Practical Guide to Applied Conformal Prediction in Python: Learn and apply the best uncertainty frameworks to your industry applications

Manokhin, Valery

ISBN 10: 1805122762 / ISBN 13: 9781805122760

Editore: Packt Publishing, 2023

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Dati bibliografici

• Titolo: Practical Guide to Applied Conformal Prediction in Python: Learn and apply the best uncertainty frameworks to your industry applications
• Editore: Packt Publishing
• Data di pubblicazione: 2023
• Lingua: Inglese
• ISBN 10: 1805122762
• ISBN 13: 9781805122760
• Rilegatura: Brossura
• Condizione: New

Descrizione articolo

Riassunto

<strong style="color: rgba(0, 0, 0, 1)">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:

<span style="color: rgba(0, 0, 0, 1)">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.</span>

<span style="color: rgba(0, 0, 0, 1)">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.</span>

<span style="color: rgba(0, 0, 0, 1)">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.</span>

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.

Informazioni sull?autore

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.