The Reality Check: Handling Imbalance, Drift, and Operational Constraints

Photo by Gabriel Dalton on Unsplash

Accuracy alone is not enough to make a wildfire risk classifier operational. Once a model is producing probabilities, new challenges emerge: imbalanced datasets, changing environmental conditions, retraining schedules, and the high cost of false positives versus missed detections. In this article, we reflect on these issues as we move from prototyping to field-ready intelligence.

We discuss how to frame evaluation in cost-sensitive terms, how to monitor for concept drift, and why observability is as important as prediction. The goal is not to present solutions but to outline the path we are beginning to prototype.

Introduction: Beyond Accuracy

In machine learning research, success is often framed as achieving higher accuracy or AUC scores. In wildfire intelligence, that framing is incomplete. An alerting system is not judged only by statistical performance — it is judged by how it performs in operational context.

A false positive may waste resources and erode trust. A missed detection may mean lives and assets lost. The balance between the two is not academic; it is the core of operational decision-making.

As we advance our wildfire risk classifier, we are beginning to explore this next layer of…