print("polynomial feature names:\ {}format(poly get_feature_names())out[ ]polynomial feature names[' '' ^ '' ^ '' ^ '' ^ '' ^ '' ^ '' ^ '' ^ '' ^ 'you can see that the first column of x_poly corresponds exactly to xwhile the other columns are the powers of the first entry it' interesting to see how large some of the values can get the second column has entries above , orders of magnitude different from the rest using polynomial features together with linear regression model yields the classical model of polynomial regression (see figure - )in[ ]reg linearregression(fit(x_polyyline_poly poly transform(lineplt plot(linereg predict(line_poly)label='polynomial linear regression'plt plot( [: ] ' ' =' 'plt ylabel("regression output"plt xlabel("input feature"plt legend(loc="best"figure - linear regression with tenth-degree polynomial features representing data and engineering features