一个样本的全景图

之所以需要学习，是因为样本不是完全的，如果训练集是一个完全的样本，一个样本总体，意味着模型的最后模样。

设计好的决策树：

学习这么多天后，隐约感觉我这里的“决策树”和DecisionTreeClaassifier中决策树的概念并不一致，具体体现在DecisionTreeClaassifier的一些参数上，如max_features、min_samples_leaf等，我用id3生成的决策树和训练样本的关系是一一对应的，是过拟合的，可以反映训练样本的面貌，但是对于预测，可能会有失误。

但是，如果一个训练样本是一个总体，那么就没有这种失误的风险。下面就是上面这棵树的一个样本总体。

特征A,特征B,特征C,特征D,特征E,结果RES
A,B,C,D,E,RES
1,5,10,12,3,no
1,8,10,12,4,yes
0,8,9,13,4,yes
2,6,11,12,3,yes
0,6,9,13,3,no
1,5,10,13,3,no
2,8,9,12,3,yes
2,8,9,13,4,no
1,6,11,13,4,no
1,7,9,12,3,yes
1,7,11,12,4,no
1,6,9,12,4,yes
2,8,10,13,3,no
2,5,10,12,3,yes
0,8,10,12,4,yes
2,8,10,12,3,yes
1,5,9,12,3,yes
1,8,10,13,3,yes
1,5,11,12,3,no
2,7,11,12,4,yes
1,8,11,12,4,no
0,5,10,12,4,yes
0,6,10,12,4,yes
2,8,11,12,4,yes
0,7,10,13,3,yes
0,7,9,12,4,yes
2,7,10,12,4,yes
0,7,9,13,4,yes
1,6,11,13,3,no
2,6,10,12,4,yes
1,8,10,12,3,yes
0,8,10,13,4,yes
2,6,9,13,4,yes
0,7,9,12,3,yes
2,6,9,12,3,yes
1,6,11,12,3,no
2,6,11,13,4,yes
2,6,9,12,4,yes
2,8,9,13,3,no
1,5,9,13,3,yes
0,5,10,13,4,yes
2,6,9,13,3,yes
0,6,10,13,3,no
2,5,10,12,4,yes
1,7,11,13,4,no
0,7,11,13,4,yes
1,7,9,13,4,yes
1,5,10,13,4,no
0,5,11,13,4,yes
1,8,9,13,3,yes
1,5,11,13,3,no
0,5,9,13,4,yes
0,6,11,12,4,yes
2,7,11,12,3,yes
1,8,9,12,3,yes
0,7,10,12,3,yes
0,7,10,13,4,yes
0,6,10,12,3,no
1,5,11,13,4,no
1,6,9,13,3,yes
2,8,10,12,4,yes
2,5,9,12,3,yes
1,5,10,12,4,no
0,6,9,13,4,yes
0,7,10,12,4,yes
1,7,11,13,3,no
0,8,9,12,4,yes
1,8,10,13,4,yes
2,5,9,12,4,yes
0,7,11,12,4,yes
0,7,11,13,3,yes
0,8,11,13,4,yes
2,8,11,13,3,no
1,5,11,12,4,no
1,8,11,13,4,no
2,8,9,12,4,yes
2,7,10,12,3,yes
1,7,9,13,3,yes
0,6,9,12,3,no
1,7,9,12,4,yes
1,6,9,12,3,yes
2,6,11,13,3,yes
2,7,9,12,3,yes
0,7,9,13,3,yes
1,8,11,13,3,no
1,6,11,12,4,no
1,8,9,12,4,yes
2,5,11,12,4,yes
0,6,11,13,3,no
1,6,9,13,4,yes
2,8,11,12,3,yes
0,6,9,12,4,yes
2,6,10,13,3,yes
0,8,11,12,4,yes
1,7,11,12,3,no
0,6,11,13,4,yes
2,8,11,13,4,no
1,5,9,12,4,yes
1,8,11,12,3,no
2,7,9,12,4,yes
0,5,9,12,4,yes
0,7,11,12,3,yes
2,6,10,13,4,yes
2,5,11,12,3,yes
0,6,11,12,3,no
2,6,10,12,3,yes
1,5,9,13,4,yes
0,6,10,13,4,yes
1,8,9,13,4,yes
2,8,10,13,4,no
0,5,11,12,4,yes
2,6,11,12,4,yes

我用sklearn生成的结果：

对总体样本而言，要的就是过拟合。我自己id3算法生成的结果：

DecisionTreeClassifier的结果是一棵二叉树，我算法中是N叉树，目前正在学习解读DecisionTreeClassifier的结果，所以，对于它们之间的区别，后面再说。