For inorganic solids, several approaches have been taken to increase the accuracy of ML-predicted band gaps trained on high-throughput DFT calculations in a computationally tractable manner. The most straightforward option is to train ML models on experimental band gap data or an ensemble of both theoretical and experimental band gap data. Unfortunately, this approach is challenging to apply to MOFs because there are relatively few reports of experimentally measured MOF band gaps. Furthermore, the reported band gaps of MOFs can vary by several tenths of an eV depending on the synthesis conditions and crystallinity of the material. Another approach is to carry out higher-accuracy DFT calculations on a subset of materials and use them to train an ML model that can make more reliable predictions. Recently, large datasets of band gaps computed with meta-GGA and hybrid functionals have been published for inorganic solids, although no such resource currently exists for MOFs.