in [ ]from skater core explanations import interpretation from skater model import inmemorymodel leveraging skater for feature importances interpreter interpretation(wqp_test_sxfeature_names=wqp_feature_nameswqp_im_model inmemorymodel(wqp_rf predict_probaexamples=wqp_train_sxtarget_names=wqp_rf classes_retrieving feature importances from the scikit-learn estimator wqp_rf_feature_importances wqp_rf feature_importances_ wqp_rf_feature_nameswqp_rf_feature_scores zip(*sorted(zip(wqp_feature_nameswqp_rf_feature_importances)key=lambda xx[ ])plot the feature importance plots (ax ax plt subplots( figsize=( ) suptitle('feature importances for random forest'fontsize= subplots_adjust(top= wspace= y_position list(range(len(wqp_rf_feature_names))ax barh(y_positionwqp_rf_feature_scoresheight= align='center'tick_label=wqp_rf_feature_namesax set_title("scikit-learn"ax set_xlabel('relative importance score'ax set_ylabel('feature'plots interpreter feature_importance plot_feature_importance(wqp_im_modelascending=falseax=ax ax set_title("skater"ax set_xlabel('relative importance score'ax set_ylabel('feature'figure - comparative feature importance analysis obtained from our tuned random forest model we can clearly observe from figure - that the most important features are consistent across the two plotswhich is expected considering we are just using different interfaces on the same model the top two features are alcohol by volume and the volatile acidity content we will be using them shortly for further analysis but right nowlet' look at the model' roc curve and the area under curve (aucstatistics plotting binary classifier' roc curve is easybut what do you do when you are dealing with multi-class classifier ( -class in our case)there are several ways to do this you would need to binarize the output once this operation is executedyou can plot one roc curve per class label besides thisyou can also follow two aggregation metrics for computing the average roc measures micro-averaging involves plotting an roc curve over the entire prediction space by considering each predicted element as binary