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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

We've all heard the stereotypes: firstborns are very responsible and bossy; the youngest is spoiled and carefree; and the middle child is a jealous introvert! It turns out prominent psychology researchers have reached out to your data science consultancy firm and have conducted several small empirical studies on how birth order affects personality. But they just got a hold of a dataset of over 40,000 online quiz entries from the Open-Source Psychometrics Project. They are skeptical because it was submitted online and they have never conducted a study of that magnitude, so it's uncharted territory. For these reasons, they would like a third party who is well versed in machine learning to approach the problem with fresh eyes. What they hope to learn is about any relation between the quiz answers and the birth order, and also to determine if there are any questions they could use in their empirical studies, or even if online quizzes are a reliable method to begin...

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