  We consider composite loss functions for multiclass prediction comprising a
proper (i.e., Fisher-consistent) loss over probability distributions and an
inverse link function. We establish conditions for their (strong) convexity and
explore the implications. We also show how the separation of concerns afforded
by using this composite representation allows for the design of families of
losses with the same Bayes risk.
