Conformal prediction is a method for uncertainty quantification that is based on
the idea of **conformal sets** (CS). A CS is a set of predictions that is
guaranteed to contain the true value with a certain probability. This
probability is called the *confidence level* of the CS. The CSs are constructed
such that the confidence level is independent of the data distribution and the
model’s structure or uncertainty. This means that the CPs are *distribution-free*
and *model-agnostic*. They are very simple to compute and involve only a
calibration step involving quantiles of a so-called **conformal score** over
unseen data.

In this talk we introduce conformal prediction and its applications to classification and regression. We highlight the key assumptions of the method and discuss its main challenges. We also discuss the main differences between conformal prediction and other uncertainty quantification methods.