  Many classification models produce a probability distribution as the outcome
of a prediction. This information is generally compressed down to the single
class with the highest associated probability. In this paper, we argue that
part of the information that is discarded in this process can be in fact used
to further evaluate the goodness of models, and in particular the confidence
with which each prediction is made. As an application of the ideas presented in
this paper, we provide a theoretical explanation of a confidence degradation
phenomenon observed in the complement approach to the (Bernoulli) Naive Bayes
generative model.
