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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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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

Reviewing traditional model interpretation methods

To explore as many model classes and interpretation methods as possible, we will fit the data to regression and classification models as well as to dimensionality reduction methods.

Predicting minutes delayed with various regression methods

To compare and contrast regression methods, we will first create a dictionary named reg_models. Each model is its own dictionary and the function that creates it in the model attribute. This structure will be used later to store the fitted model neatly and its metrics. Model classes in this dictionary have been chosen to represent several model families and to illustrate important concepts that we will discuss later:

Reg_models = {
  #Generalized Linear Models (GLMs)
  'linear':{'model': linear_model.LinearRegression()},
  'linear_poly':{'model': 
           make_pipeline...
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