Create a Decision Tree

Now that we have explored the IRIS dataset, let us create a decision tree.

We will go through this script line-by-line. Type each line in the R console as you encounter it.

• Step 1: Load the party package.
  > library(party)
  You will see the ‘party’ package and a list of dependent packages loading.

• Step 2: Let us now load the IRIS dataset that is available in R.
  > data(iris)
  You will see the IRIS dataset is loaded in the Environment and Workspace area.

• Step 3: Let us create the input data frame and assign the IRIS dataset to it.
  > input = iris
  You will see the input dataframe is loaded in the Environment and Workspace area. The following command will give you the data structure of the variable.
  class(input)
[1] “data.frame”

• Step 4: Create the decision tree.
  > output.tree <- ctree(Species ~ Sepal.Length + Sepal.Width + Petal.Length + Petal.Width, data=iris)
  The decision tree model is now created.
  Here, Species ~ Sepal.Length + Sepal.Width + Petal.Length + Petal.Width tells R that Species (the class of the flowers) is dependent on the attributes Sepal.Length, Sepal.Width, Petal.Length and Petal.Width.

• Step 5: Plot the tree
  > plot(output.tree,type="simple")
  You should be able to see the following plot in the “Files and Plots” area of the IDE.

• > print(output.tree) This will give you the following verbose output of the tree.

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