Epidemiological data often contain numerous variables and potential confounders, making models prone to overfitting. Regularization is crucial for ensuring that models remain robust and reliable, even when dealing with complex datasets. This is particularly important for public health decision-making, where inaccurate predictions can have significant consequences.
