Regression and Regularization Explained: Building Reliable Predictive ML Models

Regression is a supervised machine learning technique used to predict continuous numerical values by learning relationships between input features and a target variable, with common types including linear, multiple linear, and polynomial regression. A key challenge in building such models is overfitting, where a model learns training data too closely — including noise and irrelevant patterns — and performs poorly on new, unseen data. Regularization addresses this by adding a penalty term to the model, discouraging unnecessary complexity and helping it focus on meaningful patterns. Techniques such as Ridge, Lasso, and Elastic Net regression are widely used regularization methods that improve model generalization. Together, regression and regularization form a foundational toolkit for data scientists tackling real-world prediction problems like sales forecasting, house pricing, and salary estimation.