in [ ]from skater core explanations import interpretation from skater model import inmemorymodel wtp_interpreter interpretation(wtp_test_sxfeature_names=wtp_features columnswtp_im_model inmemorymodel(wtp_lr predict_probaexamples=wtp_train_sxtarget_names=wtp_lr classes_plots wtp_interpreter feature_importance plot_feature_importance(wtp_im_modelascending=falsefigure - feature importances obtained from our logistic regression model we can see in figure - that densitytotal sulfur dioxideand residual sugar are the top three features that contributed toward classifying wine samples as red or white another way of understand how well model is performing besides looking at metrics is to plot receiver operating characteristics curve also known popularity as roc curve this curve can be plotted using the true positive rate (tprand the false positive rate (fprof classifier tpr is known as sensitivity or recallwhich is the total number of correct positive results predicted among all the positive samples the dataset fpr is known as false alarms or ( specificity)determining the total number of incorrect positive predictions among all negative samples in the dataset the roc curve is also known as sensitivity versus ( specificityplot sometimes the following code uses our model evaluation utilities module to plot the roc curve for our logistic regression model in the roc space in [ ]meu plot_model_roc_curve(wtp_lrwtp_test_sxwtp_test_ytypically in any roc curvethe roc space is between points ( , and ( each prediction result from the confusion matrix occupies one point in this roc space ideallythe best prediction model would give point on the top-left corner ( , indicating perfect classification ( sensitivity and specificitya diagonal line depicts classifier that does random guess ideally if your roc curve occurs in the top half of the graphyou have decent classifierwhich is better than average figure - makes this clearer