可视化决策树（例如，从scikit学习）(Visualizing a decision tree (

我在使用小白sciki学习的，所以请多多包涵。

我经历的例子： 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文件就可以使用了。

但我怎么画使用graphiz文件树？ （例子中没有进入细节树如何绘制）。

示例代码和提示比欢迎更多！

谢谢！

---

更新

我使用Ubuntu 12.04，Python的2.7.3

Answer 1:

你运行的操作系统？ 你有graphviz安装？

在你的榜样， StringIO()对象，持有Graphviz的数据，这里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 ;
}

你可以把它写成.DOT文件并生成图像的输出，如源表明您链接：

$ dot -Tpng tree.dot -o tree.png （PNG格式输出）

Answer 2:

你是非常接近！ 做就是了：

graph_from_dot_data(out.getvalue()).write_pdf("somefile.pdf")

文章来源: Visualizing a decision tree ( example from scikit-learn )