  Inductive Conformal Prediction (ICP) is a set of distribution-free and model
agnostic algorithms devised to predict with a user-defined confidence with
coverage guarantee. Instead of having point predictions, i.e., a real number in
the case of regression or a single class in multi class classification, models
calibrated using ICP output an interval or a set of classes, respectively. ICP
takes special importance in high-risk settings where we want the true output to
belong to the prediction set with high probability. As an example, a
classification model might output that given a magnetic resonance image a
patient has no latent diseases to report. However, this model output was based
on the most likely class, the second most likely class might tell that the
patient has a 15% chance of brain tumor or other severe disease and therefore
further exams should be conducted. Using ICP is therefore way more informative
and we believe that should be the standard way of producing forecasts. This
paper is a hands-on introduction, this means that we will provide examples as
we introduce the theory.
