Context

SafeDrive uses a binary classification model to detect imminent collision risk from driver-assistance sensor data and trigger an in-cabin emergency alert. The current model performs well on aggregate accuracy, but operations teams report too many nuisance alerts while safety reviewers are concerned about missed dangerous events.

Current Performance

The validation set contains 200,000 driving windows, with 6,000 true safety-critical events (3.0% prevalence). At the current threshold, the model produces 4,800 true positives, 1,200 false positives, 1,200 false negatives, and 192,800 true negatives.

The Problem

Leadership wants a recommendation on how to think about false positives versus false negatives in this safety system and whether the current threshold is appropriate.

Requirements

1. Interpret the current metrics and confusion matrix in business terms.
2. Explain when false negatives should be weighted more heavily than false positives, and when the reverse may be justified.
3. Recommend whether to keep, raise, or lower the decision threshold.
4. Propose an evaluation framework beyond aggregate metrics, including segmentation and calibration checks.
5. Suggest concrete model or policy changes to reduce the most harmful error type.

Constraints

• Missed true hazards can lead to injury and regulatory escalation.
• Excessive false alerts cause driver desensitization and alert fatigue.
• The system must run on-device with low latency and limited compute.