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

Conformal Prediction for Computer Vision

In today’s fast-paced world, computer vision has grown beyond mere image recognition to be a fundamental cornerstone in numerous real-world applications. From self-driving cars navigating bustling streets to medical imaging systems that detect early signs of diseases, the demand for reliable and accurate computer vision models has never been higher. However, with the increasing complexity of these systems and their applications, a critical need arises for the ability to quantify the uncertainty associated with their predictions.

Enter conformal prediction, a ground-breaking framework that offers a robust means to encapsulate the uncertainty inherent in machine learning models. While traditional computer vision models often produce a singular prediction, the true power of conformal prediction lies in its ability to provide a set of possible outcomes, each backed by a confidence level. This offers practitioners a more informed, nuanced...