A large number of insurance records are to be examined to develop a model for predicting fraudulent claims. Of the claims in the historical database, 1% were judged to be fraudulent (class 1). A sample database is taken to develop a model, and oversampling is used to provide a balanced sample in light of the very low response rate. When applied to this sample database (total number of records, N = 800), the model ends up correctly classifying 310 frauds, and 270 non-frauds. It misses 90 frauds, and classified 130 records incorrectly as frauds when they were not.

a)What is the true positive rate when the model is applied to the data set generated by oversampling? (please give 2 digits after the decimal point, for example, 0.95)

b) What is the false negative rate when the model is applied to the data set generated by oversampling?

c) What is the percentage of fraudulent responses (class 1 response) generated by the model when the model is applied to the data set generated by oversampling?

d) What is misclassification rate (error rate) when the model is applied to the data set generated by oversampling?

e) What is the true positive rate when the model is applied to the data set generated by oversampling?

f) What is the false positive rate when the model is applied to the data set generated by oversampling?

g) What is the true negative rate when the model is applied to the data set generated by oversampling?