adabag: bagging.cv – R documentation

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bagging.cv

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, control, par=FALSE)

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.
`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.

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. (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.

Examples

## 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]

adabag

Applies Multiclass AdaBoost.M1, SAMME and Bagging

v4.2

GPL (>= 2)

Authors

Alfaro, Esteban; Gamez, Matias and Garcia, Noelia; with contributions from Li Guo

Initial release

2018-01-10