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Notes on Calibration for Trustworthy Models
[Placeholder] Why a model's confidence should mean something — and a few practical recipes for getting there.
Placeholder content. Replace with a real exploration post.
A trustworthy model knows what it doesn’t know. In practice that means its predicted probabilities should match observed frequencies — a property we call calibration.
A quick intuition
If a model says “80% confident” across many predictions, it should be right about 80% of the time. When it isn’t, downstream decisions built on those numbers break.
Recipes we reach for
- Temperature scaling as a cheap first pass.
- Selective prediction with a learned abstention threshold.
- Evaluation on shifted distributions, not just the validation set.
More to come as we write this up properly.