TracIn performs slightly better than KNN Shapley but is still outperformed by MVS-A and Cat-Boost. This is likely due to the different underlying machine learning models. While TracIn uses deep neural networks (DNNs) to identify important samples, MVS-A and CatBoost use gradient boosting algorithms. The discrepancy in the active learning performance is likely caused by the low prediction performance of DNNs, given such a small initial training set. Meanwhile, gradient boosting algorithms can already show strong predictions even with a small training set size. The discrepancy between MVS-A and CatBoost can be attributed to their difference in computing importance given the gradient boosted trees. While MVS-A focuses on the influence of a sample on the tree structure, CatBoost uses a sample's influence on leaf weights. These results suggest that analyzing splitting decisions provides more accurate influence scores compared to leaf weight analysis. Furthermore, these results further reaffirm that methods utilizing GBMs outperform deep learning approaches on small training sets. We hypothesized the bottleneck could be the importance score prediction of the separate regression model.