Methodology diagram. The procedure consists of three main parts: experiments as well as inference and catalog tests. The experiments are based on the cross-match between KiDS and SDSS data, and they include the repeatable process of training and evaluating ML models. The training is based only on the train and random test subsets, while the hyper-parameter tuning uses both random and faint extrapolation tests. The best hyper-parameters found are used in the inference to train new models, now on the whole range of magnitudes available in the training data. The raw predictions were then tested with number counts and Gaia parallaxes to calibrate the final catalog with probability cuts for the optimal purity-completeness trade-off.