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Traditional spectral energy distribution (SED) fitting codes used to derivegalaxy physical properties are often uncertain at the factor of a few levelowing to uncertainties in galaxy star formation histories and dust attenuationcurves. Beyond this, Bayesian fitting (which is typically used in SED fittingsoftware) is an intrinsically compute-intensive task, often requiring access toexpensive hardware for long periods of time. To overcome these shortcomings, wehave developed {\sc mirkwood}: a user-friendly tool comprising of an ensembleof supervised machine learning-based models capable of non-linearly mappinggalaxy fluxes to their properties. By stacking multiple models, we marginalizeagainst any individual model's poor performance in a given region of theparameter space. We demonstrate \textsc{mirkwood}'s significantly improvedperformance over traditional techniques by training it on a combined data setof mock photometry of z=0 galaxies from the \textsc{Simba}, \textsc{EAGLE} and\textsc{IllustrisTNG} cosmological simulations, and comparing the derivedresults with those obtained from traditional SED fitting techniques.\textsc{mirkwood} is also able to account for uncertainties arising both fromintrinsic noise in observations, and from finite training data and incorrectmodeling assumptions. To increase the added value to the observationalcommunity, we use Shapley value explanations (SHAP) to fairly evaluate therelative importance of different bands to understand why particular predictionswere reached. We envisage \textsc{mirkwood} to be an evolving, open-sourceframework that will provide highly accurate physical properties fromobservations of galaxies as compared to traditional SED fitting.

{\sc mirkwood:} Fast and Accurate SED Modeling Using Machine Learning