Some common performance metrics were affected by the data imbalance and yielded misleading trends. We included an imbalance-aware random baseline and defined baselineadjusted metrics to overcome this issue, especially in F1-score and accuracy. After baseline adjustment, the metrics provided a unified picture: the largest impact in performance estimates came from the application of data augmentation to the test set (resampling_before_clustering and resampling_after_clustering outperformed semi_resampling and no_resampling). How-ever, augmenting the test set may not reflect a realistic scenario.