This repository is a playground for conformal prediction (CP), a statistical framework for uncertainty quantification in machine learning. CP relies on prediction sets or intervals of possible outcomes so as to represent the uncertainty of model predictions. These sets are constructed such that they are guaranteed to contain the true outcome with a pre-specified probability.
What makes CP attractive and broadly applicable is that it comes with strong guarantees under widely met conditions. The theoretical properties do not hinge on the amount of data (finite-sample) and virtually require no distributional assumptions (distribution-free). Moreover, any model can be used (model-agnostic).
