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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

Mission accomplished

This chapter's mission was to see whether there was unfair bias in predicting whether a particular defendant would recidivate. We demonstrated that the FPR for African American defendants is 1.87 times higher than for Caucasian defendants. This disparity was confirmed with WIT, indicating that the model in question is much more likely to misclassify the positive class on the basis of race. However, this is a global interpretation method, so it doesn't answer our question regarding a specific defendant. Incidentally, in Chapter 11, Bias Mitigation and Causal Inference Methods, we will cover other global interpretation methods for unfairness.

To ascertain whether the model was racially biased toward the defendant in question, we leveraged anchor and counterfactual explanations – they both output race as a primary feature in their explanations. The anchor did it with relatively high precision and coverage, and Counterfactuals Guided by Prototypes...

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