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

Types of Conformal Predictors

This chapter describes different families of conformal predictors, exploring various approaches to quantifying uncertainty. Through practical examples, we provide an intermediate-level understanding of these techniques and how they can be applied to real-world situations.

Here are examples of how companies are using conformal prediction.

At a high-profile AI developer conference called GTC 2023 (https://www.nvidia.com/gtc/), Bill Dally, NVIDIA’s chief scientist and SVP of research, offered insights into one of NVIDIA’s R&D primary focuses, which is in conformal prediction (https://www.hpcwire.com/2023/03/28/whats-stirring-in-nvidias-rd-lab-chief-scientist-bill-dally-provides-a-peek/).

Traditional machine learning models for autonomous vehicles output a single classification (e.g., pedestrian or no pedestrian on the road) and position estimate for detected objects. However, NVIDIA wants to produce a set of potential outputs with...