In all experiments the xgboost models were comparable to the KNeighborsRegressor model (Figure ). For the multi-task case the OOPT strategy worked slightly worse or was comparable to MOT (Figures and). Contrary to findings of Janela et al. , we did not see elevated performance of the MedianModel baseline on this particular data set and benchmarking conditions. Therefore, using multiple single task models is likely a better choice for the data at hand. However, it should be noted that more models and strategies could be considered here and also compared in a follow up study. For example, deep learning models may be better-suited for multi-task modeling than the algorithms tested herein. Their architecture can be adapted in a number of ways to accommodate multi-task learning . Another point to consider is the influence of sparsity of the data . We simply imputed missing labels with a median value in our experiments, which may have introduced large bias to these central values in the multi-task models presented. Surprisingly, this did not affect the performance of the MedianModel, which should theoretically benefit from such imputation. Finally, it should also be noted that proper statistical testing should also be conducted to determine under which conditions the performance of models truly significantly differs.