hybridEnsemble: Build, Deploy and Evaluate Hybrid Ensembles
Version 1.0.0

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.

AuthorMichel Ballings, Dauwe Vercamer, and Dirk Van den Poel
Date of publication2015-05-30 16:22:16
MaintainerMichel Ballings <Michel.Ballings@GMail.com>
LicenseGPL (>= 2)
Version1.0.0
Package repositoryView on CRAN
InstallationInstall the latest version of this package by entering the following in R:
install.packages("hybridEnsemble")

Popular man pages

Credit: Credit approval (Frank and Asuncion, 2010)
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...
See all...

All man pages Function index File listing

Man pages

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...

Functions

CVhybridEnsemble Man page Source code
Credit Man page
binary Source code
bit Source code
bit.character Source code
bit.numeric Source code
calibrate Source code
hybridEnsemble Man page Source code
hybridEnsembleNews Man page Source code
importance.hybridEnsemble Man page Source code
maxBit Source code
maxBit.character Source code
maxBit.numeric Source code
onAttach Source code
partition Source code
plot.CVhybridEnsemble Man page Source code
predict.calibrate Source code
predict.hybridEnsemble Man page Source code
summary.CVhybridEnsemble Man page Source code
tuneMember Source code
unbinary Source code

Files

inst
inst/NEWS
tests
tests/testthat.R
tests/testthat
tests/testthat/test-mainfunctions.r
NAMESPACE
data
data/Credit.RData
R
R/summary.CVhybridEnsemble.R
R/hybridEnsemble.R
R/CVhybridEnsemble.R
R/hybridEnsembleNews.R
R/plot.CVhybridEnsemble.R
R/auxiliary.R
R/importance.hybridEnsemble.R
R/predict.hybridEnsemble.R
R/zzz.R
MD5
DESCRIPTION
man
man/hybridEnsembleNews.Rd
man/predict.hybridEnsemble.Rd
man/Credit.Rd
man/CVhybridEnsemble.Rd
man/summary.CVhybridEnsemble.Rd
man/hybridEnsemble.Rd
man/plot.CVhybridEnsemble.Rd
man/importance.hybridEnsemble.Rd
hybridEnsemble documentation built on May 20, 2017, 2:55 a.m.

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