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PROFESSIONAL-MACHINE-LEARNING-ENGINEER · Question #332

You are developing a model to detect fraudulent credit card transactions. You need to prioritize detection, because missing even one fraudulent transaction could severely impact the credit card…

The correct answer is D. Decrease the probability threshold to classify a fraudulent transaction. Lowering the probability threshold makes the model more sensitive to identifying positive cases (fraudulent transactions), increasing the true positive rate. This approach helps prioritize detection, even if it may lead to more false positives - which is acceptable in high-risk…

Submitted by chen.hong· Apr 18, 2026ML model development

Question

You are developing a model to detect fraudulent credit card transactions. You need to prioritize detection, because missing even one fraudulent transaction could severely impact the credit card holder. You used AutoML to train a model on users' profile information and credit card transaction data. After training the initial model, you notice that the model is failing to detect many fraudulent transactions. How should you increase the number of fraudulent transactions that are detected?

Options

  • AAdd more non-fraudulent examples to the training set.
  • BReduce the maximum number of node hours for training.
  • CIncrease the probability threshold to classify a fraudulent transaction.
  • DDecrease the probability threshold to classify a fraudulent transaction.

How the community answered

(42 responses)
  • A
    2% (1)
  • B
    5% (2)
  • C
    12% (5)
  • D
    81% (34)

Explanation

Lowering the probability threshold makes the model more sensitive to identifying positive cases (fraudulent transactions), increasing the true positive rate. This approach helps prioritize detection, even if it may lead to more false positives - which is acceptable in high-risk use cases like fraud detection.

Topics

#Classification Threshold#Recall Optimization#Fraud Detection#Model Tuning

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