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

Summary

In the rapidly evolving realm of technology, computer vision has transformed from mere image recognition into an integral component of countless real-world applications. As these applications span diverse fields such as autonomous vehicles and medical diagnostics, the pressure on computer vision models to deliver accurate and reliable predictions intensifies. With the growing sophistication of these models comes a dire need: quantifying prediction uncertainties.

This is where conformal prediction shines. Unlike traditional models that typically output a singular prediction, conformal prediction offers a range of potential outcomes, each coupled with a confidence measure. This novel approach grants users a detailed perspective on model predictions, which is invaluable for applications where precision is paramount.

This chapter delved into the symbiotic relationship between conformal prediction and computer vision. We started by emphasizing the importance of uncertainty...