摘要翻译：
随机森林回归(RF)是一种非常流行的高维数据分析工具。然而，在稀疏环境中，由于预测因子较弱，它的好处可能会降低，因此需要一个预先估计降维（目标化）步骤。我们表明，正确的目标控制了沿着强预测器放置分裂的概率，从而为RF的特征采样提供了重要的补充。这一点得到了使用有代表性的有限样本的模拟的支持。此外，我们量化了目标的直接收益，以增加个体树木的强度。宏观经济和金融应用表明，由于森林中树木之间的相关性增加，目标所隐含的偏差-方差权衡在中等程度的目标中是平衡的，选择了通常应用的10-30%的最佳预测因子。相对于普通射频，目标射频的预测精度提高了12-13%，无论是在衰退期还是扩展期，特别是在长期，都有很大的提高。
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英文标题：
《Targeting predictors in random forest regression》
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作者：
Daniel Borup, Bent Jesper Christensen, Nicolaj N{\o}rgaard M\"uhlbach,
  Mikkel Slot Nielsen
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最新提交年份：
2020
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分类信息：

一级分类：Economics        经济学
二级分类：Econometrics        计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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英文摘要：
  Random forest regression (RF) is an extremely popular tool for the analysis of high-dimensional data. Nonetheless, its benefits may be lessened in sparse settings due to weak predictors, and a pre-estimation dimension reduction (targeting) step is required. We show that proper targeting controls the probability of placing splits along strong predictors, thus providing an important complement to RF's feature sampling. This is supported by simulations using representative finite samples. Moreover, we quantify the immediate gain from targeting in terms of increased strength of individual trees. Macroeconomic and financial applications show that the bias-variance trade-off implied by targeting, due to increased correlation among trees in the forest, is balanced at a medium degree of targeting, selecting the best 10--30\% of commonly applied predictors. Improvements in predictive accuracy of targeted RF relative to ordinary RF are considerable, up to 12-13\%, occurring both in recessions and expansions, particularly at long horizons.
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PDF链接：
https://arxiv.org/pdf/2004.01411