scikit-learn在树结构中的每个叶子节点的决策标签都存放在哪里？ [英] Where does scikit-learn hold the decision labels of each leaf node in its tree structure?

问题描述

我已经使用scikit-learn训练了随机森林模型，现在我想将其树结构保存在文本文件中，以便可以在其他地方使用它。
根据此链接，一个树对象由一个并行数组的数量，每个数组包含有关树的不同节点的一些信息（例如，左子节点，右子节点，其检查的功能...）。但是，似乎没有有关与每个叶节点相对应的类标签的信息！

I have trained a random forest model using scikit-learn and now I want to save its tree structures in a text file so I can use it elsewhere. According to this link a tree object consist of a number of parallel arrays each one hold some information about different nodes of the tree (ex. left child, right child, what feature it examines,...) . However there seems to be no information about the class label corresponding to each leaf node! It's even not mentioned in the examples provided in the link above.

有人知道scikit-learn决策树结构中存储的类标签在哪里吗？

Does anyone know where are the class labels stored in the scikit-learn decision tree structure?

推荐答案

看看 sklearn.tree.DecisionTreeClassifier.tree_.value ：

from sklearn.datasets import load_iris
from sklearn.cross_validation import cross_val_score
from sklearn.tree import DecisionTreeClassifier

clf = DecisionTreeClassifier(random_state=0)
iris = load_iris()

clf.fit(iris.data, iris.target)

print(clf.classes_)

[0, 1, 2]

print(clf.tree_.value)

[[[ 50.  50.  50.]]

 [[ 50.   0.   0.]]

 [[  0.  50.  50.]]

 [[  0.  49.   5.]]

 [[  0.  47.   1.]]

 [[  0.  47.   0.]]

 [[  0.   0.   1.]]

 [[  0.   2.   4.]]

 [[  0.   0.   3.]]

 [[  0.   2.   1.]]

 [[  0.   2.   0.]]

 [[  0.   0.   1.]]

 [[  0.   1.  45.]]

 [[  0.   1.   2.]]

 [[  0.   0.   2.]]

 [[  0.   1.   0.]]

 [[  0.   0.  43.]]]

clf.tree_.value 包含每个节点的恒定预测值（ help（clf.tree _）），它对应于索引索引到 clf.classes _ 。

Each row in clf.tree_.value "contains the constant prediction value of each node," (help(clf.tree_)) which corresponds index-to-index to clf.classes_.

请参见此答案（很少）获得更多详细信息。

See this answer for (barely) more details.

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