You own a gradient-boosted binary classifier that predicts whether a support ticket should be escalated to a human specialist in a high-volume customer operations workflow. The model outputs a probability, and tickets above a fixed threshold are routed to the specialist queue while the rest stay in the standard queue. Leadership is asking whether the current 0.50 threshold is appropriate because specialist capacity is limited, but missed escalations create costly SLA breaches and customer churn. You have recent holdout results from Azure Machine Learning and estimated business costs for false positives and false negatives.
| Threshold | Precision | Recall | F1 | Escalation Rate | Daily Tickets Sent to Specialists | Estimated Daily FP Cost | Estimated Daily FN Cost |
|---|---|---|---|---|---|---|---|
| 0.30 | 0.42 | 0.91 | 0.57 | 18.0% | 3,600 | $4,176 | $2,160 |
| 0.50 | 0.61 | 0.74 | 0.67 | 10.0% | 2,000 | $1,872 | $6,240 |
| 0.70 | 0.79 | 0.48 | 0.60 | 5.0% | 1,000 | $672 | $12,480 |
| 0.85 | 0.90 | 0.25 | 0.39 | 2.0% | 400 | $144 | $18,000 |
How would you choose the operating threshold for this classifier, and what additional validation or analysis would you want before recommending a final threshold to ship?