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 why deep learning produces miscalibrated predictions

In the rapidly evolving field of NLP, deep learning played a pivotal role in enabling machines to process and generate language in ways that were once the exclusive domain of humans. The next section introduces the key concepts and milestones in deep learning that has significantly influenced NLP.

Introduction to deep learning in NLP

Deep learning, a subset of machine learning, relies on neural networks with many layers (hence “deep”) to analyze various data factors. In the context of NLP, deep learning has been a game-changer, enabling machines to understand and generate human language with unprecedented accuracy:

  • Evolution of architectures: The journey began with simpler architectures such as feedforward neural networks and recurrent neural networks (RNNs). With its ability to remember past information, the latter was particularly influential in sequence-based tasks such as language translation...