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For example, suppose the problem is to predict whether a customer is likely to respond to a promotional mailing. The target has two categories: YES (the customer responds) and NO (the customer does not respond). Suppose a positive response to the promotion generates $500 and that it costs $5 to do the mailing. After building the model, you compare the model predictions with actual data held aside for testing. At this point, you can evaluate the relative cost of different misclassification:
If the model predicts YES and the actual value is YES, the cost of misclassification is $0.
If the model predicts YES and the actual value is NO, the cost of misclassification is $5.
If the model predicts NO and the actual value is YES, the cost of misclassification is $495.
If the model predicts NO and the actual value is NO, the cost is $0.