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


This chapter explored the fascinating world of conformal predictors, their types, and their distinctive features. The key concepts and skills we touched upon include covering the foundational principles of conformal prediction and its application in machine learning. It also highlighted the differences between classical transductive and inductive conformal predictors. We also covered how to effectively choose the appropriate type of conformal predictor based on the specific requirements of the problem. Finally, the practical applications of conformal predictors in binary classification, multiclass classification, and regression were also included.

The chapter also provided a detailed algorithmic description and mathematical formulation of classical and inductive conformal predictors, adding to our theoretical understanding. To deepen our learning, we also took a hands-on approach, looking at practical examples in Python.

For those interested in further exploring conformal...