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 concept of PIs in forecasting applications

PIs are vital tools in forecasting, providing a range of plausible values within which a future observation is likely to occur. Unlike point forecasts, which give a single best estimate, PIs communicate the uncertainty surrounding that estimate.

This section explores the fundamental concepts behind PIs and their significance in various forecasting applications.

Definition and construction

PIs are constructed around a point forecast to represent the range within which future observations are expected to lie with a given confidence level. For example, a 95% PI implies that 95 of 100 future observations are expected to fall within the defined range.

PIs can take several forms, depending on the approach used to generate them. Two key distinguishing factors are as follows:

  • Symmetric versus asymmetric intervals: PIs can be symmetric, where the bounds are equidistant from the point forecast, or asymmetric, reflecting differing...