hybridEnsemble: Build, Deploy and Evaluate Hybrid Ensembles

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

Downloads:

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
OS X Snow Leopard binaries: r-release: hybridEnsemble_1.0.0.tgz, r-oldrel: hybridEnsemble_0.1.1.tgz
OS X Mavericks binaries: r-release: hybridEnsemble_1.0.0.tgz
Old sources: hybridEnsemble archive