in[ ]linear_svm linearsvc(fit(xyprint("coefficient shape"linear_svm coef_ shapeprint("intercept shape"linear_svm intercept_ shapeout[ ]coefficient shape( intercept shape( ,we see that the shape of the coef_ is ( )meaning that each row of coef_ contains the coefficient vector for one of the three classes and each column holds the coefficient value for specific feature (there are two in this datasetthe intercept_ is now one-dimensional arraystoring the intercepts for each class let' visualize the lines given by the three binary classifiers (figure - )in[ ]mglearn discrete_scatter( [: ] [: ]yline np linspace(- for coefinterceptcolor in zip(linear_svm coef_linear_svm intercept_[' '' '' '])plt plot(line-(line coef[ interceptcoef[ ] =colorplt ylim(- plt xlim(- plt xlabel("feature "plt ylabel("feature "plt legend(['class ''class ''class ''line class ''line class ''line class ']loc=( )you can see that all the points belonging to class in the training data are above the line corresponding to class which means they are on the "class side of this binary classifier the points in class are above the line corresponding to class which means they are classified as "restby the binary classifier for class the points belonging to class are to the left of the line corresponding to class which means the binary classifier for class also classifies them as "rest thereforeany point in this area will be classified as class by the final classifier (the result of the classification confidence formula for classifier is greater than zerowhile it is smaller than zero for the other two classesbut what about the triangle in the middle of the plotall three binary classifiers classify points there as "rest which class would point there be assigned tothe answer is the one with the highest value for the classification formulathe class of the closest line supervised machine learning algorithms