compare performance of models

perfCompare.R

# code copied from:
# http://heuristically.wordpress.com/2009/12/23/compare-performance-machine-learning-classifiers-r/

# load the ROCR package which draws the ROC curves
require(ROCR)
 
# create an ROCR prediction object from rpart() probabilities
x.rp.prob.rocr <- prediction(x.rp.prob[,2], BreastCancer[ind == 2,'Class'])
# prepare an ROCR performance object for ROC curve (tpr=true positive rate, fpr=false positive rate)
x.rp.perf <- performance(x.rp.prob.rocr, "tpr","fpr")
# plot it
plot(x.rp.perf, col=2, main="ROC curves comparing classification performance of five machine learning models")
 
# Draw a legend.
legend(0.6, 0.6, c('rpart', 'ctree', 'cforest','bagging','svm'), 2:6)
 
# ctree
x.ct.prob.rocr <- prediction(x.ct.prob, BreastCancer[ind == 2,'Class'])
x.ct.perf <- performance(x.ct.prob.rocr, "tpr","fpr")
# add=TRUE draws on the existing chart 
plot(x.ct.perf, col=3, add=TRUE)
 
# cforest
x.cf.prob.rocr <- prediction(x.cf.prob, BreastCancer[ind == 2,'Class'])
x.cf.perf <- performance(x.cf.prob.rocr, "tpr","fpr")
plot(x.cf.perf, col=4, add=TRUE)
 
# bagging
x.ip.prob.rocr <- prediction(x.ip.prob[,2], BreastCancer[ind == 2,'Class'])
x.ip.perf <- performance(x.ip.prob.rocr, "tpr","fpr")
plot(x.ip.perf, col=5, add=TRUE)
 
# svm
x.svm.prob.rocr <- prediction(attr(x.svm.prob, "probabilities")[,2], BreastCancer[ind == 2,'Class'])
x.svm.perf <- performance(x.svm.prob.rocr, "tpr","fpr")
plot(x.svm.perf, col=6, add=TRUE)