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.

The issue names no file, test, or entry point. Locate the manuscript source, determine where this guidance belongs and how it fits with existing content; done means the recommendation and its handling of class imbalance are clearly represented in the appropriate manuscript section.

Written by the indexing model from the issue text.