Regression vs Regularization: Key Differences Every ML Learner Should Know

Regression and regularization are two distinct but complementary concepts in machine learning and statistics. Regression models the relationship between input variables and a continuous outcome, with techniques like simple and multiple linear regression minimizing prediction error using Ordinary Least Squares. Regularization, by contrast, addresses overfitting by adding a complexity penalty to the model's objective function, discouraging overly large coefficients. The three main regularization methods are Ridge, which shrinks coefficients using squared penalties; Lasso, which can reduce some coefficients to zero for feature selection; and Elastic Net, which combines both approaches. Together, these techniques help build models that generalize better to new, unseen data.