# boosting.cv

0th

Percentile

##### Runs v-fold cross validation with AdaBoost.M1 or SAMME

The data are divided into v non-overlapping subsets of roughly equal size. Then, boosting 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.

Keywords
classif, tree
##### Usage
boosting.cv(formula, data, v = 10, boos = TRUE, mfinal = 100,
coeflearn = "Breiman", control, par=FALSE)
##### Arguments
formula

a formula, as in the lm function.

data

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

boos

if TRUE (by default), a bootstrap sample of the training set is drawn using the weights for each observation on that iteration. If FALSE, every observation is used with its weights.

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.

coeflearn

if 'Breiman'(by default), alpha=1/2ln((1-err)/err) is used. If 'Freund' alpha=ln((1-err)/err) is used. In both cases the AdaBoost.M1 algorithm is used and alpha is the weight updating coefficient. On the other hand, if coeflearn is 'Zhu' the SAMME algorithm is implemented with alpha=ln((1-err)/err)+ ln(nclasses-1).

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.

##### Value

An object of class boosting.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.

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

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.

boosting, predict.boosting

• boosting.cv
##### Examples
# NOT RUN {
## rpart library should be loaded
data(iris)
iris.boostcv <- boosting.cv(Species ~ ., v=2, data=iris, mfinal=5,
control=rpart.control(cp=0.01))
iris.boostcv[-1]

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

# }