Case context: A team checks its classifier and finds that it performs badly even on the examples it was trained on. Earlier attempts to reduce simple underfitting have already been tried.

Question: State the diagnosis and describe the next analysis the team should do, including the style of procedure to follow.

Sample answer: The model is showing high bias, since it cannot match the training examples well. As a follow-up, the team should perform an error review on the training data itself. A useful way to do that is to use a process like the one used when examining mistakes on a dev set by eye.

Key points:

• Identify the problem as high bias.
• Link that diagnosis to poor fit on the training set.
• Recommend reviewing training examples for errors.
• Use a procedure modeled on manual dev-set error review.

Rubric: The response should name high bias, advise error analysis on the training set, present it as an additional step after earlier remedies, and mention a protocol similar to manual inspection of dev-set mistakes.