利用sklearn 中的决策树方法对鸢尾花数据建立决策树，并利用pydotplus+graphviz 对决策树进行可视化

利用sklearn 中的决策树方法对鸢尾花数据建立决策树，并利用pydotplus+graphviz 对决策树进行可视化

from sklearn import  tree
from sklearn.datasets import load_iris
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
import pydotplus
iris=load_iris()
# 特征
iris_feature = iris.data
# 分类标签
iris_label = iris.target
# 数据集划分
X_train,X_test,Y_train,Y_test = train_test_split(iris_feature,iris_label,test_size=0.3,random_state=30)
clf=tree.DecisionTreeClassifier()
clf=clf.fit(X_train,Y_train)
predict=clf.predict(X_test)
print(predict)
print(Y_test)
print(accuracy_score(predict,Y_test))
dot_data = tree.export_graphviz(clf, out_file=None)
graph = pydotplus.graph_from_dot_data(dot_data)
graph.write_pdf("iris.pdf")

决策树：