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

Overview of Conformal Prediction

In today’s world, where data plays a crucial role in decision making, it has become increasingly important to measure uncertainty in predictions. To achieve this, a relatively new framework called conformal prediction is gaining popularity. This framework provides probabilistic predictions that are not only robust and reliable but also trustworthy. It is a powerful tool that offers measures of confidence, accuracy, and reliability for a given prediction, allowing users to make informed choices with more certainty.

This chapter will provide an overview of conformal prediction.

It will explain why conformal prediction is a valuable tool for quantifying the uncertainty of predictions, especially in critical settings such as healthcare, self-driving cars, and finance. We will also discuss the concept of uncertainty quantification (UQ) and how the conformal prediction framework has successfully addressed the challenge of quantifying uncertainty...