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PROFESSIONAL-MACHINE-LEARNING-ENGINEER · Question #27

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…

The correct answer is C. An optimization objective that maximizes the area under the precision-recall curve (AUC PR). AUC PR is the area under the precision-recall curve. It is a good metric to use for fraud detection because it takes into account the trade-off between precision and recall. In this case, you need to prioritize detection of fraudulent transactions while minimizing false…

Submitted by jordan8· Apr 18, 2026ML model development

Question

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?

Options

  • AAn optimization objective that minimizes Log loss
  • BAn optimization objective that maximizes the Precision at a Recall value of 0.50
  • CAn optimization objective that maximizes the area under the precision-recall curve (AUC PR)
  • DAn optimization objective that maximizes the area under the receiver operating characteristic

How the community answered

(38 responses)
  • A
    16% (6)
  • B
    3% (1)
  • C
    74% (28)
  • D
    8% (3)

Explanation

AUC PR is the area under the precision-recall curve. It is a good metric to use for fraud detection because it takes into account the trade-off between precision and recall. In this case, you need to prioritize detection of fraudulent transactions while minimizing false positives. This means that you need to maximize the precision of the model. AUC PR is a good metric to use for this because it takes into account the trade-off between precision and recall.

Topics

#Evaluation Metrics#Imbalanced Classification#Fraud Detection#Optimization Objectives

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