  Self-consistent model grids of brown dwarfs involve complex physics and
chemistry, and are often computed using proprietary computer codes, making it
challenging to identify the reasons for discrepancies between model and data as
well as between the models produced by different research groups. In the
current study, we demonstrate a novel method for analyzing brown dwarf spectra,
which combines the use of the Sonora, AMES-Cond and HELIOS model grids with the
supervised machine learning method of the random forest. Besides performing
atmospheric retrieval, the random forest enables information content analysis
of the three model grids as a natural outcome of the method, both individually
on each grid and by comparing the grids against one another, via computing
large suites of mock retrievals. Our analysis reveals that the different
choices made in modelling the alkali line shapes hinder the use of the alkali
lines as gravity indicators. Nevertheless, the spectrum longward of 1.2 micron
encodes enough information on the surface gravity to allow its inference from
retrieval. Temperature may be accurately and precisely inferred independent of
the choice of model grid, but not the surface gravity. We apply random forest
retrieval to three objects: the benchmark T7.5 brown dwarf GJ 570D; and Epsilon
Indi Ba (T1.5 brown dwarf) and Bb (T6 brown dwarf), which are part of a binary
system and have measured dynamical masses. For GJ 570D, the inferred effective
temperature and surface gravity are consistent with previous studies. For
Epsilon Indi Ba and Bb, the inferred surface gravities are broadly consistent
with the values informed by the dynamical masses.
