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

Introducing imbalanced data

In machine learning, we often come across datasets that need to be more balanced. But what does it mean for a dataset to be imbalanced?

An imbalanced dataset is one where the distribution of samples across the different classes is not uniform. In other words, one type has significantly more samples than the other(s). This is a common scenario in many real-world applications. For instance, in a dataset for fraud detection, the number of non-fraudulent transactions (majority class) is typically much higher than the number of fraudulent ones (minority class).

Imagine a medical dataset recording instances of a rare disease. Most patients will be disease-free, resulting in a large class of healthy records, while only a tiny fraction will be affected by the disease. This disproportion in the distribution of categories is what we call imbalanced data.

Imbalanced data can lead to a significant challenge in predictive modeling. By their very nature, machine...