blocks|key|4150533|text|如果在export_graphviz中为out_file提供None，则可以获得树的字符串表示。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|4150534|from+sklearn.datasets+import+load_iris
from+sklearn+import+tree

clf+=+tree.DecisionTreeClassifier()
iris+=+load_iris()

clf+=+clf.fit(iris.data,+iris.target)
string_data+=+tree.export_graphviz(clf,
++++out_file=None)

print(string_data)

#Output
digraph+Tree+{
node+[shape=box]+;
0+[label="petal+length+(cm)+<=+2.45\ngini+=+0.667\nsamples+=+150\nvalue+=+[50,+50,+50]\nclass+=+setosa"]+;
1+[label="gini+=+0.0\nsamples+=+50\nvalue+=+[50,+0,+0]\nclass+=+setosa"]+;
0+->+1+[labeldistance=2.5,+labelangle=45,+headlabel="True"]+;
2+[label="petal+width+(cm)+<=+1.75\ngini+=+0.5\nsamples+=+100\nvalue+=+[0,+50,+50]\nclass+=+versicolor"]+;
0+->+2+[labeldistance=2.5,+labelangle=-45,+headlabel="False"]+;
3+[label="petal+length+(cm)+<=+4.95\ngini+=+0.168\nsamples+=+54\nvalue+=+[0,+49,+5]\nclass+=+versicolor"]+;
2+->+3+;
4+[label="petal+width+(cm)+<=+1.65\ngini+=+0.041\nsamples+=+48\nvalue+=+[0,+47,+1]\nclass+=+versicolor"]+;
3+->+4+;
5+[label="gini+=+0.0\nsamples+=+47\nvalue+=+[0,+47,+0]\nclass+=+versicolor"]+;
4+->+5+;
6+[label="gini+=+0.0\nsamples+=+1\nvalue+=+[0,+0,+1]\nclass+=+virginica"]+;
4+->+6+;
7+[label="petal+width+(cm)+<=+1.55\ngini+=+0.444\nsamples+=+6\nvalue+=+[0,+2,+4]\nclass+=+virginica"]+;
3+->+7+;
8+[label="gini+=+0.0\nsamples+=+3\nvalue+=+[0,+0,+3]\nclass+=+virginica"]+;
7+->+8+;
9+[label="sepal+length+(cm)+<=+6.95\ngini+=+0.444\nsamples+=+3\nvalue+=+[0,+2,+1]\nclass+=+versicolor"]+;
7+->+9+;
10+[label="gini+=+0.0\nsamples+=+2\nvalue+=+[0,+2,+0]\nclass+=+versicolor"]+;
9+->+10+;
11+[label="gini+=+0.0\nsamples+=+1\nvalue+=+[0,+0,+1]\nclass+=+virginica"]+;
9+->+11+;
12+[label="petal+length+(cm)+<=+4.85\ngini+=+0.043\nsamples+=+46\nvalue+=+[0,+1,+45]\nclass+=+virginica"]+;
2+->+12+;
13+[label="sepal+length+(cm)+<=+5.95\ngini+=+0.444\nsamples+=+3\nvalue+=+[0,+1,+2]\nclass+=+virginica"]+;
12+->+13+;
14+[label="gini+=+0.0\nsamples+=+1\nvalue+=+[0,+1,+0]\nclass+=+versicolor"]+;
13+->+14+;
15+[label="gini+=+0.0\nsamples+=+2\nvalue+=+[0,+0,+2]\nclass+=+virginica"]+;
13+->+15+;
16+[label="gini+=+0.0\nsamples+=+43\nvalue+=+[0,+0,+43]\nclass+=+virginica"]+;
12+->+16+;
}|code-block|syntax|javascript|4150535|这里会有你想要的东西。然后，您可以很容易地编写一个程序来解析它，以便按照您的需要进行处理。|4150536|entityMap^0|3|F|K|8|U|4|0|0|0^^$0|@$1|2|3|4|5|6|7|O|8|@$9|P|A|Q|B|C]|$9|R|A|S|B|C]|$9|T|A|U|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|V|8|@]|D|@]|E|$I|J]]|$1|K|3|L|5|6|7|W|8|@]|D|@]|E|$]]|$1|M|3|-4|5|6|7|X|8|@]|D|@]|E|$]]]|N|$]]

If you supply <code>None</code> to the <code>out_file</code> in <code>export_graphviz</code>, you can get a string representation of the tree.

