Interpretable Machine Learning with Python

4 (3 reviews total)
By Serg Masís
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    Chapter 1: Interpretation, Interpretability, and Explainability; and Why Does It All Matter?

About this book

Do you want to understand your models and mitigate risks associated with poor predictions using machine learning (ML) interpretation? Interpretable Machine Learning with Python can help you work effectively with ML models.

The first section of the book is a beginner's guide to interpretability, covering its relevance in business and exploring its key aspects and challenges. You'll focus on how white-box models work, compare them to black-box and glass-box models, and examine their trade-off. The second section will get you up to speed with a vast array of interpretation methods, also known as Explainable AI (XAI) methods, and how to apply them to different use cases, be it for classification or regression, for tabular, time-series, image or text. In addition to the step-by-step code, the book also helps the reader to interpret model outcomes using examples. In the third section, you’ll get hands-on with tuning models and training data for interpretability by reducing complexity, mitigating bias, placing guardrails, and enhancing reliability. The methods you’ll explore here range from state-of-the-art feature selection and dataset debiasing methods to monotonic constraints and adversarial retraining.

By the end of this book, you'll be able to understand ML models better and enhance them through interpretability tuning.

Publication date:
March 2021


Section 1: Introduction to Machine Learning Interpretation

In this section, you will recognize the importance of interpretability in business and understand its key aspects and challenges.

This section includes the following chapters:

  • Chapter 1, Interpretation, Interpretability and Explainability; and why does it all matter?
  • Chapter 2, Key Concepts of Interpretability
  • Chapter 3, Interpretation Challenges

About the Author

  • Serg Masís

    Serg Masís has been at the confluence of the internet, application development, and analytics for the last two decades. Currently, he's 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 startup, incubated by Harvard Innovation Labs, that combined the power of cloud computing and machine learning with principles in decision-making science to expose users to new places and events. 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.

    Browse publications by this author

Latest Reviews

(3 reviews total)
Excelente livros valem a pena, muito aprendizado
I bought it for my students, and they are having a good and profitable time with it. Interpretability has recently become a must have for sophisticated machine learning methods and my students are elaborating some smart projects.
This book is well presented, it has constructive explanation but some how, a few library cannot run on Jupyter notebook

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