintainer: Mikhail Popov <mikhail at mpopov.com>
BugReports: https://github.com/bearloga/MLPUGS/issues
License: MIT + file LICENSE
URL: https://github.com/bearloga/MLPUGS
NeedsCompilation: no
Materials: README
CRAN checks: MLPUGS results

Downloads:

Reference manual: MLPUGS.pdf
Vignettes: Multi-label Classification with MLPUGS
Package source: MLPUGS_0.2.0.tar.gz
Windows binaries: r-devel: MLPUGS_0.2.0.zip, r-release: MLPUGS_0.2.0.zip, r-oldrel: MLPUGS_0.2.0.zip
OS X El Capitan binaries: r-release: MLPUGS_0.2.0.tgz
OS X Mavericks binaries: r-oldrel: MLPUGS_0.2.0.tgz

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