  Density functional theory within the local or semilocal density
approximations (DFT-LDA/GGA) has become a workhorse in electronic structure
theory of solids, being extremely fast and reliable for energetics and
structural properties, yet remaining highly inaccurate for predicting band gaps
of semiconductors and insulators. Accurate prediction of band gaps using
firstprinciples methods is time consuming, requiring hybrid functionals,
quasi-particle GW, or quantum Monte Carlo methods. Efficiently correcting
DFT-LDA/GGA band gaps and unveiling the main chemical and structural factors
involved in this correction is desirable for discovering novel materials in
high-throughput calculations. In this direction, we use DFT and machine
learning techniques to correct band gaps and band-edge positions of a
representative subset of ABO3 perovskite oxides. Relying on results of HSE06
hybrid functional calculations as target values of band gaps, we find a
systematic band gap correction of ~1.5 eV for this class of materials, where ~1
eV comes from downward shifting the valence band and ~0.5 eV from uplifting the
conduction band. The main chemical and structural factors determining the band
gap correction are determined through a feature selection procedure.
