# plot.margins

0th

Percentile

##### Plots the margins of the ensemble

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

Keywords
classif, tree
##### Usage
# S3 method for margins
plot(x, y = NULL, ...)
##### Arguments
x

An object of class margins. This is assumed to be the result of some function that produces an object with a component named margins as that returned by the margins function.

y

This argument can be used to represent in the same plot the margins in the test and train sets, x and y, respectively. Should be NULL (by default) or an object of class margins.

further arguments passed to or from other methods.

##### 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

##### Value

A labeled plot is produced on the current graphics device (one being opened if needed).

##### 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.

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

• plot.margins
##### Examples
# NOT RUN {
library(mlbench)
data(BreastCancer)
l <- length(BreastCancer[,1])
sub <- sample(1:l,2*l/3)
cntrl <- rpart.control(maxdepth = 3, minsplit = 0,  cp = -1)

BC.adaboost <- boosting(Class ~.,data=BreastCancer[sub,-1],mfinal=5, control=cntrl)