What is linear regression?

Linear regression models the relationship between input features and a continuous target variable as a linear combination: y = w₁x₁ + w₂x₂ + ... + b. The weights (coefficients) and bias are learned by minimizing Mean Squared Error. Key assumptions: linearity, independence of errors, homoscedasticity, and normality of residuals. It is fast, highly interpretable, and serves as a baseline for regression tasks. For non-linear relationships, polynomial regression or more complex models are needed.