Explain output of a given classifier w.r.t features Given a binary classifier, is it always possible to explain why it has classified some input as a positive class ? And by that I mean, if we have a big set of features, is there a tool that says : 'For this output, these are the features that were the most responsible for labeling it as a positive' ? 

Thanks  !
Try out some of the examples in this library, which attempts to use machine learning to understand black box machine learning models in Python.