pheble: Classifying High-Dimensional Phenotypes with Ensemble Learning (original) (raw)
A system for binary and multi-class classification of high-dimensional phenotypic data using ensemble learning. By combining predictions from different classification models, this package attempts to improve performance over individual learners. The pre-processing, training, validation, and testing are performed end-to-end to minimize user input and simplify the process of classification.
| Version: | 0.1.0 |
|---|---|
| Depends: | R (≥ 2.10) |
| Imports: | adabag, base, C50, caret, caTools, data.table, doParallel, dplyr, e1071, earth, evtree, frbs, glmnet, gmodels, hda, HDclassif, ipred, kernlab, kknn, klaR, magrittr, MASS, Matrix, mda, MLmetrics, nnet, parallel, party, pls, randomForest, rpartScore, sparseLDA, stats, themis, utils |
| Suggests: | h2o |
| Published: | 2023-05-17 |
| DOI: | 10.32614/CRAN.package.pheble |
| Author: | Jay Devine [aut, cre, cph], Bened'ikt Hallgrimsson [aut] |
| Maintainer: | Jay Devine <jay.devine1 at ucalgary.ca> |
| License: | GPL (≥ 3) |
| NeedsCompilation: | no |
| Citation: | pheble citation info |
| Materials: | README NEWS |
| CRAN checks: | pheble results |
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