Practical guide to applied conformal prediction in Python

• Verfasst von: Manokhin, Valery [VerfasserIn]
• Titel: Practical guide to applied conformal prediction in Python
• Titelzusatz: learn and apply the best uncertainty frameworks to your industry applications
• Mitwirkende: Sudjianto, Agus [MitwirkendeR]
• Verf.angabe: Valery Manokhin ; foreword by Agus Sudjianto
• Ausgabe: 1st edition.
• Verlagsort: Birmingham, UK
• Verlag: Packt Publishing Ltd.
• Jahr: 2023
• Umfang: 1 online resource
• Fussnoten: Includes index. - Modern machine learning approaches. - Description based on online resource; title from digital title page (viewed on January 18, 2024)
• ISBN: 978-1-80512-091-9
• ISBN: 1-80512-091-3
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: Python (Langage de programmation)
• Sach-SW: Apprentissage automatique
• K10plus-PPN: 1879407167
• K10plus-PPN:
  Universitätsbibliothek
• Lokale URL UB: Zum Volltext
• Lokale URL UB: Bibliothek der Medizinischen Fakultät Mannheim der Universität Heidelberg
• Bibliothek/Idn: UW / m4471895451
• Lokale URL Inst.: Zum Volltext

Abstract

"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.".