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

How conformal prediction can be applied to multi-class classification problems

conformal prediction is a powerful framework that can be applied to multi-class classification problems. It provides a way to make predictions with a measure of certainty, which is particularly useful when dealing with multiple classes.

In the previous chapters, we have already looked at how conformal prediction assigns a p-value to each class for a given instance in the context of multi-class classification.

The p-value represents the confidence level of the prediction for that class. The higher the p-value, the more confident the model is that the instance belongs to that class.

The procedure for applying conformal prediction to multi-class classification is as follows:

  1. Calibration: A portion of the training data, known as the calibration set, is set aside. The model is trained on the remaining data.
  2. Prediction: For each class, the model predicts class scores. The conformity score...