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One thing I encountered today was a trick using bagging as a way to go beyond a point estimate and get an approximation for the full distribution. This can then be used to penalize predictions with larger uncertainty, which helps reducing false positives. To me it sounds like a useful trick that I found roughly 0 hits on Google for, so I thought I’d share it. Of course, it might be that I’ve completely gotten something backwards (my ML skills have some notable gaps), so let me know if that’s the...