Confusion matrices (upper panels) for the SVC, RF, and MLP methods, respectively, along with the characteristic metrics (precision, recall, and F1 score; lower panels). These results originate from single runs, i.e., by using 70% of the initial sample for the training sample, which is then resampled to produce a balanced sample before training each model and applying the model to the remaining 30% of the sample (the validation). In general, the algorithms perform well except for the cases of LBVs and WRs (see Sect. 4.1 for more details).