comparison between LogisticRegression and RandomForest with "Census Income" dataset. please refer to http://archive.ics.uci.edu/ml/datasets/Adult

adult.R

library(randomForest)
library(ROCR)

init <- function() {
  df1 <- read.csv("adult.data",header=F)
  df2 <- read.csv("adult.test",header=F)
  
  df2$V15 <- gsub("\\.$","",df2$V15)
  
  df <- rbind(df1,df2)
  write.csv(df,"adult.all",row.names=F)
  
  return(c(nrow(df1),nrow(df2)))
}

#--comment out this block only the first time--
#sz <- init() 
#df <- read.csv("adult.all",header=T)
#train <- df[1:sz[1],]
#test <- df[sz[1]+(1:sz[2]),]
#rm(df)
#--comment out this block only the first time--

model1 <- glm(V15 ~.,family=binomial(link='logit'),data=train)
result1 <- predict(model1,newdata=test[,-15],type="response")
pred1 <- prediction(result1,test[,15])
perf1 <- performance(pred1,"prec","rec")
plot(perf1,colorize = TRUE)
  
model2 <- randomForest(V15 ~.,data=train,mtry=2)
result2 <- predict(model2,newdata=test[,-15],type="prob")
pred2 <- prediction(result2[,2],test[,15])
perf2 <- performance(pred2,"prec","rec")
plot(perf2,add=TRUE,colorize = TRUE)