  Wide-area imaging surveys are one of the key ways of advancing our
understanding of cosmology, galaxy formation physics, and the large-scale
structure of the Universe in the coming years. These surveys typically require
calculating redshifts for huge numbers (hundreds of millions to billions) of
galaxies - almost all of which must be derived from photometry rather than
spectroscopy. In this paper we investigate how using statistical models to
understand the populations that make up the colour-magnitude distribution of
galaxies can be combined with machine learning photometric redshift codes to
improve redshift estimates. In particular we combine the use of Gaussian
Mixture Models with the high performing machine learning photo-z algorithm GPz
and show that modelling and accounting for the different colour-magnitude
distributions of training and test data separately can give improved redshift
estimates, reduce the bias on estimates by up to a half, and speed up the
run-time of the algorithm. These methods are illustrated using data from deep
optical and near infrared data in two separate deep fields, where training and
test data of different colour-magnitude distributions are constructed from the
galaxies with known spectroscopic redshifts, derived from several heterogeneous
surveys.
