  Constraining the frequency of energy deposition in magnetically-closed active
region cores requires sophisticated hydrodynamic simulations of the coronal
plasma and detailed forward modeling of the optically-thin line-of-sight
integrated emission. However, understanding which set of model inputs best
matches a set of observations is complicated by the need for any proposed
heating model to simultaneously satisfy multiple observable constraints. In
this paper, we train a random forest classification model on a set of
forward-modeled observable quantities, namely the emission measure slope, the
peak temperature of the emission measure distribution, and the time lag and
maximum cross-correlation between multiple pairs of AIA channels. We then use
our trained model to classify the heating frequency in every pixel of active
region NOAA 1158 using the observed emission measure slopes, peak temperatures,
time lags, and maximum cross-correlations and are able to map the heating
frequency across the entire active region. We find that high-frequency heating
dominates in the inner core of the active region while intermediate frequency
dominates closer to the periphery of the active region. Additionally, we assess
the importance of each observed quantity in our trained classification model
and find that the emission measure slope is the dominant feature in deciding
with which heating frequency a given pixel is most consistent. The technique
presented here offers a very promising and widely applicable method for
assessing observations in terms of detailed forward models given an arbitrary
number of observable constraints.
