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
binary model is a good classifier.

Interpretation

The area under the ROC curve values range from 0.5 to 1. When the binary
model can perfectly separate the classes, then the area under the curve is 1.
When the binary model cannot separate the classes better than a random
assignment, then the area under the curve is 0.5.

When no separate test set is used, Minitab creates the ROC curve with the
data set.

With a validation method, Minitab creates two ROC curves. One curve is
for the training data and the other is for the validation data. The validation
results indicate whether the model can adequately predict the response values
for new observations, or properly summarize the relationships between the
response and the predictor variables. The training results are usually more
ideal than actual and are for reference only.

A k-fold area under the ROC curve that is substantially less than the area
under the ROC curve can indicate that the model is over-fit. An over-fit model
occurs when the model includes terms that are not important in the population.
The model becomes tailored to the training data and, therefore, might not be
useful for making predictions about the population.