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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
Author Profile Icon Serg Masís
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

Further reading

  1. Chang, C., Chang, H.H., & Tien, J. (2017). A Study on the Coping Strategy of Financial Supervisory Organization under Information Asymmetry: Case Study of Taiwan's Credit Card Market. Universal Journal of Management, 5, 429-436. http://doi.org/10.13189/ujm.2017.050903
  2. Foulds, J., & Pan, S. (2020). An Intersectional Definition of Fairness. 2020 IEEE 36th International Conference on Data Engineering (ICDE), 1918-1921. https://arxiv.org/abs/1807.08362
  3. Kamiran, F., & Calders, T. (2011). Data preprocessing techniques for classification without discrimination. Knowledge and Information Systems, 33, 1-33. https://link.springer.com/article/10.1007/s10115-011-0463-8
  4. Feldman, M., Friedler, S., Moeller, J., Scheidegger, C., & Venkatasubramanian, S. (2015). Certifying and Removing DI. Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. https://arxiv.org/abs/1412.3756
  5. Kamishima, T., Akaho,...
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