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

View More author details

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

Why subscribe?

25. Other Books You May Enjoy

Test-driven development for machine learning modeling

One approach to reducing the risks of developing unreliable models and pushing them to production is test-driven development. We aim to design unit tests (that is, tests designed to test individual components of software) that reduce the risks of code revision either within the same or in different life cycles. To better understand this concept, we need to understand what unit tests are and how we can design and use them in Python.

Unit testing

Unit tests are designed to test the smallest components, or units, in the code and software we design. In machine learning modeling, we might have many modules taking care of different steps of a machine learning life cycle, such as data curation and wrangling or model evaluation. Unit tests help us avoid errors and mistakes, and design our code without the need to worry about whether we made a mistake that will not be detected early on. Detecting issues in our code early has lower...

The rest of the chapter is locked

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

Test-driven development for machine learning modeling

Unit testing

Authors (1)

Personalised recommendations for you

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

Test-driven development for machine learning modeling

Unit testing

Unlock this book and the full library FREE for 7 days

Authors (1)

Personalised recommendations for you