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

A business case for interpretability

This section describes several practical business benefits of machine learning interpretability, such as better decisions, as well as being more trusted, ethical, and profitable.

Better decisions

Typically, machine learning models are trained and then evaluated against the desired metrics. If they pass quality control against a hold-out dataset, they are deployed. However, once tested in the real world, things can get wild, as in the following hypothetical scenarios:

A high-frequency trading algorithm could single-handedly crash the stock market.
Hundreds of smart home devices might inexplicably burst into unprompted laughter, terrifying their users.
License-plate recognition systems could incorrectly read a new kind of license plate and fine the wrong drivers.
A racially biased surveillance system could incorrectly detect an intruder, and because of this guards shoot an innocent office worker.
A self...

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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

A business case for interpretability

Better decisions

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

A business case for interpretability

Better decisions

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Authors (1)

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