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
1
Part 1: Introduction
4
Part 2: Conformal Prediction Framework
8
Part 3: Applications of Conformal Prediction
14
Part 4: Advanced Topics

Conformal prediction for NLP

Conformal prediction is a flexible and statistically robust approach to uncertainty quantification. It is a distribution-free framework that can estimate uncertainty for machine learning models without requiring model retraining or access to limited APIs. The central idea behind conformal prediction is to output a set of predictions containing the correct output with a user-specified probability. Conformal prediction can help quantify the uncertainty associated with the model’s predictions in language models.

Conformal prediction is a framework that delivers valid confidence intervals for predictions, irrespective of the underlying machine learning model. In the NLP landscape, with its inherent challenges of ambiguity, context sensitivity, and linguistic diversity, conformal prediction offers a structured way to quantify uncertainty.

Validity and efficiency are the two fundamental principles of conformal prediction. Validity ensures that the...