转自：https://blog.csdn.net/gracejpw/article/details/102593225

1.sklearn建立随机森林分类器

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import load_wine
from sklearn.model_selection import train_test_split
wine = load_wine()
wine
wine.data
wine.target
#切分训练集和测试集
Xtrain, Xtest, Ytrain, Ytest = train_test_split(wine.data,wine.target,test_size=0.3)
#建立模型
clf = DecisionTreeClassifier(random_state=0)
rfc = RandomForestClassifier(random_state=0)
clf = clf.fit(Xtrain,Ytrain)
rfc = rfc.fit(Xtrain,Ytrain)
#查看模型效果
score_c = clf.score(Xtest,Ytest)
score_r = rfc.score(Xtest,Ytest)
#打印最后结果
print("Single Tree:",score_c)
print("Random Forest:",score_r)

Single Tree: 0.8888888888888888
Random Forest: 0.9444444444444444

2.红酒数据集

 

 它包含11个特征，以及quality分数，从0至9表示10个级别，随机森林可以将它们成功地多分类。