论文标题
随机森林的多元预测间隔
Multivariate Prediction Intervals for Random Forests
论文作者
论文摘要
准确的不确定性估计可以显着改善实验迭代设计的性能，例如顺序和增强学习。对于工程和物理科学中的许多此类问题，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，我们提出了一种重新校准的自举方法，以生成装袋模型的多元预测间隔，并表明其已易受校准。我们将重新校准的引导程序应用于具有多个目标的模拟顺序学习问题，并表明它会导致寻找令人满意的候选者所需的迭代次数显着减少。这表明重新校准的引导程序可能是使用机器学习来优化具有多个竞争目标的系统的从业者的宝贵工具。
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.
