The random forest (RF) algorithm is a very practical and excellent ensemble learning algorithm. In this paper, we improve the random forest algorithm and propose an algorithm called ‘post-selection boosting random forest’ (PBRF). This algorithm combines the original random forest and the Lasso method, without giving the number of decision trees for final prediction in advance, it can dynamically obtain the decision trees according to different input samples to output the prediction results. Meanwhile, we verify that the proposed algorithm can improve the performance of the model through simulation studies and real data analysis.

中文翻译：

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用Lasso方法改进随机森林算法

随机森林（RF）算法是一种非常实用且优秀的集成学习算法。在本文中，我们改进了随机森林算法并提出了一种称为“选择后提升随机森林”（PBRF）的算法。该算法结合了原始随机森林和Lasso方法，无需事先给出最终预测的决策树数量，可以根据不同的输入样本动态获取决策树输出预测结果。同时，我们通过仿真研究和真实数据分析验证了所提出的算法可以提高模型的性能。