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This article presents Individual Conditional Expectation (ICE) plots, a toolfor visualizing the model estimated by any supervised learning algorithm.Classical partial dependence plots (PDPs) help visualize the average partialrelationship between the predicted response and one or more features. In thepresence of substantial interaction effects, the partial response relationshipcan be heterogeneous. Thus, an average curve, such as the PDP, can obfuscatethe complexity of the modeled relationship. Accordingly, ICE plots refine thepartial dependence plot by graphing the functional relationship between thepredicted response and the feature for individual observations. Specifically,ICE plots highlight the variation in the fitted values across the range of acovariate, suggesting where and to what extent heterogeneities might exist. Inaddition to providing a plotting suite for exploratory analysis, we include avisual test for additive structure in the data generating model. Throughsimulated examples and real data sets, we demonstrate how ICE plots can shedlight on estimated models in ways PDPs cannot. Procedures outlined areavailable in the R package ICEbox.

Peeking Inside the Black Box: Visualizing Statistical Learning with Plots of Individual Conditional Expectation