Package: CCNR 0.2.0
CCNR: Classifier Chain Network for Multi-Label Classification
Fits a Classifier Chain Network (CCN) for multi-label binary classification. The model jointly learns label dependencies through a chain structure and optimizes an Lq norm aggregated cross-entropy loss. Provides methods for prediction, model summary, and k-fold cross-validation with grid search over hyperparameters. The methodology is described in Touw and Van de Velden (2025) <doi:10.1016/j.eswa.2025.128048>.
Authors:
CCNR_0.2.0.tar.gz
CCNR_0.2.0.zip(r-4.7-x86_64)CCNR_0.2.0.zip(r-4.7-arm64)CCNR_0.2.0.zip(r-4.6-x86_64)CCNR_0.2.0.zip(r-4.6-arm64)
CCNR_0.2.0.tar.gz(r-4.7-arm64)CCNR_0.2.0.tar.gz(r-4.7-x86_64)CCNR_0.2.0.tar.gz(r-4.6-arm64)CCNR_0.2.0.tar.gz(r-4.6-x86_64)
CCNR_0.2.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
CCNR/json (API)
| # Install 'CCNR' in R: |
| install.packages('CCNR', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/djwtouw/ccnr/issues
- amphibians - Presence of amphibian species at Polish water reservoirs
Last updated from:9f665b7348. Checks:10 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-arm64 | OK | 103 | ||
| linux-devel-x86_64 | OK | 130 | ||
| source / vignettes | OK | 206 | ||
| linux-release-arm64 | OK | 108 | ||
| linux-release-x86_64 | OK | 143 | ||
| windows-devel-arm64 | OK | 206 | ||
| windows-devel-x86_64 | OK | 153 | ||
| windows-release-arm64 | OK | 193 | ||
| windows-release-x86_64 | OK | 160 | ||
| wasm-release | OK | 100 |
Exports:ccnccn_cvccn_exact_matchccn_hamming_accuracyccn_log_likelihoodccn_macro_f1ccn_mean_aucccn_micro_f1conditional_entropy_matrixentropy_label_ordergenerate_datasetmultilabel_stratified_folds
