data howeverpolynomials of high degree tend to behave in extreme ways on the boundaries or in regions with little data as comparisonhere is kernel svm model learned on the original datawithout any transformation (see figure - )in[ ]from sklearn svm import svr for gamma in [ ]svr svr(gamma=gammafit(xyplt plot(linesvr predict(line)label='svr gamma={}format(gamma)plt plot( [: ] ' ' =' 'plt ylabel("regression output"plt xlabel("input feature"plt legend(loc="best"figure - comparison of different gamma parameters for an svm with rbf kernel using more complex modela kernel svmwe are able to learn similarly complex prediction to the polynomial regression without an explicit transformation of the features interactions and polynomials