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.
Interpretation
For classification trees, the area under the ROC curve values range from
0.5 to 1. When a classification tree can perfectly separate the classes, then
the area under the curve is 1. When a classification tree cannot separate the
classes better than a random assignment, then the area under the curve is 0.5.
The red dotted line indicates the random assignment case.
In this example, the training and cross-validation curves are similar to each other. The
area under the cross-validation curve is 0.820.
