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
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
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Very informative book.
Further steps could be taken to elaborate on a number of concepts. The CV and NLP sections have little material and could use a lot of work in later editions with later research in the application of CP in these fields.
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.
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