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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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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

Picture yourself, a data science consultant, in a conference room in Forth Worth, Texas, during early January 2019. In this conference room, executives for one of the world's largest airlines, American Airlines (AA), are briefing you on their on-time performance (OTP). OTP is a widely accepted key performance indicator for flight punctuality. It is measured as the percentage of flights that arrived within 15 minutes of the scheduled arrival. It turns out that AA has achieved an OTP of just over 80% for 3 years in a row, which is already acceptable, and much better than before, but they are still ninth in the world and fifth in North America. To brag about it next year in their advertising, they aspire to achieve, at least, number one in North America for 2019, besting their biggest rivals.

On the financial front, it is estimated that delays cost the airline close to $2 billion, so reducing this by even 25-35% to be on parity with their competitors could produce...

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