Glossary · Phase 3 · Machine learning
R²
How much better a model predicts than always guessing the average: 1 is perfect, 0 is no better, and below 0 is worse.
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.
- 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.