  We present a machine learning analysis of five labelled galaxy catalogues
from the Galaxy And Mass Assembly (GAMA): The SersicCatVIKING and
SersicCatUKIDSS catalogues containing morphological features, the
GaussFitSimple catalogue containing spectroscopic features, the MagPhys
catalogue including physical parameters for galaxies, and the Lambdar
catalogue, which contains photometric measurements. Extending work previously
presented at the ESANN 2018 conference - in an analysis based on Generalized
Relevance Matrix Learning Vector Quantization and Random Forests - we find that
neither the data from the individual catalogues nor a combined dataset based on
all 5 catalogues fully supports the visual-inspection-based galaxy
classification scheme employed to categorise the galaxies. In particular, only
one class, the Little Blue Spheroids, is consistently separable from the other
classes. To aid further insight into the nature of the employed visual-based
classification scheme with respect to physical and morphological features, we
present the galaxy parameters that are discriminative for the achieved class
distinctions.
