  Traditional spectral energy distribution (SED) fitting codes used to derive
galaxy physical properties are often uncertain at the factor of a few level
owing to uncertainties in galaxy star formation histories and dust attenuation
curves. Beyond this, Bayesian fitting (which is typically used in SED fitting
software) is an intrinsically compute-intensive task, often requiring access to
expensive hardware for long periods of time. To overcome these shortcomings, we
have developed {\sc mirkwood}: a user-friendly tool comprising of an ensemble
of supervised machine learning-based models capable of non-linearly mapping
galaxy fluxes to their properties. By stacking multiple models, we marginalize
against any individual model's poor performance in a given region of the
parameter space. We demonstrate \textsc{mirkwood}'s significantly improved
performance over traditional techniques by training it on a combined data set
of mock photometry of z=0 galaxies from the \textsc{Simba}, \textsc{EAGLE} and
\textsc{IllustrisTNG} cosmological simulations, and comparing the derived
results with those obtained from traditional SED fitting techniques.
\textsc{mirkwood} is also able to account for uncertainties arising both from
intrinsic noise in observations, and from finite training data and incorrect
modeling assumptions. To increase the added value to the observational
community, we use Shapley value explanations (SHAP) to fairly evaluate the
relative importance of different bands to understand why particular predictions
were reached. We envisage \textsc{mirkwood} to be an evolving, open-source
framework that will provide highly accurate physical properties from
observations of galaxies as compared to traditional SED fitting.
