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Practical Guide to Applied Conformal Prediction in Python

You're reading from  Practical Guide to Applied Conformal Prediction in Python

Product type Book
Published in Dec 2023
Publisher Packt
ISBN-13 9781805122760
Pages 240 pages
Edition 1st Edition
Languages
Concepts
Author (1):
Valery Manokhin Valery Manokhin
Profile icon Valery Manokhin
LinkedIn
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.
See other products by Valery Manokhin

Table of Contents (19) Chapters

Preface 1. Part 1: Introduction
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 18. Other Books You May Enjoy

Open source tools for conformal prediction in classification problems

While deep-diving into the intricacies of conformal prediction for classification, it has become evident that the right tools can significantly enhance our implementation efficiency. Recognizing this, the open source community has made remarkable contributions by providing various tools tailored for this purpose. In this section, we will explore some of the prominent open source tools for conformal prediction that can seamlessly integrate into your projects and elevate your predictive capabilities.

Nonconformist

nonconformist (https://github.com/donlnz/nonconformist) is a classical conformal prediction package that can be used for conformal prediction in classification problems.

Let’s illustrate how to create an ICP using nonconformist. You can find the Jupyter notebook containing the relevant code at https://github.com/PacktPublishing/Practical-Guide-to-Applied-Conformal-Prediction/blob/main/Chapter_06...

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Authors (1)

Profile icon Valery Manokhin
LinkedIn
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.
Read more
See other products by Valery Manokhin

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