  Neutrinos in dense environments like core-collapse supernovae (CCSNe) and
neutron star mergers (NSMs) can undergo fast flavor conversions (FFCs) once the
angular distribution of neutrino lepton number crosses zero along a certain
direction. Recent advancements have demonstrated the effectiveness of machine
learning (ML) in detecting these crossings. In this study, we enhance prior
research in two significant ways. Firstly, we utilize realistic data from CCSN
simulations, where neutrino transport is solved using the full Boltzmann
equation. We evaluate the ML methods' adaptability in a real-world context,
enhancing their robustness. In particular, we demonstrate that when working
with artificial data, simpler models outperform their more complex
counterparts, a noteworthy illustration of the bias-variance tradeoff in the
context of ML. We also explore methods to improve artificial datasets for ML
training. In addition, we extend our ML techniques to detect the crossings in
the heavy-leptonic channels, accommodating scenarios where $\nu_x$ and
$\bar\nu_x$ may differ. Our research highlights the extensive versatility and
effectiveness of ML techniques, presenting an unparalleled opportunity to
evaluate the occurrence of FFCs in CCSN and NSM simulations.
