  In particle physics, semi-supervised machine learning is an attractive option
to reduce model dependencies searches beyond the Standard Model. When utilizing
semi-supervised techniques in training machine learning models in the search
for bosons at the Large Hadron Collider, the over-training of the model must be
investigated. Internal fluctuations of the phase space and bias in training can
cause semi-supervised models to label false signals within the phase space due
to over-fitting. The issue of false signal generation in semi-supervised models
has not been fully analyzed and therefore utilizing a toy Monte Carlo model,
the probability of such situations occurring must be quantified. This
investigation of $Z\gamma$ resonances is performed using a pure background
Monte Carlo sample. Through unique pure background samples extracted to mimic
ATLAS data in a background-plus-signal region, multiple runs enable the
probability of these fake signals occurring due to over-training to be
thoroughly investigated.
