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.
|Author||Koen W. De Bock, Kristof Coussement and Dirk Van den Poel|
|Date of publication||2016-03-02 01:56:37|
|Maintainer||Koen W. De Bock <K.DeBock@ieseg.fr>|
|License||GPL (>= 2)|
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