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.

Install the latest version of this package by entering the following in R:
install.packages("MLPUGS")
AuthorMikhail Popov [aut, cre] (@bearloga on Twitter)
Date of publication2016-07-06 09:43:54
MaintainerMikhail Popov <mikhail@mpopov.com>
LicenseMIT + file LICENSE
Version0.2.0
https://github.com/bearloga/MLPUGS

View on CRAN

Files

inst
inst/doc
inst/doc/tutorial.R
inst/doc/tutorial.html
inst/doc/tutorial.Rmd
NAMESPACE
data
data/movies_train.rda
data/movies_test.rda
data/movies.rda
R
R/MLPUGS.R R/data.R R/pugs.R R/ecc.R
vignettes
vignettes/tutorial.Rmd
README.md
MD5
build
build/vignette.rds
DESCRIPTION
man
man/ecc.Rd man/movies.Rd man/predict.ECC.Rd man/summary.PUGS.Rd man/MLPUGS-package.Rd man/validate_pugs.Rd
LICENSE

Questions? Problems? Suggestions? or email at ian@mutexlabs.com.

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