Let us try and improve our performance. Rather than accept the default classification of all applicants’ credit status, use the estimated probabilities

(propensities) from the logistic regression (where success means 1) as a basis for selecting the best credit risks first, followed by poorer-risk applicants.

Create a vector containing the net profit for each record in the validation set. Use this vector to create a decile-wise lift chart for the validation set that incorporates the net profit.

a. How far into the validation data should you go to get maximum net profit? (Often, this is specified as a percentile or rounded to deciles.)

b. If this logistic regression model is used to score to future applicants, what “probability of success” cutoff should be used in extending credit?