Regression vs Regularization: A Clear Guide for ML Practitioners

Regression models relationships to predict continuous outcomes, while regularization prevents overfitting by penalizing complexity. Ridge (L2) shrinks coefficients but keeps all features, Lasso (L1) can zero out features for selection, and Elastic Net combines both. Understanding the distinction helps build models that generalize better to new data.