Question

我正在尝试找到Kaggle TFI数据集的解决方案。

但是在使用随机森林在R中构建模型时，我发现了以下错误：

[.data.frame（数据、、 all.vars（Terms），drop = FALSE）中的错误：未定义的列已选择

代码：

library(Boruta)
library(caret)

train<-read.csv("train.csv")
names(train)
View(train)
str(train)
test<-read.csv("test.csv")

n.train<-nrow(train)

test$revenue<-1
myData<-rbind(train,test)
rm(train,test)

myData$Open.Date<-as.POSIXlt("01/01/2015", format="%m/%d/%Y") - 
as.POSIXlt(myData$Open.Date, format="%m/%d/%Y")
myData$Open.Date<-as.numeric(myData$Open.Date/1000)

myData$City<-as.character(myData$City)
myData$City[myData$City.Group=="Other"]<-"Other"
myData$City[myData$City==unique(myData$City)[4]]<-unique(myData$City)[2]
myData$City<-as.factor(myData$City)
myData$City.Group<-NULL

myData$Type<-as.character(myData$Type)
myData$Type[myData$Type=="DT"]<-"IL"
myData$Type[myData$Type=="MB"]<-"FC"
myData$Type<-as.factor(myData$Type)

hist(train$revenue)
hist(log(train$revenue))

myData[,paste("P",1:37,sep="")]<-log(1+myData[,paste("P",1:37,sep="")])

myData$revenue<-log(myData$revenue)
head(myData,2)

important<-Boruta(revenue~.,data=myData[1:n.train,])

important$finalDecision

library(randomForest)
model<-train(myData$revenue[1:n.train]~.,method="rf",
         data=myData[1:n.train,c(important$finalDecision!="Rejected",TRUE)])

R中的随机森林错误：[.. data.frame`（data，，all.vars（Terms），drop = FALSE）错误：未定义的列已选中

0 个答案: