Question: Explain why a model should be evaluated with an entire learning curve, showing training and dev error at several training-set sizes, rather than only at the final point using all available data.

Sample answer: A single endpoint only tells you how the system behaves with the largest dataset you currently have. It does not show whether the model improves steadily, flattens out, or still has room to benefit from more examples. A full learning curve shows how both training and dev error change as more data is added. That broader view helps you judge whether more labeled data is likely to help and lets you estimate the dev-error trend with more confidence.

Key points:

• One endpoint summarizes only the largest available training set.
• The full curve shows behavior across multiple dataset sizes.
• Training and dev error together reveal learning trends.
• The dev curve can be projected more reliably when the whole curve is available.

Rubric: A strong response should contrast the limited information from the final point with the richer trend information from the full curve, and it should explain that the curve makes extrapolation of dev error more reliable.