bagging

0th

Percentile

Applies the Bagging algorithm to a data set

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

Keywords
classif, tree
Usage
bagging(formula, data, mfinal = 100, control, par=FALSE,...)
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.

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.

...

further arguments passed to or from other methods.

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.

a matrix describing, for each observation, the number of trees that assigned it to each class.

prob

a matrix describing, for each observation, the posterior probability or degree of support of each class. These probabilities are calculated using the proportion of votes in the final ensemble.

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 takes into account the gain of the Gini index given by a variable in each tree.

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. (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.

predict.bagging, bagging.cv

• bagging
Examples
# NOT RUN {
## rpart library should be loaded
#This example has been hidden to fulfill execution time <5s
#library(rpart)
#data(iris)
#iris.bagging <- bagging(Species~., data=iris, mfinal=10)

# 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=5,
control=rpart.control(maxdepth=5, minsplit=15))
#Using the pruning option
Vehicle.bagging.pred <- predict.bagging(Vehicle.bagging,newdata=Vehicle[-sub, ], newmfinal=3)
Vehicle.bagging.pred$confusion Vehicle.bagging.pred$error

# }