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% was 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. If the positive sample number is fixed (400), the sample ratio is 1:99 (fraudulent vs. non-fraudulent, positive vs. negative)1. What is the adjusted misclassification rate (error rate) that should be in the original non-oversampled database?2. What is the total number of false positive records that should be in the original non-oversampled database?