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


The challenge of imbalanced datasets in machine learning often results in biased predictions and compromised model outcomes. This chapter delves deep into the complexities of such datasets and illuminates the path through conformal prediction, a groundbreaking approach to handling these scenarios.

Traditional methods, such as resampling techniques, and metrics, such as ROC AUC, often fail to address the imbalances effectively. Furthermore, they can sometimes lead to even more skewed results. On the other hand, conformal prediction emerges as a robust solution, offering calibrated and reliable probability estimates.

The practical implications of these methods are illustrated using the Credit Card Fraud Detection dataset from Kaggle, an inherently imbalanced dataset. The exploration underscores the significance of understanding the data, using robust metrics, and the transformative potential of conformal prediction.

In essence, while imbalanced data presents challenges...