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Decision Tree

Decision tree is a machine learning algorithm used to classify data into categories. It helps us to explore the data set. This section will use the kyphosis data set provided by gam package.

Classification tree

Below example creates a decision tree on Kyphosis from Age, Number, Start columns.

# Classification Tree with rpart
library(rpart)

# grow tree 
fit <- rpart(Kyphosis ~ Age + Number + Start,
  	method="class", data=kyphosis)

printcp(fit) # display the results 
plotcp(fit) # visualize cross-validation results 
summary(fit) # detailed summary of splits

# plot tree 
plot(fit, uniform=TRUE, 
  	main="Classification Tree for Kyphosis")
text(fit, use.n=TRUE, all=TRUE, cex=.8)

# create attractive postscript plot of tree 
post(fit, file = "/tmp/tree.ps", 
  	title = "Classification Tree for Kyphosis")

Prune the tree

# prune the tree 
pfit<- prune(fit, cp=   fit$cptable[which.min(fit$cptable[,"xerror"]),"CP"])

# plot the pruned tree 
plot(pfit, uniform=TRUE, 
  	main="Pruned Classification Tree for Kyphosis")
text(pfit, use.n=TRUE, all=TRUE, cex=.8)
post(pfit, file = "~/tmp/ptree.ps", 
  	title = "Pruned Classification Tree for Kyphosis")