Functions to build and deploy a hybrid ensemble consisting of eight different sub-ensembles: bagged logistic regressions, random forest, stochastic boosting, kernel factory, bagged neural networks, bagged support vector machines, rotation forest, and bagged k-nearest neighbors. Functions to cross-validate the hybrid ensemble and plot and summarize the results are also provided. There is also a function to assess the importance of the predictors.
| Version: | 1.0.0 |
| Imports: | randomForest, kernelFactory, ada, rpart, ROCR, nnet, e1071, NMOF, GenSA, Rmalschains, pso, AUC, soma, genalg, reportr, nnls, quadprog, tabuSearch, rotationForest, FNN, glmnet |
| Suggests: | testthat |
| Published: | 2015-05-30 |
| Author: | Michel Ballings, Dauwe Vercamer, and Dirk Van den Poel |
| Maintainer: | Michel Ballings <Michel.Ballings at GMail.com> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | no |
| Materials: | NEWS |
| CRAN checks: | hybridEnsemble results |
| Reference manual: | hybridEnsemble.pdf |
| Package source: | hybridEnsemble_1.0.0.tar.gz |
| Windows binaries: | r-devel: hybridEnsemble_1.0.0.zip, r-release: hybridEnsemble_1.0.0.zip, r-oldrel: hybridEnsemble_1.0.0.zip |
| macOS binaries: | r-release: hybridEnsemble_1.0.0.tgz, r-oldrel: hybridEnsemble_1.0.0.tgz |
| Old sources: | hybridEnsemble archive |
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