iris_deicison_tree.R

# An example of using decision tree to classify iris data 

library("party")

splitdf <- function(dataframe, ratio=0.8, seed=NULL) {
  if (!is.null(seed)) set.seed(seed)
  index <- 1:nrow(dataframe)
  trainindex = sample(1:nrow(dataframe), size=ratio*nrow(dataframe))
  trainset <- dataframe[trainindex, ]
  testset <- dataframe[-trainindex, ]
  list(train=trainset,test=testset)
}

split = splitdf(iris)
tree <- ctree(Species ~ Sepal.Length + Sepal.Width + Petal.Length + Petal.Width, data=split$train)
plot(tree)

predicted = predict(tree, split$test)

calculate_accuracy = function(predicted, actual) {
  d = table(predicted, actual)
  sum(diag(d))/sum(d)
}
calculate_accuracy(predicted, split$test$Species)