Consequently, various regression models were tested in the influence active learning pipeline on the same dataset as before, always using MVS-A for influence calculation, as MVS-A has shown to have the best active learning performance (Fig. ). Among them, the sklearn implementation of a Gaussian process regressor showed the best active learning performance, improving the overall pipeline. Finally, the optimized pipeline was benchmarked against the state-of-the-art on the remaining 24 datasets. As seen in Figure , the influence-based active learning approach significantly outperforms the greedy benchmark at all steps across all datasets.