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

Product type Paperback

Published in Mar 2021

Publisher Packt

ISBN-13 9781800203907

Length 736 pages

Edition 1st Edition

Languages

Python

Tools

TensorFlow

Concepts

Machine Learning

Author (1):

Serg Masís

View More author details

Table of Contents (19) Chapters

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

Technical requirements

This chapter's example uses the mldatasets, pandas, numpy, sklearn, skimage, tensorflow, matplotlib, seaborn, cv2, tf-explain, tf-keras-vis, lime, alibi, and shap libraries. Instructions on how to install all of these libraries are in the preface. The code for this chapter is located here: https://github.com/PacktPublishing/Interpretable-Machine-Learning-with-Python/tree/master/Chapter08.