Case context: A development team has produced many classifiers by changing algorithm architectures, model parameters, and feature choices. Discussions about which classifier to pursue are slow because the team lacks one common basis for ranking them.

Question: What should the team adopt, and how would it improve the decision process?

Sample answer: The team should choose a single-number evaluation metric to optimize. It can then evaluate and sort all classifiers using that metric. The resulting preference ranking will show which classifier performs best under the chosen measure, speed the selection decision, and give the team a clear direction for further progress.

Key points:

• Choose one evaluation number for the team to optimize.
• Evaluate all classifiers on the same metric.
• Sort the classifiers to create a clear preference ranking.
• Use the ranking to decide quickly and guide further iteration.

Rubric: The answer should recommend a single-number evaluation metric and explain its effects on model sorting, preference ranking, decision speed, and direction for progress.