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

Introduction to conformal prediction

In this section, we will introduce conformal prediction and explain how it can be used to improve the reliability of predictions produced by statistical, machine learning, and deep learning models. We will provide an overview of the key ideas and concepts behind conformal prediction, including its underlying principles and benefits. By the end of this section, you will have a solid understanding of conformal prediction and why it is an important framework to know.

Conformal prediction is a powerful machine learning framework that provides valid confidence measures for individual predictions. This means that when you make a prediction using any model from the conformal prediction framework, you can also measure your confidence in that prediction.

This is incredibly useful in many practical applications where it is crucial to have reliable and interpretable predictions. For example, in medical diagnosis, conformal prediction can provide a confidence...