How conformal prediction differs from traditional machine learning

Conformal prediction allows the production of well-calibrated probabilistic predictions for any statistical, machine learning, or deep learning model. This is achieved without relying on restrictive assumptions required by other methods such as Bayesian techniques, Monte Carlo simulation, and bootstrapping. Importantly, conformal prediction does not require subjective priors. It provides mathematically guaranteed, well-calibrated predictions every time – regardless of the underlying prediction model, data distribution, or dataset size.

A key limitation of traditional machine learning is the need for more reasonable confidence measures for individual predictions. Models may have excellent overall performance but not be able to quantify uncertainty for a given input reliably.

Conformal prediction solves this by outputting prediction regions and confidence measures with statistical validity guarantees. It...