What does AUC stand for in predictive analytics?

In predictive analytics, AUC stands for "Area Under the Curve." This concept is primarily associated with the Receiver Operating Characteristic (ROC) curve, which is a graphical representation used to evaluate the performance of a binary classification model. The ROC curve illustrates the trade-off between true positive rates (sensitivity) and false positive rates (1-specificity) at various threshold settings.

The AUC quantifies the overall ability of the model to discriminate between the positive and negative classes. A value of 0.5 indicates no discrimination (equivalent to random chance), while a value of 1.0 represents perfect discrimination. Thus, a higher AUC value reflects a better-performing model. It serves as a standardized measure for comparing different models and understanding their predictive power in a given context.