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 efficiency of probabilistic predictors

Efficiency is a performance metric used to evaluate probabilistic predictors. It measures how precise or informative the prediction intervals or regions are. In other words, efficiency indicates how tight or narrow the predicted probability distributions are. Smaller intervals or regions are considered more efficient, as they convey more certainty about the predicted outcomes.

While validity focuses on ensuring that the error rate is controlled, efficiency assesses the usefulness and precision of the predictions. An efficient predictor provides more specific information about the possible outcomes, whereas a less efficient predictor generates wider intervals or regions, resulting in less precise information.

There is an inherent trade-off between validity and efficiency. A conformal predictor can always achieve perfect validity by outputting very wide prediction sets that encompass all possible outcomes. However, this lacks efficiency...