Table shows the average testing errors on the original test of A obtained by training different ML estimators after different AB aggregation strategies. A color scheme is used to guide the interpretation. Our experiments reveal that classical ML approaches fail to leverage the advantages offered by any of the considered aggregation strategies. Among the strategies, the plain Concatenation performs the worst, followed by DiSCoVeR, and finally ElemConc. This observation suggests that the contamination in the original dataset increases as a function of the number of added points, irrespective of the aggregation strategy. Contrary to classical ML approaches, DL models exhibit much greater stability. A possible explanation for this phenomenon can be attributed to the choice of loss function employed at the training stage. Notably, both Roost and CrabNet utilize a customized variant of the L1 loss referred to as 'robust' [Goodall and . The rationale behind employing this modified loss function is the ability to capture and incorporate the inherent noise associated with individual data points. Therefore, this approach may facilitate a more robust and stable data aggregation process. Despite that, except for sporadic cases, improvements or degradations in accuracy are not significant. Interestingly, the majority of the best overall results are found in the correspondence of the CrabNet model after performing a full concatenation with dataset B. Further investigation into the reasons behind this could provide valuable insights for future research. By comparing the results obtained for the calculated datasets, we also observe that maintaining consistency between the nature of datasets A and B led to slightly better performance.