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
1
Part 1: Introduction
4
Part 2: Conformal Prediction Framework
8
Part 3: Applications of Conformal Prediction
14
Part 4: Advanced Topics

Multi-class classification problems

In ML, classification problems are ubiquitous. They involve predicting a discrete class label output for an instance. While binary classification – predicting one of two possible outcomes – is a common scenario, many real-world problems require predicting more than two classes. This is where multi-class classification comes into play.

Multi-class classification is a problem where an instance can belong to one of many classes. For example, consider an ML model designed to categorize news articles into topics. The articles could be classified into categories such as Sports, Politics, Technology, Health, and so on. Each of these categories represents a class, and since there are more than two classes, this is a multi-class classification problem.

It’s important to note that each instance belongs to exactly one class in multi-class classification. If each instance could belong to multiple classes, it would be a multi-label...