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Our goal is to estimate the mass of the Local Group (LG) and the individualmasses of its primary galaxies, the M31 and the Milky Way (MW). We do this bymeans of a supervised machine learning algorithm, the gradient boosted decisiontrees (GBDT) and using the observed distance and relative velocity of the twoas input parameters. The GBDT is applied to a sample of 2148 mock LGs drawnfrom a set of 5 dark matter (DM)-only simulations, ran within the standard$\Lambda$CDM\ cosmological model. The selection of the mock LGs is guided by aLG model, which defines such objects. The role of the observationaluncertainties of the input parameters is gauged by applying the model to anensemble of mock LGs pairs whose observables are these input parametersperturbed by their corresponding observational errors. Finally theobservational data of the actual LG is used to infer its relevant masses. Ourmain results are the sum and the individual masses of the MW and M31: $M_{tot}= 3.31 ^{+0.79}_{-0.67} $, $M_{MW}=1.15^{+0.25}_{-0.22}$ and$M_{M31}=2.01^{+0.65}_{-0.39} \ \ \times 10^{12}M_{\odot}$ (corresponding tothe median and the 1st and 3rd quartiles). The ratio of the masses is$M_{M31}/M_{MW}=1.75^{+0.54}_{-0.28}$, where by convention the M31 is definedhere to be the more massive of the two halos.

Estimation of the masses in the Local Group by Gradient Boosted Decision Trees