The ROC curve plots the true positive rate (TPR), also known as power, on the y-axis. The ROC curve plots the false positive rate (FPR), also known as type 1 error, on the x-axis. The area under an ROC curve indicates whether the classification tree is a good classifier.
In this example, the training and test curves are similar to each other. The area under the test curve is 0.820.