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

Open source tools for conformal prediction in classification problems

While deep-diving into the intricacies of conformal prediction for classification, it has become evident that the right tools can significantly enhance our implementation efficiency. Recognizing this, the open source community has made remarkable contributions by providing various tools tailored for this purpose. In this section, we will explore some of the prominent open source tools for conformal prediction that can seamlessly integrate into your projects and elevate your predictive capabilities.

Nonconformist

nonconformist (https://github.com/donlnz/nonconformist) is a classical conformal prediction package that can be used for conformal prediction in classification problems.

Let’s illustrate how to create an ICP using nonconformist. You can find the Jupyter notebook containing the relevant code at https://github.com/PacktPublishing/Practical-Guide-to-Applied-Conformal-Prediction/blob/main/Chapter_06...