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

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

Mitigating bias

We can mitigate bias at three different levels with methods that operate at these individual levels:

Preprocessing: These are interventions to detect and remove bias from the training data before training the model. Methods that leverage pre-processing have the advantage that they tackle bias at the source. On the other hand, any undetected bias could still be amplified by the model.
In-processing: These methods mitigate bias during the model training and are, therefore, highly dependent on the model and tend to not be model-agnostic like the pre-processing and post-processing methods. They also require hyperparameter tuning to calibrate fairness metrics.
Post-processing: These methods mitigate bias during model inference. In Chapter 6, Anchors and Counterfactual Explanations, we touched on the subject of using the What-If tool to choose the right thresholds (see Figure 6.13 in that chapter), and we manually adjusted them to achieve parity with...

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