  We initiate a novel approach to explain the predictions and out of sample
performance of random forest (RF) regression and classification models by
exploiting the fact that any RF can be mathematically formulated as an adaptive
weighted K nearest-neighbors model. Specifically, we employ a recent result
that, for both regression and classification tasks, any RF prediction can be
rewritten exactly as a weighted sum of the training targets, where the weights
are RF proximities between the corresponding pairs of data points. We show that
this linearity facilitates a local notion of explainability of RF predictions
that generates attributions for any model prediction across observations in the
training set, and thereby complements established feature-based methods like
SHAP, which generate attributions for a model prediction across input features.
We show how this proximity-based approach to explainability can be used in
conjunction with SHAP to explain not just the model predictions, but also
out-of-sample performance, in the sense that proximities furnish a novel means
of assessing when a given model prediction is more or less likely to be
correct. We demonstrate this approach in the modeling of US corporate bond
prices and returns in both regression and classification cases.
