  In a previous paper we presented the results of applying machine learning to
classify whether an HI 21-cm absorption spectrum arises in a source intervening
the sight-line to a more distant radio source or within the host of the radio
source itself. This is usually determined from an optical spectrum giving the
source redshift. However, not only will this be impractical for the large
number of sources expected to be detected with the Square Kilometre Array, but
bright optical sources are the most ultra-violet luminous at high redshift and
so bias against the detection of cool, neutral gas. Adding another 44, mostly
newly detected absorbers, to the previous sample of 92, we test four different
machine learning algorithms, again using the line properties (width, depth and
number of Gaussian fits) as features. Of these algorithms, three gave a some
improvement over the previous sample, with a logistic regression model giving
the best results. This suggests that the inclusion of further training data, as
new absorbers are detected, will further increase the prediction accuracy above
the current 80%. We use the logistic regression model to classify the z = 0.42
absorption towards PKS 1657-298 and find this to be associated, which is
consistent with a previous study which determined a similar redshift from the
K-band magnitude-redshift relation.
