Conformal prediction: turn any model into an interval with a coverage guarantee using one calibration quantile

A model that only ever answers "the value is 3.2" is hiding how sure it is. Conformal prediction wraps any trained model in an interval — "the value is in [2.4, 4.0]" — that comes with a genuine mathematical promise: the true value lands inside at least 90% of the time (or 95%, or whatever 1−α you ask for), and the promise holds for any data distribution, with finite data, no Gaussian assumption, no faith in the model being right. The recipe is almost embarrassingly simple, and I built a demo…