Besides the overall changes in data imbalance, strategies differed in how the imbalance of a certain protein in the training set would translate to the test set. The positive trend in no_resampling suggests that existing data imbalances tended to persist after the clustering and splitting. The negative trend in resampling_before_clustering hints that, in the absence of imbalance, clustering will induce it. The flat trend in semi_resampling supports that the imbalance induced with the clustering in the training set, which was balanced with SMOTE beforehand, is independent from the original imbalance in the dataset (present in the test set).