This paper introduces Energy Bagging Tree (EBT) for multivariate nonparametric regression problems. The EBT makes use of a measure of dispersion constructed from a generalized Gini's mean difference as node impurity, and the tree split function therefore corresponds to the product of energy distance and descendants' proportions. As a non-parametric extension of the between-sample variation in the analysis of variance, this measure of dispersion serves well for EBT in understanding certain complex data. Extensive simulation studies indicate that EBT is highly competitive with existing regression tree methods. We also assess the performance of the EBT through a real data analysis on forest fires.

中文翻译：

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能量装袋树

本文介绍了用于多元非参数回归问题的能量袋装树 (EBT)。EBT 使用由广义基尼平均差构建的离散度量作为节点杂质，因此树分裂函数对应于能量距离和后代比例的乘积。作为方差分析中样本间变异的非参数扩展，这种离散度量有助于 EBT 理解某些复杂数据。广泛的模拟研究表明，EBT 与现有的回归树方法相比具有很强的竞争力。我们还通过对森林火灾的真实数据分析来评估 EBT 的性能。