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 classifier calibration

Conformal prediction is a powerful framework for probabilistic prediction that provides valid and well-calibrated prediction sets and prediction intervals. It offers a principled approach to quantify and control the uncertainty associated with the predictions.

We have already seen how conformal prediction approaches, such as inductive conformal prediction (ICP) and transductive conformal prediction (TCP), aim to generate sets that have accurate coverage probabilities. To recap, conformal prediction computes p-values and constructs prediction sets by comparing the p-values of each potential label with a selected significance level.

Unlike Platt scaling, histogram binning, and isotonic regression, which focus on calibrating the predicted probabilities or scores, conformal prediction takes a more comprehensive approach by providing prediction sets that encompass the uncertainty associated with the predictions and enhances the reliability...