scikit-learn 中的叶排序

Question

我正在 scikit-learn 中构建决策树，树缺少叶 #2。我想知道为什么？这是我的例子：

import numpy as np
from sklearn.tree import DecisionTreeClassifier, export_graphviz

def leaf_ordering():
    X = np.genfromtxt('X.csv', delimiter=',')
    Y = np.genfromtxt('Y.csv',delimiter=',')
    dt = DecisionTreeClassifier(min_samples_leaf=100, random_state=99)
    dt.fit(X, Y)
    print(set(dt.apply(X)))

leaf_ordering()

link 到文件 X link 到文件 Y

这是输出：{1, 3, 4}。如您所见，没有叶 #2。

Answer 1

示例中的节点 0 和 2 都是非叶节点。在我下面的例子中，从导出可以看出0、1、4都是内部树节点，而2、3、5 和 6 是叶子，所以所有的预测都将在这 4 个中的一个中。

In [35]: X = np.random.random([100, 5])

In [36]: y = X.sum(axis=1) + np.random.random(100)

In [37]: dt = DecisionTreeRegressor(max_depth=2)

In [38]: dt.fit(X, y)
Out[38]:
DecisionTreeRegressor(criterion='mse', max_depth=2, max_features=None,
           max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2,
           min_weight_fraction_leaf=0.0, presort=False, random_state=None,
           splitter='best')

In [39]: dt.apply(X)
Out[39]:
array([6, 3, 3, 3, 6, 6, 3, 6, 3, 6, 2, 3, 3, 5, 3, 5, 5, 6, 3, 3, 3, 3, 3,
       3, 3, 6, 6, 3, 3, 3, 3, 5, 3, 5, 3, 3, 3, 3, 2, 3, 3, 3, 6, 3, 3, 3,
       3, 6, 3, 5, 2, 3, 3, 6, 3, 3, 3, 3, 3, 6, 6, 3, 6, 6, 3, 5, 6, 3, 3,
       3, 3, 6, 3, 3, 2, 3, 6, 2, 6, 2, 3, 3, 6, 2, 5, 6, 3, 3, 3, 6, 5, 3,
       3, 3, 6, 6, 3, 3, 6, 5])

In [40]: export_graphviz(dt)

In [41]: !cat tree.dot
digraph Tree {
node [shape=box] ;
0 [label="X[2] <= 0.7003\nmse = 0.4442\nsamples = 100\nvalue = 3.0586"] ;
1 [label="X[4] <= 0.1842\nmse = 0.3332\nsamples = 65\nvalue = 2.8321"] ;
0 -> 1 [labeldistance=2.5, labelangle=45, headlabel="True"] ;
2 [label="mse = 0.0426\nsamples = 7\nvalue = 1.9334"] ;
1 -> 2 ;
3 [label="mse = 0.2591\nsamples = 58\nvalue = 2.9406"] ;
1 -> 3 ;
4 [label="X[0] <= 0.3576\nmse = 0.3782\nsamples = 35\nvalue = 3.4791"] ;
0 -> 4 [labeldistance=2.5, labelangle=-45, headlabel="False"] ;
5 [label="mse = 0.1212\nsamples = 10\nvalue = 2.9395"] ;
4 -> 5 ;
6 [label="mse = 0.3179\nsamples = 25\nvalue = 3.695"] ;
4 -> 6 ;
}

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