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, minsplit = 5, cp = 0.01, maxdepth = nlevels(vardep))
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. |
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. (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.
## 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]