Machine Learning with R Cookbook - Second Edition

How it works...

In this recipe, we demonstrated how to generate an ROC curve to illustrate the performance of a binary classifier. First, we should install and load the library, ROCR. Then, we use svm, from the e1071 package, to train a classification model, and then use the model to predict labels for the testing dataset. Next, we use the prediction function (from the package, ROCR) to generate prediction results. We then adapt the performance function to obtain the performance measurement of the true positive rate against the false positive rate. Finally, we use the plot function to visualize the ROC plot, and add the value of AUC to the title. In this example, the AUC value is 0.92, which indicates that the svm classifier performs well ...