Linear Models for Prediction

Linear regression is your first credible baseline — transparent, fast, and fully inspectable. This lecture covers OLS regression, then introduces ridge and lasso as the first upgrade when you worry about overfitting or too many features. We discuss when to penalise, how to tune the regularisation parameter with cross-validation, and what the regularisation path tells you about your features.

Interactive: Regularisation path

Regularisation path: as λ increases (slider right), coefficients shrink toward zero. Stronger features resist longer. Ridge shrinks smoothly; lasso would send some coefficients exactly to zero.