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 test set, Minitab creates two ROC curves. One curve is for the
training data and the other is for the test data. The test 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.

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