We suggest an approach using machine learning random forest algorithms to comparing and calibrating the results of calculations of transition energies in organic molecules by ZINDO/S (Zerner's intermediate neglect of differential overlap) and TDDFT (time-dependent density-functional theory) methods. We show how our machine learning model, trained on a relatively small data set can improve the results of semi-empirical methods and obtain absorption spectra comparable to TDDFT calculations.