Your question is Evaluate Precision-Recall Tradeoff in Screening. Take a moment with it on the right.
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MediScan Health built a binary classification model to flag patients as high risk for diabetic retinopathy so they can be referred for specialist review. The current model is being considered for rollout across primary care clinics, but clinical leaders are concerned about whether it is missing too many true cases or sending too many healthy patients to follow-up.
| Metric | Model A (Current) | Model B (Alternative) |
|---|---|---|
| Precision | 0.91 | 0.72 |
| Recall | 0.58 | 0.86 |
| F1 Score | 0.71 | 0.78 |
| Accuracy | 0.94 | 0.89 |
| AUC-ROC | 0.88 | 0.90 |
| Positive prediction rate | 6.4% | 13.9% |
| Test set size: 20,000 patients. Disease prevalence in the test set is 8.0% (1,600 actual positive cases). |
The product team wants a clear recommendation on the difference between precision and recall, which metric should matter more in this deployment, and whether Model A or Model B is better aligned with the business and clinical objective.