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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
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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 (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

Creating a causal model

Decision-making will often involve understanding cause and effect. If the effect is desirable, you can decide to replicate its cause, or otherwise avoid it. You can change something on purpose to observe how it changes outcomes, or to trace back an accidental effect to its cause, or to simulate which change will produce the most beneficial impact. Causal inference can help us do all this by creating causal graphs and models. These tie all variables together and estimate effects to make more principled decisions. However, to properly assess the impact of a cause, whether by design or accident, you'll need to separate its effect from confounding variables.

The reason causal inference is relevant to this chapter is that the bank's policy decisions have the power to impact cardholder livelihoods significantly and, given the rise in suicides, even to the degree of life and death. Therefore, there's a moral imperative to assess policy decisions with...

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