Elastic Net

Elastic Net is a statistical method used to select important features from data when building predictive models. It combines two techniques—Lasso and Ridge regression—to handle situations where many predictors are involved, especially when some are related. This approach not only helps identify the most relevant variables but also manages issues like multicollinearity, ensuring the model remains stable and accurate. Essentially, Elastic Net balances the simplicity of selecting fewer variables with the robustness of handling correlated data, making it a versatile tool for creating reliable predictions from complex datasets.