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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
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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

Over 2.8 billion credit cards are circulating worldwide, and they collectively spend over $25 trillion (US) every year (https://www.ft.com/content/ad826e32-2ee8-11e9-ba00-0251022932c8). These are astronomic amounts, no doubt, but the credit card industry's size is best measured not by what is spent, but by what is owed. Card issuers such as banks make the bulk of their money from interest. So, the over-$60 trillion owed by consumers, of which credit card debt is a sizable portion, provides a steady income to lenders in the form of interest. It could be argued this is good for business, but it also poses ample risk because if a borrower defaults before the principal plus operation costs have been repaid, the lender could lose, especially once they've exhausted legal avenues to collect the debt.

When there's a credit bubble, this problem is compounded because an unhealthy level of debt can compromise lender finances and take their stakeholders down with...

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