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

Trying SHAP for NLP

Most of SHAP’s explainers will work with tabular data. DeepExplainer can do text but is restricted to deep learning models, and, as we will cover in Chapter 7, Visualizing Convolutional Neural Networks, three of them do images, including KernelExplainer. In fact, SHAP’s KernelExplainer was designed to be a general-purpose, truly model-agnostic method, but it’s not promoted as an option for NLP. It is easy to understand why: it’s slow, and NLP models tend to be very complex and with hundreds—if not thousands—of features to boot. In cases such as this one, where word order is not a factor and you have a few hundred features, but the top 100 are present in most of your observations, KernelExplainer could work.

In addition to overcoming the high computation cost, there are a couple of technical hurdles you would need to overcome. One of them is that KernelExplainer is compatible with a pipeline, but it expects a single...

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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

Trying SHAP for NLP

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

Trying SHAP for NLP

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