A data mining routine has been applied to a data set and has classified 88 records as fraudulent (30 correctly so) and 952 as non-fraudulent (920 correctly so). Construct the confusion matrix and calculate the error rate.
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It shows the number of true positives (TP), false positives (FP), true negatives (TN), and false negatives (FN). Show more…
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Data mining routine has been applied to a transaction dataset and has classified 88 records as fraudulent (30 correctly so) and 952 as non-fraudulent (920 correctly so) using 0.5 as the cutoff. Note: fraudulent is the class of interest here. Specificity is: 0.516 0.915 0.941 0.966
Adi S.
An accounting firm is interested in estimating the error rate in a compliance audit it is conducting. The population contains 828 claims, and the firm audits a simple random sample (SRS) of 85 of those claims. For each of the 85 sampled claims, there are 215 fields, all of which are checked for errors. One claim has errors in 25 fields, another claim has errors in 22 fields, and the remaining 57 claims have no errors.
Error Analysis Describe the error. $\langle5,8\rangle \cdot\langle=2,7\rangle=\langle-10,56\rangle$
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