deep-rules
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Strive for balance between training, tuning, and testing datasets
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.
With augmentation, one needs to be careful to augment the training, testing, and validation data separately, not together (and then splitting) to prevent overfitting.