Introducing Conformal Prediction

This book is about conformal prediction, a modern framework for uncertainty quantification that is becoming increasingly popular in industry and academia.

Machine learning and AI applications are everywhere. In the realm of machine learning, prediction is a fundamental task. Given a training dataset, we train a machine learning model to make predictions on new data.

Figure 1.1 – Machine learning prediction model

However, in many real-world applications, the predictions made by statistical, machine learning, and deep learning models are often incorrect or unreliable because of various factors, such as insufficient or incomplete data, issues arising during the modeling process, or simply because of the randomness and complexities of the underlying problem.

Predictions made by machine learning models often come without the uncertainty quantification required for confident and reliable decision-making. This is...