Scenario: A fraud-detection model has a training error of 18%, which is still worse than the team's target of 8%. Its development error is 27%, so the dev set performs even worse than the training set. The team suggests that the only action needed is to collect more training examples.

Question: Is that plan enough to reach the target error? Explain using the relationship between training error, development error, and more data.

Sample answer: No. In this situation, collecting more data by itself is not enough. If the model already has training error above the target, more examples do not fix the fact that the model is underperforming on the training set. With the stated learning-curve pattern, additional data tends to reduce variance, but it does not directly solve the high-bias problem reflected by the high training error. The development error is also above the training error, so more data alone is not a complete remedy.

Key points:

• Training error is already higher than the target.
• More data does not by itself drive training error down to a much lower level.
• Dev error exceeds training error.
• The proposal is therefore insufficient on its own.

Rubric: The response should say that more data alone is not sufficient and should justify the answer by referring to the high training error and the dev-versus-training error gap.