Final models get trained twice, once with standard params, once with jittered ones. I compare validation curves, SHAP outputs, and final decisions. If they diverge significantly, it’s a sign of instability or overfitting. It’s slow, but it’s the best way I’ve found to confirm that a model isn’t just lucky on a particular split. [[ML]] [[Serendipity]]