Anirban Nandi - Learn Model Interpretability and Explainability Methods

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Interpretability and explainability have become two of the top trending search words in machine learning. This book explains machine learning interpretability by using different explainability algorithms. The book begins by talking about the theoretical aspects of machine learning interpretability. The first few chapters explain interpretability, the common properties of interpretability methods, the general taxonomy for classifying methods into different sections, and how methods should be assessed in terms of human factors and technical requirements.

In the first few chapters, readers holistically learn about choosing an interpretability method. These chapters are designed to provide information about interpretability in an academic style, with each section explaining the significance in detail with proper examples. We include quotes from actual business leaders and technical experts to showcase how the real-life users perceive interpretability and its related methods, goals, stages, and properties.

In the next few sections of the book, we deep dive into the technical details of the interpretability domain. Starting with the general frameworks of different methods, we then use a data set to show how each method generates output with actual codes and implementations. The various methods are divided into different types based on their explanation frameworks. Common categories are feature importance-based methods, rule-based methods, saliency maps methods, counterfactuals, and concept attribution. The book concludes with how data affects interpretability and the common pitfalls of explainability methods.

On completing the book, you will understand the working of model interpretability and explainability methods whenever you encounter them and select and apply the most suitable interpretation method for a machine learning project. After reading this book, readers will be easily able to convert a black-box model into a white box.

This book is a motivation-driven endeavor. Over the last few years, we got fascinated by the importance of building models, which can be explained since we had to interact with stakeholders from different businesses as a part of our daily work. We found one common problem when we started adopting data sciences to solve business problems our clients struggle to understand the recommendations made by us. We felt an urgent need to find explainable models. We started reading about model interpretability and found that the domain is very new and has a lot of potential. For a few use cases, we got fascinated by the kind of difference it could bring to our analysis.

We would like to thank Takuya Kitagawa, Kazuhito Nomura, and Yusuke Kaji at Rakuten. They introduced us to this domain and helped us experiment across various methods and use cases to understand the true potential of model interpretability.

Finally, we would like to acknowledge the invaluable help and guidance of the Apress publishing team for giving us this opportunity to present our work. Special thanks to all the reviewers for patiently reviewing our work and working with us through multiple iterations to give the best version to the readers.