Fundamentals of conformal prediction
In this section, we will cover the fundamentals of conformal prediction. There are two variants of conformal prediction – inductive conformal prediction (ICP) and transductive conformal prediction (TCP). We will discuss the benefits of the conformal prediction framework and learn about the basic components of conformal predictors and the different types of nonconformity measures. We will also learn how to use nonconformity measures to create probabilistic prediction sets in classification tasks.
Definition and principles
Conformal prediction is a machine learning framework that quantifies uncertainty to produce probabilistic predictions. These predictions can be prediction sets for classification tasks or prediction intervals for regression tasks. Conformal prediction has significant advantages in equipping statistical, machine learning, and deep learning models with valuable additional features that instill confidence in their predictions...
