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- Auto=read.csv("Auto.csv",header=T,na.strings="?")
- attach(Auto)
- #Stablo odluke
- library(tree)
- library(ISLR)
- attach(Carseats)
- High=ifelse(Sales<=8,"No","Yes")
- Carseats=data.frame(Carseats,High)
- tree.carseats=tree(High~.-Sales,Carseats)
- summary(tree.carseats)
- plot(tree.carseats)
- text(tree.carseats,pretty=0)
- tree.carseats
- set.seed(2)
- train=sample(1:nrow(Carseats), 200)
- Carseats.test=Carseats[-train,]
- High.test=High[-train]
- tree.carseats=tree(High~.-Sales,Carseats,subset=train)
- tree.pred=predict(tree.carseats,Carseats.test,type="class")
- table(tree.pred,High.test)
- (86+57)/200
- set.seed(3)
- cv.carseats=cv.tree(tree.carseats,FUN=prune.misclass)
- names(cv.carseats)
- cv.carseats
- par(mfrow=c(1,2))
- plot(cv.carseats$size,cv.carseats$dev,type="b")
- plot(cv.carseats$k,cv.carseats$dev,type="b")
- prune.carseats=prune.misclass(tree.carseats,best=9)
- plot(prune.carseats)
- text(prune.carseats,pretty=0)
- tree.pred=predict(prune.carseats,Carseats.test,type="class")
- table(tree.pred,High.test)
- (94+60)/200
- prune.carseats=prune.misclass(tree.carseats,best=15)
- plot(prune.carseats)
- text(prune.carseats,pretty=0)
- tree.pred=predict(prune.carseats,Carseats.test,type="class")
- table(tree.pred,High.test)
- (86+62)/200
- #Bagging
- library(tree)
- library(ISLR)
- library(MASS)
- library(randomForest)
- set.seed(1)
- train = sample(1:nrow(Boston), nrow(Boston)/2)
- bag.boston=randomForest(medv~.,data=Boston,subset=train,mtry=13,importance=TRUE)
- bag.boston
- boston.test=Boston[-train,"medv"]
- yhat.bag = predict(bag.boston,newdata=Boston[-train,])
- plot(yhat.bag, boston.test)
- abline(0,1)
- mean((yhat.bag-boston.test)^2)
- bag.boston=randomForest(medv~.,data=Boston,subset=train,mtry=13,ntree=25)
- yhat.bag = predict(bag.boston,newdata=Boston[-train,])
- mean((yhat.bag-boston.test)^2)
- #Random forest
- library(tree)
- library(ISLR)
- library(MASS)
- library(randomForest)
- set.seed(1)
- rf.boston=randomForest(medv~.,data=Boston,subset=train,mtry=6,importance=TRUE)
- yhat.rf = predict(rf.boston,newdata=Boston[-train,])
- mean((yhat.rf-boston.test)^2)
- importance(rf.boston)
- varImpPlot(rf.boston)
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