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


In the chapter, we have explored the inherent uncertainty challenges in the NLP domain. Recognizing the pivotal role of NLP models in today’s critical systems, the chapter emphasizes the importance of ensuring these models’ predictions are trustworthy and reliable. The chapter introduces conformal prediction as a solution to address the miscalibration seen in deep learning models’ outputs, offering a means to quantify the confidence of predictions robustly. Throughout this chapter, you gained insights into the intricacies of uncertainty quantification specific to NLP, the reasons why deep learning models often produce miscalibrated predictions, and various methods of quantifying uncertainty in NLP. Finally, we deeply studied the conformal prediction technique tailored for NLP tasks.

At the end of this chapter, you should have a holistic understanding of the challenges of uncertainty in NLP, the merits and mechanics of conformal prediction, and practical...