xuefliang

--- title: "caret学习" output: html_document --- ```{r setup, include=FALSE} knitr::opts_chunk$set(echo = TRUE) ``` ```{r include=FALSE} library(caret) data(mdrr) ``` #数据预处理 ##常数自变量，或者是方差极小的自变量 ```{r} zerovar <- nearZeroVar(mdrrDescr) newdata1 <- mdrrDescr[,-zerovar] ``` ##删除的是与其它自变量有很强相关性的变量 ```{r} descrCorr <- cor(newdata1) highCorr <- findCorrelation(descrCorr,0.9) newdata2 <- newdata1[,-highCorr] ``` ##自变量多重共线性 ```{r} comboinfo <- findLinearCombos(newdata1) newdata2 <- newdata2[,-comboinfo$remove] ``` ##标准化并补足缺失值 ```{r} Process <- preProcess(newdata2) newdata3 <- predict(Process,newdata2) ``` ##用createDataPartition将数据进行划分，分成75%的训练样本和25%检验样本 ```{r} inTrain <- createDataPartition(mdrrClass,p=3/4,list = F) trainx <- newdata3[inTrain,] testx <- newdata3[-inTrain,] trainy <- mdrrClass[inTrain] testy <- mdrrClass[-inTrain] ``` ##建模前还可以对样本数据进行图形观察，例如对前两个变量绘制箱线图 ```{r} featurePlot(trainx[,1: