我对scikit-learn的使用还是个新手，请多包涵。

我正在研究这个示例：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，graphviz文件已经准备好使用了。

但是，我该如何使用graphviz文件来绘制决策树呢？（示例中没有详细说明如何绘制决策树）。

欢迎提供示例代码和建议！

谢谢！

更新

我使用的是Ubuntu 12.04，Python 2.7.3

回答：

你使用的是什么操作系统？你有安装graphviz吗？

在你的示例中，StringIO()对象保存了graphviz数据，以下是一种检查数据的方法：

...>>> 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 ;}

你可以将其保存为.dot文件，并生成图像输出，如你所链接的源代码中所示：

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