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Interpretable Machine Learning with Python

You're reading from Interpretable Machine Learning with Python Learn to build interpretable high-performance models with hands-on real-world examples

Product type Paperback

Published in Mar 2021

Publisher Packt

ISBN-13 9781800203907

Length 736 pages

Edition 1st Edition

Languages

Python

Tools

TensorFlow

Concepts

Machine Learning

Author (1):

Serg Masís

View More author details

Table of Contents (19) Chapters

Preface

1. Section 1: Introduction to Machine Learning Interpretation

2. Chapter 1: Interpretation, Interpretability, and Explainability; and Why Does It All Matter? FREE CHAPTER

3. Chapter 2: Key Concepts of Interpretability

4. Chapter 3: Interpretation Challenges

5. Section 2: Mastering Interpretation Methods

6. Chapter 4: Fundamentals of Feature Importance and Impact

7. Chapter 5: Global Model-Agnostic Interpretation Methods

8. Chapter 6: Local Model-Agnostic Interpretation Methods

9. Chapter 7: Anchor and Counterfactual Explanations

10. Chapter 8: Visualizing Convolutional Neural Networks

11. Chapter 9: Interpretation Methods for Multivariate Forecasting and Sensitivity Analysis

12. Section 3:Tuning for Interpretability

13. Chapter 10: Feature Selection and Engineering for Interpretability

14. Chapter 11: Bias Mitigation and Causal Inference Methods

15. Chapter 12: Monotonic Constraints and Model Tuning for Interpretability

16. Chapter 13: Adversarial Robustness

17. Chapter 14: What's Next for Machine Learning Interpretability?

18. Other Books You May Enjoy

Dataset and image sources

Baron, Karl (photographer). (2003, March 26). Local Daiei got a self-checkout [digital image]. CC 2.0 License: https://www.flickr.com/photos/82365211@N00/9244253015.
Muresan, H., and Oltean, M. (2017). Fruit recognition from images using deep learning. Acta Universitatis Sapientiae, Informatica, 10, 26 – 42: https://arxiv.org/abs/1712.00580.
Oltean, M. (2020). Fruits 360: https://www.kaggle.com/moltean/fruits (CC BY-SA 4.0).

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Authors (1)

Serg Masís

Serg Masís

For the last two decades, Serg Masis has been at the confluence of the internet, application development, and analytics. Currently, he is a Climate and Agronomic Data Scientist at Syngenta, a leading agribusiness company with a mission to improve global food security. Before that role, he co founded a search engine startup incubated by Harvard Innovation Labs, combining the power of cloud computing and machine learning with principles in decision making science to expose users to new places and events efficiently. Whether it pertains to leisure activities, plant diseases, or customer lifetime value, Serg is passionate about providing the often missing link between data and decision making and machine learning interpretation helps bridge this gap more robustly.

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