Boosting.R

#Boosting in R


require(gbm)
require(MASS)


Boston.boost<-gbm(medv ~ . ,data = Boston[-train,],distribution = "gaussian",n.trees = 10000,
                  shrinkage = 0.01, interaction.depth = 4)
Boston.boost

summary(Boston.boost) #Summary gives a table of Variable Importance and a plot of Variable Importance


plot(Boston.boost,i="lstat") #Plot of Response variable with lstat variable
#Inverse relation with lstat variable ie

plot(Boston.boost,i="rm") 
#as the average number of rooms increases the the price increases


n.trees = seq(from=100 ,to=10000, by=100) #no of trees-a vector of 100 values 

#Generating a Prediction matrix for each Tree
predmatrix<-predict(Boston.boost,Boston[-train,],n.trees = n.trees)
dim(predmatrix) #dimentions of the Prediction Matrix

#Calculating The Mean squared Test Error
test.error<-with(Boston[-train,],apply( (predmatrix-medv)^2,2,mean))
head(test.error)

#Plotting 

plot(n.trees , test.error , pch=19,col="blue",xlab="Number of Trees",ylab="Test Error", main = "Perfomance of Boosting on Test Set")

#adding the RandomForests Minimum Error line trained on same data and similar parameters
abline(h = min(test.err),col="red")
legend("topright",c("Minimum Test error Line for Random Forests"),col="red",lty=1,lwd=1)