  Building on the first paper in this series (Duncan et al. 2018), we present a
study investigating the performance of Gaussian process photometric redshift
(photo-z) estimates for galaxies and active galactic nuclei detected in deep
radio continuum surveys. A Gaussian process redshift code is used to produce
photo-z estimates targeting specific subsets of both the AGN population -
infrared, X-ray and optically selected AGN - and the general galaxy population.
The new estimates for the AGN population are found to perform significantly
better at z > 1 than the template-based photo-z estimates presented in our
previous study. Our new photo-z estimates are then combined with template
estimates through hierarchical Bayesian combination to produce a hybrid
consensus estimate that outperforms either of the individual methods across all
source types. Photo-z estimates for radio sources that are X-ray sources or
optical/IR AGN are signficantly improved in comparison to previous
template-only estimates, with outlier fractions and robust scatter reduced by
up to a factor of ~4. The ability of our method to combine the strengths of the
two input photo-z techniques and the large improvements we observe illustrate
its potential for enabling future exploitation of deep radio continuum surveys
for both the study of galaxy and black hole co-evolution and for cosmological
studies.
