Photometric redshift - empirical methods (machine learning)
This was too long for a comment, but is not a real answer since I'm not completely sure, but:



My guess is that "representing the galaxies" refers to the "type" of galaxy that you intent to observe, where by "type" I mean e.g.
Lyman-break galaxies (Steidel et al. 1996),
Lyman $\alpha$ emitters (Partridge &amp; Peebles 1967),
sub-millimeter galaxies (Blain et al. 2002),
(U)LIRGS, etc.

These terms all refer to selection methods (i.e. observational techniques), and hence also to physical differences. The closer the training data are to the observed sample, the better your algorithm will be at linking their redshifts to their photometric properties.

Mass is just one property, there's also e.g. dustiness, star-burstiness, stellar population, age, and others. "Region" is probably less important, but since the clustering of galaxies also affect their properties (e.g. their morphology, Dressler 1980), it could potentially influence the result.