aCRM (version 0.1.1)

cocktailEnsemble: Cocktail Ensemble: build a model consisting of multiple classifiers.

Description

cocktailEnsemble is a classification algorithm. It builds four models by calling glm (logit), kernelFactory, randomForest, and ada.

Usage

cocktailEnsemble(x, y)

Arguments

x

A data frame containing the predictors.

y

The response vector.

Value

An object of type cocktailEnsemble containing the four aforementioned models.

References

Van den Poel, D., Ballings, M., Volkov, A., D''haen, J., Vanherwegen, M., Predictive Analytics for analytical Customer Relationship Management using SAS, Oracle and R, Springer, Forthcoming.

glm:

• Dobson, A. J. (1990) An Introduction to Generalized Linear Models. London: Chapman and Hall.
• Hastie, T. J., & Pregibon, D. (1992) Generalized linear models. Chapter 6 of Statistical Models in S eds J. M. Chambers and T. J. Hastie, Wadsworth & Brooks/Cole.
• McCullagh P., & Nelder, J. A. (1989) Generalized Linear Models. London: Chapman and Hall.
• Venables, W. N., & Ripley, B. D. (2002) Modern Applied Statistics with S. New York: Springer.

randomForest:

• Liaw, A. & Wiener, M. (2002). Classification and Regression by randomForest. R News 2(3), 18--22.
• Breiman, L. (2001), Random Forests, Machine Learning 45(1), 5-32.

kernelFactory:

• Ballings, M., & Van den Poel, D. (2012). Kernel Factory: An ensemble of Kernel Machines. Expert Systems With Applications. Forthcoming.
• Ballings, M., & Van den Poel, D. (2012). kernelFactory: An ensemble of kernel machines. R package version 0.1.1 http://cran.r-project.org/web/packages/kernelFactory.

ada:

• Culp, M., Johnson, K., & Michailidis, G. (2012). ada: ada: an R package for stochastic boosting. R package version 2.0-3. http://CRAN.R-project.org/package=ada
• Friedman, J. (1999). Greedy Function Approximation: A Gradient Boosting Machine. Technical Report, Department of Statistics, Standford University.
• Friedman, J., Hastie, T., and Tibshirani, R. (2000). Additive Logistic Regression: A statistical view of boosting. Annals of Statistics, 28(2), 337-374.
• Friedman, J. (2002). Stochastic Gradient Boosting. Coputational Statistics \& Data Analysis 38.
• Culp, M., Johnson, K., & Michailidis, G. (2006). ada: an R Package for Stochastic Boosting Journal of Statistical Software, 16.

See Also

Other functions in this package: imputeMissings, Aggregate, cocktailEnsemble, predict.cocktailEnsemble

Examples

#Credit Approval data available at UCI Machine Learning Repository
data(Credit)

#Create training set (take a small subset for demonstration purposes)
Credit <- data.frame(Credit[order(runif(nrow(Credit ))),])[1:100,c('V2','V3','V8','V11','V14','V15','Response')]
trainingset <- Credit[1:1:floor(0.50*nrow(Credit)),]
#Create test set
#testset <- Credit[(floor(0.50*nrow(Credit))+1 ):nrow(Credit),]


#Train Cocktail Ensemble on training data
cE <- cocktailEnsemble(x=trainingset[,names(trainingset)!= "Response"],y=trainingset$Response)

#Deploy Kernel Factory to predict response for test data
#pred <- predict(cE,testset[,names(testset)!= "Response"])