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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

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Product type Paperback
Published in Dec 2023
Publisher Packt
ISBN-13 9781805122760
Length 240 pages
Edition 1st Edition
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Author (1):
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Valery Manokhin Valery Manokhin
Author Profile Icon Valery Manokhin
Valery Manokhin
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Table of Contents (19) Chapters Close

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 18. Other Books You May Enjoy

Conformal prediction for computer vision

In this section, we will dive deeper into the diverse applications of conformal prediction in computer vision. With its broad range of problems, from image classification to object detection, computer vision presents challenges that require precise and reliable machine learning models. As we navigate these applications, we will demonstrate how conformal prediction is a robust tool to quantify the uncertainty associated with these models.

By exploring these practical examples, we aim to underscore the importance of understanding the model’s confidence in its predictions. Understanding is crucial, especially when decisions based on these predictions could have significant consequences. Conformal prediction, with its ability to provide a measure of uncertainty, can greatly aid researchers and practitioners in making informed decisions based on the outputs of their models. This improves the system’s reliability and paves the way...

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