> library(C50)
> data(churn)
> str(churnTrain)
> churnTrain=churnTrain[,!names(churnTrain)%in%c("state","area_code","account_length")]
> str(churnTrain)
> set.seed(2)
> ind=sample(2,nrow(churnTrain),replace=T,prob=c(0.7,0.3))
> trainset=churnTrain[ind==1,]
> testset=churnTrain[ind==2,]
> dim(trainset)
[1] 2315 17
> dim(testset)
[1] 1018 17
> library(e1071)
> library(ROCR)
> library(gplots)
> library(ROCR)
> svmfit=svm(churn~.,data=trainset,prob=T)
> pred=predict(svmfit,testset[,!names(testset)%in%c("churn")],probability=T)
> pred.prob=attr(pred,"probabilities")
> pred.to.roc=pred.prob[,2]
> pred.rocr=prediction(pred.to.roc,testset$churn)
> pred.rocr
> perf.rocr=performance(pred.rocr,measure="auc",x.measure="cutoff")
> perf.tpr.rocr=performance(pred.rocr,"tpr","fpr")
> plot(perf.tpr.rocr,colorize=T,main=paste("AUC:",(perf.rocr@y.values)))
> library(pROC)
> data(aSAH)
> roc(aSAH$outcome,aSAH$s100b)
> roc(aSAH$outcome,aSAH$s100b,plot=T,print.thres=T,print.auc=T)
