Your question is Choose the Right Classification Metric. Take a moment with it on the right.
Talk me through your thinking if you like. When you're confident, submit your answer and I'll grade it like a real screen (7/10 or better passes).
You are reviewing a binary classification model and the team is debating which metric should drive the final decision. The model scores each case and a threshold turns the score into an action. Different stakeholders care about different kinds of mistakes, so the same model can look strong on one metric and weak on another.
How do you decide whether to use Precision, Recall, F1-score, or ROC-AUC?