Why is Stratified K-Fold Cross Validation Important in Epidemiology?
In epidemiology, datasets often contain imbalanced classes, such as different proportions of disease cases versus controls. Using standard cross-validation methods may lead to subsets that do not adequately represent the overall population. This can result in models that perform well on some folds but poorly on others. Stratified K-Fold Cross Validation ensures that each fold is representative of the original dataset's class distribution, leading to more accurate and reliable predictive models.
