  We prove optimal embeddings for nonlinear approximation spaces in terms of
weighted Lorentz sequence spaces, with the weights depending on the democracy
functions of the basis. As applications we recover known embeddings for
$N$-term wavelet approximation in Lebesgue, Orlicz, and Lorentz norms. We also
study the "greedy classes" introduced by Gribonval and Nielsen.
