Q2. Consider the following decile-wise lift chart for the transaction data model, applied to new data Decile-wise Lift Chart (Validate Date) 450 4.00 3 50 3.00 250 2.00 150 1.00 050 0.00 9 10 Deciles a. Interpret the meaning of the first and second bars from the left. b. Explain how you might use this information in practice. c. A data mining technique has been applied to a transaction dataset and has classified 88 records as fraudulent (30 correctly so) and 912 as nonfraudulent (880 correctly so). Another analyst comments that you could improve the accuracy of the model by classifying everything as nonfraudulent. If you do that, what is the error rate? d. Comment on the usefulness, in this situation, of these two metrics of model performance (error rate and lift)