Question

A data scientist trains a binary classifier to detect fraudulent transactions. Fraud represents only 1.5% of all transactions. On the validation set, the model achieves 98.5% accuracy, but the business reports that many fraudulent transactions still slip through undetected. The confusion matrix shows a very high number of false negatives and very few false positives. Which action will BEST address the business concern?

Accepted Answer

Lower the classification threshold to increase the model's recall for the fraud (positive) class, and evaluate using the F1 score or recall rather than accuracy.

Answer

Continue optimizing for overall accuracy, since 98.5% already exceeds typical production targets.

Answer

Raise the classification threshold so the model is more confident before flagging fraud, reducing false negatives.

Answer

Add more features to the majority (non-fraud) class to strengthen the model's ability to identify legitimate transactions.