# ML Evaluation Memory

Apply this inside `ml/evaluation/`.

- Report validation setup, data splits, metrics, and known limitations.
- Prefer clinically cautious interpretation of model quality.
- Track false positives, false negatives, calibration, and subgroup concerns when relevant.
- Evaluation artifacts should support review, not overstate readiness.
- Summaries should state what the model can and cannot support.