Situation: A product analytics group has built a collection of fraud-detection models. The models differ in network size, input features, regularization, and training settings. Team members keep debating which version is strongest because they are looking at several separate measures at once.

Prompt: What decision rule should the group adopt, and why does it help?

Model response: The group should agree on a single main score to rank the candidates. After that, every model can be tested against the same measure, making comparison straightforward. A one-score ranking clarifies which candidate is currently best, reduces debate over mixed signals from multiple metrics, and gives the team a concrete target for future improvement.

What to remember:

• Pick one metric as the main criterion for the team.
• Judge each model with that same criterion.
• Rank the models from strongest to weakest using that number.
• Use the ranking to make faster choices and set the next development step.

Rubric: The answer should recommend using one primary evaluation score and explain that it enables consistent comparison, ranking, quicker selection, and clearer next-step guidance.