  Machine learning has become widely used in astronomy. Gaussian Process (GP)
regression in particular has been employed a number of times to fit or
re-sample supernova (SN) light-curves, however by their nature typical GP
models are not suited to fit SN photometric data and they will be prone to
over-fitting. Recently GP re-sampling was used in the context of studying the
morphologies of type II and IIb SNe and they were found to be clearly distinct
with respect to four parameters: the rise time (t$_{\rm rise}$), the magnitude
difference between 40 and 30 days post explosion ($\Delta m_{\rm 40-30}$), the
earliest maximum (post-peak) of the first derivative (dm1) and minimum of the
second derivative (dm2). Here we take a close look at GP regression and its
limitations in the context of SN light-curves in general, and we also discuss
the uncertainties on these specific parameters, finding that dm1 and dm2 cannot
give reliable astrophysical information. We do reproduce the clustering in
t$_{\rm rise}$--$\Delta m_{\rm 40-30}$ space although it is not as clear cut as
previously presented. The best strategy to accurately populate the t$_{\rm
rise}$-- $\Delta m_{\rm 40-30}$ space will be to use an expanded sample of high
quality light-curves (such as those in the ATLAS transient survey) and
analytical fitting methods. Finally, using the BPASS fiducial models, we
predict that future photometric studies will reveal clear clustering of the
type IIb and II light curve morphologies with a distinct continuum of
transitional events.
