Understanding classical predictors

Before we deep dive into the intricacies of conformal predictors, let’s briefly recap the key concepts from the previous chapters. Conformal prediction is a framework that enables creating confidence regions for our predictions while controlling the error rate.

This approach is especially beneficial in situations where a measure of uncertainty is essential, such as in medical diagnosis, self-driving cars, or financial risk management. The framework encompasses two main types of conformal predictors: classical and inductive.

Classical transductive conformal prediction (TCP) is the original form of conformal prediction developed by the inventors of Conformal prediction. It forms the basis for understanding the general principles of conformal predictors. Classical Conformal prediction was developed to construct prediction regions that conform to a specified confidence level. The critical aspect of classical Conformal prediction is its distribution...