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Interpretable Machine Learning with Python

You're reading from   Interpretable Machine Learning with Python Learn to build interpretable high-performance models with hands-on real-world examples

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Product type Paperback
Published in Mar 2021
Publisher Packt
ISBN-13 9781800203907
Length 736 pages
Edition 1st Edition
Languages
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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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Toc

Table of Contents (19) Chapters Close

Preface 1. Section 1: Introduction to Machine Learning Interpretation
2. Chapter 1: Interpretation, Interpretability, and Explainability; and Why Does It All Matter? FREE CHAPTER 3. Chapter 2: Key Concepts of Interpretability 4. Chapter 3: Interpretation Challenges 5. Section 2: Mastering Interpretation Methods
6. Chapter 4: Fundamentals of Feature Importance and Impact 7. Chapter 5: Global Model-Agnostic Interpretation Methods 8. Chapter 6: Local Model-Agnostic Interpretation Methods 9. Chapter 7: Anchor and Counterfactual Explanations 10. Chapter 8: Visualizing Convolutional Neural Networks 11. Chapter 9: Interpretation Methods for Multivariate Forecasting and Sensitivity Analysis 12. Section 3:Tuning for Interpretability
13. Chapter 10: Feature Selection and Engineering for Interpretability 14. Chapter 11: Bias Mitigation and Causal Inference Methods 15. Chapter 12: Monotonic Constraints and Model Tuning for Interpretability 16. Chapter 13: Adversarial Robustness 17. Chapter 14: What's Next for Machine Learning Interpretability? 18. Other Books You May Enjoy

The mission

Energy efficiency is a significant concern to consumers that want to spend or pollute less. Therefore, it is in the purview of policymakers, regulators, environmental activists, public health officials, and manufacturers of energy-consuming technologies. In the United States alone, the transportation sector accounted for 28% (https://www.eia.gov/energyexplained/use-of-energy/transportation.php) of total energy consumption in 2019, of which more than half is consumed by light-duty passenger vehicles. And even though there has been an increase in the USA's electric car fleet over the last decade, most of their electricity still comes from fossil fuel power plants. Ultimately, this means that all passenger vehicles have a carbon footprint regardless of their fuel type.

For this exercise, let's say the US-based consumer advocacy non-profit that you work for has traditionally focused on car safety, and fraudulent sales practices are shifting their attention to energy...

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