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