feature derived from feature in the new representation of the datait is now indeed possible to separate the two classes using linear modela plane in three dimensions we can confirm this by fitting linear model to the augmented data (see figure - )in[ ]linear_svm_ linearsvc(fit(x_newycoefintercept linear_svm_ coef_ ravel()linear_svm_ intercept_ show linear decision boundary figure plt figure(ax axes (figureelev=- azim=- xx np linspace(x_new[: min( x_new[: max( yy np linspace(x_new[: min( x_new[: max( xxyy np meshgrid(xxyyzz (coef[ xx coef[ yy intercept-coef[ ax plot_surface(xxyyzzrstride= cstride= alpha= ax scatter(x_new[mask ]x_new[mask ]x_new[mask ] =' 'cmap=mglearn cm = ax scatter(x_new[~mask ]x_new[~mask ]x_new[~mask ] =' 'marker='^'cmap=mglearn cm = ax set_xlabel("feature "ax set_ylabel("feature "ax set_zlabel("feature * "supervised machine learning algorithms