Exam Professional Machine Learning Engineer topic 1 question 169 discussion

B. Decrease the score threshold and C. Add more positive examples to the training set.

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)

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.

B&C - Fraudulent transactions are often rare events, so the model might not have enough exposure to learn their patterns effectively.

B & C They are the options

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

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.

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?)

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)