[Data Mining]Decision Trees using R

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Decision Trees using R

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 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) > 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.

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关键词：Data Mining Decision Trees Using Mini library levels party

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thanks for sharing