Case-Based Explanation for Artificial Neural Nets
Sometimes the most accurate models are the least intelligible. We show how to generate case-based explanations for non-case-based machine learning methods such as artificial neural nets. The method uses the neural net as a distance metric to determine which cases in the training set are most similar to a test case that needs to be explained.