Case context: A cat classifier has 0% training error on two examples. After the training set grows to 100 and then 10,000 examples, its training accuracy drops slightly while its dev error decreases. Some examples are ambiguous or mislabeled.

Question: What should the learner conclude about these changes based on the source?

Sample answer: The changes match the expected pattern. The two-example set was easy to memorize, but fitting all examples became harder as the set grew, particularly because some were ambiguous or mislabeled. Thus, training error increased while dev error decreased.

Key points:

• Zero training error on two examples can result from memorization.
• Training error usually rises as the training set grows.
• Dev error should fall as the training set grows.
• Ambiguous or mislabeled examples make perfect fitting harder.

Rubric: The response should diagnose both trends, explain the initial memorization, and identify dataset size plus ambiguous or mislabeled examples as reasons that perfect training fit becomes harder.