Thermoelectric materials generate an electrical potential in response to a difference in temperature across the material and vice versa. Thermoelectric materials have many applications, with one of the most notable being a solid state replacement for chemical refrigerants. The suitability of materials as thermoelectrics is a combination of a low thermal conductivity, high electrical conductivity, and a high seebeck coefficient. To screen materials for use as thermoelectrics, all three properties are of importance, we present a front end to a RF model of the thermal conductivity of thermoelectrics. The model presented here is an updated version of a previously reported thermal conductivity prediction model 29 for probing the Ba-Y-Ti-O phase space of thermoelectric materials. The RF was trained on a mixture of experimentally measured and DFT computed thermal conductivities for a range of thermoelectric materials. The RF is trained using a composition based featurisation, and as such predictions can be obtained from a chemical formula only.