Book Image

Practical Guide to Applied Conformal Prediction in Python

By : Valery Manokhin
4 (1)
Book Image

Practical Guide to Applied Conformal Prediction in Python

4 (1)
By: Valery Manokhin

Overview of this book

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.
Table of Contents (19 chapters)
Free Chapter
Part 1: Introduction
Part 2: Conformal Prediction Framework
Part 3: Applications of Conformal Prediction
Part 4: Advanced Topics

Multi-Class Conformal Prediction

Welcome to the last chapter of this book, where we delve into the fascinating world of multi-class Conformal Prediction. This chapter introduces you to various conformal prediction methods that can be effectively applied to multi-class classification problems.

We will explore the concept of multi-class classification, a common scenario in machine learning (ML), where an instance can belong to one of many classes. Understanding this problem is the first step toward applying conformal prediction techniques effectively.

Next, we will investigate the metrics used to evaluate multi-class classification problems. These metrics provide a quantitative measure of the performance of our models, and understanding them is crucial for effective model evaluation and selection.

Finally, we will learn how to apply conformal prediction to multi-class classification problems. This section will provide practical insights and techniques to apply directly to your...