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


Embark on an insightful journey with “Practical Guide to Applied Conformal Prediction in Python,” your comprehensive guide to mastering uncertainty quantification in machine learning. This book unfolds the complexities of Conformal Prediction, focusing on practical applications that span classification, regression, forecasting, computer vision, and natural language processing. It also delves into sophisticated techniques for addressing imbalanced datasets and multi-class classification challenges, presenting case studies that bridge theory with real-world practice.

This resource is meticulously crafted for a diverse readership, including data scientists, machine learning engineers, industry professionals, researchers, academics, and students interested in mastering uncertainty quantification and conformal prediction within their respective fields.

Whether you’re starting your journey in data science or looking to deepen your existing expertise, this book provides the foundational knowledge and advanced strategies necessary to navigate uncertainty quantification in machine learning confidently.

With “Practical Guide to Applied Conformal Prediction in Python,” you gain more than knowledge; you gain the power to apply cutting-edge techniques to industry applications, enhancing the precision and reliability of your predictive models. Embrace this opportunity to elevate your career in machine learning by harnessing the potential of Conformal Prediction.