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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

Speculating on the future of ML interpretability

I'm used to hearing the metaphor of this period being the "Wild West of AI", or worse, an "AI Gold Rush"! It conjures images of unexplored and untamed territory being eagerly conquered, or worse, civilized. Yet, in the 19th century, the United States' western areas were not too different from other regions on the planet and had already been inhabited by Native Americans for millennia, so the metaphor doesn't quite work. Predicting with the accuracy and confidence that we can achieve with ML would spook our ancestors and is not a "natural" position for us humans. It's more akin to flying than exploring unknown land.

The article Toward the Jet Age of machine learning (linked in the Further reading section at the end of this chapter) presents a much more fitting metaphor of AI being like the dawn of aviation. It's new and exciting, and people still marvel at what we can do from down...

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