Topic 1 Question 125
2 つ選択A financial company is trying to detect credit card fraud. The company observed that, on average, 2% of credit card transactions were fraudulent. A data scientist trained a classifier on a year's worth of credit card transactions data. The model needs to identify the fraudulent transactions (positives) from the regular ones (negatives). The company's goal is to accurately capture as many positives as possible. Which metrics should the data scientist use to optimize the model?
Specificity
False positive rate
Accuracy
Area under the precision-recall curve
True positive rate
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コメント(11)
D, E is the answer. we need to make the recall rate(not precision) high.
👍 35littlewat2021/10/16To maximize detection of fraud in real-world, imbalanced datasets, D and E should always be applied.
https://en.wikipedia.org/wiki/Sensitivity_and_specificity https://machinelearningmastery.com/roc-curves-and-precision-recall-curves-for-imbalanced-classification/
👍 13joep212021/09/21- 正解だと思う選択肢: DE
agreed with DE
👍 4ystotest2022/11/26
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