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Answer: An optimization objective that maximizes the area under the precision-recall curve (AUC PR) value
The correct answer is C. In the context of credit card fraud detection, the dataset is typically imbalanced with far more legitimate transactions than fraudulent ones. The goal is to prioritize the detection of fraudulent transactions while minimizing false positives. The area under the precision-recall curve (AUC PR) is a better metric for imbalanced datasets compared to the area under the receiver operating characteristic curve (AUC ROC). AUC PR focuses on the trade-off between precision (proportion of true positives among predicted positives) and recall (proportion of true positives among actual positives), which is more relevant for applications like fraud detection where the costs of false positives and false negatives are not the same.
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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. Given the nature of fraud detection, the dataset is highly imbalanced with a far greater number of legitimate transactions compared to fraudulent ones. You need to prioritize detection of fraudulent transactions while minimizing false positives to avoid unnecessarily flagging valid transactions. 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