The Baseline: Why We Started Simple with Logistic Regression

When building a wildfire risk classifier, complexity is tempting. Gradient boosting, deep learning, and ensemble models promise high accuracy. But we chose to begin with something deceptively simple: logistic regression. This article explains why interpretability and transparency were more valuable than complexity at the start, how we implemented a baseline model using a clean pipeline, and what we learned from its performance. The story demonstrates how a simple model can set the stage for operational trust and informed iteration in geospatial intelligence.

Introduction: Why Start Simple?

Every wildfire starts small, and so should every model. We wanted predictions that could make a difference on the ground — but more importantly, predictions that field operators could trust.

In the world of geospatial intelligence, trust is not earned by accuracy alone. It is earned by clarity, transparency, and the ability to explain why the model flagged a risk. For this reason, we deliberately avoided jumping straight into black-box approaches. Instead, we asked:

What if the first step isn’t about maximizing performance, but about creating a reliable foundation that people can understand and improve upon?

Building the First Pipeline

We began by preparing a structured dataset of sensor readings. The features included temperature, humidity, wind speed…