RandomForests.R

#ENSEMBLE LEARNING in R
require(randomForest)
require(MASS)#Package which contains the Boston housing dataset

dim(Boston)
attach(Boston)
set.seed(101)


#training Sample with 300 observations
train<-sample(1:nrow(Boston),300)
?Boston

Boston.rf<-randomForest(medv ~ . , data = Boston , subset = train)
Boston.rf

plot(Boston.rf)


oob.err<-double(13)
test.err<-double(13)

#mtry is no of Variables randomly chosen at each split
for(mtry in 1:13) 
{
  rf=randomForest(medv ~ . , data = Boston , subset = train,mtry=mtry,ntree=400) 
  oob.err[mtry] = rf$mse[400] #Error of all Trees fitted
  
  pred<-predict(rf,Boston[-train,]) #Predictions on Test Set for each Tree
  test.err[mtry]= with(Boston[-train,], mean( (medv - pred)^2)) #Mean Squared Test Error
  
  cat(mtry," ")
  
}

test.err  
oob.err


matplot(1:mtry , cbind(oob.err,test.err), pch=19 , col=c("red","blue"),type="b",ylab="Mean Squared Error",xlab="Number of Predictors Considered at each Split")
legend("topright",legend=c("Out of Bag Error","Test Error"),pch=19, col=c("red","blue"))