Sample based Explanations via Generalized Representers
Che-Ping Tsai Chih-Kuan Yeh Pradeep Ravikumar
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. A key contribution of the paper is to show that
generalized representers are the only class of sample based explanations
satisfying a natural set of axiomatic properties. We discuss approaches to
extract global importances given a kernel, and also natural choices of kernels
given modern non-linear models. As we show, many popular existing sample based
explanations could be cast as generalized representers with particular choices
of kernels and approaches to extract global importances. Additionally, we
conduct empirical comparisons of different generalized representers on two
image and two text classification datasets.