9) Grading the ROC Performance with AUC.

This Blog entry is from the Logistic Regression section in Learn R.

Visually the ROC Curve plot created in the previous Blog entry suggests a that the model created has some predictive power.  A more succinct method to measure model performance is the Area Under Curve statistics which can be calculated with ease by requesting "auc" as the measure to the performance object:

AUC <- performance(ROCRPredictions,measure = "auc")

Run the line of script to console:


To write out the contents of the AUC object:



Run the line of script to console:


The value to gravitate towards is the y.values,  which will have a value ranging between 0.5 and 1:


In this example, the AUC value is 0.827767 which suggests that the model has an excellent utility. By way of grading, AUC scores would correspond:

·         A: Outstanding > 0.9

·         B: Excellent > 0.8 and <= 0.9

·         C: Acceptable > 0.7 and <= 0.8

·         D: Poor > 0.6 and <= 0.7

·         E: Junk > 0.5 and <= 0.6