 Proper linear models represent another important class of optimization models. A proper linear model is one where predictor variables are assigned weights, which are selected so that the linear combination of those weighted predictor variables optimally predicts a target variable of interest. For example, linear regression is a proper linear model that selects weights such that the squared “distance” between the model’s predicted value of the target variable and the actual value (given in the data set) is minimized.