mapping scikit-learn DecisionTreeClassifier.tree_.value to predicted class

问题

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]

This will print out:

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

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)

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

回答1:

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

回答2:

No, it is not clf.classes_ but clf.tree_.feature that contain the column index of X. And, if X is Pandas DataFrame, X.columns contains the name. You can find more detailed information in a similar question.

来源：https://stackoverflow.com/questions/26207451/mapping-scikit-learn-decisiontreeclassifier-tree-value-to-predicted-class