An algorithm that trains a meta-learning procedure that combines
screening and wrapper methods to find a set of extremely low-dimensional attribute
combinations. This package works on top of the 'caret' package and proceeds in a
forward-step manner. More specifically, it builds and tests learners starting
from very few attributes until it includes a maximal number of attributes by
increasing the number of attributes at each step. Hence, for each fixed number
of attributes, the algorithm tests various (randomly selected) learners and
picks those with the best performance in terms of training error. Throughout,
the algorithm uses the information coming from the best learners at the previous
step to build and test learners in the following step. In the end, it outputs
a set of strong low-dimensional learners.
| Version: |
0.1.0 |
| Depends: |
R (≥ 4.0.0) |
| Imports: |
caret, Rdpack (≥ 0.7), stats |
| Suggests: |
doParallel, e1071, foreach, ggplot2, glmnet, grDevices, iterators, kernlab, knitr, lattice, methods, mlbench, ModelMetrics, nlme, parallel, plyr, pROC, randomForest, recipes, remotes, reshape2, stats4, rmarkdown, utils, withr |
| Published: |
2020-11-10 |
| DOI: |
10.32614/CRAN.package.swag |
| Author: |
Samuel Orso [aut, cre],
Gaetan Bakalli [aut],
Cesare Miglioli [aut],
Stephane Guerrier [ctb],
Roberto Molinari [ctb] |
| Maintainer: |
Samuel Orso <Samuel.Orso at unige.ch> |
| BugReports: |
https://github.com/SMAC-Group/SWAG-R-Package/issues/ |
| License: |
GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: |
https://github.com/SMAC-Group/SWAG-R-Package/ |
| NeedsCompilation: |
no |
| Materials: |
README, NEWS |
| CRAN checks: |
swag results |