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.
翻译:我们为一个机器学习模型提出了一个模型,用于生成热后解释和不确定性间隔,而只有该模型的投入和产出样本,而不是直接进入模型本身。当模型评估费用昂贵时,当隐私、安全和带宽受限时,或当需要实时、即时、即时解释时,可能会出现这种情况。我们的算法使用局部多元回归法构建解释,并用靴子方法量化解释的不确定性。通过模拟研究,我们发现,我们的算法产生的不确定性间隔表明,与古典回归分析的天性信任间隔相比,间隔宽度和覆盖概率是有利的。我们进一步展示了我们的方法能力,将它应用到经过两个真实数据集培训的黑盒模型中。