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Practical Guide to Applied Conformal Prediction in Python

You're reading from Practical Guide to Applied Conformal Prediction in Python Learn and apply the best uncertainty frameworks to your industry applications

Product type Paperback

Published in Dec 2023

Publisher Packt

ISBN-13 9781805122760

Length 240 pages

Edition 1st Edition

Languages

Python

Tools

Boost

Concepts

Machine Learning

Author (1):

Valery Manokhin

View More author details

Table of Contents (19) Chapters

Preface

1. Part 1: Introduction FREE CHAPTER

2. Chapter 1: Introducing Conformal Prediction

3. Chapter 2: Overview of Conformal Prediction

4. Part 2: Conformal Prediction Framework

5. Chapter 3: Fundamentals of Conformal Prediction

6. Chapter 4: Validity and Efficiency of Conformal Prediction

7. Chapter 5: Types of Conformal Predictors

8. Part 3: Applications of Conformal Prediction

9. Chapter 6: Conformal Prediction for Classification

10. Chapter 7: Conformal Prediction for Regression

11. Chapter 8: Conformal Prediction for Time Series and Forecasting

12. Chapter 9: Conformal Prediction for Computer Vision

13. Chapter 10: Conformal Prediction for Natural Language Processing

14. Part 4: Advanced Topics

15. Chapter 11: Handling Imbalanced Data

16. Chapter 12: Multi-Class Conformal Prediction

17. Index

Why subscribe?

18. Other Books You May Enjoy

Part 4: Advanced Topics

This part will provide illustrations on how conformal prediction can be used to solve imbalanced data problems, introducing you to various conformal prediction methods that can be used for multi-class classification problems.

This section has the following chapters:

Chapter 11, Handling Imbalanced Data
Chapter 12, Multi-Class Conformal Prediction

The rest of the chapter is locked

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Authors (1)

Valery Manokhin

Valery Manokhin

Valeriy Manokhin is the leading expert in the field of machine learning and Conformal Prediction. He holds a Ph.D.in Machine Learning from Royal Holloway, University of London. His doctoral work was supervised by the creator of Conformal Prediction, Vladimir Vovk, and focused on developing new methods for quantifying uncertainty in machine learning models. Valeriy has published extensively in leading machine learning journals, and his Ph.D. dissertation 'Machine Learning for Probabilistic Prediction' is read by thousands of people across the world. He is also the creator of "Awesome Conformal Prediction," the most popular resource and GitHub repository for all things Conformal Prediction.

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