  We consider Type IIB compactifications on an isotropic torus $T^6$ threaded
by geometric and non geometric fluxes. For this particular setup we apply
supervised machine learning techniques, namely an artificial neural network
coupled to a genetic algorithm, in order to obtain more than sixty thousand
flux configurations yielding to a scalar potential with at least one critical
point. We observe that both stable AdS vacua with large moduli masses and small
vacuum energy as well as unstable dS vacua with small tachyonic mass and large
energy are absent, in accordance to the Refined de Sitter Conjecture. Moreover,
by considering a hierarchy among fluxes, we observe that perturbative solutions
with small values for the vacuum energy and moduli masses are favored, as well
as scenarios in which the lightest modulus mass is much greater than the
corresponding AdS vacuum scale. Finally we apply some results on Random Matrix
Theory to conclude that the most probable mass spectrum derived from this
string setup is that satisfying the Refined de Sitter and AdS scale
conjectures.
