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

Feature summary explanations

This section will cover a number of methods used to visualize how an individual feature impacts the outcome.

Partial dependence plots

Partial Dependence Plots (PDPs) display a feature’s relationship with the outcome according to the model. In essence, the PDP illustrates the marginal effect of a feature on the model’s predicted output across all possible values of that feature.

The calculation involves two steps:

Initially, conduct a simulation where the feature value for each observation is altered to a range of different values, and predict the model using those values. For example, if the year varies between 1984 and 2022, create copies of each observation with year values ranging between these two numbers. Then, run the model using these values. This first step can be plotted as the Individual Conditional Expectation (ICE) plot, with simulated values for year on the X-axis and the model output on the Y-axis, and...

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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

Feature summary explanations

Partial dependence plots

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

Feature summary explanations

Partial dependence plots

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Authors (1)

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