in [ ]wtp_dnn_ypred wtp_dnn_model predict_classes(wtp_test_sxwtp_dnn_predictions le inverse_transform(wtp_dnn_ypredmeu display_model_performance_metrics(true_labels=wtp_test_ypredicted_labels=wtp_dnn_predictionsclasses=['red''white']figure - model performance metrics for deep neural network for wine type predictive model we get an overall score and model accuracy of %as depicted in figure - which is even better than our previous modelthis goes to prove you don' always need big data but good quality data and features even for deep learning models the loss and accuracy measures at each epoch are depicted in figure - with the detailed code present in the notebook figure - model performance metrics for dnn model per epoch now that we have working wine type classification systemlet' try to interpret one of these predictive models one of the key aspects in model interpretation is to try to understand the importance of each feature from the dataset we will be using the skater package that we used in the previous for our model interpretation needs the following code helps visualize feature importances for our logistic regression model