figure - model interpretation for our wine quality model' prediction for low quality wine the results depicted in figure - show us the features that were primarily responsible for the model to predict the wine quality as low we can see that the most important feature was alcoholwhich makes sense considering what we obtained in our analyses so far from feature importances and model decision surface interpretations the values for each corresponding feature depicted here are the scaled values obtained after feature scaling let' interpret another predictionthis time for wine of high quality exp explain_instance(wqp_test_sx[ ]wqp_rf predict_probatop_labels= show_in_notebook(figure - model interpretation for our wine quality model' prediction for high quality wine from the interpretation in figure - we can see the features responsible for the model correctly predicting the wine quality as high and the primary feature was again alcohol by volume (besides other features like densityvolatile acidityand so onalso you can notice stark difference in the scaled values of alcohol for the two instances depicted in figure - and figure -