Tuned Data Mining in R ('TDMR') performs the complete tuning of a data mining task (predictive analytics, that is classification and regression). Preprocessing parameters and modeling parameters can be tuned simultaneously. It incorporates a variety of tuners (among them 'SPOT' and 'CMA' with package 'rCMA') and allows integration of additional tuners. Noise handling in the data mining optimization process is supported, see Koch et al. (2015) <doi:10.1016/j.asoc.2015.01.005>.
| Version: | 2.2 |
| Depends: | R (≥ 3.0.0), SPOT (≥ 2.0), twiddler |
| Imports: | testit, methods, adabag |
| Suggests: | cmaes, parallel, e1071, ROCR, randomForest, rCMA, rSFA |
| Published: | 2020-03-02 |
| Author: | Wolfgang Konen, Patrick Koch |
| Maintainer: | Wolfgang Konen <wolfgang.konen at fh-koeln.de> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | no |
| CRAN checks: | TDMR results |
| Reference manual: | TDMR.pdf |
| Package source: | TDMR_2.2.tar.gz |
| Windows binaries: | r-devel: TDMR_2.2.zip, r-release: TDMR_2.2.zip, r-oldrel: TDMR_2.2.zip |
| macOS binaries: | r-release: TDMR_2.2.tgz, r-oldrel: TDMR_2.2.tgz |
| Old sources: | TDMR archive |
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