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
Part 1: Introduction
Part 2: Conformal Prediction Framework
Part 3: Applications of Conformal Prediction
Part 4: Advanced Topics


In this chapter, we have introduced conformal prediction and explained the multiple benefits of this powerful framework for reliably quantifying the uncertainty of predictions to improve trust in machine learning models.

We explained that the key benefit of conformal prediction is that, unlike any other probabilistic prediction framework, it provides valid probabilistic predictions accompanied by confidence measures, regardless of the underlying model, the dataset size, and the data distribution.

We then explored the origins of conformal prediction and saw how it has recently become a very popular framework adopted by leading universities and companies.

Finally, we looked at how conformal prediction differs from traditional machine learning and learned about the role of p-values in conformal prediction.

In Chapter 2, we will explain why conformal prediction is a valuable tool for quantifying the uncertainty of predictions, especially in critical settings such as...