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Interpretable Machine Learning with Python

You're reading from   Interpretable Machine Learning with Python Learn to build interpretable high-performance models with hands-on real-world examples

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Product type Paperback
Published in Mar 2021
Publisher Packt
ISBN-13 9781800203907
Length 736 pages
Edition 1st Edition
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Author (1):
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Serg Masís Serg Masís
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Serg Masís
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Table of Contents (19) Chapters Close

Preface 1. Section 1: Introduction to Machine Learning Interpretation
2. Chapter 1: Interpretation, Interpretability, and Explainability; and Why Does It All Matter? FREE CHAPTER 3. Chapter 2: Key Concepts of Interpretability 4. Chapter 3: Interpretation Challenges 5. Section 2: Mastering Interpretation Methods
6. Chapter 4: Fundamentals of Feature Importance and Impact 7. Chapter 5: Global Model-Agnostic Interpretation Methods 8. Chapter 6: Local Model-Agnostic Interpretation Methods 9. Chapter 7: Anchor and Counterfactual Explanations 10. Chapter 8: Visualizing Convolutional Neural Networks 11. Chapter 9: Interpretation Methods for Multivariate Forecasting and Sensitivity Analysis 12. Section 3:Tuning for Interpretability
13. Chapter 10: Feature Selection and Engineering for Interpretability 14. Chapter 11: Bias Mitigation and Causal Inference Methods 15. Chapter 12: Monotonic Constraints and Model Tuning for Interpretability 16. Chapter 13: Adversarial Robustness 17. Chapter 14: What's Next for Machine Learning Interpretability? 18. Other Books You May Enjoy

Mission accomplished

The mission was to train a traffic prediction model and understand what factors create uncertainty and possibly increase costs for the construction company. We can conclude that a large portion of the potential $20,000/year in fines can be attributed to the is_holiday factor. Therefore, the construction company should rethink working holidays. There are only five or six holidays between May and October, and they could cost more because of the fines than working on a few Sundays instead. Of course, these conclusions are for the chosen model – which we can compare with other, better models. With this caveat, the mission was successful, but there's still a lot of room for improvement.

For instance, one thing that could be covered in further depth is the true impact of temp and rain_1h, and features for snow. Our prediction approximation method precluded Sobol from testing the effect of extreme weather events. If we modified the model to train on aggregated...

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