Confusion Matrix - True Positive, True Negative, False Positive, False Negative
AUC or ROC curve - a receiver operating characteristic curve, is a graphical plot that illustrates the diagnostic ability of a binary classifier system as its discrimination threshold is varied. Binary classification system has four possible outcomes/combination. Speaking in plain English, True positive (TP) - equivalent (eqv.) with hit, patient is diagnosed with disease and patient has disease. True negative (TN) - eqv. with correct rejection, patient is diagnosed without disease and patient does not have disease. False positive (FP) - eqv. with false alarm, Type I error. Patient is diagnosed with disease but in reality does not have one. False negative (FN) - eqv. with miss, Type II error. Patient is diagnose without disease but in reality does have disease. Or speaking in computer language, Condition positive (P) - the number of real positive cases in the data Condition negative (N) - the number of real negative cases in the data True Positive (TP) - if the