<pre><code>from sklearn.datasets import load_iris
from sklearn import tree

clf = tree.DecisionTreeClassifier()
iris = load_iris()

clf = clf.fit(iris.data, iris.target)
string_data = tree.export_graphviz(clf,
 out_file=None)

print(string_data)

#Output
digraph Tree {
node [shape=box] ;
0 [label="petal length (cm) &lt;= 2.45\ngini = 0.667\nsamples = 150\nvalue = [50, 50, 50]\nclass = setosa"] ;
1 [label="gini = 0.0\nsamples = 50\nvalue = [50, 0, 0]\nclass = setosa"] ;
0 -&gt; 1 [labeldistance=2.5, labelangle=45, headlabel="True"] ;
2 [label="petal width (cm) &lt;= 1.75\ngini = 0.5\nsamples = 100\nvalue = [0, 50, 50]\nclass = versicolor"] ;
0 -&gt; 2 [labeldistance=2.5, labelangle=-45, headlabel="False"] ;
3 [label="petal length (cm) &lt;= 4.95\ngini = 0.168\nsamples = 54\nvalue = [0, 49, 5]\nclass = versicolor"] ;
2 -&gt; 3 ;
4 [label="petal width (cm) &lt;= 1.65\ngini = 0.041\nsamples = 48\nvalue = [0, 47, 1]\nclass = versicolor"] ;
3 -&gt; 4 ;
5 [label="gini = 0.0\nsamples = 47\nvalue = [0, 47, 0]\nclass = versicolor"] ;
4 -&gt; 5 ;
6 [label="gini = 0.0\nsamples = 1\nvalue = [0, 0, 1]\nclass = virginica"] ;
4 -&gt; 6 ;
7 [label="petal width (cm) &lt;= 1.55\ngini = 0.444\nsamples = 6\nvalue = [0, 2, 4]\nclass = virginica"] ;
3 -&gt; 7 ;
8 [label="gini = 0.0\nsamples = 3\nvalue = [0, 0, 3]\nclass = virginica"] ;
7 -&gt; 8 ;
9 [label="sepal length (cm) &lt;= 6.95\ngini = 0.444\nsamples = 3\nvalue = [0, 2, 1]\nclass = versicolor"] ;
7 -&gt; 9 ;
10 [label="gini = 0.0\nsamples = 2\nvalue = [0, 2, 0]\nclass = versicolor"] ;
9 -&gt; 10 ;
11 [label="gini = 0.0\nsamples = 1\nvalue = [0, 0, 1]\nclass = virginica"] ;
9 -&gt; 11 ;
12 [label="petal length (cm) &lt;= 4.85\ngini = 0.043\nsamples = 46\nvalue = [0, 1, 45]\nclass = virginica"] ;
2 -&gt; 12 ;
13 [label="sepal length (cm) &lt;= 5.95\ngini = 0.444\nsamples = 3\nvalue = [0, 1, 2]\nclass = virginica"] ;
12 -&gt; 13 ;
14 [label="gini = 0.0\nsamples = 1\nvalue = [0, 1, 0]\nclass = versicolor"] ;
13 -&gt; 14 ;
15 [label="gini = 0.0\nsamples = 2\nvalue = [0, 0, 2]\nclass = virginica"] ;
13 -&gt; 15 ;
16 [label="gini = 0.0\nsamples = 43\nvalue = [0, 0, 43]\nclass = virginica"] ;
12 -&gt; 16 ;
}
</code></pre>

This will have what you want. You can then easily write a program to parse this to handle as you want.

I wanted the decision path (i.e the set of rules) from the root node to a given node (which I supply) in a decision tree (DecisionTreeClassifier) in scikit-learn. <code>clf.decision_path</code> specifies the nodes a sample goes through, which may help in getting the set of rules followed by the sample, but how do you get the set of rules up to a particular node in the tree?

Getting decision path to a node in sklearn

翻译质量差，导致语言生硬或混乱。

没有提供实际的解决方法或示例。

解答不清晰，无法理解或解决问题。

页面排版不美观，阅读体验差。

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我希望在scikit-learn中的决策树(DecisionTreeClassifier)中有从根节点到给定节点(我提供的)的决策路径(即规则集)。clf.decision_path指定了样本所经过的节点，这可能有助于获取样本所遵循的规则集，但是如何将规则集提升到树中的特定节点呢？

获取sklearn中节点的决策路径-腾讯云开发者社区-腾讯云

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