Sample based Explanations via Generalized Representers

We propose a general class of sample based explanations of machine learning models, which we term generalized representers . To measure the effect of a training sample on a model's test prediction, generalized representers use two components: a global sample importance that quantifies the importance of the training point to the model and is invariant to test samples, and a local sample importance that measures similarity between the training sample and the test point with a kernel.