Area Under the ROC Curve

This is a test that determines the ranking power of a scoring system. It is equal to the probability that a classifier will rank a randomly chosen positive instance higher than a randomly chosen negative one. The ROC curve is created by plotting the true positive rate (Sensitivity) against the false positive rate (1-Specificity) at various threshold settings.

The Sensitivity or True Positive rate measures the proportion of actual positives, that is, a BAD condition, which is correctly identified as such. The specificity measures the proportion of negatives which are correctly identified, that is, a GOOD condition,

The expected results are as follows:

·        Random model: 0.5

·        Perfect model: 1.0

·        Acceptable: 0.7 < AUC < 0.8

·        Excellent: 0.8 < AUC < 0.9

·        Exceptional: 0.9 < AUC < 1

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