Conformal Prediction

A rigorous framework for quantifying uncertainty in machine learning by producing prediction sets with guaranteed coverage.

Conformal Prediction (CP) transforms point estimates into statistically valid intervals. Unlike standard heuristics, CP provides a distribution-free guarantee: the true label will fall within the predicted set at a user-defined confidence level (e.g., 95%). It works on top of any model (from XGBoost to GPT-4) without requiring retraining. By using a small calibration dataset of 500 to 1,000 samples, CP calculates a non-conformity score to bound error rates precisely. It is currently the industry standard for high-stakes deployments in medical diagnostics and autonomous systems where knowing when a model is uncertain is as critical as the prediction itself.

https://cparchive.org