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.
|Author||Michel Ballings, Dauwe Vercamer, and Dirk Van den Poel|
|Date of publication||2015-05-30 16:22:16|
|Maintainer||Michel Ballings <Michel.Ballings@GMail.com>|
|License||GPL (>= 2)|
Credit: Credit approval (Frank and Asuncion, 2010)
CVhybridEnsemble: Five times twofold cross-validation for the Hybrid Ensemble...
hybridEnsemble: Binary classification with Hybrid Ensemble
hybridEnsembleNews: Display the NEWS file
importance.hybridEnsemble: Importance method for hybridEnsemble objects
plot.CVhybridEnsemble: Plot the performance of the cross-validated Hybrid Ensemble
predict.hybridEnsemble: Predict method for hybridEnsemble objects
summary.CVhybridEnsemble: Summarize the performance of the cross-validated Hybrid...