【相关推荐】
Xtrain, Xtest, Ytrain, Ytest = train_test_split(wine.data,wine.target,test_size=0.3)
clf = tree.DecisionTreeClassifier(criterion="entropy")
clf = clf.fit(Xtrain, Ytrain)
score = clf.score(Xtest, Ytest) #返回预测的准确度
print(score)
feature_name = ['酒精',
'苹果酸',
'灰',
'灰的碱性',
'镁','总酚','类黄酮','非黄烷类酚类','花青素',
'颜色强度','色调','od280/od315稀释葡萄酒','脯氨酸']
#安装graphviz并配置电脑的环境变量,否则显示不了图片
import graphviz
dot_data = tree.export_graphviz(clf,
feature_names= feature_name,
class_names=["琴酒","雪莉","贝尔摩德"]
,filled=True
,rounded=True
)
# 出图
graph = graphviz.Source(dot_data)
graph