LIME – Local Interpretable Model-Agnostic Explanations

In this post, we’ll talk about the method for explaining the predictions of any classifier described in this paper, and implemented in this open source package. Motivation: why do we want to understand predictions? Machine learning is a buzzword these days. With computers beating professionals in games like Go, many people have started asking if machines would also make for better drivers, or even doctors. Many of the state of the art machine learning models are functionally black boxes, as it is nearly impossible to get a feeling for its inner workings. This brings us to a question of trust: do I trust that a certain prediction from the model is correct? Or do I even trust that the model is making reasonable predictions in general? While the stakes are…

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