Question: In a concise analytical response, explain why training and dev error can move in opposite directions as training-set size grows.

Sample answer: A very small training set can often be memorized, producing near-zero training error. As more examples are added, fitting every training example becomes harder, especially when some examples are ambiguous or mislabeled, so training error usually rises. At the same time, dev error should decrease as the training set grows.

Key points:

• Small training sets can be memorized.
• Training error usually increases with training-set size.
• Ambiguous or mislabeled examples make perfect fitting harder.
• Dev error should decrease as training-set size grows.

Rubric: A strong response accurately identifies both error trends and connects rising training error to reduced memorization and the difficulty of fitting larger, potentially ambiguous or mislabeled datasets.