将scikit学习的DecisionTreeClassifier.tree_.value映射到预测的类 [英] mapping scikit-learn DecisionTreeClassifier.tree_.value to predicted class

问题描述

我在3类数据集上使用了scikit-learn DecissionTreeClassifier。在适合分类器之后，我访问tree_属性上的所有叶节点，以获得每个类在给定节点中结束的实例数量。

I am using a scikit-learn DecissionTreeClassifier on a 3 class dataset. After I fit the classifier I access all leaf nodes on the tree_ attribute in order to get the amount of instances that end up in a given node for each class.

clf = tree.DecisionTreeClassifier(max_depth=5)
clf.fit(X, y)
# lets assume there is a leaf node with id 5
print clf.tree_.value[5]

这将打印出来：

>>> array([[  0.,   1.,  68.]])

但是...怎么办我知道该数组中的哪个位置属于哪个类？
分类器具有classes_属性，该属性也是一个列表

but ... how do I know which position in that array belongs to which class ? The classifier has a classes_ attribute which is also a list

>>> clf.classes_
array(['CLASS_1', 'CLASS_2', 'CLASS_3'], dtype=object)

也许值数组上的索引1与类数组的索引1上的类匹配，依此类推？

Maybe index 1 on the value array matches the class on index 1 of the classes array and so on?

推荐答案

在scikit-learm邮件列表中对此进行了询问，我的猜测是正确的。原来值数组上的索引1与class数组的索引1上的类匹配，依此类推

Asked about this on the scikit-learm mailing list and my guess was right. Turns out the index 1 on the value array matches the class on index 1 of the classes array and so on

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