NEWBIE
使用微软版本10,python 3.5.2,dot - graphviz版本2.38.0(正确安装)
尝试使用export_graphviz来显示决策树。 认为它非常接近,只是做不到最后一步。
这是示例代码
from sklearn.datasets import load_iris
from sklearn import tree
clf = tree.DecisionTreeClassifier()
iris = load_iris()
clf = clf.fit(iris.data, iris.target)
tree.export_graphviz(clf, out_file='tree.dot')
`
输出'tree.dot'文件。双击时,它会调用Microsoft Word并显示以下文本。
digraph Tree {
node [shape=box] ;
0 [label="X[2] <= 2.45\ngini = 0.6667\nsamples = 150\nvalue = [50, 50, 50]"] ;
1 [label="gini = 0.0\nsamples = 50\nvalue = [50, 0, 0]"] ;
0 -> 1 [labeldistance=2.5, labelangle=45, headlabel="True"] ;
2 [label="X[3] <= 1.75\ngini = 0.5\nsamples = 100\nvalue = [0, 50, 50]"] ;
0 -> 2 [labeldistance=2.5, labelangle=-45, headlabel="False"] ;
3 [label="X[2] <= 4.95\ngini = 0.168\nsamples = 54\nvalue = [0, 49, 5]"] ;
2 -> 3 ;
4 [label="X[3] <= 1.65\ngini = 0.0408\nsamples = 48\nvalue = [0, 47, 1]"] ;
3 -> 4 ;
5 [label="gini = 0.0\nsamples = 47\nvalue = [0, 47, 0]"] ;
4 -> 5 ;
6 [label="gini = 0.0\nsamples = 1\nvalue = [0, 0, 1]"] ;
4 -> 6 ;
7 [label="X[3] <= 1.55\ngini = 0.4444\nsamples = 6\nvalue = [0, 2, 4]"] ;
3 -> 7 ;
8 [label="gini = 0.0\nsamples = 3\nvalue = [0, 0, 3]"] ;
7 -> 8 ;
9 [label="X[0] <= 6.95\ngini = 0.4444\nsamples = 3\nvalue = [0, 2, 1]"] ;
7 -> 9 ;
10 [label="gini = 0.0\nsamples = 2\nvalue = [0, 2, 0]"] ;
9 -> 10 ;
11 [label="gini = 0.0\nsamples = 1\nvalue = [0, 0, 1]"] ;
9 -> 11 ;
12 [label="X[2] <= 4.85\ngini = 0.0425\nsamples = 46\nvalue = [0, 1, 45]"] ;
2 -> 12 ;
13 [label="X[1] <= 3.1\ngini = 0.4444\nsamples = 3\nvalue = [0, 1, 2]"] ;
12 -> 13 ;
14 [label="gini = 0.0\nsamples = 2\nvalue = [0, 0, 2]"] ;
13 -> 14 ;
15 [label="gini = 0.0\nsamples = 1\nvalue = [0, 1, 0]"] ;
13 -> 15 ;
16 [label="gini = 0.0\nsamples = 43\nvalue = [0, 0, 43]"] ;
12 -> 16 ;
}
此示例代码正常运行
提前致谢
答案 0 :(得分:0)
这个答案对我很有用!谢谢阿什利!
对于windows:dl msi并安装;在你的程序列表中找到gvedit.exe;打开有问题的.dot文件;单击工具栏上的运行人员;转到图表 - &gt;设置;将输出文件类型更改为您喜欢的文件类型,然后按确定..它没有说什么,您只需在与.dot文件相同的目录中找到该文件。 - ashley 2015年3月26日9:15