Case context: A machine learning team has been using a classification metric to select their final models. However, they discover that the selected models frequently perform poorly in the actual product environment because the metric is measuring something other than what the project needs to optimize.

Question: Based on the source concepts, diagnose why the team's current model selection process is failing and recommend the appropriate action.

Sample answer: The process is failing because the evaluation metric does not align with the project's actual optimization needs, meaning it can no longer be trusted to pick the best algorithm. The team must change their evaluation metric to one that accurately reflects their product objectives.

Key points:

• The metric is measuring something other than the project's optimization needs.
• The metric can no longer be trusted to pick the best algorithm.
• The team must change the evaluation metric to guide the project correctly.

Rubric: The student must diagnose that the metric cannot be trusted to select the best algorithm due to misaligned objectives, and recommend changing the evaluation metric.