hybridEnsemble: Build, Deploy and Evaluate Hybrid Ensembles
Functions to build and deploy a hybrid ensemble consisting of different sub-ensembles such as bagged logistic regressions, random forest, stochastic boosting, kernel factory, bagged neural networks, bagged support vector machines, rotation forest, bagged k-nearest neighbors, and bagged naive Bayes. 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: |
1.7.8 |
| Imports: |
randomForest, kernelFactory, ada, rpart, ROCR, nnet, e1071, NMOF, GenSA, Rmalschains, pso, AUC, soma, genalg, reportr, nnls, quadprog, tabuSearch, rotationForest, FNN, glmnet, foreach, doParallel, parallel |
| Suggests: |
testthat |
| Published: |
2022-05-10 |
| Author: |
Michel Ballings, Dauwe Vercamer, Matthias Bogaert, 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 |
Documentation:
Downloads:
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