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 resultsRetroSearch is an open source project built by @garambo | Open a GitHub Issue
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