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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

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.

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Authors (1)

Serg Masís

Serg Masís

Serg Masís has been at the confluence of the internet, application development, and analytics for the last two decades. Currently, he's a climate and agronomic data scientist at Syngenta, a leading agribusiness company with a mission to improve global food security. Before that role, he co-founded a start-up, incubated by Harvard Innovation Labs, that combined the power of cloud computing and machine learning with principles in decision-making science to expose users to new places and events. Whether it pertains to leisure activities, plant diseases, or customer lifetime value, Serg is passionate about providing the often-missing link between data and decision-making—and machine learning interpretation helps bridge this gap robustly.

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