Glossary · Phase 3 · Machine learning
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.
Learn it properly
Linear regression · Fit a line, and predict numbers from features. About 30 minutes.
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.
- linear regression: Predicting a number with a straight-line formula: a weight for each input, added up, plus a starting value.
- 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.
- weight: An adjustable number inside a model that says how much an input matters. Training sets the weights.