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Answer: Decrease the score threshold., Add more positive examples to the training set
The correct answers are B (Decrease the score threshold) and C (Add more positive examples to the training set). B. Decreasing the score threshold will cause the model to flag more transactions as suspicious, potentially catching more fraudulent activities that were previously missed. However, be aware that this might also increase the number of false positives, so a balance must be achieved. C. Adding more positive examples (fraudulent transactions) to the training set helps the model better learn from and identify patterns associated with fraud. Fraudulent transactions are typically a minority class, so increasing the number of these examples can significantly enhance the model's ability to detect them.
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You are developing a model to detect fraudulent credit card transactions. It is crucial to prioritize detection accuracy because missing even a single fraudulent transaction could severely impact the credit card holder. You've used AutoML to train a model based on users' profile information and credit card transaction data. However, after training the initial model, you notice that the model is failing to detect a significant number of fraudulent transactions. To improve the model's performance in accurately identifying fraud, which two training parameters should you adjust in AutoML?
A
Increase the score threshold
B
Decrease the score threshold.
C
Add more positive examples to the training set
D
Add more negative examples to the training set
E
Reduce the maximum number of node hours for training
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