摘要
随机森林是一种基于决策树集成的方法,能够处理高维特征数据,对缺失值和噪声数据都具有很好的容忍度。本文首先介绍了随机森林算法的基本原理及其性质,进而详细分析了随机森林算法的优化方法及其在不同领域的应用。
Random forest is a method based on decision tree integration,which can process high-dimensional feature data and has a good tolerance for missing values and noise data.Firstly,the basic principle and properties of random forest algorithm are introduced.Secondly,the optimization method of random forest algorithm and its application in different fields are analyzed in detail.
作者
董红瑶
王弈丹
李丽红
DONG Hongyao;WANG Yidan;LI Lihong(College of Science,North China University of Science and Technology,Tangshan Hebei 063210,China;Hebei Province Key Laboratory of Data Science and Application,Tangshan Hebei 063210,China;Tangshan Key Laboratory of Data Science,Tangshan Hebei 063210,China)
出处
《信息与电脑》
2021年第17期34-37,共4页
Information & Computer
关键词
决策树
随机森林
集成方法
优化
decision tree
random forest
integration method
optimizing
