Decision Trees and Random Forests

Tom Rainforth
{"title":"Decision Trees and Random Forests","authors":"Tom Rainforth","doi":"10.1002/9781119544678.ch10","DOIUrl":null,"url":null,"abstract":"Y = { 1 if X1 > 0.4 and X2 > 0.6 0 otherwise We construct the dataset: n <5000 x <cbind(runif(n), runif(n)) y <factor(ifelse(x[,1] > .4, x[,2] > .6, 0)) r <data.frame(x, y) We construct a decision tree for this using rpart: tree <rpart(y ~ X1 + X2, data = r, method = \"class\") printcp(tree) We can generate a simple diagram of this tree: plot(tree, compress = TRUE, mar = c(.2, .2, .2, .2)) text(tree, use.n = TRUE) We can generate predictions using this tree on data using the predict function. Here we generate a testing set the same way as the training set above and we find the accuracy of our classifier:","PeriodicalId":344200,"journal":{"name":"Condition Monitoring with Vibration Signals","volume":"55 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Condition Monitoring with Vibration Signals","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1002/9781119544678.ch10","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Y = { 1 if X1 > 0.4 and X2 > 0.6 0 otherwise We construct the dataset: n <5000 x .4, x[,2] > .6, 0)) r
决策树和随机森林
我们构建数据集:n .4, x[,2] > .6, 0)) r
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信