Was comparing SHAP importances across different model runs and noticed wildly different rankings. Turns out: SHAP only makes sense _per model_. If your models are trained on different feature subsets or data windows, you’re not comparing apples to apples. Now I snapshot SHAP per model version, per regime. That gives me clarity over time ... not misleading cross-model noise. [[ML]] [[Serendipity]]