in this casethe svm yields very smooth and nonlinear (not straight lineboundary we adjusted two parameters herethe parameter and the gamma parameterwhich we will now discuss in detail tuning svm parameters the gamma parameter is the one shown in the formula given in the previous sectionwhich controls the width of the gaussian kernel it determines the scale of what it means for points to be close together the parameter is regularization parametersimilar to that used in the linear models it limits the importance of each point (or more preciselytheir dual_coef_let' have look at what happens when we vary these parameters (figure - )in[ ]figaxes plt subplots( figsize=( )for axc in zip(axes[- ])for agamma in zip(axrange(- ))mglearn plots plot_svm(log_c=clog_gamma=gammaax=aaxes[ legend(["class ""class ""sv class ""sv class "]ncol= loc= )supervised machine learning algorithms