Question: A classifier performs very well on its training data but much worse on new examples. Why is reducing overfitting likely to help more than trying to reduce bias?

Sample answer: Because the model already fits the training set closely, the bias is probably small. The large gap between training performance and development performance shows that generalization is the main weakness, so there is more opportunity to reduce variance than to reduce bias.

Key points:

• Very low training error suggests limited bias.
• A large train-dev gap indicates high variance.
• The most promising gains come from reducing overfitting.

Rubric: The answer must explain that near-optimal training performance leaves little bias to remove, while a large train-dev gap means variance reduction offers more improvement.