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

Comparing SHAP with LIME

As you will have noticed by now, both SHAP and LIME have limitations, but they also have strengths. SHAP is grounded in game theory and approximate Shapley values, so its SHAP values are supported by theory. These have great properties such as additivity, efficiency, and substitutability that make them consistent but violate the dummy property. It always adds up and doesn’t need parameter tuning to accomplish this. However, it’s more suited for global interpretations, and one of its most model-agnostic explainers, KernelExplainer, is painfully slow. KernelExplainer also deals with missing values by using random ones, which can put too much weight on unlikely observations.

LIME is speedy, very model-agnostic, and adaptable to all kinds of data. However, it’s not grounded on strict and consistent principles but has the intuition that neighbors are alike. Because of this, it can require tricky parameter tuning to define the neighborhood...

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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

Comparing SHAP with LIME

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

Comparing SHAP with LIME

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Authors (1)

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