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

Understanding uncertainty quantification

Uncertainty is an inherent part of any prediction, as there are always factors that are unknown or difficult to measure. Predictions are typically made based on incomplete data or models that are unable to capture the full complexity of the real world. As a result, the predictions are subject to various sources of uncertainty, including randomness, bias, and model errors.

To mitigate the risks associated with uncertainty, it is essential to quantify it accurately. By quantifying uncertainty, we can estimate the range of possible outcomes and assess the degree of confidence we can have in our predictions. This information can be used to make informed decisions and to identify areas where further research or data collection is needed.

UQ is a field of study that helps us measure how much we don’t know when we make predictions. UQ tries to estimate the probability of outcomes even if some aspects of the system under study are not known...