How Should You Adjust AutoML Parameters for Fraud Detection?

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 tram 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 adjust the training parameters in AutoML to improve model performance? (Choose two.)

  1. Increase the score threshold
  2. Decrease the score threshold. Source Reference Answer
  3. Add more positive examples to the training set
  4. Add more negative examples to the training set
  5. Reduce the maximum number of node hours for training

Community Votes

B
67%
C
33%

67% of anonymous learners picked answer B. Votes are pick records left by other test-takers — they are not the verified answer.

Community Insight

This question tests your ability to align model tuning with business risk by adjusting classification thresholds and addressing class imbalance, while avoiding the trap of misidentifying the target class or modifying irrelevant compute parameters.

Optimizing machine learning models for high-stakes detection requires balancing precision and recall through threshold management and dataset composition. The community consensus confirms that lowering the classification threshold and augmenting positive examples are the most effective strategies to minimize missed fraudulent transactions.

Candidates frequently select option D due to confusing which class is labeled positive versus negative, but adding more legitimate transaction examples would further suppress the model's ability to identify rare fraud cases.

Community Discussion (9 comments)

NamitSehgal 👍 1 Selected: B
B. Decrease the score threshold and C. Add more positive examples to the training set.
tardigradum 👍 3 Selected: B
B&C If we want to increase the detection rate of fraudulent transactions, we can lower the classification threshold. By doing so, the model becomes less strict and classifies more transactions as potentially fraudulent. This implies including a higher number of false positives in our results. To improve the performance, we can also add more fradulent transactions examples to the dataset (fraudulent transactions are the positivies, in this case)
fitri001 👍 2 Selected: B
B & D D. Add more negative examples to the training set: Fraudulent transactions are typically a minority compared to legitimate transactions. By increasing the number of negative examples (fraudulent transactions) in your training data, you provide AutoML with more information about the patterns of fraudulent activity. This can help the model better distinguish between legitimate and fraudulent transactions. B. Decrease the score threshold: The score threshold determines the level of suspicion assigned to a transaction by the model. A lower threshold means the model flags more transactions as suspicious, potentially catching more fraudulent activities. However, this might also lead to an increase in false positives (flagging legitimate transactions). You'll need to find a balance between fraud detection and acceptable false positive rates based on your business needs.
shadz10 👍 2
B&C - Fraudulent transactions are often rare events, so the model might not have enough exposure to learn their patterns effectively.
36bdc1e 👍 2
B & C They are the options
BlehMaks 👍 3 Selected: C
BC B. More suspicious transactions are marked as fraudulent C. Usually real fraudulent transactions are rare in datasets so we need to add more examples to make our model focus more on them
pikachu007 👍 2 Selected: B
B & D B. Decrease the score threshold: This adjustment could make the model more sensitive, potentially reducing the chance of missing fraudulent transactions, but might increase false positives. D. Add more negative examples to the training set: Providing more examples of non-fraudulent transactions could help the model better distinguish between legitimate and fraudulent transactions, improving its overall performance.
b1a8fae 👍 2 Selected: C
Regarding the 2nd choice (did not notice), I would choose C: adding more positive examples to the training set. It did not sound like a change of parameter to me, but apparently AutoML allows parametrization of data split: https://cloud.google.com/vertex-ai/docs/general/ml-use. I am not entirely convinced but it seems more likely than any other option (reducing max number of hours per node for training can only affect performance negatively I reckon?)
b1a8fae 👍 2 Selected: B
B. Decreasing the score threshold will cause the model to make more positive predictions and potentially decrease the number of false negatives (non detected fraudulent transactions)

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Expert Analysis

Why the Answer Is Correct

Lowering the classification threshold increases model sensitivity, directly reducing false negatives at the expense of higher false positives, which aligns perfectly with scenarios where missing fraud carries severe consequences. Furthermore, real-world fraud datasets suffer from extreme class imbalance; explicitly adding more positive (fraudulent) examples to the training set forces AutoML to learn minority-class decision boundaries more effectively, boosting recall.

Why the Other Options Are Wrong

Increasing the score threshold tightens classification criteria, which inevitably raises false negatives and contradicts the priority of maximizing detection. Adding more negative examples deepens the class imbalance, causing the model to bias toward predicting legitimate transactions and further dropping recall. Reducing maximum node hours restricts computational resources and training epochs, preventing the model from converging and degrading overall predictive performance.

Community Comment Notes

The voting distribution and top comments strongly validate B and C, with multiple candidates noting that decreasing the threshold captures more suspicious activity while accepting manageable false positives [Comment 1, 2]. Several discussions clarify the terminology confusion around class labeling, reinforcing that fraud must be treated as the positive class regardless of its statistical rarity [Comment 4, 6]. Users also emphasize linking metric adjustments directly to business impact, demonstrating practical AutoML operational knowledge [Comment 7, 8].

Official Reference

Exam Strategy

Always translate business risk tolerance into specific ML metrics before selecting tuning actions. When missing a rare event is unacceptable, prioritize recall optimization through threshold reduction and positive class augmentation rather than precision-focused adjustments.

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