Scientists should strive for balance in the training, tuning, and testing datasets to assure that different phenotypic groups are represented appropriately/similarly. This refers to the balanced proportion of different classes of outcome or target variables. If class imbalance is inevitable, appropriate strategies like augmentation or bootstrapping can be used.