linear models are class of models that are widely used in practice and have been studied extensively in the last few decadeswith roots going back over hundred years linear models make prediction using linear function of the input featureswhich we will explain shortly linear models for regression for regressionthe general prediction formula for linear model looks as followsy [ [ [ [ [px[pb herex[ to [pdenotes the features (in this examplethe number of features is pof single data pointw and are parameters of the model that are learnedand is the prediction the model makes for dataset with single featurethis isy [ [ which you might remember from high school mathematics as the equation for line herew[ is the slope and is the -axis offset for more featuresw contains the slopes along each feature axis alternativelyyou can think of the predicted response as being weighted sum of the input featureswith weights (which can be negativegiven by the entries of trying to learn the parameters [ and on our one-dimensional wave dataset might lead to the following line (see figure - )in[ ]mglearn plots plot_linear_regression_wave(out[ ] [ ] - supervised machine learning algorithms