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
1
Part 1: Introduction
4
Part 2: Conformal Prediction Framework
8
Part 3: Applications of Conformal Prediction
14
Part 4: Advanced Topics

Fundamentals of conformal prediction

In this section, we will cover the fundamentals of conformal prediction. There are two variants of conformal prediction – inductive conformal prediction (ICP) and transductive conformal prediction (TCP). We will discuss the benefits of the conformal prediction framework and learn about the basic components of conformal predictors and the different types of nonconformity measures. We will also learn how to use nonconformity measures to create probabilistic prediction sets in classification tasks.

Definition and principles

Conformal prediction is a machine learning framework that quantifies uncertainty to produce probabilistic predictions. These predictions can be prediction sets for classification tasks or prediction intervals for regression tasks. Conformal prediction has significant advantages in equipping statistical, machine learning, and deep learning models with valuable additional features that instill confidence in their predictions...