基于随机森林的变量选择方法研究 Alternative Title Studies on the Variable Selection Methods Based on Random Forests 卢天驰 Subtype 学士 Thesis Advisor 李周平 2021-05-20 Degree Grantor 兰州大学 Place of Conferral 兰州 Degree Name 理学学士 Degree Discipline 数学与应用数学 Keyword 变量选择 随机森林 正则化惩罚方法 Abstract 在现代统计模型中, 变量选择问题因为可以增强模型解释率, 减小计算量而一直备受关注. 本文基于随机森林对变量选择进行研究, 同时将随机森林与正则化方法进行比较, 旨在通过数值模拟和数据分析的方法来综合比较这两类变量选择方法, 具体研究步骤如下：第一, 对随机森林及正则化惩罚方法相关理论进行了阐述. 第二, 在线性和Logistic 分类模型中进行模拟, 比较出各个方法的适用性和有效性. 第三, 应用这些方法对一些实际数据进行了分析. 最终, 本文通过模拟和数据分析得到了各种方法在不同数据集上的优劣, 特点. Other Abstract In modern statistical models, the problem of variable selection has been paid much attentionbecause it can enhance the explanatory rate of models and reduce the amount of calculation. Thisthesis studies variable selection based on random forests, and compares random forests with regularization penalty methods. The specifific research steps are as follows: First, the related theoriesof random forests and regularization penalty methods are explained.The applicability and validityof each method are compared by simulation in linear and Logistic classifification models. Third,some actual data are analyzed by these methods. Finally, the advantages and disadvantages ofeach method on different datasets are obtained through simulation and data analysis. Pages 42 URL 查看原文 Language 中文 Document Type 学位论文 Identifier https://ir.lzu.edu.cn/handle/262010/460975 Collection 数学与统计学院 Affiliation 数学与统计学院 First Author Affilication School of Mathematics and Statistics Recommended CitationGB/T 7714 卢天驰. 基于随机森林的变量选择方法研究[D]. 兰州. 兰州大学,2021.
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