Which optimization objective should you use when training the model?

You work for a credit card company and have been asked to create a custom fraud detection model based on historical data using AutoML Tables. You need to prioritize detection of fraudulent transactions while minimizing false positives.

Which optimization objective should you use when training the model?
A . An optimization objective that minimizes Log loss
B. An optimization objective that maximizes the Precision at a Recall value of 0.50
C. An optimization objective that maximizes the area under the precision-recall curve (AUC PR) value
D. An optimization objective that maximizes the area under the receiver operating characteristic curve (AUC ROC) value

Answer: C

Explanation:

https://stats.stackexchange.com/questions/262616/roc-vs-precision-recall-curves-on-imbalanced-dataset

https://neptune.ai/blog/f1-score-accuracy-roc-auc-pr-auc

https://icaiit.org/proceedings/6th_ICAIIT/1_3Fayzrakhmanov.pdf The problem of fraudulent transactions detection, which is an imbalanced classification problem (most transactions are not fraudulent), you want to maximize both precision and recall; so the area under the PR curve. As a matter of fact, the question asks you to focus on detecting fraudulent transactions (maximize true positive rate, a.k.a. Recall) while minimizing false positives (a.k.a. maximizing Precision). Another way to see it is this: for imbalanced problems like this one you’ll get a lot of true negatives even from a bad model (it’s easy to guess a transaction as "non-fraudulent" because most of them are!), and with high TN the ROC curve goes high fast, which would be misleading. So you wanna avoid dealing with true negatives in your evaluation, which is precisely what the PR curve allows you to do.