"使用 sklearn，我如何找到决策树的深度？"

Question

I am training a decision tree with sklearn. When I use:

dt_clf = tree.DecisionTreeClassifier()

Answer 1

Access the max_depth for the underlying Tree object:

from sklearn import tree
X = [[0, 0], [1, 1]]
Y = [0, 1]
clf = tree.DecisionTreeClassifier()
clf = clf.fit(X, Y)
print(clf.tree_.max_depth)
>>> 1

You may get more accessible attributes from the underlying tree object using:

help(clf.tree_)

These include max_depth , node_count , and other lower-level parameters.

Answer 2

根据文档https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html#sklearn.tree.DecisionTreeClassifier.get_depth的答案是使用tree.get_depth()函数。