Epidemiological data can be complex, with many potential predictors influencing health outcomes. Traditional regression models may struggle with multicollinearity and overfitting, leading to less reliable results. Elastic Net addresses these issues by:
1. Combining Lasso and Ridge: It blends Lasso's ability to perform variable selection (shrink some coefficients to zero) with Ridge's ability to handle multicollinearity (shrink coefficients of correlated variables towards each other). 2. Improving Prediction Accuracy: By balancing bias and variance through regularization, Elastic Net often yields more accurate predictions. 3. Enhancing Interpretability: It can simplify models by excluding irrelevant predictors, making results easier to interpret.
