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

Employing LIME

Until now, the model-agnostic interpretation methods we’ve covered attempt to reconcile the totality of outputs of a model with its inputs. For these methods to get a good idea of how and why X becomes y_pred, we need some data first. Then, we perform simulations with this data, pushing variations of it into a model and evaluating what comes out of the model. Sometimes, they even leverage a global surrogate to connect the dots. By using what we learned in this process, we yield feature importance values that quantify a feature’s impact, interactions, or decisions on a global level. For many methods such as SHAP, these can be observed locally too. However, even when they can be observed locally, what was quantified globally may not apply locally. For this reason, there should be another approach that quantifies the local effects of features solely for local interpretation—one such as LIME!