Case context: A team trains a spam filter that scores better on its training emails than on its validation emails. The team wants to understand what that gap means.

Question: What should the team compare, and what does the size of the drop indicate?

Sample answer: The team should compare the model's validation performance with its training performance. The amount by which validation performance is worse than training performance is informally called the model's variance.

Key points:

• Compare validation performance against training performance.
• Focus on how much worse the validation result is.
• Treat that drop as an informal measure of variance.

Rubric: The response should name both training and validation performance, keep the comparison direction correct, and identify the performance drop on validation as informal variance.