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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

Interpreting PDPs

A PDP conveys the marginal effect of a feature on the prediction throughout all (or interpolated) possible values for that feature. It's a global model interpretation method that can visually demonstrate the impact of a feature and the nature of the relationship with the target (linear, exponential, monotonic, and so on).

It can also be extended to include two features, to illustrate the effect of their interaction on the model. One feature plot shows in the y axis the predicted outcome or relative change in this outcome, and the x axis shows all possible values of the feature. The plotted line is calculated by changing the value of the feature to the one in the x axis for all the observations and averaging the predictions if this single feature were to change, to get the y axis coordinate.

One variation of the PDP deducts the expected value for all observations from the y axis, thus centering the marginal effect to the expected value. Another PDP variation...

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