标题： 针对分位数估计和预测区间的随机森林定向调优 显示英文标题

标题： Targeted tuning of random forests for quantile estimation and prediction intervals

摘要： 我们提出了一种随机森林（RFs）的新调参方法，该方法提高了估计分位数的准确性，并产生了有效且相对较窄的预测区间。 虽然随机森林通常用于估计协变量条件下的均值响应，但也可以通过估计响应的完整分布来估计分位数。 然而，构建随机森林的标准方法往往导致分位数估计过度偏差。 为了减少这种偏差，我们提出的调参方法最小化了“分位数覆盖损失”（QCL），我们将其定义为基于袋外样本的边际分位数覆盖概率估计的估计偏差。 我们将QCL调参方法适应于处理删失数据，并用随机生存森林展示了其应用。 我们表明，QCL调参方法得到的分位数估计具有比使用默认参数值或传统调参（对于无删失数据使用MSPE，对于删失数据使用C指数）更准确的覆盖概率，同时减少了这些覆盖概率的估计MSE。 我们讨论了QCL调参性能优越与其与估计目标的一致性之间的联系。 最后，我们探讨了使用此方法生成的预测区间的有效性与宽度。 显示英文摘要

摘要： We present a novel tuning procedure for random forests (RFs) that improves the accuracy of estimated quantiles and produces valid, relatively narrow prediction intervals. While RFs are typically used to estimate mean responses (conditional on covariates), they can also be used to estimate quantiles by estimating the full distribution of the response. However, standard approaches for building RFs often result in excessively biased quantile estimates. To reduce this bias, our proposed tuning procedure minimizes "quantile coverage loss" (QCL), which we define as the estimated bias of the marginal quantile coverage probability estimate based on the out-of-bag sample. We adapt QCL tuning to handle censored data and demonstrate its use with random survival forests. We show that QCL tuning results in quantile estimates with more accurate coverage probabilities than those achieved using default parameter values or traditional tuning (using MSPE for uncensored data and C-index for censored data), while also reducing the estimated MSE of these coverage probabilities. We discuss how the superior performance of QCL tuning is linked to its alignment with the estimation goal. Finally, we explore the validity and width of prediction intervals created using this method.