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  1. SATimage = read.table(file.choose(), header = T, sep = ",")
  2. SATimage = data.frame(class = as.factor(SATimage$class), SATimage[,1:36])
  3.  
  4. set.seed(888)
  5. testcases = sample(1:dim(SATimage)[1],1000,replace=F)
  6. SATtest = SATimage[testcases,]
  7. SATtrain = SATimage[-testcases,]
  8.  
  9. library(rpart)
  10. SI.rpart = rpart(class~., data = SATimage)
  11. SI.rpart
  12.  
  13. library(ipred)
  14. SI.bag = bagging(class~.,data = SATtrain,SATtrain$class, coob = T)
  15. SI.bag
  16. ybag = predict(SI.bag, newdata = SATtest)
  17. SI.bagMis = misclass(ybag,SATtest$class)
  18.  
  19. library(randomForest)
  20. SI.rf = randomForest(class~.,data = SATtrain, mtry = 2, importance = T)
  21. yfor = predict(SI.rf, newdata = SATtest)
  22. SI.rfMis = misclass(yfor, SATtest$class)
  23. SI.rfMis
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