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AI & Machine Learning · Foundational

Linear Regression

A regression algorithm that models the target as a weighted sum of input features plus an intercept.

Definition

Linear regression fits coefficients that minimize the sum of squared errors between predictions and actual values. It is fast, interpretable and a strong baseline, but assumes a linear relationship and is sensitive to outliers and multicollinearity. Ridge and Lasso add L2 and L1 regularization.

Example

Predicting electricity usage from temperature and day of week.

Exam tip

Coefficients are directly interpretable as the effect of a one-unit change in a feature.

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