我是使用 sciki-learn 的菜鸟，所以请多多包涵。
我正在浏览这个例子： http ://scikit-learn.org/stable/modules/tree.html#tree
>>> from sklearn.datasets import load_iris
>>> from sklearn import tree
>>> iris = load_iris()
>>> clf = tree.DecisionTreeClassifier()
>>> clf = clf.fit(iris.data, iris.target)
>>> from StringIO import StringIO
>>> out = StringIO()
>>> out = tree.export_graphviz(clf, out_file=out)
显然，graphiz 文件已经可以使用了。
但是如何使用 graphiz 文件绘制树呢？（该示例没有详细说明如何绘制树）。
示例代码和提示非常受欢迎！
谢谢！
更新
我正在使用 ubuntu 12.04，Python 2.7.3
您运行哪个操作系统？你graphviz安装了吗？
在您的示例中，StringIO()对象包含 graphviz 数据，这是检查数据的一种方法：
...
>>> print out.getvalue()
digraph Tree {
0 [label="X[2] <= 2.4500\nerror = 0.666667\nsamples = 150\nvalue = [ 50. 50. 50.]", shape="box"] ;
1 [label="error = 0.0000\nsamples = 50\nvalue = [ 50. 0. 0.]", shape="box"] ;
0 -> 1 ;
2 [label="X[3] <= 1.7500\nerror = 0.5\nsamples = 100\nvalue = [ 0. 50. 50.]", shape="box"] ;
0 -> 2 ;
3 [label="X[2] <= 4.9500\nerror = 0.168038\nsamples = 54\nvalue = [ 0. 49. 5.]", shape="box"] ;
2 -> 3 ;
4 [label="X[3] <= 1.6500\nerror = 0.0407986\nsamples = 48\nvalue = [ 0. 47. 1.]", shape="box"] ;
3 -> 4 ;
5 [label="error = 0.0000\nsamples = 47\nvalue = [ 0. 47. 0.]", shape="box"] ;
4 -> 5 ;
6 [label="error = 0.0000\nsamples = 1\nvalue = [ 0. 0. 1.]", shape="box"] ;
4 -> 6 ;
7 [label="X[3] <= 1.5500\nerror = 0.444444\nsamples = 6\nvalue = [ 0. 2. 4.]", shape="box"] ;
3 -> 7 ;
8 [label="error = 0.0000\nsamples = 3\nvalue = [ 0. 0. 3.]", shape="box"] ;
7 -> 8 ;
9 [label="X[0] <= 6.9500\nerror = 0.444444\nsamples = 3\nvalue = [ 0. 2. 1.]", shape="box"] ;
7 -> 9 ;
10 [label="error = 0.0000\nsamples = 2\nvalue = [ 0. 2. 0.]", shape="box"] ;
9 -> 10 ;
11 [label="error = 0.0000\nsamples = 1\nvalue = [ 0. 0. 1.]", shape="box"] ;
9 -> 11 ;
12 [label="X[2] <= 4.8500\nerror = 0.0425331\nsamples = 46\nvalue = [ 0. 1. 45.]", shape="box"] ;
2 -> 12 ;
13 [label="X[0] <= 5.9500\nerror = 0.444444\nsamples = 3\nvalue = [ 0. 1. 2.]", shape="box"] ;
12 -> 13 ;
14 [label="error = 0.0000\nsamples = 1\nvalue = [ 0. 1. 0.]", shape="box"] ;
13 -> 14 ;
15 [label="error = 0.0000\nsamples = 2\nvalue = [ 0. 0. 2.]", shape="box"] ;
13 -> 15 ;
16 [label="error = 0.0000\nsamples = 43\nvalue = [ 0. 0. 43.]", shape="box"] ;
12 -> 16 ;
}
您可以将其编写为.dot 文件并生成图像输出，如您链接的源代码所示：
$ dot -Tpng tree.dot -o tree.png （PNG格式输出）
你很亲近！做就是了：
graph_from_dot_data(out.getvalue()).write_pdf("somefile.pdf")