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

View More author details

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

Interpretable Machine Learning with Python, Second Edition: Build Your Own Interpretable Models

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Chapter 1: Interpretation, Interpretability and Explainability; and why does it all matter?
Chapter 2: Key Concepts of Interpretability
Chapter 3: Interpretation Challenges
Chapter 4: Global Model-agnostic Interpretation Methods
Chapter 5: Local...

The rest of the chapter is locked

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

Interpretable Machine Learning with Python, Second Edition: Build Your Own Interpretable Models

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

Interpretable Machine Learning with Python, Second Edition: Build Your Own Interpretable Models

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