  In preparation for photometric classification of transients from the Legacy
Survey of Space and Time (LSST) we run tests with different training data sets.
Using estimates of the depth to which the 4-metre Multi-Object Spectroscopic
Telescope (4MOST) Time Domain Extragalactic Survey (TiDES) can classify
transients, we simulate a magnitude-limited sample reaching $r_{\textrm{AB}}
\approx$ 22.5 mag. We run our simulations with the software snmachine, a
photometric classification pipeline using machine learning. The
machine-learning algorithms struggle to classify supernovae when the training
sample is magnitude-limited, in contrast to representative training samples.
Classification performance noticeably improves when we simulate adding just a
few additional fainter supernovae to the magnitude-limited training sample;
average area under ROC curve (AUC) score over 10 runs increases from 0.554 to
0.760 for a k-nearest neighbours (KNN) algorithm. By creating new, artificial
light curves using the augmentation software avocado, we achieve a purity in
our classified sample of 95 per cent using an artificial neural network, with
completeness $\approx$ 0.4 in 9 out of 10 runs. We also reach a highest average
AUC score of 0.962 with KNN. Our results are a proof of concept that
augmentation is a crucial requirement in optimisation of a 4MOST spectroscopic
training sample. However, to create the optimal training sample and achieve the
best classification results, it is necessary to have at least a few `true'
faint supernovae to complement our magnitude-limited sample before augmenting.
