We initiate a novel approach to explain the out of sample performance of
random forest (RF) models by exploiting the fact that any RF can be formulated
as an adaptive weighted K nearest-neighbors model. Specifically, we use the
proximity between points in the feature space learned by the RF to re-write
random forest predictions exactly as a weighted average of the target labels of
training data points. 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 methods like SHAP, which instead generates attributions for a model
prediction across dimensions of the feature space. We demonstrate this approach
in the context of a bond pricing model trained on US corporate bond trades, and
compare our approach to various existing approaches to model explainability.

通过利用随机森林模型中的特征空间中的点之间的相似性，我们提出了一种新的方法来解释随机森林的样本外表现，这使得随机森林的预测可以准确地重写为训练数据点目标标签的加权平均值，并为模型预测生成任何观测的归因，从而补充了 SHAP 等现有的针对模型预测在特征空间维度上生成的归因的方法。我们在美国公司债券交易的债券定价模型的背景下演示了这种方法，并将其与各种现有的模型可解释性方法进行了比较。