bagging.cv {adabag} | R Documentation |
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.
bagging.cv(formula, data, v = 10, mfinal = 100, control, par=FALSE)
formula |
a formula, as in the |
data |
a data frame in which to interpret the variables named in |
v |
An integer, specifying the type of v-fold cross validation. Defaults to 10.
If |
mfinal |
an integer, the number of iterations for which boosting is run
or the number of trees to use. Defaults to |
control |
options that control details of the rpart algorithm. See rpart.control for more details. |
par |
if |
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. |
Esteban Alfaro-Cortes Esteban.Alfaro@uclm.es, Matias Gamez-Martinez Matias.Gamez@uclm.es and Noelia Garcia-Rubio Noelia.Garcia@uclm.es
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.
## 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]