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

UQ for time series and forecasting problems

UQ is not just a sophisticated addition to time series forecasting; it is a fundamental aspect that provides invaluable insights into the nature of the predictions. Let’s look at why it’s important and a brief history of its development.

The importance of UQ

UQ is a critical component of time series forecasting. While a forecast model may provide accurate predictions on average, understanding the uncertainty around those predictions is equally essential. There are several key reasons why properly quantifying uncertainty is vital for practical time series forecasting:

  • Risk assessment: In many domains, such as finance, healthcare, and environmental science, forecasting is closely linked with decision-making. Understanding the uncertainty in predictions aids in assessing potential risks, thus enabling informed decisions.
  • Model confidence: UQ provides an understanding of the confidence in each model’s predictions...