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Interpretable Machine Learning with Python - Second Edition

You're reading from  Interpretable Machine Learning with Python - Second Edition

Product type Book
Published in Oct 2023
Publisher Packt
ISBN-13 9781803235424
Pages 606 pages
Edition 2nd Edition
Languages
Author (1):
Serg Masís Serg Masís
Profile icon Serg Masís

Table of Contents (17) Chapters

Preface 1. Interpretation, Interpretability, and Explainability; and Why Does It All Matter? 2. Key Concepts of Interpretability 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

Further reading

  • Friedman, J., & Popescu, B. (2008). Predictive Learning via Rule Ensembles. The Annals of Applied Statistics, 2(3), 916-954. http://doi.org/10.1214/07-AOAS148
  • Hastie, T., R. Tibshirani, and M. Wainwright. 2015. Statistical Learning with Sparsity: The Lasso and Generalizations. Chapman & Hall/Crc Monographs on Statistics & Applied Probability. Taylor & Francis
  • Thomas, D.R., Hughes, E. & Zumbo, B.D. On Variable Importance in Linear Regression. Social Indicators Research 45, 253–275 (1998). https://doi.org/10.1023/A:1006954016433
  • Nori, H., Jenkins, S., Koch, P., & Caruana, R. (2019). InterpretML: A unified framework for machine learning interpretability. arXiv preprint https://arxiv.org/pdf/1909.09223.pdf
  • Hastie, T and Tibshirani, R. Generalized additive models: some applications. Journal of the American Statistical Association, 82(398):371–386, 1987. http://doi.org/10.2307%2F2289439
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