plt plot(ridge coef_' 'label="ridge alpha= "plt plot(ridge coef_'^'label="ridge alpha= "plt plot(ridge coef_' 'label="ridge alpha= "plt plot(lr coef_' 'label="linearregression"plt xlabel("coefficient index"plt ylabel("coefficient magnitude"plt hlines( len(lr coef_)plt ylim(- plt legend(figure - comparing coefficient magnitudes for ridge regression with different values of alpha and linear regression herethe -axis enumerates the entries of coef_x= shows the coefficient associated with the first featurex= the coefficient associated with the second featureand so on up to = the -axis shows the numeric values of the corresponding values of the coefficients the main takeaway here is that for alpha= the coefficients are mostly between around - and the coefficients for the ridge model with alpha= are somewhat larger the dots corresponding to alpha= have larger magnitude stilland many of the dots corresponding to linear regression without any regularization (which would be alpha= are so large they are outside of the chart supervised machine learning algorithms