We present a model-agnostic algorithm for generating post-hoc explanations
and uncertainty intervals for a machine learning model when only a sample of
inputs and outputs from the model is available, rather than direct access to
the model itself. This situation may arise when model evaluations are
expensive; when privacy, security and bandwidth constraints are imposed; or
when there is a need for real-time, on-device explanations. Our algorithm
constructs explanations using local polynomial regression and quantifies the
uncertainty of the explanations using a bootstrapping approach. Through a
simulation study, we show that the uncertainty intervals generated by our
algorithm exhibit a favorable trade-off between interval width and coverage
probability compared to the naive confidence intervals from classical
regression analysis. We further demonstrate the capabilities of our method by
applying it to black-box models trained on two real datasets.

我们提出了一个针对机器学习模型的模型无关算法，用于在仅可得到模型的输入输出样本而非对模型本身直接访问时生成事后解释和不确定性区间。