This post is not about a new technique or package, but rather combining existing functionality in interpretable machine learning and data visualization in a way to facilitate analyses of model results. We’ll make use of two packages DALEX and PLOTLY ot create interactive Individual Conditional Expectation (ICE) plots show how to use them to find interesting behavior. Let’s take a random forest (RF) trained on an imputed version of the titanic data as an example, on which we create a DALEX explainer.