Tham khảo tài liệu 'new frontiers in banking services emerging needs and tailored products for untapped markets_8', tài chính - ngân hàng, tài chính doanh nghiệp phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả | 200 8. Classification Credit Card Default and Bank Failures When working with any nonlinear function however we should never underestimate the difficulties of obtaining optima even with simple probit or Weibull models used for classification. The logit model of course is a special case of the neural network since a neural network with one logsigmoid neuron reduces to the logit model. But the same tools we examined in previous chapters particularly hybridization or coupling the genetic algorithm with quasi-Newton gradient methods come in very handy. Classification problems involving nonlinear functions have all of the same problems as other models especially when we work with a large number of variables. Credit Card Risk For examining credit card risk we make use of a data set used by Baesens Setiono Mues and Vanthienen 2003 on German credit card default rates. The data set we use for classification of default no default for German credit cards consists of 1000 observations. The Data Table lists the twenty arguments a mix of categorical and continuous variables. Table also gives the maximum minimum and median values of each of the variables. The dependent variable y takes on a value of 0 if there is no default and a value of 1 if there is a default. There are 300 cases of defaults in this sample with y 1. As we can see in the mix of variables there is considerable discretion about how to categorize the information. In-Sample Performance The in-sample performance of the five methods appears in Table . This table pictures both the likelihood functions for the four nonlinear alternatives to the discriminant analysis and the error percentages of all five methods. There are two types of errors as taught from statistical decision theory. False positives take place when we incorrectly label the dependent variables as 1 with y 1 when y 0. Similarly false negatives occur when we have y 0 when y 1. The overall error ratio in Table is simply a .
