Question: A classifier has a training error that is almost the best achievable on the training set, but its development-set error is much larger. Explain what this suggests about bias and variance, and where improvement is most likely to come from.

Sample answer: Because the model is already near the best possible performance on the training data, it has little bias left to remove. The much larger development error means the model performs poorly on new examples, which is a sign of high variance. So the main opportunity is not to reduce bias, but to reduce variance. A practical way to do that is to use more training data, which often narrows the gap between training and development performance.

Key points:

• Near-best training error implies low bias.
• A much worse development error implies high variance.
• There is little remaining room to improve bias.
• There is substantial room to improve variance.
• More training data may reduce the generalization gap.

Rubric: A strong response identifies low bias and high variance from the error pattern and explains that the likely gain comes from reducing variance rather than from further bias reduction.