of alpha and ridge regression for alpha= we not only see that most of the coefficients are zero (which we already knew)but that the remaining coefficients are also small in magnitude decreasing alpha to we obtain the solution shown as the green dotswhich causes most features to be exactly zero using alpha= we get model that is quite unregularizedwith most coefficients nonzero and of large magnitude for comparisonthe best ridge solution is shown in teal the ridge model with alpha= has similar predictive performance as the lasso model with alpha= but using ridgeall coefficients are nonzero in practiceridge regression is usually the first choice between these two models howeverif you have large amount of features and expect only few of them to be importantlasso might be better choice similarlyif you would like to have model that is easy to interpretlasso will provide model that is easier to understandas it will select only subset of the input features scikit-learn also provides the elasticnet classwhich combines the penalties of lasso and ridge in practicethis combination works bestthough at the price of having two parameters to adjustone for the regularizationand one for the regularization supervised machine learning algorithms