# 第十二章：决策树

![](http://windmissing.github.io/images/2019/261.jpg) 假设上图是某公司招聘算法工程师的过程。\
图中的叶子节点代码决策，在寻找决策过程是形成了树是决策树。\
这棵树的深度为3，因为最多经过3次判断就能做出决策。

## 使用sklearn中的决策树

```python
import numpy as np
import matplotlib.pyplot as plt
from sklearn import datasets

iris = datasets.load_iris()
X = iris.data[:, 2:]
y = iris.target

plt.scatter(X[y==0,0],X[y==0,1])
plt.scatter(X[y==1,0],X[y==1,1])
plt.scatter(X[y==2,0],X[y==2,1])
plt.show()
```

![](http://windmissing.github.io/images/2019/260.png)

```python
from sklearn.tree import DecisionTreeClassifier

dt_clf = DecisionTreeClassifier(max_depth=2, criterion='entropy') # entropy = 熵
dt_clf.fit(X, y)

plot_decision_boundary(dt_clf, axis=[0.5, 7.5, 0, 3])
plt.scatter(X[y==0,0],X[y==0,1])
plt.scatter(X[y==1,0],X[y==1,1])
plt.scatter(X[y==2,0],X[y==2,1])
plt.show()
```

![](http://windmissing.github.io/images/2019/259.png)\
\&#xNAN;*这个分类结果与视频中的分类结果不同*

## 总结

### 决策树怎样做决策的

![](http://windmissing.github.io/images/2019/262.jpg) 当属性是数值特征时，在每一个节点上，选择某一个维度上的数值，以它为域值将样本分成两类。

### 决策树的特点

1. 是非参数学习算法   
2. 可以分类问题  
3. 天然可以解决多分类问题  
4. 也可以解决回归问题（用分类算法将样本归到某个叶子上，该叶子上所有样本的平均值即输出标记）  
5. 有非常好的可解释性  

### 怎样创建决策树

1. 每个结点在哪个维度做划分？  
2. 某个维度的哪个值上做划分？  


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