Explainable boosting machines (EBMs) are popular "glass-box" models that learn a set of univariate functions using boosting trees. These achieve explainability through visualizations of each feature's effect. However, unlike linear model coefficients, uncertainty quantification for the learned univariate functions requires computationally intensive bootstrapping, making it hard to know which features truly matter. We provide an alternative using recent advances in statistical inference for gradient boosting, deriving methods for statistical inference as well as end-to-end theoretical guarantees. Using a moving average instead of a sum of trees (Boulevard regularization) allows the boosting process to converge to a feature-wise kernel ridge regression. This produces asymptotically normal predictions that achieve the minimax-optimal mean squared error for fitting Lipschitz GAMs with $p$ features at rate $O(pn^{-2/3})$, successfully avoiding the curse of dimensionality. We then construct prediction intervals for the response and confidence intervals for each learned univariate function with a runtime independent of the number of datapoints, enabling further explainability within EBMs.

翻译：可解释提升机（EBMs）是一种流行的"玻璃盒"模型，它通过提升树学习一组单变量函数。这些模型通过可视化每个特征的影响来实现可解释性。然而，与线性模型系数不同，学习到的单变量函数的不确定性量化需要计算密集的自举法，这使得难以确定哪些特征真正重要。我们利用梯度提升统计推断的最新进展提供了一种替代方案，推导出统计推断方法以及端到端的理论保证。使用移动平均而非树求和（Boulevard正则化）使得提升过程收敛到特征级核岭回归。这产生了渐近正态的预测，在拟合具有$p$个特征的Lipschitz广义可加模型时达到了极小极大最优均方误差$O(pn^{-2/3})$，成功避免了维数灾难。随后，我们构建了响应的预测区间和每个学习到的单变量函数的置信区间，其运行时间与数据点数量无关，从而在EBMs中实现了进一步的可解释性。