Regularized Linear Models

Regularized linear models are statistical tools used to predict outcomes based on input data, like estimating house prices from features such as size and location. They extend basic linear models by adding a penalty term that discourages overly complex or extreme coefficients, which helps prevent overfitting—where a model captures noise instead of true patterns. This regularization improves the model's ability to generalize to new data, making predictions more reliable. Essentially, regularized models balance fitting the data well while keeping their complexity in check for better performance on unseen data.