  Deep forest is a non-differentiable deep model which has achieved impressive
empirical success across a wide variety of applications, especially on
categorical/symbolic or mixed modeling tasks. Many of the application fields
prefer explainable models, such as random forests with feature contributions
that can provide local explanation for each prediction, and Mean Decrease
Impurity (MDI) that can provide global feature importance. However, deep
forest, as a cascade of random forests, possesses interpretability only at the
first layer. From the second layer on, many of the tree splits occur on the new
features generated by the previous layer, which makes existing explanatory
tools for random forests inapplicable. To disclose the impact of the original
features in the deep layers, we design a calculation method with an estimation
step followed by a calibration step for each layer, and propose our feature
contribution and MDI feature importance calculation tools for deep forest.
Experimental results on both simulated data and real world data verify the
effectiveness of our methods.
