There is a quiet kind of courage in admitting that a model's improved score might be a trap. The engineer who asked about range as a feature in automotive radar classification isn't just troubleshooting a pipeline; they are questioning whether their model learned to see objects or simply learned to see distance. That distinction matters far more than a few extra F1 points. When radar returns fewer points for farther objects, and the model leans on range to make predictions, the model is not reasoning about classes. It is reasoning about the environment. The fact that validation and test sets both improved only proves the model is consistent, not that it is generalizing. Consistency and correctness are not the same thing. This is the kind of tension that separates a model that works in a demo from one that works in the world. We have covered similar ground in Exploring Real-World Computer Vision: Deployments, Edge Models, and Current Challenges, where the gap between controlled performance and field behavior becomes the real story.
The instinct to stress test the claim is exactly right, but the method needs to be sharper than just splitting data by range. A random split that happens to balance range distributions will not expose the confound. The model could still be latching onto range within each fold. What the engineer needs is a deliberate intervention: train on data where the range distribution is intentionally shifted, then test on the original distribution. If performance collapses, the model was leaning on range as a crutch. If it holds, the feature may be genuinely useful. This is not a trivial exercise. It requires collecting or annotating data with range diversity in mind, which is often expensive and tedious. But the alternative, accepting a higher score that encodes a bias toward larger objects, is worse. We have talked about tools that go beyond mathematics, like the Explore the Forrester Function: Beyond Mathematics, a Tool for Machine Learning, and this situation is a perfect example: the math of the loss function will not save you from a poorly framed feature.
Here is the honest take: sometimes the lower performance is the correct result. The engineer should not be afraid to drop range entirely and accept the hit, because that hit is the true measure of what the model can learn from object shape, reflectivity, and point cloud structure alone. A model that scores 0.75 without range but generalizes across distances is more valuable than a model that scores 0.85 with range and fails at 60 meters. The fear that the model is learning "big range means big object" is not paranoia; it is the most likely outcome given the artifact described. Radar point clouds are sparse at distance, and the model will happily exploit that statistical regularity. The fact that there is no classical overfitting is irrelevant. Overfitting to the training distribution is only one failure mode. Learning the environment's priors instead of the object's identity is another, and it is sneakier because it survives cross-validation. We would tell this engineer to run the range-shift experiment, but also to look at the model's failure cases at close range where the point density is high. If the model still confuses classes there, range was never the real signal. If it does well there, range is masking a weakness. Either way, the answer is in the data, not in the score. The specific takeaway to quote: "A model that generalizes across distance is more valuable than one that only memorizes the environment." That is the principle worth building around.