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MLPUGS  

Multi-Label Prediction Using Gibbs Sampling (and Classifier Chains)
View on CRAN: Click here


Download and install MLPUGS package within the R console
Install from CRAN:
install.packages("MLPUGS")

Install from Github:
library("remotes")
install_github("cran/MLPUGS")

Install by package version:
library("remotes")
install_version("MLPUGS", "0.2.0")



Attach the package and use:
library("MLPUGS")
Maintained by
Mikhail Popov
[Scholar Profile | Author Map]
First Published: 2016-07-06
Latest Update: 2016-07-06
Description:
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.
How to cite:
Mikhail Popov (2016). MLPUGS: Multi-Label Prediction Using Gibbs Sampling (and Classifier Chains). R package version 0.2.0, https://cran.r-project.org/web/packages/MLPUGS. Accessed 29 Mar. 2025.
Previous versions and publish date:
No previous versions
Other packages that cited MLPUGS R package
View MLPUGS citation profile
Other R packages that MLPUGS depends, imports, suggests or enhances
Complete documentation for MLPUGS
Functions, R codes and Examples using the MLPUGS R package
Some associated functions: MLPUGS-package . ecc . movies . predict.ECC . summary.PUGS . validate_pugs . 
Some associated R codes: MLPUGS.R . data.R . ecc.R . pugs.R .  Full MLPUGS package functions and examples
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