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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
Author Profile Icon Serg Masís
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

Mission accomplished

The mission was to train models that could predict preventable delays with enough accuracy to be useful, and then, to understand the factors that impacted these delays, according to these models, to improve OTP. The resulting regression models all predicted delays, on average, well below the 15-minute threshold according to the RMSE. And most of the classification models achieved an F1 score well above 50% – one of them reached 98.8%! We also managed to find factors that impacted delays for all white-box models, some of which performed reasonably well. So, it seems like it was a resounding success!

Don’t celebrate just yet! Despite the high metrics, this mission was a failure. Through interpretation methods, we realized that the models were accurate mostly for the wrong reasons. This realization helps underpin the mission-critical lesson that a model can easily be right for the wrong reasons, so the question “why?” is not a question...

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