Machine Learning & AI

Basic

Linear Regression Model

Predicts a continuous value as a weighted sum of input features plus a bias.

Formula

\hat{y} = \mathbf{w}^\top \mathbf{x} + b

Variables

\hat{y}Prediction

\mathbf{w}Weight vector

\mathbf{x}Feature vector

bBias

Example

Weights [2,3], x=[1,4], b=1: y = 2+12+1 = 15

Did You Know?

Linear regression dates to Gauss and Legendre around 1800 — the oldest algorithm still core to modern ML.

regression model supervised

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