Translations:Linear Regression/39/en

• Feature scaling: Standardizing features (zero mean, unit variance) improves gradient descent convergence and makes regularization fair across features.
• Polynomial features: Adding polynomial terms (e.g., $ x^2, x_1 x_2 $) allows linear regression to capture nonlinear relationships.
• Outliers: OLS is sensitive to outliers because of the squared loss. Robust alternatives include Huber regression and RANSAC.
• Diagnostic plots: Residual plots help detect violations of assumptions (non-linearity, heteroscedasticity, non-normality).