Introduction
Explainability of machine learning models is a hot topic right now - particularly in deep learning where models are that bit harder to reason about and understand. These models are often called ‘black boxes’ because you put something in, you get something out and you don’t really know how that outcome was achieved. The ability to explain machine learning model’s decisions in terms of the features passed in is both useful from a debugging standpoint (identifying features with weird weights) and with legislation like GDPR’s Right to an Explanation it is becoming important in a commercial setting to be able to explain why models behave a certain way.
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