  Identifying merging galaxies is an important - but difficult - step in galaxy
evolution studies. We present random forest classifications of galaxy mergers
from simulated JWST images based on various standard morphological parameters.
We describe (a) constructing the simulated images from IllustrisTNG and the
Santa Cruz SAM, and modifying them to mimic future CEERS observations as well
as nearly noiseless observations, (b) measuring morphological parameters from
these images, and (c) constructing and training the random forests using the
merger history information for the simulated galaxies available from
IllustrisTNG. The random forests correctly classify $\sim60\%$ of non-merging
and merging galaxies across $0.5 < z < 4.0$. Rest-frame asymmetry parameters
appear more important for lower redshift merger classifications, while
rest-frame bulge and clump parameters appear more important for higher redshift
classifications. Adjusting the classification probability threshold does not
improve the performance of the forests. Finally, the shape and slope of the
resulting merger fraction and merger rate derived from the random forest
classifications match with theoretical Illustris predictions, but are
underestimated by a factor of $\sim 0.5$.
