Book Image

Practical Guide to Applied Conformal Prediction in Python

By : Valery Manokhin
4 (1)
Book Image

Practical Guide to Applied Conformal Prediction in Python

4 (1)
By: Valery Manokhin

Overview of this book

In the rapidly evolving landscape of machine learning, the ability to accurately quantify uncertainty is pivotal. The book addresses this need by offering an in-depth exploration of Conformal Prediction, a cutting-edge framework to manage uncertainty in various ML applications. Learn how Conformal Prediction excels in calibrating classification models, produces well-calibrated prediction intervals for regression, and resolves challenges in time series forecasting and imbalanced data. Discover specialised applications of conformal prediction in cutting-edge domains like computer vision and NLP. Each chapter delves into specific aspects, offering hands-on insights and best practices for enhancing prediction reliability. The book concludes with a focus on multi-class classification nuances, providing expert-level proficiency to seamlessly integrate Conformal Prediction into diverse industries. With practical examples in Python using real-world datasets, expert insights, and open-source library applications, you will gain a solid understanding of this modern framework for uncertainty quantification. By the end of this book, you will be able to master Conformal Prediction in Python with a blend of theory and practical application, enabling you to confidently apply this powerful framework to quantify uncertainty in diverse fields.
Table of Contents (19 chapters)
Free Chapter
1
Part 1: Introduction
4
Part 2: Conformal Prediction Framework
8
Part 3: Applications of Conformal Prediction
14
Part 4: Advanced Topics

The origins of conformal prediction

The origins of conformal prediction are documented in Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification by Anastasios N. Angelopoulos and Stephen Bates (https://arxiv.org/abs/2107.07511).

Note

Conformal prediction was invented by my PhD supervisor Prof. Vladimir Vovk, a professor at Royal Holloway University of London. Vladimir Vovk graduated from Moscow State University, where he studied mathematics and became a student of one of the most notable mathematicians of the 20th century, Andrey Kolmogorov. During this time, initial ideas that later gave rise to the invention of conformal prediction appeared.

The first edition of Algorithmic Learning in a Random World (https://link.springer.com/book/10.1007/b106715) by Vladimir Vovk, Alexander Gammerman, and Glenn Shafer was published in 2005. The second edition of the book was published in 2022 (https://link.springer.com/book/10.1007/978-3-031-06649...