# Decision Trees

> library("party")

> str(iris)

'data.frame': 150 obs. of 5 variables:

$ Sepal.Length: num 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...

$ Sepal.Width : num 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...

$ Petal.Length: num 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...

$ Petal.Width : num 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...

$ Species : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...

Call function ctree to build a decision tree. The first parameter is a formula, which defines a target variable and a list of independent variables.

> iris_ctree <- ctree(Species ~ Sepal.Length + Sepal.Width + Petal.Length + Petal.Width, data=iris)

> print(iris_ctree)

Conditional inference tree with 4 terminal nodes

Response: Species

Inputs: Sepal.Length, Sepal.Width, Petal.Length, Petal.Width

Number of observations: 150

1) Petal.Length <= 1.9; criterion = 1, statistic = 140.264

2)* weights = 50

1) Petal.Length > 1.9

3) Petal.Width <= 1.7; criterion = 1, statistic = 67.894

4) Petal.Length <= 4.8; criterion = 0.999, statistic = 13.865

5)* weights = 46

4) Petal.Length > 4.8

6)* weights = 8

3) Petal.Width > 1.7

7)* weights = 46

> plot(iris_ctree)

This page shows how to build a decision tree with R.

*> plot(iris_ctree, type="simple")*

More examples on decision trees with R and other data mining techniques can be found in my book "R and Data Mining: Examples and Case Studies", which is downloadable as a .PDF file at the link.