Question: Explain how comparing a 2% error rate on a fresh holdout drawn from the training distribution with both a 0.8% training error and an 11% dev-set error helps identify distribution mismatch as the main issue.

Sample answer: The small difference between the 0.8% training error and the 2% holdout error suggests that the model is not badly overfitting; its variance is low because it still performs almost as well on another sample from the same distribution. In contrast, the jump from 2% on the same-distribution holdout to 11% on the dev set is much larger. That pattern shows the model is handling the training distribution reasonably well but failing on the dev-set distribution. Because variance is not the main explanation, the most likely cause is a mismatch between the training data and the dev data.

Key points:

• Compare training error with a separate sample from the same distribution to check variance.
• Compare the same-distribution error with dev-set error to look for distribution shift.
• A large dev gap alongside a small same-distribution gap points to mismatch between data distributions.

Rubric: The response should explain the role of both comparisons and conclude that the model generalizes well within the training distribution but performs poorly on the dev distribution because the data sources differ.