Redshifts of radio sources in the Million Quasars Catalogue from machine
  learning
  With the aim of using machine learning techniques to obtain photometric
redshifts based upon a source's radio spectrum alone, we have extracted the
radio sources from the Million Quasars Catalogue. Of these, 44,119 have a
spectroscopic redshift, required for model validation, and for which photometry
could be obtained. Using the radio spectral properties as features, we fail to
find a model which can reliably predict the redshifts, although there is the
suggestion that the models improve with the size of the training sample. Using
the near-infrared--optical--ultraviolet bands magnitudes, we obtain reliable
predictions based on the 12,503 radio sources which have all of the required
photometry. From the 80:20 training--validation split, this gives only 2501
validation sources, although training the sample upon our previous SDSS model
gives comparable results for all 12,503 sources. This makes us confident that
SkyMapper, which will survey southern sky in the u, v, g, r, i, z bands, can be
used to predict the redshifts of radio sources detected with the Square
Kilometre Array. By using machine learning to impute the magnitudes missing
from much of the sample, we can predict the redshifts for 32,698 sources, an
increase from 28% to 74% of the sample, at the cost of increasing the outlier
fraction by a factor of 1.4. While the "optical" band data prove successful, at
this stage we cannot rule out the possibility of a radio photometric redshift,
given sufficient data which may be necessary to overcome the relatively
featureless radio spectra.
