car <- read.table("data/car.data",sep = ",")
colnames(car) <- c("buy","main","doors","capacity",
"lug_boot","safety","accept")
library(caret)
library(ggplot2)
library(lattice)
ind <- createDataPartition(car$accept,times=1,p=0.75,list=FALSE)
carTR <- car[ind,]
carTE <- car[-ind,]
carTR<- within(carTR,accept <- factor(accept,levels=c("unacc","acc","good","vgood")))
carTE<- within(carTE,accept <- factor(accept,levels=c("unacc","acc","good","vgood")))
library(adabag)
bagging.model <- bagging(accept~.,data=carTR)
boosting.model <- boosting(accept~.,data=carTR)
library(randomForest)
randomForest.model <- randomForest(accept~.,data=carTR,ntree=500,mtry=3)
result <- data.frame(arithmetic=c("bagging","boosting","随机森林"),
errTR=rep(0,3),errTE=rep(0,3))
for(i in 1:3){
carTR_predict <- predict(switch(i,bagging.model,boosting.model,randomForest.model),
newdata=carTR)
carTE_predict <- predict(switch(i,bagging.model,boosting.model,randomForest.model),
newdata=carTE)
tableTR <- table(actual=carTR$accept,
predict=switch(i,carTR_predict$class,carTR_predict$class,carTR_predict))
tableTE <- table(actual=carTE$accept,
predict=switch(i,carTE_predict$class,carTE_predict$class,carTE_predict))
result[i,2] <- paste0(round((sum(tableTR)-sum(diag(tableTR)))*100/sum(tableTR),
2),"%")
result[i,3] <- paste0(round((sum(tableTE)-sum(diag(tableTE)))*100/sum(tableTE),
2),"%")
}
result
