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You can also try the decile table, which can be construct by searching ”Two-by-Two Classification and Decile Tables”. Meanwhile, I am also studying on this problem and will give better measure. Xiaorui Zhu Xiaorui Zhu. $\begingroup$ If you care about the performance of a method, you'd better use ROC to show its classification performance, But if you care more about the actual prediction of true positive, the F1-score is welcome in industry. $\endgroup$ $\begingroup$ In a real business setting, costs of false positives and costs of false negative can be estimated. Then the final classification should be based off a probabilistic model and a classification threshold chosen to minimize the cost of false classifications. I don't really think accuracy, or F score have many actual applications for the disciplined data scientist. $\endgroup$ $\begingroup$ Yes, I agree with the procedure of decision method that minimize the cost of false classification w.r.t cut-off probability and model. And in some cases, asymmetric cost can be applied to FP and FN. Racing celtic.37.4563 EUR.
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