bagging {adabag}R Documentation

Applies the Bagging algorithm to a data set.

Description

Fits the Bagging algorithm proposed by Breiman in 1996 using classification trees as single classifiers.

Usage

bagging(formula, data, 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 the formula
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.

Details

Unlike boosting, individual classifiers are independent among them in bagging

Value

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

formula the formula used.
trees the trees grown along the iterations.
votes a matrix describing, for each observation, the number of trees that assigned it to each class.
class the class predicted by the ensemble classifier.
samples the bootstrap samples used along the iterations.
importance returns the relative importance of each variable in the classification task. This measure is the number of times each variable is selected to split.

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

predict.bagging, bagging.cv

Examples

## rpart library should be loaded
library(rpart)
data(iris)
names(iris)<-c("LS","AS","LP","AP","Especies")
lirios.bagging <- bagging(Especies~LS +AS +LP+ AP, data=iris, mfinal=10)

## rpart and mlbench libraries should be loaded
library(rpart)
library(mlbench)
data(BreastCancer)
l <- length(BreastCancer[,1])
sub <- sample(1:l,2*l/3)
BC.bagging <- bagging(Class ~.,data=BreastCancer[,-1],mfinal=25, maxdepth=3)
BC.bagging.pred <- predict.bagging(BC.bagging,newdata=BreastCancer[-sub,-1])
BC.bagging.pred[-1]

# Data Vehicle (four classes)
library(rpart)
library(mlbench)
data(Vehicle)
l <- length(Vehicle[,1])
sub <- sample(1:l,2*l/3)
Vehicle.bagging <- bagging(Class ~.,data=Vehicle[sub, ],mfinal=50, maxdepth=5)
Vehicle.bagging.pred <- predict.bagging(Vehicle.bagging,newdata=Vehicle[-sub, ])
Vehicle.bagging.pred[-1]


[Package adabag version 1.1 Index]