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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
Languages
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Author (1):
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Serg Masís Serg Masís
Author Profile Icon Serg Masís
Serg Masís
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Toc

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

The mission

Highway traffic congestion is a problem that's affecting cities across the world. As vehicle per capita steadily increases across the developing world with not enough road and parking infrastructure to keep up with it, congestion has been increasing at alarming levels. In the United States, the vehicle per capita statistic is among the highest in the world (838 per 1,000 people for 2019). For this reason, US cities represent 62 out of the 381 cities worldwide. with at least a 15% congestion level.

Minneapolis is one such city (see the following screenshot) where that threshold was recently surpassed and keeps rising. To put this metropolitan area into context, congestion levels are extremely severe above 50%, but moderate level congestion (15-25%) is already a warning sign of bad congestion to come. It's challenging to reverse congestion once it reaches 25% because any infrastructure improvement will be costly to implement without disrupting traffic even further...

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