You're reading from Interpretable Machine Learning with Python Build explainable, fair, and robust high-performance models with hands-on, real-world examples

Product type Paperback

Published in Oct 2023

Publisher Packt

ISBN-13 9781803235424

Length 606 pages

Edition 2nd Edition

Languages

Python

Tools

BERT

Concepts

Machine Learning

Author (1):

Serg Masís

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Table of Contents (17) Chapters

Preface

1. Interpretation, Interpretability, and Explainability; and Why Does It All Matter?

2. Key Concepts of Interpretability FREE CHAPTER

3. Interpretation Challenges

4. Global Model-Agnostic Interpretation Methods

5. Local Model-Agnostic Interpretation Methods

6. Anchors and Counterfactual Explanations

7. Visualizing Convolutional Neural Networks

8. Interpreting NLP Transformers

9. Interpretation Methods for Multivariate Forecasting and Sensitivity Analysis

10. Feature Selection and Engineering for Interpretability

11. Bias Mitigation and Causal Inference Methods

12. Monotonic Constraints and Model Tuning for Interpretability

13. Adversarial Robustness

14. What’s Next for Machine Learning Interpretability?

15. Other Books You May Enjoy

16. Index

Speculating on the future of ML interpretability

I'm used to hearing the metaphor of this period being the "Wild West of AI", or worse, an "AI Gold Rush"! It conjures images of unexplored and untamed territory being eagerly conquered, or worse, civilized. Yet, in the 19th century, the United States' western areas were not too different from other regions on the planet and had already been inhabited by Native Americans for millennia, so the metaphor doesn't quite work. Predicting with the accuracy and confidence that we can achieve with ML would spook our ancestors and is not a "natural" position for us humans. It's more akin to flying than exploring unknown land.

The article Toward the Jet Age of machine learning (linked in the Further reading section at the end of this chapter) presents a much more fitting metaphor of AI being like the dawn of aviation. It's new and exciting, and people still marvel at what we can do from down below...

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You're reading from Interpretable Machine Learning with Python Build explainable, fair, and robust high-performance models with hands-on, real-world examples

Table of Contents (17) Chapters

Speculating on the future of ML interpretability

Authors (1)

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You're reading from Interpretable Machine Learning with Python Build explainable, fair, and robust high-performance models with hands-on, real-world examples

Table of Contents (17) Chapters

Speculating on the future of ML interpretability

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