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

Conformal Prediction for Regression

In this chapter, we will cover conformal prediction for regression problems.

Regression is a cornerstone of machine learning, enabling us to predict continuous outcomes from given data. However, as with many predictive tasks, the predictions are never free from uncertainty. Traditional regression techniques give us a point estimate but fail to measure the uncertainty. This is where the power of conformal prediction comes into play, extending our regression models to produce well-calibrated prediction intervals.

This chapter delves deep into conformal prediction tailored specifically for regression problems. By understanding and appreciating the importance of quantifying uncertainty, we will explore how conformal prediction augments regression to provide not just a point prediction but an entire interval or even a distribution where the actual outcome will likely fall with pre-specified confidence. This is invaluable in many real-world scenarios...