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

Summary

In this chapter, we embarked on an enlightening exploration of conformal prediction specifically tailored to classification tasks. We began by underscoring the significance of calibration in the realm of classification, emphasizing its role in ensuring the reliability and trustworthiness of model predictions. Through our journey, we were introduced to various calibration methods, including the various approaches to conformal prediction. We observed how conformal prediction uniquely addresses the challenges of calibration, providing both a theoretical and practical edge over traditional methods.

We also delved into the nuanced realms of Venn-ABERS predictors, shedding light on their roles and implications in the calibration process.

Lastly, we underscored the invaluable contribution of the open source community in this domain. We highlighted tools such as the nonconformist library, which serve as essential resources for practitioners who are keen on implementing conformal...