我有一个决策树graphviz文件，是通过使用ScikitLearn的export_graphviz函数获得的。为了简单起见，我将深度限制为3，因此得到以下输出：
digraph Tree {
node [shape=box] ;
0 [label="userAcceleration-magnitude-mean <= 0.973\ngini = 0.875\nsamples = 3878\nvalue = [471, 467, 485, 484, 486, 486, 513, 486]\nclass = Walking"] ;
1 [label="userAcceleration-x-IQR <= 0.073\ngini = 0.834\nsamples = 2881\nvalue = [471, 443, 476, 484, 9, 486, 512, 0]\nclass = Walking"] ;
0 -> 1 [labeldistance=2.5, labelangle=45, headlabel="True"] ;
2 [label="rotationRate-z-IQR <= 0.396\ngini = 0.606\nsamples = 1020\nvalue = [466, 80, 43, 2, 0, 429, 0, 0]\nclass = Push-ups"] ;
1 -> 2 ;
3 [label="gini = 0.355\nsamples = 515\nvalue = [5, 74, 28, 2, 0, 406, 0, 0]\nclass = Resting"] ;
2 -> 3 ;
4 [label="gini = 0.164\nsamples = 505\nvalue = [461, 6, 15, 0, 0, 23, 0, 0]\nclass = Push-ups"] ;
2 -> 4 ;
5 [label="rotationRate-magnitude-median <= 0.844\ngini = 0.764\nsamples = 1861\nvalue = [5, 363, 433, 482, 9, 57, 512, 0]\nclass = Walking"] ;
1 -> 5 ;
6 [label="gini = 0.596\nsamples = 974\nvalue = [2, 73, 388, 476, 0, 23, 12, 0]\nclass = Lunges"] ;
5 -> 6 ;
7 [label="gini = 0.571\nsamples = 887\nvalue = [3, 290, 45, 6, 9, 34, 500, 0]\nclass = Walking"] ;
5 -> 7 ;
8 [label="userAcceleration-y-max <= 2.702\ngini = 0.533\nsamples = 997\nvalue = [0, 24, 9, 0, 477, 0, 1, 486]\nclass = Running"] ;
0 -> 8 [labeldistance=2.5, labelangle=-45, headlabel="False"] ;
9 [label="rotationRate-z-IQR <= 2.4\ngini = 0.236\nsamples = 536\nvalue = [0, 22, 6, 0, 466, 0, 1, 41]\nclass = Jump Rope"] ;
8 -> 9 ;
10 [label="gini = 0.553\nsamples = 53\nvalue = [0, 11, 6, 0, 3, 0, 0, 33]\nclass = Running"] ;
9 -> 10 ;
11 [label="gini = 0.08\nsamples = 483\nvalue = [0, 11, 0, 0, 463, 0, 1, 8]\nclass = Jump Rope"] ;
9 -> 11 ;
12 [label="altitude-median <= 5.0\ngini = 0.068\nsamples = 461\nvalue = [0, 2, 3, 0, 11, 0, 0, 445]\nclass = Running"] ;
8 -> 12 ;
13 [label="gini = 0.0\nsamples = 445\nvalue = [0, 0, 0, 0, 0, 0, 0, 445]\nclass = Running"] ;
12 -> 13 ;
14 [label="gini = 0.477\nsamples = 16\nvalue = [0, 2, 3, 0, 11, 0, 0, 0]\nclass = Jump Rope"] ;
12 -> 14 ;
}
让我们集中讨论前两个节点：
0 [label="userAcceleration-magnitude-mean <= 0.973\ngini = 0.875\nsamples = 3878\nvalue = [471, 467, 485, 484, 486, 486, 513, 486]\nclass = Walking"] ;
1 [label="userAcceleration-x-IQR <= 0.073\ngini = 0.834\nsamples = 2881\nvalue = [471, 443, 476, 484, 9, 486, 512, 0]\nclass = Walking"] ;
0 -> 1 [labeldistance=2.5, labelangle=45, headlabel="True"] ;
2 [label="rotationRate-z-IQR <= 0.396\ngini = 0.606\nsamples = 1020\nvalue = [466, 80, 43, 2, 0, 429, 0, 0]\nclass = Push-ups"] ;
1 -> 2 ;
这是我不明白的：
1：如果用户加速度幅值平均值小于或等于0.973，则遵循True。（这将继续向下延伸到树中。）
他说：我在谷歌上搜索了一下，发现了“基尼系数：一种统计指标，用来衡量一组值所代表的变化程度，特别是在分析收入不平等时”。我认为这是从经济的角度考虑的，但我不确定情况是否如此。
3:这里面有一个基本的结构samples是应用于该节点的样本量。根有3878个样本，左子有2881个样本，右子有997个样本。由于2881 997=3878，我认为对于2881个样本，用户加速度幅度平均值
这些值也有某种潜在的结构。value列表中每个值的总和等于该节点中示例的数量。
