adabag (version 5.0)

margins: Calculates the margins

Description

Calculates the margins of an AdaBoost.M1, AdaBoost-SAMME or Bagging classifier for a data frame

Usage

margins(object, newdata)

Value

An object of class margins, which is a list with only one component:

margins

a vector with the margins.

Arguments

object

This object must be the output of one of the functions bagging, boosting, predict.bagging or predict.boosting. This is assumed to be the result of some function that produces an object with two components named formula and class, as those returned for instance by the bagging function.

newdata

The same data frame used for building the object

Author

Esteban Alfaro-Cortes , Matias Gamez-Martinez and Noelia Garcia-Rubio

Details

Intuitively, the margin for an observation is related to the certainty of its classification. It is calculated as the difference between the support of the correct class and the maximum support of an incorrect class

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.

Schapire, R.E., Freund, Y., Bartlett, P. and Lee, W.S. (1998): ``Boosting the margin: A new explanation for the effectiveness of voting methods''. The Annals of Statistics, vol 26, 5, pp. 1651--1686.

See Also

bagging, boosting, plot.margins, predict.boosting, predict.bagging

Examples

#Iris example
library(rpart)
data(iris)
sub <- c(sample(1:50, 25), sample(51:100, 25), sample(101:150, 25))
iris.adaboost <- boosting(Species ~ ., data=iris[sub,], mfinal=3)
margins(iris.adaboost,iris[sub,])->iris.margins # training set
plot.margins(iris.margins)

# test set
iris.predboosting<- predict.boosting(iris.adaboost, newdata=iris[-sub,])
margins(iris.predboosting,iris[-sub,])->iris.predmargins 
plot.margins(iris.predmargins,iris.margins)

#Examples with bagging
iris.bagging <- bagging(Species ~ ., data=iris[sub,], mfinal=3)
margins(iris.bagging,iris[sub,])->iris.bagging.margins # training set

iris.predbagging<- predict.bagging(iris.bagging, newdata=iris[-sub,])
margins(iris.predbagging,iris[-sub,])->iris.bagging.predmargins # test set
par(bg="lightyellow")
plot.margins(iris.bagging.predmargins,iris.bagging.margins)