How to Prevent Overfitting in Epidemiological Studies?
Cross-validation: Use techniques like k-fold cross-validation to assess model performance on different subsets of data. Regularization: Apply regularization methods such as Lasso or Ridge Regression to penalize overly complex models. Pruning: Simplify models by removing less significant variables to avoid unnecessary complexity. Data Splitting: Split the dataset into training, validation, and test sets to ensure that the model generalizes well to unseen data.
