We show that the byproducts of the standard training process of a random forest yield not only the well known and almost computationally free out-of-bag point estimate of the model generalization error, but also open a direct path to compute confidence intervals for the generalization error which avoids processes of data splitting and model retraining. Besides the low computational cost involved in their construction, these confidence intervals are shown through simulations to have good coverage and appropriate shrinking rate of their width in terms of the training sample size.

中文翻译：

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随机森林泛化误差的置信区间

我们表明，随机森林的标准训练过程的副产品不仅产生了众所周知且几乎无需计算的模型泛化误差的袋外点估计，而且还开辟了一条直接计算泛化置信区间的途径避免数据拆分和模型再训练过程的错误。除了构建过程中涉及的低计算成本外，这些置信区间通过模拟显示具有良好的覆盖率和就训练样本大小而言其宽度的适当收缩率。