Interpretable Machine Learning
This book is 70% complete
Last updated on 2018-04-16
About the Book
"Book on interpretability of ML models, such an important topic often neglected" - @prdeepakbabu
Machine learning has a huge potential to improve products, processes and research. But machines usually don’t give an explanation for their predictions, which creates a barrier for the adoption of machine learning. This book is about making machine learning models and their decisions interpretable, with a focus on supervised machine learning and tabular data.
After exploring the concepts of interpretability, you will learn about simple, interpretable models and how to interpret them. The later chapters focus on general model-agnostic tools for analysing complex models and making their decisions interpretable. In an ideal future, machines will be able to explain their decisions and the algorithmic age we are moving towards will be as human as possible.
This book is recommended to machine learning practitioners, data scientists, statisticians and anyone else interested in making machine decisions more human.
A free HTML version of the book can be found at: https://christophm.github.io/interpretable-ml-book/
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