Choose the right evaluation metric for a classifier
Use when class imbalance or asymmetric costs make accuracy the wrong metric.
Act as an ML evaluation expert.
Problem: {{problem_description}}
Class balance: {{class_balance}}
Cost of a false positive: {{fp_cost}}
Cost of a false negative: {{fn_cost}}
How predictions will be used: {{usage}}
Task:
1. Recommend the primary evaluation metric and 1-2 secondary metrics, and explain why accuracy is or is not appropriate here.
2. Recommend a decision threshold strategy given the cost asymmetry.
3. Specify what the confusion matrix should optimize for.
4. Describe the validation scheme to avoid optimistic estimates (stratification, cross-validation folds).
5. Give the Python snippet to compute the chosen metrics.
Be blunt about which common metric would mislead here.Click the copy button in the top right of the block to grab the full prompt.
Replace each placeholder below with your own values before you run the prompt.
- {{problem_description}}
- {{class_balance}}
- {{fp_cost}}
- {{fn_cost}}
- {{usage}}
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