Case context: A fruit-quality classifier has 0% training error on a tiny set of 3 examples. After the training set grows to 120 examples and then to 8,000 examples, the training error rises a little while the dev error falls. Some of the labels are uncertain or inconsistent.

Question: What should the learner conclude about these changes?

Sample answer: This pattern is expected. The 3-example set was easy to memorize, but fitting every example becomes harder as the training set gets larger. In addition, uncertain or inconsistent labels make perfect fitting more difficult. The rise in training error together with the drop in dev error shows that the model is learning a more useful pattern from the larger dataset.

Key points:

• Very low training error on a tiny set can come from memorization.
• Training error often increases as the training set becomes larger.
• Dev error should usually decrease as more training data is added.
• Uncertain or inconsistent labels make perfect training fit harder.

Rubric: The response should explain both error trends, note that the smallest dataset could be memorized, and identify larger dataset size plus label inconsistency as reasons that perfect training fit becomes harder.