Question: A classifier is retrained several times using progressively larger training sets. Explain why the validation error often goes down as more training examples are added. What does that say about the model's generalization?

Sample answer: When the training set is expanded, the model sees more of the patterns and variation present in the data source. With a broader sample, it is less likely to memorize quirks from a small dataset and more likely to learn relationships that hold beyond the training examples. As a result, performance on new, unseen data improves, so the validation error typically decreases.

Key points:

• More training data gives the model a broader view of the data distribution.
• Extra examples make it harder to fit only small accidental patterns in the training set.
• Better generalization usually shows up as lower validation error.

Rubric: The response should explain that increasing the training set size usually improves generalization by reducing overfitting, which in turn lowers validation or dev-set error.