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
1
Part 1: Introduction
4
Part 2: Conformal Prediction Framework
8
Part 3: Applications of Conformal Prediction
14
Part 4: Advanced Topics

Building prediction intervals and predictive distributions using conformal prediction

ICP is a computationally efficient variant of the original transductive conformal prediction framework. Like all other models from the conformal prediction family, ICP is model-agnostic in terms of the underlying point prediction model and data distribution and comes with automatic validity guarantees for final samples of any size.

The key advantage of ICP compared to the original variant of conformal prediction (transductive conformal prediction) is that ICP requires training the underlying regression model only once, leading to efficient computations during the calibration and prediction phases. ICP is highly computationally efficient as the conformal layer requires very little additional computation overhead compared to training the underlying model.

The ICP process involves splitting the dataset into a proper training set and a calibration set. The training set is used to create the initial...