决策树:https://www.cnblogs.com/molieren/articles/10664954.html
from sklearn import datasets
from sklearn.model_selection import train_test_split,cross_val_score
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier,ExtraTreesClassifier
import random
if __name__ == '__main__':
datas = datasets.load_iris()
#打散数据:x_train,80%训练的data x_test 20%测试的data ,y_train 80%训练的target,y_test 20%测试的target
x_train,x_test,y_train,y_test = train_test_split(datas.data, datas.target,test_size=0.2, random_state=6)
#决策树分类器
model = DecisionTreeClassifier(criterion="entropy").fit(x_train,y_train)
#用80%的数据训练得出模型,然后用20%去预测,验证模型是否正确