Context

HelpHive uses a text classification model to label incoming customer support tickets as Urgent or Non-Urgent so urgent cases can be prioritized by agents. The model was recently deployed, but operations leaders report that too many urgent tickets are still reaching the standard queue.

Current Performance

The model was evaluated on a held-out test set of 10,000 labeled tickets.

The Problem

Although overall accuracy looks strong, the support team is concerned because many truly urgent tickets are not being flagged. Missing an urgent ticket can delay resolution for outages, billing failures, or security issues, while false positives mainly create extra review work.

Your Task

1. Explain whether this model is performing well enough for the business use case.
2. Interpret what the metrics say about class imbalance and the tradeoff between precision and recall.
3. Use the confusion matrix implications to identify the most important failure mode.
4. Recommend how you would validate the model beyond a single test-set accuracy number.
5. Propose specific changes to improve performance, including any threshold or data strategy changes.

Constraints

• The support team can handle at most 300 escalated tickets per day.
• Missing an urgent ticket is estimated to be 8x more costly than reviewing a false positive.
• Labels come from agent triage outcomes and may contain some inconsistency across teams.