随机森林算法使用介绍

随机森林是多个决策树的组合，最后的结果是各个决策树结果的综合考量。

1) 导入相关模块

In [1]: from sklearn import datasets
   ...: from sklearn.model_selection import train_test_split
   ...: from sklearn.ensemble import RandomForestClassifier

2) 导入相关数据

In [2]: wine = datasets.load_wine()

3) 分割测试集和训练集

In [3]: X_train, X_test, y_train, y_test = train_test_split(wine.data, wine.target, test_size=0.2)

4) 创建随机森林分类器对象

In [4]: dt = RandomForestClassifier()

5) 训练模型

In [5]: dt = dt.fit(X_train, y_train)

6) 模型评分

In [6]: dt.score(X_test,y_test) Out[6]: 1.0