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 book’s final chapter, we explored the intriguing domain of multi-class conformal prediction. We began by understanding the concept of multi-class classification, a prevalent scenario in ML where an instance can belong to one of many classes. This understanding is crucial for effectively applying conformal prediction techniques.

We then delved into the metrics used for evaluating multi-class classification problems. These metrics quantitatively measure our model’s performance and are vital for effective model evaluation and selection.

Finally, we learned how to apply conformal prediction to multi-class classification problems. This section provided practical insights and techniques to apply to your industrial applications directly.

By the end of this chapter, you should have gained valuable skills and knowledge in multi-class classification and how conformal prediction can be effectively applied to these problems. This knowledge will prove invaluable...