森林绞喉
Forest Garrote
摘要：
最近，高维线性模型的变量选择受到了很多关注，主要是在l1正则化的背景下。吸引人的部分原因是变量选择效应：得到了非常适合解释的简约模型。然而，就预测能力而言，这些正则化线性模型通常略逊于树集成等机器学习过程。另一方面，树系综通常缺乏变量选择的正式方式，并且难以可视化。提出了一种适用于树系综，特别是随机森林的Garrote式凸罚函数。惩罚选择树中节点的功能组。这些可以简单到单个预测变量的单调函数。这产生了一个简约的函数拟合，它很容易可视化和解释。预测能力至少保持在与原始树集合相同的水平。该方法的一个关键特征是，一旦一个树系综拟合，没有进一步的调整参数需要选择。实证表现证明了广泛的数据集。
英文摘要：
Variable selection for high-dimensional linear models has received a lot of attention lately, mostly in the context of l1-regularization. Part of the attraction is the variable selection effect: parsimonious models are obtained, which are very suitable for interpretation. In terms of predictive power, however, these regularized linear models are often slightly inferior to machine learning procedures like tree ensembles. Tree ensembles, on the other hand, lack usually a formal way of variable selection and are difficult to visualize. A Garrote-style convex penalty for trees ensembles, in particular Random Forests, is proposed. The penalty selects functional groups of nodes in the trees. These could be as simple as monotone functions of individual predictor variables. This yields a parsimonious function fit, which lends itself easily to visualization and interpretation. The predictive power is maintained at least at the same level as the original tree ensemble. A key feature of the method is that, once a tree ensemble is fitted, no further tuning parameter needs to be selected. The empirical performance is demonstrated on a wide array of datasets.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
