# 随机森林

## Random Forest

​ 随机森林RF是Bagging的扩展变体，在其基础上，进一步在决策树上引入了随机性：

* 传统是基于最优属性划分，而**RF先从属性集合中随机选出k个属性作为候选属性集，再从k个属性集中挑一个最佳属性（按照决策树属性划分的那些方式）作为划分点**。
* 显然**k=1的时候就是每次都随机划分属性了，k=所有属性个数则就是传统决策树划分形式**了。通常按照划分，其中d为所有属性数量
* 值得说明的是，**随机森林通常优于Bagging**，因为显然二者都是基于随机的，但是随机森林随机的更加彻底

## 1基本流程

> 1）按照Bagging那样随机自助采样得到m个数据集
>
> 2）运用决策树算法，利用采样数据进行弱学习器学习，但是决策树每次划分的时候是在当且可选属性d中挑选k个(k<=d)然后再选择最优属性划分。
>
> 3）重复采样、学习T次，得到T个弱学习器，
>
> 4）和Bagging一样，对分类任务：使用投票多数表决了；回归：简单平均。

## 随机森林的使用：

中ensemble下

```python
from sklearn.model_selection import cross_val_score
from sklearn.datasets import make_blobs
import matplotlib.pyplot as plt
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import ExtraTreesClassifier

from sklearn.tree import DecisionTreeClassifier

seed = 1
X, y = make_blobs(n_samples=1000, n_features=6, centers=50, random_state=seed)

# 普通决策树
clf = DecisionTreeClassifier(max_depth=None, min_samples_split=2, random_state=seed)
scores = cross_val_score(clf, X, y)
print(scores.mean())

# 随机森林
clf = RandomForestClassifier(n_estimators=10, max_depth=None, min_samples_split=2, random_state=seed)
scores = cross_val_score(clf, X, y)
print(scores.mean())

# RF随机森林的变种
clf = ExtraTreesClassifier(n_estimators=10, max_depth=None, min_samples_split=2, random_state=seed)
scores = cross_val_score(clf, X, y)
print(scores.mean())
```


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