sklearn決定木tree_

Question

のchildren_left属性を解釈する方法sklearn DecisionTreeClassifierの 'tree_'メソッドを使用して最も深いノードの規則を抽出しようとしています。私は、 'children_left'と 'children_right'の配列がモデルから何を意味しているのかを理解するのに苦労しています。誰でも説明できる？ Sklearnの例では

estimator = DecisionTreeClassifier(max_depth=4, random_state=0) 
estimator.fit(X_train, y_train) 
estimator.tree_.children_left 

[6] array([ 1, 2, 3, 4, 5, -1, -1, 8, -1, -1, 11, 12, -1, -1, 15, -1, -1, 
    18, 19, 20, -1, -1, 23, -1, -1, 26, 27, -1, -1, 30, -1, -1, 33, 34, 
    35, 36, -1, -1, 39, -1, -1, 42, 43, -1, -1, 46, -1, -1, 49, 50, 51, 
    -1, -1, 54, -1, -1, 57, 58, -1, -1, 61, -1, -1]) 

tree_model.tree_.children_right 

[7] array([32, 17, 10, 7, 6, -1, -1, 9, -1, -1, 14, 13, -1, -1, 16, -1, -1, 
    25, 22, 21, -1, -1, 24, -1, -1, 29, 28, -1, -1, 31, -1, -1, 48, 41, 
    38, 37, -1, -1, 40, -1, -1, 45, 44, -1, -1, 47, -1, -1, 56, 53, 52, 
    -1, -1, 55, -1, -1, 60, 59, -1, -1, 62, -1, -1]) 

、http://scikit-learn.org/stable/auto_examples/tree/plot_unveil_tree_structure.html、それは言う：

`# The decision estimator has an attribute called tree_ which stores the entire 
# tree structure and allows access to low level attributes. The binary tree 
# tree_ is represented as a number of parallel arrays. The i-th element of each 
# array holds information about the node `i`. Node 0 is the tree's root. NOTE: 
# Some of the arrays only apply to either leaves or split nodes, resp.` 

しかし、それはポストからchildren_left配列

Answer 1

の数字の意味を説明していません：https://github.com/scikit-learn/scikit-learn/blob/4907029b1ddff16b111c501ad010d5207e0bd177/sklearn/tree/_tree.pyx

children_left : array of int, shape [node_count] 
    children_left[i] holds the node id of the left child of node i. 
    For leaves, children_left[i] == TREE_LEAF. Otherwise, 
    children_left[i] > i. This child handles the case where 
    X[:, feature[i]] <= threshold[i]. 
children_right : array of int, shape [node_count] 
    children_right[i] holds the node id of the right child of node i. 
    For leaves, children_right[i] == TREE_LEAF. Otherwise, 
    children_right[i] > i. This child handles the case where 
    X[:, feature[i]] > threshold[i].