 The accuracy paradox is one motivation for introducing other measures of predictive performance. For our fraud detection problem there are two ways your prediction can be correct and two ways it can be wrong. A prediction can be correct by predicting that \(Y=1\) when in fact a transaction is fraudulent (a true positive) or predicting \(Y=0\) when in fact a transaction is legitimate (a true negative). Correspondingly, one may err by either predicting \(Y=1\) when in fact \(Y=0\) (a false positive) or predicting \(Y=0\) when in fact a transaction is fraudulent (a false negative). These four possibilities are presented in the following two-by-two contingency table, which is sometimes referred to as a confusion matrix: