Linear regression

Predicting a number with a straight-line formula: a weight for each input, added up, plus a starting value.

Other terms from the same lesson

• bias: In a model, a starting value added before anything else (the intercept). It can also mean a systematic error; the context tells you which.
• intercept: In a straight-line model, the prediction when every feature is 0: where the line crosses the vertical axis.
• least squares: Fitting a model by choosing the weights that make the sum of squared errors as small as possible.
• R²: How much better a model predicts than always guessing the average: 1 is perfect, 0 is no better, and below 0 is worse.
• residual: How far one prediction missed: the true value minus the predicted value.
• sum of squared errors: Square each prediction's miss and add them all up. Squaring makes every miss count as positive and big misses count much more.
• weight: An adjustable number inside a model that says how much an input matters. Training sets the weights.