如何在决策树中获取所有基尼指数？

Question

I have made a decision tree using sklearn, here, under the SciKit learn DL package, viz. sklearn.tree.DecisionTreeClassifier().fit(x,y) .

How do I get the gini indices for all possible nodes at each step? graphviz only gives me the gini index of the node with the lowest gini index, ie the node used for split.

For example, the image below (from graphviz ) tells me the gini score of the Pclass_lowVMid right index which is 0.408, but not the gini index of the Pclass_lower or Sex_male at that step. I just know the Gini index of Pclass_lower and Sex_male must be greater than (0.408*0.7 + 0) but that's it.

Answer 1

pclass node的gini索引=左节点的gini索引*（左节点的样本数/左节点的样本数+右节点的样本数）+右节点的gini索引*（左边的样本数）节点/没有左边节点的样本+右边节点的样本数量）所以这里它将是

Gini index of pclass = 0 + .408 *(7/10) = 0.2856

Answer 2

Using export_graphviz shows impurity for all nodes, at least in version 0.20.1 .

from sklearn.datasets import load_iris
from sklearn.tree import DecisionTreeClassifier, export_graphviz
from graphviz import Source

data = load_iris()
X, y = data.data, data.target

clf = DecisionTreeClassifier(max_depth=2, random_state=42)
clf.fit(X, y)

graph = Source(export_graphviz(clf, out_file=None, feature_names=data.feature_names))
graph.format = 'png'
graph.render('dt', view=True);

The impurity values for all nodes are also accessible in the impurity attribute of the tree .

clf.tree_.impurity
array([0.66666667, 0.        , 0.5       , 0.16803841, 0.04253308])