figure - visualizing the model decision surface for our tuned random forest model the plot depicted in figure - shows us that the three classes are definitely not as easily distinguishable as our wine type classifier for red and white wine of coursevisualizing the hypersurfaces with multiple features becomes difficult as compared to visualizing with the two most important featuresbut the plot should give you good idea that the model is able to distinguish well between the classesalthough there is certain amount of overlapespecially with the wine samples of medium quality rating with high and low quality rated wine samples let' look at some model prediction interpretations similar to what we did in where we analyzed movie review sentiments for thiswe will be leveraging skater and look at model predictions we will try to interpret why the model predicted class label and which features were influential in its decision first we build limetabularexplainer object using the following snippetwhich will help us in interpreting and explaining predictions from skater core local_interpretation lime lime_tabular import limetabularexplainer exp limetabularexplainer(wqp_train_sxfeature_names=wqp_feature_namesdiscretize_continuous=trueclass_names=wqp_rf classes_let' now look at two wine sample instances from our test dataset the first instance is wine of low quality rating we show the interpretation for the predicted class with maximum probability\confidence using the top_labels parameter you can set it to to view the same for all the three class labels exp explain_instance(wqp_test_sx[ ]wqp_rf predict_probatop_labels= show_in_notebook(