Question: Explain why examining mistakes on the training set can still be worthwhile when a model is underfitting.

Sample answer: If a model performs poorly on the training set, that is a sign of high bias, meaning it is not capturing even the patterns present in the data it has already seen. In that situation, checking which training examples are being missed can help diagnose the source of the problem. This kind of review can use a process similar to the one used for inspecting errors on a small development sample, and it can be done alongside other steps used to reduce bias.

Key points:

• High bias shows up as weak performance on the training set.
• Training-set error review is useful because it focuses on the same data the model is failing to fit.
• The workflow can resemble error inspection on a dev sample.
• It is an extra diagnostic step, not a replacement for other bias-reduction methods.

Rubric: A strong response states that high bias means the model fits the training set poorly, explains that training-set errors are therefore the right place to look, notes that the review can follow a similar error-analysis procedure used on a dev set, and makes clear that this analysis complements other bias-reduction techniques.