Evaluation criteria Condition (as determined by "Gold standard ") Positive Negative False Positive Positive predictive value Positive True Positive ∑ True Positive Test (Type I error) 2 Test outcome positive outcome Negative predictive value Negative False Negative (ype ll error) True Negative ∑ True Negative ∑ Test outcome Negati Ive Sensitivity Specificity ∑ True Positive ∑ True Negative ∑ Condition Positive∑ Condition Negativel AUC (Area Under receives operating characteristic(ROC) Curve) represents the probability that a randomly chosen positive example is correctly rated with greater suspicion than a randomly chosen negative exampleEvaluation Criteria AUC (Area Under receives operating characteristic (ROC) Curve) represents the probability that a randomly chosen positive example is correctly rated with greater suspicion than a randomly chosen negative example