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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 Interpretation, Interpretability, and Explainability; and Why Does It All Matter? Key Concepts of Interpretability Interpretation Challenges Global Model-Agnostic Interpretation Methods Local Model-Agnostic Interpretation Methods Anchors and Counterfactual Explanations Visualizing Convolutional Neural Networks Interpreting NLP Transformers Interpretation Methods for Multivariate Forecasting and Sensitivity Analysis Feature Selection and Engineering for Interpretability Bias Mitigation and Causal Inference Methods Monotonic Constraints and Model Tuning for Interpretability Adversarial Robustness What’s Next for Machine Learning Interpretability? Other Books You May Enjoy
Index

Further reading

  • Platt, J. C. (1999). Probabilistic Outputs for Support Vector Machines and Comparisons to Regularized Likelihood Methods. Advances in Large Margin Classifiers, MIT Press. https://www.cs.colorado.edu/~mozer/Teaching/syllabi/6622/papers/Platt1999.pdf
  • Lundberg, S. & Lee, S. (2017). A Unified Approach to Interpreting Model Predictions. Advances in Neural Information Processing Systems, 30. https://arxiv.org/abs/1705.07874 (documentation for SHAP: https://github.com/slundberg/shap)
  • Ribeiro, M. T., Singh, S. & Guestrin, C. (2016). "Why Should I Trust You?": Explaining the Predictions of Any Classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. http://arxiv.org/abs/1602.04938
  • Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q. & Liu, T. (2017). LightGBM: A Highly Efficient Gradient Boosting Decision Tree. Advances in Neural Information Processing Systems vol. 30, pp. 3149-3157. https...
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