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

Metrics for multi-class classification problems

In the multi-class classification field, evaluating models’ performance is as crucial as developing them. Effective evaluation hinges upon utilizing the right metrics that can accurately measure the performance of the multi-class classification models and provide insights for improvement. This section demystifies the various metrics essential for assessing the performance of multi-class classification models, providing a solid foundation for selecting and employing the right metric for your specific use case.

Confusion matrix

One of the fundamental metrics for evaluating multi-class classification models is the confusion matrix. It provides a visualization of the performance of an algorithm, typically a supervised learning (SL) one. Each row of the confusion matrix represents the instances of an actual class, while each column represents the instances of a predicted class. It’s an essential tool for understanding the...