This page shows how to build a decision tree with R.
> 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 nodesResponse: 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) > plot(iris_ctree, type="simple") |