R-package hybridEnsemble 1.0.0 is now on CRAN
hybridEnsemble contains functions to build and deploy a hybrid ensemble consisting of eight different sub-ensembles: bagged logistic regressions, random forest, stochastic adaboost, 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.
This version of the package is a substantial upgrade:
- added a seventh base classifier: rotation forest
- add an eight base classifier: bagged nearest neighbors
- added parameters for size of sub-ensembles
- added oversampling to alleviate problems related to class imbalance and subsequent subsetting
- added a filter parameter to remove near constants that often produced problems in subsetting
- added error handling
- added formal automated tests
- refactored for faster and shorter code
- changed to [-1,1] scaling in neural networks
Here is the link to the package on CRAN:
http://cran.r-project.org/web/packages/hybridEnsemble/index.html