Preface
From this book's title, you can infer that this book is about three things: Interpretation, Machine Learning, and Python. And they are precisely in that order of importance!
"Why?", you might ask.
Interpretable Machine Learning, also known as Explainable AI (XAI), is an ever-increasing family of methods that we can leverage to learn from models and make them safe, fair, and reliable, which is something, I hope, we all want for our models.
However, since AI is replacing software (and humans), machine learning models are seen as a more "intelligent" form of software. Yes, they are ones and zeros, but they are not software in the sense that their logic is programmed by people and does as intended, by design. So, interpretation is how ...
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