Question: What happens if a machine learning evaluation score measures something different from the project’s real objective, and what should the team do to fix it?

Sample answer: If the score is not aligned with the project’s true goal, it stops being a dependable way to compare models. The team may end up choosing a model that looks good on the score but performs poorly on the real task. The fix is to replace or redesign the evaluation metric so it matches the outcome the project actually needs.

Key points:

• The score is tracking the wrong target.
• It can no longer be relied on to pick the best model.
• The remedy is to change the evaluation metric so it reflects the real objective.

Rubric: The response should clearly state that (1) the metric is misaligned with the project goal, (2) it is no longer trustworthy for model selection, and (3) the appropriate response is to revise the evaluation metric.