once this is completewe are ready to run model interpretation algorithms we will start by trying to generate feature importances this will give us an idea of the degree to which our predictive model relies on particular features the skater framework' feature importance implementation is based on an information theory criterionwhere it measures the entropy in the change of predictionsgiven perturbation of specific feature the idea is that the more model' decision making criteria depends on featurethe more the predictions will change as function of perturbing the feature plots interpreter feature_importance plot_feature_importance(modelascending=falsefigure - feature importances obtained from our logistic regression model we can clearly observe from figure - that the most important feature in our model is worst areafollowed by mean perimeter and area error let' now consider the most important featureworst areaand think about ways it might influence the model decision making process during predictions partial dependence plots are an excellent tool to leverage to visualize this in generalpartial dependence plots help describe the marginal impact of specific feature on model prediction by holding the other features in the model constant the derivative of partial dependencedescribes the impact of feature the following code helps build the partial dependence plot for the worst area feature in our model interpreter partial_dependence plot_partial_dependence(['worst area']modelgrid_resolution= with_variance=truefigsize ( )