linear model for classification can only separate points using lineand will not be able to do very good job on this dataset (see figure - )in[ ]from sklearn svm import linearsvc linear_svm linearsvc(fit(xymglearn plots plot_ d_separator(linear_svmxmglearn discrete_scatter( [: ] [: ]yplt xlabel("feature "plt ylabel("feature "now let' expand the set of input featuressay by also adding feature * the square of the second featureas new feature instead of representing each data point as two-dimensional point(feature feature )we now represent it as threedimensional point(feature feature feature * this new representation is illustrated in figure - in three-dimensional scatter plot we picked this particular feature to add for illustration purposes the choice is not particularly important supervised machine learning algorithms