论文标题
多元增强的树木和预测和控制的应用
Multivariate Boosted Trees and Applications to Forecasting and Control
论文作者
论文摘要
梯度增强的树木是竞争获奖，通用，非参数回归器，它们利用顺序模型拟合和梯度下降以最大程度地减少特定的损失函数。最受欢迎的实现是针对单变量回归和分类任务量身定制的，排除了捕获多变量目标互相关的可能性，并将结构化的惩罚应用于预测。在本文中，我们提出了一种用于拟合多元增强树的计算有效算法。我们表明，当预测相关时，多元树可以胜过它们的单变量。此外，该算法允许任意正规化预测，以便可以执行平滑度，一致性和功能关系之类的属性。我们提出了与预测和控制有关的应用程序和数值结果。
Gradient boosted trees are competition-winning, general-purpose, non-parametric regressors, which exploit sequential model fitting and gradient descent to minimize a specific loss function. The most popular implementations are tailored to univariate regression and classification tasks, precluding the possibility of capturing multivariate target cross-correlations and applying structured penalties to the predictions. In this paper, we present a computationally efficient algorithm for fitting multivariate boosted trees. We show that multivariate trees can outperform their univariate counterpart when the predictions are correlated. Furthermore, the algorithm allows to arbitrarily regularize the predictions, so that properties like smoothness, consistency and functional relations can be enforced. We present applications and numerical results related to forecasting and control.
