Case context: You are building a fraud detection model for an online marketplace, and its development-set error is still too high. You are considering whether collecting several more months of labeled transaction data is worth the time and cost.

Question: How can a learning curve help you decide whether more training data is likely to improve performance?

Sample answer: Plot the development-set error for several training-set sizes taken from your current data. Then examine how the error changes as the training set gets larger. If the curve is still dropping, the model may benefit from additional labeled examples. If the curve has become nearly flat, more data is unlikely to produce a large improvement.

Key points:

• Plot development-set error against training-set size
• Check how the error changes as more examples are added
• Use the trend to decide whether gathering more data is worthwhile

Rubric: The answer should describe plotting development-set error versus the number of training examples, using the trend to judge whether additional data is likely to help.