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

The mission

Picture yourself, a data science consultant, in a conference room in Fort Worth, Texas, during early January 2019. In this conference room, executives for one of the world’s largest airlines, American Airlines (AA), are briefing you on their On-Time Performance (OTP). OTP is a widely accepted Key Performance Indicator (KPI) for flight punctuality. It is measured as the percentage of flights that arrived within 15 minutes of the scheduled arrival. It turns out that AA has achieved an OTP of just over 80% for 3 years in a row, which is acceptable, and a significant improvement, but they are still ninth in the world and fifth in North America. To brag about it next year in their advertising, they aspire to achieve, at least, number one in North America for 2019, besting their biggest rivals.

On the financial front, it is estimated that delays cost the airline close to $2 billion, so reducing this by 25–35% to be on parity with their competitors could produce...

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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

The mission

Authors (1)

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Table of Contents (17) Chapters

The mission

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