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Interpretable Machine Learning with Python

You're reading from   Interpretable Machine Learning with Python Build explainable, fair, and robust high-performance models with hands-on, real-world examples

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Product type Paperback
Published in Oct 2023
Publisher Packt
ISBN-13 9781803235424
Length 606 pages
Edition 2nd 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 (17) Chapters Close

Preface 1. Interpretation, Interpretability, and Explainability; and Why Does It All Matter? 2. Key Concepts of Interpretability FREE CHAPTER 3. Interpretation Challenges 4. Global Model-Agnostic Interpretation Methods 5. Local Model-Agnostic Interpretation Methods 6. Anchors and Counterfactual Explanations 7. Visualizing Convolutional Neural Networks 8. Interpreting NLP Transformers 9. Interpretation Methods for Multivariate Forecasting and Sensitivity Analysis 10. Feature Selection and Engineering for Interpretability 11. Bias Mitigation and Causal Inference Methods 12. Monotonic Constraints and Model Tuning for Interpretability 13. Adversarial Robustness 14. What’s Next for Machine Learning Interpretability? 15. Other Books You May Enjoy
16. Index

The approach

The bank has stressed to you how important it is that there’s fairness embedded in your methods because the regulators and the public at large want assurance that banks will not cause any more harm. Their reputation depends on it too, because in recent months, the media has been relentless in blaming them for dishonest and predatory lending practices, causing distrust in consumers. For this reason, they want to use state-of-the-art robustness testing to demonstrate that the prescribed policies will alleviate the problem. Your proposed approach includes the following points:

  • Younger lenders have been reported to be more prone to defaulting on repayment, so you expect to find age bias, but you will also look for bias with other protected groups such as gender.
  • Once you have detected bias, you can mitigate bias with preprocessing, in-processing, and post-processing algorithms using the AI Fairness 360 (AIF360) library. In this process, you will train...
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