to wrap up our discussion on model interpretationwe will be talking about partial dependence plots and how they are useful in our scenario in generalpartial dependence helps describe the marginal impact or influence of feature on the model prediction decision by holding the other features constant because it is very difficult to visualize high dimensional feature spacestypically one or two influential and important features are used to visualize partial dependence plots the scikit-learn framework has functions like partial_dependenceand plot_partial_dependence)but unfortunately as of the time of writing this bookthese functions only work on boosting models like gbm the beauty of skater is that we can build partial dependence plots on any model including our tuned random forest model we will leverage skater' interpretation objectinterpreterand the inmemorymodel objectwqp_im_modelbased on our random forest model that we had created earlier when we computed the feature importances the following code depicts one-way partial dependence plots for our model prediction function based on the most important featurealcohol in [ ]axes_list interpreter partial_dependence plot_partial_dependence(['alcohol']wqp_im_modelgrid_resolution= with_variance=truefigsize ( )axs axes_list[ ][ :[ax set_ylim( for ax in axs]figure - one-way partial dependence plots for our random forest model predictor based on alcohol from the plots in figure - we can see that with an increase in the quantity of alcohol contentthe confidence\probability of the model predictor increases in predicting the wine to be either medium or high and similarly it decreases for the probability of wine to be of low quality this shows there is definitely some relationship between the class predictions with the alcohol content and again the influence of alcohol for predictions of class high is pretty lowwhich is expected considering training samples for high quality wine are less let' now plot two-way partial dependence plots for interpreting our random forest predictor' dependence on alcohol and volatile aciditythe top two influential features in [ ]plots_list interpreter partial_dependence plot_partial_dependence([('alcohol''volatile acidity')]wqp_im_modeln_samples= figsize=( )grid_resolution= axs plots_list[ ][ :[ax set_zlim( for ax in axs]