a ' c 1 1 1 8 EVALUATlflG 5.2 53 Consider Figure 514, the decile-w: nationality; Avi". , c . A. c and. ciagsified 88 galen: le correctly so). ConSt RT on}' fun [he v y '. 1. (threshold) 216223312?v}:ch ymca" Suppose that Chi? mainefsds classified as flandulcnt. Descn C t {at 6 Cum up alter the proportxon 0 Fe or down would aflect the following: (is that a a The classification error rate for recor . records that are se lift chart for the transaction data model, applied to re truly fraudulent truly nonfraudulent b. The classification error rate for new data IN (0 LO Q' C") N v- 0 cream seams) / ueeuu anoac 12345678910 Deciles DEClLE-WlSE UFT CHART FOR TRANSACTlON DATA ' how you might use this information in practice. alter analyst comments that you could improve the accuracy of the model bY atfymg everythmg as nonfraudulent. If you do that, wh ' ' 'T rent on the "serIYleSS, in this situation, of these two metrics of model Pam" WC "(error rate and lift). are to be examined to develop a model for predicting 6 historical database, 1% Were judged to be fraudult'm del, and oversampling is used to provide a bam" correctly classifyln . . , " g310frau ' a . "Cords incorrectly as Frauds vizihlalmd 270 nonfiaUds' It missed go fraud" he 1 1 they Were not. mple as it stands adjustmg for the oversamplinglv Xpect to be classified as friiudulcnt.'