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Practical Guide to Applied Conformal Prediction in Python: Learn and apply the best uncertainty frameworks to your industry applications
by 作者: Valery Manokhin (Author), Agus Sudjianto (Foreword)
Publisher Finelybook 出版社: Packt Publishing
Publication Date 出版日期: 2023-12-20
Language 语言: English
pages 页数: : 240 pages
ISBN-10 书号: 1805122762
ISBN-13 书号: 9781805122760
Book Description
Take your machine learning skills to the next level by mastering the best framework for uncertainty quantification - Conformal Prediction
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.
- The book will explain 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. "Practical Guide to Applied Conformal Prediction in Python" addresses this need by offering an in-depth exploration of Conformal Prediction, a cutting-edge framework set to revolutionize uncertainty management in various ML applications.
Embark on a comprehensive journey through Conformal Prediction, exploring its fundamentals and practical applications in binary classification, regression, time series forecasting, imbalanced data, 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. Practical examples in Python using real-world datasets reinforce intuitive explanations, ensuring you acquire a robust understanding of this modern framework for uncertainty quantification.
This guide is a beacon for mastering Conformal Prediction in Python, providing a blend of theory and practical application. It serves as a comprehensive toolkit to enhance machine learning skills, catering to professionals from data scientists to ML engineers.
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.
Table of contents
- Introducing Conformal Prediction
- Overview of Conformal Prediction
- Fundamentals of Conformal Prediction
- Validity and Efficiency of Conformal Prediction
- Types of Conformal Predictors
- Conformal Prediction for Classification
- Conformal Prediction for Regression
- Conformal Prediction for Time Series and Forecasting
- Conformal Prediction for Computer Vision
- Conformal Prediction for Natural Language Processing
- Handling Imbalanced Data
- Multi-Class Conformal Prediction
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
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.
