标题： 随机森林的多变量预测区间 显示英文标题

标题： Multivariate Prediction Intervals for Random Forests

摘要： 准确的不确定性估计可以显著提高实验设计迭代的性能，如顺序学习和强化学习中所示。 在工程和物理科学中的许多此类问题中，设计任务取决于多个相关的模型输出作为目标和/或约束。 为了更好地解决这些问题，我们提出了一种校准后的自助法，用于生成集成模型的多变量预测区间，并表明它是良好校准的。 我们将校准后的自助法应用于一个具有多个目标的模拟顺序学习问题，并表明它能显著减少找到满意候选方案所需的迭代次数。 这表明，校准后的自助法可能是使用机器学习优化多个竞争目标系统的实践者的一个有价值工具。 显示英文摘要

摘要： Accurate uncertainty estimates can significantly improve the performance of iterative design of experiments, as in Sequential and Reinforcement learning. For many such problems in engineering and the physical sciences, the design task depends on multiple correlated model outputs as objectives and/or constraints. To better solve these problems, we propose a recalibrated bootstrap method to generate multivariate prediction intervals for bagged models and show that it is well-calibrated. We apply the recalibrated bootstrap to a simulated sequential learning problem with multiple objectives and show that it leads to a marked decrease in the number of iterations required to find a satisfactory candidate. This indicates that the recalibrated bootstrap could be a valuable tool for practitioners using machine learning to optimize systems with multiple competing targets.