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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

Learning about Shapley values

Several chapters in this book will revisit one method in particular: SHAP. So, it's best that we get an overview now of the mathematical foundation and the properties behind it. We will do this through a basketball analogy.

Imagine you are blindfolded at a basketball game where a loudspeaker announces whenever a player for your team enters or exits the court or the team scores. The loudspeaker won't tell you who scored and you are blindfolded, so you don't know who scored or who even assisted! They only refer to players by number, and you don't know who they are anyway. They could be good players or bad players. At any given time, your best guess would be that whoever last joined had something to do with the latest outcome, whether good or bad. Therefore, over time you start getting a sense of which players correlate the most with the better results and which have the opposite effect or none at all.

What if we were able to simulate...

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