Conformal Prediction Offers Valid Uncertainty Estimates Without Distributional Assumptions

Standard machine learning models typically output a single point prediction, which can look precise without conveying how reliable it is. Conformal prediction wraps almost any predictor in a prediction set or interval that carries a finite-sample coverage guarantee. Unlike many alternatives, it does not require assumptions such as Gaussian residuals, a correctly specified model, or knowledge of the true data-generating process. It needs only a held-out calibration set and the assumption of exchangeability between calibration and future data. The guarantee is marginal, meaning it holds on average across inputs rather than for each individual input or subgroup, and it does not protect against distribution shift.