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

Various approaches to classifier calibration

Before exploring how conformal prediction can provide calibrated probabilities, we will first discuss some common non-conformal calibration techniques and their strengths and weaknesses. These include histogram binning, Platt scaling, and isotonic regression.

It is important to note that the following methods are not part of the conformal prediction framework. We are covering them to build intuition about calibration and highlight some of the challenges with conventional calibration approaches. This background will motivate the need for and benefits of the conformal prediction perspective so that we can obtain reliable probability estimates.

The calibration techniques we will explore, including histogram binning, Platt scaling, and isotonic regression, represent widely used approaches for adjusting classifier confidence values. However, as we will discuss, they have certain limitations regarding model flexibility, computational expense...