论文标题
用于多输出回归的机器学习：何时应该优先使用整体多变量方法？
Machine Learning for Multi-Output Regression: When should a holistic multivariate approach be preferred over separate univariate ones?
论文作者
论文摘要
基于树的合奏，例如随机森林是统计学习方法中的现代经典。特别是，它们用于预测单变量响应。对于多个输出，出现的问题是我们分别拟合单变量模型还是直接遵循多变量方法。对于后者，存在几种可能性，例如基于修改后的分裂或停止规则多输出回归。在这项工作中，我们将这些方法比较了广泛的模拟，以帮助回答何时使用多元合奏技术的主要问题。
Tree-based ensembles such as the Random Forest are modern classics among statistical learning methods. In particular, they are used for predicting univariate responses. In case of multiple outputs the question arises whether we separately fit univariate models or directly follow a multivariate approach. For the latter, several possibilities exist that are, e.g. based on modified splitting or stopping rules for multi-output regression. In this work we compare these methods in extensive simulations to help in answering the primary question when to use multivariate ensemble techniques.
