Measure AUC

In this exercise, you will compute the AUC of your churn prediction models to find the best one. Use the auc() function in the pROC package. The function has two arguments:

1. The true churn label in the test set, test_set$Future.
2. The model prediction:
   a. For logistic regression, it is the prediction obtained from the predict function.
   b. For random forest, it is the second column of the prediction obtained from the predict function.

The objects firstPredictions, secondPredictions, thirdPredictions, and rfPredictions have been loaded for you.

Which model has the highest AUC value?