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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

Technical requirements

Although we began the book with a “toy example,” we will be leveraging real datasets throughout this book to be used in specific interpretation use cases. These come from many different sources and are often used only once.

To avoid that, readers spend a lot of time downloading, loading, and preparing datasets for single examples; there’s a library called mldatasets that takes care of most of this. Instructions on how to install this library are located in the Preface. In addition to mldatasets, this chapter’s examples also use the pandas, numpy, statsmodel, sklearn, seaborn, and matplotlib libraries.

The code for this chapter is located here: https://packt.link/DgnVj.

You have been reading a chapter from
Interpretable Machine Learning with Python - Second Edition
Published in: Oct 2023
Publisher: Packt
ISBN-13: 9781803235424
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