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

Conformal Prediction for Natural Language Processing

Natural language processing (NLP) grapples with the complexities of human language, where uncertainty is an inherent challenge. As NLP models become integral to risk-sensitive and critical applications, ensuring their reliability is paramount. Conformal prediction emerges as a promising technique, offering a way to quantify the trustworthiness of these models’ predictions, particularly when faced with miscalibrated outputs from deep learning models.

In this chapter, we will navigate the NLP conformal prediction world, understand its significance, and learn how to harness its power for more reliable and confident predictions.

In this chapter, we’re going to cover the following main topics:

  • Uncertainty quantification for NLP
  • Why deep learning produces miscalibrated predictions
  • Various approaches to quantify uncertainty in NLP problems
  • Conformal prediction for NLP
  • Building NLP classifiers...