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 taught you how to apply conformal prediction to time series forecasting. Conformal prediction is a powerful technique for crafting PIs for point forecasting models.

This chapter also offered insights into how to harness this method using open source platforms.

We began by exploring UQ in a time series, delving into the significance of PIs, and showcasing various strategies to generate them.

The concept of conformal prediction and its application in forecasting scenarios was central to this chapter. At this point, you are equipped with the knowledge to apply these methodologies in real-world settings, empowering your forecasting models with precise uncertainty bounds. Adding confidence measures to predictions ensures that the forecasts are accurate and reliable.

With a solid understanding of conformal prediction for time series, we will now focus on another critical application area – computer vision.