CRAN/E | MLPUGS

MLPUGS

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

Installation

About

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.

github.com/bearloga/MLPUGS
Bug report File report

Key Metrics

Version 0.2.0
R ≥ 3.1.2
Published 2016-07-06 2823 days ago
Needs compilation? no
License MIT
License File
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Maintainer

Maintainer

Mikhail Popov

mikhail@mpopov.com

Authors

Mikhail Popov

aut / cre

(@bearloga on Twitter)

Material

README
Reference manual
Package source

Vignettes

Multi-label Classification with MLPUGS

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Depends

R ≥ 3.1.2

Suggests

knitr
progress
C50
randomForest