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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

Mission accomplished

The mission was to train a traffic prediction model and understand what factors create uncertainty and possibly increase costs for the construction company. We can conclude a significant portion of the potential $35,000/year in fines can be attributed to the is_holiday factor. Therefore, the construction company should rethink working holidays. There are only seven or eight holidays between March and November, and they could cost more because of the fines than working on a few Sundays instead. With this caveat, the mission was successful, but there’s still a lot of room for improvement.

Of course, these conclusions are for the LSTM_traffic_168_compact1 model – which we can compare with other models. Try replacing the model_name at the beginning of the notebook with LSTM_traffic_168_compact2, an equally small but significantly more robust model, or LSTM_traffic_168_optimal, a larger slightly better-performing model, and re-running the notebook...

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