adabag: errorevol – R documentation

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errorevol

Shows the error evolution of the ensemble

Description

Calculates the error evolution of an AdaBoost.M1, AdaBoost-SAMME or Bagging classifier for a data frame as the ensemble size grows

Usage

errorevol(object, newdata)

Arguments

`object`	This object must be the output of one of the functions `bagging` or `boosting`. This is assumed to be the result of some function that produces an object with two components named `formula` and `trees`, as those returned for instance by the `bagging` function.
`newdata`	Could be the same data frame used in `object` or a new one

Details

This can be useful to see how fast Bagging, boosting reduce the error of the ensemble. in addition, it can detect the presence of overfitting and, therefore, the convenience of pruning the ensemble using predict.bagging or predict.boosting.

Value

An object of class errorevol, which is a list with only one component:

error

a vector with the error evolution.

Author(s)

Esteban Alfaro-Cortes Esteban.Alfaro@uclm.es, Matias Gamez-Martinez Matias.Gamez@uclm.es and Noelia Garcia-Rubio Noelia.Garcia@uclm.es

References

Alfaro, E., Gamez, M. and Garcia, N. (2013): “adabag: An R Package for Classification with Boosting and Bagging”. Journal of Statistical Software, Vol 54, 2, pp. 1–35.

Alfaro, E., Garcia, N., Gamez, M. and Elizondo, D. (2008): “Bankruptcy forecasting: An empirical comparison of AdaBoost and neural networks”. Decision Support Systems, 45, pp. 110–122.

Breiman, L. (1996): “Bagging predictors”. Machine Learning, Vol 24, 2, pp.123–140.

Freund, Y. and Schapire, R.E. (1996): “Experiments with a new boosting algorithm”. In Proceedings of the Thirteenth International Conference on Machine Learning, pp. 148–156, Morgan Kaufmann.

Zhu, J., Zou, H., Rosset, S. and Hastie, T. (2009): “Multi-class AdaBoost”. Statistics and Its Interface, 2, pp. 349–360.

Examples

library(mlbench)
data(BreastCancer)
l <- length(BreastCancer[,1])
sub <- sample(1:l,2*l/3)
cntrl <- rpart.control(maxdepth = 3, minsplit = 0,  cp = -1)

BC.adaboost <- boosting(Class ~.,data=BreastCancer[sub,-1],mfinal=5, control=cntrl)
BC.adaboost.pred <- predict.boosting(BC.adaboost,newdata=BreastCancer[-sub,-1])

errorevol(BC.adaboost,newdata=BreastCancer[-sub,-1])->evol.test
errorevol(BC.adaboost,newdata=BreastCancer[sub,-1])->evol.train

plot.errorevol(evol.test,evol.train)
abline(h=min(evol.test[[1]]), col="red",lty=2,lwd=2)
abline(h=min(evol.train[[1]]), col="blue",lty=2,lwd=2)

adabag

Applies Multiclass AdaBoost.M1, SAMME and Bagging

v4.2

GPL (>= 2)

Authors

Alfaro, Esteban; Gamez, Matias and Garcia, Noelia; with contributions from Li Guo

Initial release

2018-01-10