  To predict the immediate evolution of a plasma system, one needs to identify
the nature of the dominant instability. In this work, a neural network is
employed to address a non-binary classification problem of instabilities in
astrophysical jets, whose natural oscillations and instabilities are quantified
with the magnetohydrodynamic spectroscopy code Legolas. The trained models
exhibit reliable performance in the identification of the two instability types
supported by these jets. To improve the neural network aided classification
process, techniques for training data augmentation and refinement of
predictions for general eigenproblems are discussed.
