RForML之焦点包：rpart

**作者：陆勤<br /> **</p> <p>>**摘要：**rpart是Recursive Partitioning and Regression Trees的简称，它实现了 Breiman, Friedman, Olshen and Stone这三位大佬所写书籍《Classification and Regression Trees》大大都算法的成果。</p> <p>文章《[**RForML之焦点包：e1071**](http://shujuren.org/article/304.html "RForML之焦点包：e1071")》先容e1071包及应用。<br /> 文章《[**RForML之焦点包：gbm**](http://shujuren.org/article/305.html "RForML之焦点包：gbm")》先容gbm包及应用。<br /> 文章《[**RForML之焦点包：kernlab**](http://shujuren.org/article/308.html "RForML之焦点包：kernlab")》先容kernlab包及应用。<br /> 文章《[**RForML之焦点包：mboost**](http://shujuren.org/article/310.html "RForML之焦点包：mboost")》先容mboost包及应用。<br /> 文章《[**RForML之焦点包：nnet**](http://shujuren.org/article/311.html "RForML之焦点包：nnet")》先容nnet包及应用。<br /> 文章《[**RForML之焦点包：randomForest**](http://shujuren.org/article/315.html "RForML之焦点包：randomForest")》先容randomForest包及应用。</p> <p>### 一、rpart包先容<br /> **rpart**是Recursive Partitioning and Regression Trees的简称，它实现了 Breiman, Friedman, Olshen and Stone这三位大佬所写书籍《Classification and Regression Trees》大大都算法的成果。</p> <p>### 二、rpart包安装与加载<br /> rpart包是扩展包，需要先安装和加载，才气利用。<br /> “`{r}<br /> if(!require(rpart))<br /> {<br /> install.packages(‘rpart’)<br /> require(rpart)<br /> }<br /> “`</p> <p>###三、rpart包应用<br /> 操作rpart包实现决定树算法，从ecoli数据会合操作决定树进修到分类器模子，然后操作模子做预测。<br /> #### ecoli数据集<br /> “`{r}<br /> url <- ‘http://www.wekaleamstudios.co.uk/wp-content/uploads/2010/09/ecoli.txt’<br /> ecoli.data <- read.csv(url)<br /> str(ecoli.data)<br /> ## 生存数据<br /> write.csv(ecoli.data, ‘ecoli.txt’, row.names = FALSE)<br /> “`<br /> ### 决定树模子建设<br /> “`{r}<br /> ecoli.tree <- rpart(class ~ ., data = ecoli.data[,-1])<br /> “`<br /> ### 决定树修剪<br /> 操作plotcp函数可视化可能printcp函数选取符合cp值，然后操作prune函数对决定树举办修剪和优化<br /> “`{r}<br /> plotcp(ecoli.tree)<br /> “`<br /> 功效示意图<br /> ![](http://img.shujuren.org/pictures/WX/58c5377fabdd1.jpg)</p> <p>“`{r}<br /> ecoli.tree1 <- prune(ecoli.tree, cp = 0.02)<br /> “`<br /> #### 决定树功效可视化<br /> “`{r}<br /> if(!(require(rattle) & require(rpart.plot)))<br /> {<br /> install.packages(c(‘rattle’, ‘rpart.plot’))<br /> require(rattle)<br /> require(rpart.plot)<br /> }<br /> fancyRpartPlot(ecoli.tree1)<br /> “`<br /> 功效示意图<br /> ![](http://img.shujuren.org/pictures/KA/58c542fae692b.jpg)<br /> #### 决定树法则解读<br /> “`r<br /> asRules(ecoli.tree1)<br /> “`<br /> 部门功效如下<br /> ![](http://img.shujuren.org/pictures/6L/58c5438a3c9d1.jpg)<br /> #### 模子应用<br /> 操作predict函数，对数据集举办预测，而且量化预测结果。</p> <p>### 参考资料<br /> 1 rpart包文档：https://cran.r-project.org/web/packages/rpart/rpart.pdf<br /> 2 rpart应用：https://cran.r-project.org/web/packages/rpart/vignettes/longintro.pdf</p> <p>您在阅读中，有什么发起可能想法，请留言，也可以加微信交换。</p> </div> </article> <div class="tagcloud"><ul class='wp-tag-cloud' role='list'><h3>关键字：</h3> </ul> </div> <div class="col-sm-6 col-box bg-gray-light-2 contact-form"> <div role="form" class="wpcf7" id="wpcf7-f17097-o1" lang="zh-CN" dir="ltr"> <div class="screen-reader-response"></div> <form action="/rforml%e4%b9%8b%e7%84%a6%e7%82%b9%e5%8c%85%ef%bc%9arpart/#wpcf7-f17097-o1" method="post" 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