GAMens: Applies GAMbag, GAMrsm and GAMens Ensemble Classifiers for Binary Classification

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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)
Version
1.2

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Man pages

GAMens
Applies the GAMbag, GAMrsm or GAMens ensemble classifier to a...
GAMens.cv
Runs v-fold cross validation with GAMbag, GAMrsm or GAMens...
predict.GAMens
Predicts from a fitted GAMens object (i.e., GAMbag, GAMrsm or...

Files in this package

GAMens
GAMens/NAMESPACE
GAMens/R
GAMens/R/GAMens.cv.R
GAMens/R/GAMens.R
GAMens/R/predict.GAMens.R
GAMens/MD5
GAMens/DESCRIPTION
GAMens/man
GAMens/man/predict.GAMens.Rd
GAMens/man/GAMens.cv.Rd
GAMens/man/GAMens.Rd
GAMens/INDEX