Abstract

The article discusses a new two-level regression analysis method in which a corrective procedure is applied to optimal ensembles of regression trees. Optimization is carried out based on the simultaneous achievement of the divergence of the algorithms in the forecast space and a good approximation of the data by individual algorithms of the ensemble. Simple averaging, random regression forest, and gradient boosting are used as corrective procedures. Experiments are presented comparing the proposed method with the standard decision forest and the standard gradient boosting method for decision trees.

中文翻译：

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使用具有最优散度的树的集合的两级回归方法

摘要

本文讨论了一种新的两级回归分析方法，其中将校正程序应用于回归树的最佳集合。优化是基于在预测空间中同时实现算法的发散性和集合的各个算法对数据的良好近似来进行的。简单平均、随机回归森林和梯度提升被用作校正程序。实验将所提出的方法与标准决策森林和决策树的标准梯度提升方法进行比较。