adabag (version 5.0)

bagging.cv: Runs v-fold cross validation with Bagging

Description

The data are divided into v non-overlapping subsets of roughly equal size. Then, bagging is applied on (v-1) of the subsets. Finally, predictions are made for the left out subsets, and the process is repeated for each of the v subsets.

Usage

bagging.cv(formula, data, v = 10, mfinal = 100, control, par=FALSE)

Value

An object of class bagging.cv, which is a list with the following components:

class

the class predicted by the ensemble classifier.

confusion

the confusion matrix which compares the real class with the predicted one.

error

returns the average error.

Arguments

formula

a formula, as in the lm function.

data

a data frame in which to interpret the variables named in formula

v

An integer, specifying the type of v-fold cross validation. Defaults to 10. If v is set as the number of observations, leave-one-out cross validation is carried out. Besides this, every value between two and the number of observations is valid and means that roughly every v-th observation is left out.

mfinal

an integer, the number of iterations for which boosting is run or the number of trees to use. Defaults to mfinal=100 iterations.

control

options that control details of the rpart algorithm. See rpart.control for more details.

par

if TRUE, the cross validation process is runned in parallel. If FALSE (by default), the function runs without parallelization.

Author

Esteban Alfaro-Cortes , Matias Gamez-Martinez and Noelia Garcia-Rubio

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.

Breiman, L. (1998). "Arcing classifiers". The Annals of Statistics, Vol 26, 3, pp. 801--849.

See Also

bagging, predict.bagging

Examples

## rpart library should be loaded
library(rpart)
data(iris)
iris.baggingcv <- bagging.cv(Species ~ ., v=2, data=iris, mfinal=3,
control=rpart.control(cp=0.01))
iris.baggingcv[-1]


## rpart and mlbench libraries should be loaded
## Data Vehicle (four classes) 
#This example has been hidden to keep execution time <5s
#data(Vehicle)
#Vehicle.bagging.cv <- bagging.cv(Class ~.,data=Vehicle,v=5,mfinal=10, 
#control=rpart.control(maxdepth=5))
#Vehicle.bagging.cv[-1]