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

Mission accomplished

The mission was to understand why one of your client's bars is Outstanding while another one is Disappointing. Your approach employed the interpretation of machine learning models to arrive at the following conclusions:

According to SHAP on the tabular model, the Outstanding bar owes that rating to its berry taste and its cocoa percentage of 70%. On the other hand, the unfavorable rating for the Disappointing bar is due mostly to its earthy flavor and bean country of origin (Other). Review date plays a smaller role, but it seems that chocolate bars reviewed in that period (2013-15) were at an advantage.
LIME confirms that cocoa_percent<=70 is a desirable property, and that, in addition to berry, creamy, cocoa, and rich are favorable tastes, while sweet, sour, and molasses are unfavorable.
The commonality between both methods using the tabular model is that despite the many non-taste-related attributes, taste features are among the most salient. Therefore...

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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

Mission accomplished

Authors (1)

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Table of Contents (17) Chapters

Mission accomplished

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