Perform the following steps to generate two different classification examples with different costs:

1. First, you should install and load the ROCR package:

        > install.packages("ROCR")
        > library(ROCR)  

2. Train the svm model using the training dataset with a probability equal to TRUE:

        > svmfit=svm(churn~ ., data=trainset, prob=TRUE)

3. Make predictions based on the trained model on the testing dataset with the probability set as TRUE:

        >pred=predict(svmfit,testset[, !names(testset) %in% c("churn")],
         probability=TRUE)

4. Obtain the probability of labels with yes:

        > pred.prob = attr(pred, "probabilities") 
        > pred.to.roc = pred.prob[, 2] 

5. Use the prediction function to generate a prediction result:

        > pred.rocr = prediction(pred.to.roc, testset$churn)  

6. Use the performance function to obtain the performance measurement:

        > perf.rocr = performance(pred.rocr, measure = "auc", x.measure =
        "cutoff") 
        > perf.tpr.rocr = performance(pred.rocr, "tpr","fpr") 

7. Visualize the ROC curve using the plot function:

        > plot(perf.tpr.rocr, colorize=T,
        main=paste("AUC:",([email protected])))