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

Introducing Conformal Prediction

This book is about conformal prediction, a modern framework for uncertainty quantification that is becoming increasingly popular in industry and academia.

Machine learning and AI applications are everywhere. In the realm of machine learning, prediction is a fundamental task. Given a training dataset, we train a machine learning model to make predictions on new data.

Figure 1.1 – Machine learning prediction model

Figure 1.1 – Machine learning prediction model

However, in many real-world applications, the predictions made by statistical, machine learning, and deep learning models are often incorrect or unreliable because of various factors, such as insufficient or incomplete data, issues arising during the modeling process, or simply because of the randomness and complexities of the underlying problem.

Predictions made by machine learning models often come without the uncertainty quantification required for confident and reliable decision-making. This is...