Learning histories for the XGB models. Left: classification. Center: QSO redshift. Right: galaxy redshift. The x-axis shows the number of trees created iteratively during the model training, and the y-axis shows the classification error rate and redshift root mean square error on two different scales for the random and faint extrapolation tests. The errors in the faint test are higher than in the random tests due to extrapolation and higher noise. The models were stopped if the results on the faint test did not improve for 200 consecutive trees. For classification, which is easier to solve than redshift regression, the random test shows minimums sooner, followed by oscillations, while the faint test suggests longer training. For redshifts, which is a more complicated problem, the faint test achieves minimum quickly and then shows overfitting, while the random test suggests longer training.