可视化决策树（来自scikit-learn的示例）

可视化决策树（来自scikit-learn的示例）

您运行哪个 *** 作系统？你已经

graphviz

安装好了吗？

在您的示例中，

StringIO()

对象保存

graphviz

数据，这是一种检查数据的方法：

...>>> print out.getvalue()digraph Tree {0 [label="X[2] <= 2.4500nerror = 0.666667nsamples = 150nvalue = [ 50.  50.  50.]", shape="box"] ;1 [label="error = 0.0000nsamples = 50nvalue = [ 50.   0.   0.]", shape="box"] ;0 -> 1 ;2 [label="X[3] <= 1.7500nerror = 0.5nsamples = 100nvalue = [  0.  50.  50.]", shape="box"] ;0 -> 2 ;3 [label="X[2] <= 4.9500nerror = 0.168038nsamples = 54nvalue = [  0.  49.   5.]", shape="box"] ;2 -> 3 ;4 [label="X[3] <= 1.6500nerror = 0.0407986nsamples = 48nvalue = [  0.  47.   1.]", shape="box"] ;3 -> 4 ;5 [label="error = 0.0000nsamples = 47nvalue = [  0.  47.   0.]", shape="box"] ;4 -> 5 ;6 [label="error = 0.0000nsamples = 1nvalue = [ 0.  0.  1.]", shape="box"] ;4 -> 6 ;7 [label="X[3] <= 1.5500nerror = 0.444444nsamples = 6nvalue = [ 0.  2.  4.]", shape="box"] ;3 -> 7 ;8 [label="error = 0.0000nsamples = 3nvalue = [ 0.  0.  3.]", shape="box"] ;7 -> 8 ;9 [label="X[0] <= 6.9500nerror = 0.444444nsamples = 3nvalue = [ 0.  2.  1.]", shape="box"] ;7 -> 9 ;10 [label="error = 0.0000nsamples = 2nvalue = [ 0.  2.  0.]", shape="box"] ;9 -> 10 ;11 [label="error = 0.0000nsamples = 1nvalue = [ 0.  0.  1.]", shape="box"] ;9 -> 11 ;12 [label="X[2] <= 4.8500nerror = 0.0425331nsamples = 46nvalue = [  0.   1.  45.]", shape="box"] ;2 -> 12 ;13 [label="X[0] <= 5.9500nerror = 0.444444nsamples = 3nvalue = [ 0.  1.  2.]", shape="box"] ;12 -> 13 ;14 [label="error = 0.0000nsamples = 1nvalue = [ 0.  1.  0.]", shape="box"] ;13 -> 14 ;15 [label="error = 0.0000nsamples = 2nvalue = [ 0.  0.  2.]", shape="box"] ;13 -> 15 ;16 [label="error = 0.0000nsamples = 43nvalue = [  0.   0.  43.]", shape="box"] ;12 -> 16 ;}

您可以将其编写为.dot文件并产生图像输出，如链接的源中所示：

$ dot -Tpng tree.dot -o tree.png

（PNG格式输出）