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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
Author (1):
Valery Manokhin Valery Manokhin
Profile icon 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

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...

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