A large number of insurance records are to be examined to develop a model for predictive...

A large number of insurance records are to be examined to develop a model for predictive fraudulent claims. One of the claims in the historical database, 1% were judged to be fraudulent. A sample is taken to develop a model, and oversampling is used to provide a balance sample in light oath very low response date. When applied to this sample (n=800), the model ends up correctly classifying 310 frauds, and 270 nonfrauds. it missed 90 frauds, and classified 130 records incorrectly as frauds when they were not. a. Produce the classification matrix for the sample as it stands. b. Find the adjusted misclassification rate (adjusting for the oversampling). c. What percentage of new records would you expect to be classified as fraudulent? I already received an answer for the misclassification 220/800=27.54. then the model ends up classifying 57.51% of the frauds 1's... PLEASE explain how to get 57.5%. Thanks

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orchestra answered 2 years ago

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