Wow! eBook
Practical Guide to Applied Conformal Prediction in Python: Learn and apply the best uncertainty frameworks to your industry applications
Author(s): Valery Manokhin (Author)
- Publisher: Packt Publishing
- Publication Date: 20 Dec. 2023
- Language: English
- Print length: 240 pages
- ISBN-10: 1805122762
- ISBN-13: 9781805122760
Book Description
Elevate your machine learning skills using the Conformal Prediction framework for uncertainty quantification. Dive into unique strategies, overcome real-world challenges, and become confident and precise with forecasting.
Key Features:
- Master Conformal Prediction, a fast-growing ML framework, with Python applications
- Explore cutting-edge methods to measure and manage uncertainty in industry applications
- Understand how Conformal Prediction differs from traditional machine learning
Book Description:
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.
What You Will Learn:
- The fundamental concepts and principles of conformal prediction
- Learn how conformal prediction differs from traditional ML methods
- Apply real-world examples to your own industry applications
- Explore advanced topics – imbalanced data and multi-class CP
- Dive into the details of the conformal prediction framework
- Boost your career as a data scientist, ML engineer, or researcher
- Learn to apply conformal prediction to forecasting and NLP
Who this book is for:
Ideal for readers with a basic understanding of machine learning concepts and Python programming, this book caters to data scientists, ML engineers, academics, and anyone keen on advancing their skills in uncertainty quantification in ML.
Editorial Reviews
Review
“In statistical and machine learning, it is rare to encounter a technique that blends deep mathematical rigor with practical simplicity. Conformal Prediction is one such gem. Rooted in solid probability theory, it transcends academic theory to find wide-ranging applications in the real world. Valery, studied under the inventor of Conformal Prediction, compiles in this book a treasure trove of practical knowledge, tailored for practicing data scientists. His work makes Conformal Prediction not only accessible but intuitively understandable, bridging the gap between complex theory and practical application.
This book stands out for its unique approach to demystifying Conformal Prediction. It eschews the often esoteric and dense theoretical exposition common in statistical texts, opting instead for clarity and comprehensibility. This approach makes the powerful techniques of Conformal Prediction accessible to a broader range of machine learning practitioners.
The applications of Conformal Prediction are vast and varied, and this book delves into them with meticulous detail. From classification and regression to time series analysis to computer vision, and language models. Each application is explored thoroughly with examples to provide practitioners with practical guidance on applying these methods in their work.
This book will be an essential reference for machine learning engineers and data scientists who seek to incorporate uncertainty quantification (UQ) to models that they develop and deploy, a critical element that has been missing in machine learning. UQ is critical to understand prediction reliability, providing safety during model deployment and potential model weakness identification during model development and testing.”
—
Agus Sudjianto, PhD, Executive Vice President, Head of Corporate Model Risk Wells Fargo
“Practical Guide to Applied Conformal Prediction in Python” is an invaluable asset for data scientists, ML engineers, academics, and anyone interested in advancing their understanding of uncertainty quantification in machine learning. Whether you’re a novice seeking to learn about CP or a seasoned practitioner aiming to refine your skills, this book is a must-have. It’s not just a guide; it’s a comprehensive toolkit that will undoubtedly enhance your machine learning endeavors.”
—
Serg Masís, Lead Data Scientist, Computational Agronomy, North America at Syngenta. Author of Interpretable Machine Learning
About the Author
Valeriy Manokhin is the leading expert in the field of machine learning and Conformal Prediction. He holds a Ph.D.in Machine Learning from Royal Holloway, University of London. His doctoral work was supervised by the creator of Conformal Prediction, Vladimir Vovk, and focused on developing new methods for quantifying uncertainty in machine learning models. Valeriy has published extensively in leading machine learning journals, and his Ph.D. dissertation ‘Machine Learning for Probabilistic Prediction’ is read by thousands of people across the world. He is also the creator of “Awesome Conformal Prediction,” the most popular resource and GitHub repository for all things Conformal Prediction.
