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

Uncertainty quantification for NLP

Uncertainty quantification in NLP is an essential yet often overlooked aspect of model development and deployment. As NLP models become increasingly integrated into critical applications—from healthcare diagnostics to financial predictions—the need to understand and convey the confidence level of their outputs becomes paramount. Uncertainty quantification provides a framework for assessing the reliability of predictions, allowing users and developers to gauge the model’s decisiveness and the potential risks of relying on its results. This section delves into the importance, methodologies, and practical considerations of uncertainty quantification in NLP, highlighting its pivotal role in building robust and trustworthy language models.

We will now explore uncertainty in NLP and the benefits and challenges of quantifying uncertainty in NLP applications.

What is uncertainty in NLP?

NLP, at its core, is about making sense...