bagging.cv {adabag}R Documentation

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, minsplit = 5, 
        cp = 0.01, maxdepth = nlevels(vardep))

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.
minsplit the minimum number of observations that must exist in a node in order for a split to be attempted.
cp complexity parameter. Any split that does not decrease the overall lack of fit by a factor of cp is not attempted.
maxdepth set the maximum depth of any node of the final tree, with the root node counted as depth 0 (past 30 rpart will give nonsense results on 32-bit machines). Defaults to the number of classes.

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.

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. (2007): ``Multiclass corporate failure prediction by Adaboost.M1''. International Advances in Economic Research, Vol 13, 3, pp. 301–312.

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)
names(iris)<-c("LS","AS","LP","AP","Especies")
iris.baggingcv <- bagging.cv(Especies ~ ., v=10, data=iris, mfinal=10,maxdepth=3)

data(kyphosis)
kyphosis.baggingcv <- bagging.cv(Kyphosis ~ Age + Number + Start, 
        data=kyphosis, mfinal=15)

## rpart and mlbench libraries should be loaded
## Data Vehicle (four classes) 
library(rpart)
library(mlbench)
data(Vehicle)
Vehicle.bagging.cv <- bagging.cv(Class ~.,data=Vehicle,mfinal=25, maxdepth=5)
Vehicle.bagging.cv[-1]


[Package adabag version 1.1 Index]