# 7.3.2 Properties of the input data

Machine-learning classifiers were trained with Gaia sources selected from over seven hundred fifty thousand objects crossmatched with the literature, representing a large number of variability types as well as non-varying objects. The training set included about thirty-three thousand sources filtered according to their distribution in the sky, their number of FoV transits, and their median magnitudes in the $G$ band, as described in more details in Section 7.3.3.

All sources with two or more FoV transits in the $G$ band were processed by the classifiers. Photometric time series in the $G$, $G_{\rm BP}$, and $G_{\rm RP}$ bands were used after the pre-processing steps described in Section 7.2.3 and astrometric quantities (such as parallax and proper motion) were employed without specific selections. The results of the Statistical Parameter Computation module (Section 7.2.3) provided additional input information which was used directly as classification attributes or in the computation thereof.