Package: MLPUGS 0.2.0
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.
Authors:
MLPUGS_0.2.0.tar.gz
MLPUGS_0.2.0.zip(r-4.5)MLPUGS_0.2.0.zip(r-4.4)MLPUGS_0.2.0.zip(r-4.3)
MLPUGS_0.2.0.tgz(r-4.4-any)MLPUGS_0.2.0.tgz(r-4.3-any)
MLPUGS_0.2.0.tar.gz(r-4.5-noble)MLPUGS_0.2.0.tar.gz(r-4.4-noble)
MLPUGS_0.2.0.tgz(r-4.4-emscripten)MLPUGS_0.2.0.tgz(r-4.3-emscripten)
MLPUGS.pdf |MLPUGS.html✨
MLPUGS/json (API)
# Install 'MLPUGS' in R: |
install.packages('MLPUGS', repos = c('https://bearloga.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/bearloga/mlpugs/issues
- movies - FiveThirtyEight's Movie Scores
- movies_test - FiveThirtyEight's Movie Scores
- movies_train - FiveThirtyEight's Movie Scores
classificationmachine-learningmcmcmulti-label-classificationsupervised-learning
Last updated 5 years agofrom:4737d1d72f. Checks:OK: 7. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Oct 16 2024 |
R-4.5-win | OK | Oct 16 2024 |
R-4.5-linux | OK | Oct 16 2024 |
R-4.4-win | OK | Oct 16 2024 |
R-4.4-mac | OK | Oct 16 2024 |
R-4.3-win | OK | Oct 16 2024 |
R-4.3-mac | OK | Oct 16 2024 |
Exports:eccvalidate_pugs
Dependencies:
Readme and manuals
Help Manual
Help page | Topics |
---|---|
MLPUGS: Multi-Label Prediction Using Gibbs Sampling (and Classifier Chains) | MLPUGS-package MLPUGS |
Fit an Ensemble of Classifier Chains (ECC) | ecc |
FiveThirtyEight's Movie Scores | movies movies_test movies_train |
Classify new samples using an Ensemble of Classifier Chains | predict.ECC |
Gather samples of predictions | summary.PUGS |
Assess multi-label prediction accuracy | validate_pugs |