Practical Guide to Applied Conformal Prediction in Python
Packt Publishing Limited (Verlag)
978-1-80512-276-0 (ISBN)
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 DescriptionIn 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 forIdeal 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.
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.
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
| Erscheinungsdatum | 14.10.2023 |
|---|---|
| Vorwort | Agus Sudjianto |
| Verlagsort | Birmingham |
| Sprache | englisch |
| Maße | 191 x 235 mm |
| Themenwelt | Informatik ► Software Entwicklung ► User Interfaces (HCI) |
| Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
| Mathematik / Informatik ► Mathematik | |
| Technik ► Maschinenbau | |
| ISBN-10 | 1-80512-276-2 / 1805122762 |
| ISBN-13 | 978-1-80512-276-0 / 9781805122760 |
| Zustand | Neuware |
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