ROC Curve

Figure 1. ROC curve
ROC curve

An ROC curve gives you a visual display of the sensitivity by specificity for all possible classification cutoffs. The chart shown here displays four curves, one for each category of the target variable.

Note that this chart is based on the combined training and testing samples. To produce an ROC chart for the holdout sample, split the file on the partition variable and run the ROC Curve procedure on the predicted pseudo-probabilities.

Figure 2. Area under the curve
Area under the curve

The area under the curve is a numerical summary of the ROC curve, and the values in the table represent, for each category, the probability that the predicted pseudo-probability of being in that category is higher for a randomly chosen case in that category than for a randomly chosen case not in that category. For example, for a randomly selected customer in Plus service and a randomly selected customer in Basic service, E-Service, or Total service, there is a 0.668 probability that the model-predicted pseudo-probability of default will be higher for the customer in Plus service.

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