Bagging is an important technique for stabilizing machine learning models. In this paper, we derive a finite-sample guarantee on the stability of bagging for any model. Our result places no assumptions on the distribution of the data, on the properties of the base algorithm, or on the dimensionality of the covariates. Our guarantee applies to many variants of bagging and is optimal up to a constant. Empirical results validate our findings, showing that bagging successfully stabilizes even highly unstable base algorithms.

翻译：Bagging是稳定机器学习模型的重要技术。本文推导了任意模型下bagging稳定性的有限样本保证。我们的结果对数据分布、基础算法的性质或协变量的维度均不施加任何假设。该保证适用于bagging的多种变体，且在常数因子内达到最优。实证结果验证了我们的发现，表明bagging即使对高度不稳定的基础算法也能成功实现稳定化。