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

Generating LSTM attributions with integrated gradients

We first learned about integrated gradients (IG) in Chapter 7, Visualizing Convolutional Neural Networks. Unlike the other gradient-based attribution methods studied in that chapter, path-integrated gradients is not contingent on convolutional layers, nor is it limited to classification problems.

In fact, since it computes the gradients of the output concerning the inputs averaged along the path, the input and output could be anything! It is common to use integrated gradients with Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), like the one we are interpreting in this chapter. Frankly, when you see an IG LSTM example online, it has an embedding layer and is an NLP classifier, but IG could be used very effectively for LSTMs that even process sounds or genetic data!

The integrated gradient explainer and the explainers that we will use moving forward can access any part of the traffic dataset....

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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

Generating LSTM attributions with integrated gradients

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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

Generating LSTM attributions with integrated gradients

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