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 Time Series and Forecasting

In this chapter, we will explore the exciting field of conformal prediction for time series and forecasting. Conformal prediction is a powerful tool for producing prediction intervals (PIs) for point forecasting models, and we will show you how to apply this technique to your data using open source libraries. This chapter will take you on a journey from understanding the fundamentals of uncertainty quantification (UQ) in time series to the intricate mechanisms behind conformal prediction in forecasting.

With this chapter, you will have a solid understanding of the various approaches to producing PIs, and you will be able to build your PIs using conformal prediction.

In this chapter, we’re going to cover the following main topics:

  • UQ for time series and forecasting problems
  • The concept of PIs in forecasting applications
  • Various approaches to producing PIs
  • Conformal prediction for time series and forecasting...