  We conduct a post hoc analysis of solar flare predictions made by a Long
Short Term Memory (LSTM) model employing data in the form of Space-weather HMI
Active Region Patches (SHARP) parameters calculated from data in proximity to
the magnetic polarity inversion line where the flares originate. We train the
the LSTM model for binary classification to provide a prediction score for the
probability of M/X class flares to occur in next hour. We then develop a
dimension-reduction technique to reduce the dimensions of SHARP parameter (LSTM
inputs) and demonstrate the different patterns of SHARP parameters
corresponding to the transition from low to high prediction score. Our work
shows that a subset of SHARP parameters contain the key signals that strong
solar flare eruptions are imminent. The dynamics of these parameters have a
highly uniform trajectory for many events whose LSTM prediction scores for M/X
class flares transition from very low to very high. The results demonstrate the
existence of a few threshold values of SHARP parameters that when surpassed
indicate a high probability of the eruption of a strong flare. Our method has
distilled the knowledge of solar flare eruption learnt by deep learning model
and provides a more interpretable approximation, which provides physical
insight to processes driving solar flares.
