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.
|Author||Mikhail Popov [aut, cre] (@bearloga on Twitter)|
|Date of publication||2016-07-06 09:43:54|
|Maintainer||Mikhail Popov <email@example.com>|
|License||MIT + file LICENSE|
ecc: Fit an Ensemble of Classifier Chains (ECC)
MLPUGS-package: MLPUGS: Multi-Label Prediction Using Gibbs Sampling (and...
movies: FiveThirtyEight's Movie Scores
predict.ECC: Classify new samples using an Ensemble of Classifier Chains
summary.PUGS: Gather samples of predictions
validate_pugs: Assess multi-label prediction accuracy