An Introduction to Machine Learning Interpretability

An Applied Perspective on Fairness, Accountability, Transparency, and Explainable AI

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Book Description

Innovation and competition are driving analysts and data scientists toward increasingly complex predictive modeling and machine learning algorithms. This complexity makes these models accurate but also makes their predictions difficult to understand. When accuracy outpaces interpretability, human trust suffers, affecting business adoption, regulatory oversight, and model documentation.

Banking, insurance, and healthcare in particular require predictive models that are interpretable. In this ebook, Patrick Hall and Navdeep Gill from H2O.ai thoroughly introduce the idea of machine learning interpretability and examine a set of machine learning techniques, algorithms, and models to help data scientists improve the accuracy of their predictive models while maintaining interpretability.

- Learn how machine learning and predictive modeling are applied in practice
- Understand social and commercial motivations for machine learning interpretability, fairness, accountability, and transparency
- Explore the differences between linear models and more accurate machine learning models
- Get a definition of interpretability and learn about the groups leading interpretability research
- Examine a taxonomy for classifying and describing interpretable machine learning approaches
- Learn several practical techniques for data visualization, training interpretable machine learning models, and generating explanations for complex model predictions
- Explore automated approaches for testing model interpretability

This book is published as open-access, which means it is freely available to read, download, and share without restrictions.

If you enjoyed the book and would like to support the author, you can purchase a printed copy (hardcover or paperback) from official retailers.

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