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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,andoversamplingis used to provide abalancedsample 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 classifying310frauds,and270non-frauds.It misses90frauds,and classified130records incorrectly as frauds when they were not.

If the positive sample number is fixed as 400, what is the misclassification ratethat should be in theoriginal non-oversampleddata set?

The sample ratio is 1:99 (fraudulent vs. non-fraudulent, positive vs. negative)

Please give 2 digits after the decimal point, for example, 0.95.

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,andoversamplingis used to provide abalancedsample 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 classifying310frauds,and270non-frauds.It misses90frauds,and classified130records incorrectly as frauds when they were not.

What is the true negative rate when the model is applied to the data setgeneratedby oversampling?

(please give2digits after the decimal point,for example,0.95)

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,andoversamplingis used to provide abalancedsample 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 classifying310frauds,and270non-frauds.It misses90frauds,and classified130records incorrectly as frauds when they were not.

If the positive sample number is fixed as 400, what is the total number offalse positiverecords that should be in the original non-over-sampled data set?

The sample ratio is 1:99 (fraudulent vs. non-fraudulent, positive vs. negative)

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,andoversamplingis used to provide abalancedsample 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 classifying310frauds,and270non-frauds.It misses90frauds,and classified130records incorrectly as frauds when they were not.

If the positive sample number is fixed as 400, what is the total number ofnegativerecords that should be in the original non-oversampled data set (in terms of actual sample ratio)?

The sample ratio is 1:99 (fraudulent vs. non-fraudulent, positive vs. negative)

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,andoversamplingis used to provide abalancedsample 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 classifying310frauds,and270non-frauds.It misses90frauds,and classified130records incorrectly as frauds when they were not.

If the positive sample number is fixed as 400, what is the misclassification ratethat should be in theoriginal non-oversampleddata set?

The sample ratio is 1:99 (fraudulent vs. non-fraudulent, positive vs. negative)

Please give 2 digits after the decimal point, for example, 0.95.