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

The superiority of conformal prediction in uncertainty quantification

Quantifying uncertainty is fundamental to building robust and reliable machine learning models. Several methodologies have emerged over the years, each with its own merits. However, conformal prediction stands out as a particularly compelling framework. Let’s explain why:

  • Distribution-free framework: One of the most notable features of conformal prediction is that it doesn’t make any assumptions about the distribution of the data. Many uncertainty quantification methods are based on certain probabilistic assumptions or rely on specific data distributions to function effectively. In contrast, conformal prediction remains agnostic to these considerations, making it versatile and widely applicable across diverse datasets.
  • Theoretical guarantees: conformal prediction offers robust theoretical guarantees for its predictions. Specifically, it provides a set of potential outcomes for a prediction...