its the model to using only few features here is the coefficient plot and classification accuracies for regularization (figure - )in[ ]for cmarker in zip([ ][' ''^'' '])lr_l logisticregression( =cpenalty=" "fit(x_trainy_trainprint("training accuracy of logreg with ={ }{ }formatclr_l score(x_trainy_train))print("test accuracy of logreg with ={ }{ }formatclr_l score(x_testy_test))plt plot(lr_l coef_ tmarkerlabel=" ={ }format( )plt xticks(range(cancer data shape[ ])cancer feature_namesrotation= plt hlines( cancer data shape[ ]plt xlabel("coefficient index"plt ylabel("coefficient magnitude"plt ylim(- plt legend(loc= out[ ]training accuracy of logreg with = test accuracy of logreg with = training accuracy of logreg with = test accuracy of logreg with = training accuracy of logreg with = test accuracy of logreg with = as you can seethere are many parallels between linear models for binary classification and linear models for regression as in regressionthe main difference between the models is the penalty parameterwhich influences the regularization and whether the model will use all available features or select only subset supervised learning