Context

At NovaDesk, a customer support routing model predicts whether an incoming ticket should be marked urgent and escalated to the priority queue. The team recently noticed more customer complaints about delayed handling, even though the model’s overall accuracy still looks acceptable.

You are given the latest evaluation results for the production logistic regression classifier on a 20,000-ticket labeled validation set.

Current Performance

The Problem

The model is now much more conservative: precision improved, but recall dropped sharply. As a result, many truly urgent tickets are not being escalated, increasing SLA breaches and customer dissatisfaction.

Requirements

1. Explain what the metric pattern suggests about how the model is behaving.
2. Walk through a structured troubleshooting process to diagnose the underperformance.
3. Identify likely root causes across data quality, thresholding, class imbalance, and feature issues.
4. Recommend specific fixes and how you would validate them before deployment.
5. Discuss which metric(s) should matter most for this use case and why.

Constraints

• Missing an urgent ticket is estimated to cost $45 in churn risk and SLA penalties.
• Escalating a non-urgent ticket costs about $4 in agent time.
• Priority queue capacity can increase by at most 15% without hiring.