Ensemble classifiers based upon generalized additive models for binary classification (De Bock et al. (2010) <doi:10.1016/j.csda.2009.12.013>). The ensembles implement Bagging (Breiman (1996) <doi:10.1023/A:1018054314350>), the Random Subspace Method (Ho (1998) <doi:10.1109/34.709601>), or both, and use Hastie and Tibshirani's (1990) generalized additive models (GAMs) as base classifiers. Once an ensemble classifier has been trained, it can be used for predictions on new data. A function for cross validation is also included.
| Version: | 1.2 |
| Depends: | R (≥ 2.4.0), splines, gam, mlbench, caTools |
| Published: | 2016-03-02 |
| Author: | Koen W. De Bock, Kristof Coussement and Dirk Van den Poel |
| Maintainer: | Koen W. De Bock <K.DeBock at ieseg.fr> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | no |
| CRAN checks: | GAMens results |
| Reference manual: | GAMens.pdf |
| Package source: | GAMens_1.2.tar.gz |
| Windows binaries: | r-devel: GAMens_1.2.zip, r-release: GAMens_1.2.zip, r-oldrel: GAMens_1.2.zip |
| OS X El Capitan binaries: | r-release: GAMens_1.2.tgz |
| OS X Mavericks binaries: | r-oldrel: GAMens_1.2.tgz |
| Old sources: | GAMens archive |
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