You're reading from Debugging Machine Learning Models with Python Develop high-performance, low-bias, and explainable machine learning and deep learning models

Product type Paperback

Published in Sep 2023

Publisher Packt

ISBN-13 9781800208582

Length 344 pages

Edition 1st Edition

Languages

Python

Tools

TensorFlow

Concepts

Deep Learning

Author (1):

Ali Madani

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Table of Contents (26) Chapters

Preface

1. Part 1:Debugging for Machine Learning Modeling

2. Chapter 1: Beyond Code Debugging FREE CHAPTER

3. Chapter 2: Machine Learning Life Cycle

4. Chapter 3: Debugging toward Responsible AI

5. Part 2:Improving Machine Learning Models

6. Chapter 4: Detecting Performance and Efficiency Issues in Machine Learning Models

7. Chapter 5: Improving the Performance of Machine Learning Models

8. Chapter 6: Interpretability and Explainability in Machine Learning Modeling

9. Chapter 7: Decreasing Bias and Achieving Fairness

10. Part 3:Low-Bug Machine Learning Development and Deployment

11. Chapter 8: Controlling Risks Using Test-Driven Development

12. Chapter 9: Testing and Debugging for Production

13. Chapter 10: Versioning and Reproducible Machine Learning Modeling

14. Chapter 11: Avoiding and Detecting Data and Concept Drifts

15. Part 4:Deep Learning Modeling

16. Chapter 12: Going Beyond ML Debugging with Deep Learning

17. Chapter 13: Advanced Deep Learning Techniques

18. Chapter 14: Introduction to Recent Advancements in Machine Learning

19. Part 5:Advanced Topics in Model Debugging

20. Chapter 15: Correlation versus Causality

21. Chapter 16: Security and Privacy in Machine Learning

22. Chapter 17: Human-in-the-Loop Machine Learning

23. Assessments

24. Index

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Decreasing Bias and Achieving Fairness

Fairness is an important topic when it comes to using machine learning across different industries, as we discussed in Chapter 3, Debugging toward Responsible AI. In this chapter, we will provide you with some widely used notions and definitions of fairness in machine learning settings, as well as how to use fairness and explainability Python libraries that are designed to not only help you in assessing fairness in your models but also improve them in this regard.

This chapter includes many figures and code examples to help you better understand these concepts and start benefiting from them in your projects. Note that one chapter is far from enough to make you an expert on the topic of fairness, but this chapter will provide you with the necessary knowledge and tools to start practicing this subject in your projects. You can learn more about this topic using more advanced resources dedicated to machine learning fairness.

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You're reading from Debugging Machine Learning Models with Python Develop high-performance, low-bias, and explainable machine learning and deep learning models

Table of Contents (26) Chapters

Decreasing Bias and Achieving Fairness

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Table of Contents (26) Chapters

Decreasing Bias and Achieving Fairness

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