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

Why imbalanced data problems are complex to solve

Addressing imbalanced data is no walk in the park, and here’s why. At the core of the challenge is the nature of conventional machine learning algorithms. These algorithms minimize overall error and are designed with the assumption of balanced class distributions. This becomes problematic when faced with imbalanced datasets, leading to a pronounced bias toward the majority class.

The gravity of this problem becomes evident when we realize that in many scenarios, it’s the minority class that carries more significance. Take fraud detection or medical diagnoses as cases in point. While fraudulent transactions or disease instances might be sparse, their correct identification is paramount. Yet, a model trained on skewed data might often lean toward predicting the majority class, achieving superficially high accuracy but failing its core objective.

To add to the challenge, conventional metrics, such as accuracy, are only...