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.
||randomForest, kernelFactory, ada, rpart, ROCR, nnet, e1071, NMOF, GenSA, Rmalschains, pso, AUC, soma, genalg, reportr, nnls, quadprog, tabuSearch, rotationForest, FNN, glmnet
||Michel Ballings, Dauwe Vercamer, and Dirk Van den Poel
||Michel Ballings <Michel.Ballings at GMail.com>
||GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
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