Practical Guide to Applied Conformal Prediction in Python

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:
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

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.