Topic 1 Question 169
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?
Increase the score threshold
Decrease the score threshold.
Add more positive examples to the training set
Add more negative examples to the training set
Reduce the maximum number of node hours for training
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- 正解だと思う選択肢: 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.
👍 1pikachu0072024/01/10 - 正解だと思う選択肢: 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
👍 1BlehMaks2024/01/12 B & C They are the options
👍 136bdc1e2024/01/13
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