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Deep forest is a non-differentiable deep model which has achieved impressiveempirical success across a wide variety of applications, especially oncategorical/symbolic or mixed modeling tasks. Many of the application fieldsprefer explainable models, such as random forests with feature contributionsthat can provide local explanation for each prediction, and Mean DecreaseImpurity (MDI) that can provide global feature importance. However, deepforest, as a cascade of random forests, possesses interpretability only at thefirst layer. From the second layer on, many of the tree splits occur on the newfeatures generated by the previous layer, which makes existing explanatorytools for random forests inapplicable. To disclose the impact of the originalfeatures in the deep layers, we design a calculation method with an estimationstep followed by a calibration step for each layer, and propose our featurecontribution and MDI feature importance calculation tools for deep forest.Experimental results on both simulated data and real world data verify theeffectiveness of our methods.

Interpreting Deep Forest through Feature Contribution and MDI Feature Importance