A linear regression model assumes that the regression function E(Y|X) is linear in the inputs \(X_1,...,X_p\). For prediction purposes they can sometimes outperform nonlinear models, e.g in situations with small numbers of training cases, low signal-to-noise ratio or sparse data. Finally, linear methods can be applied to transformations of inputs and this considerably expands their scope.