决策树的iris的分类

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import numpy as np
from sklearn import tree
from sklearn import metrics
from sklearn import datasets
iris = datasets.load_iris()
X = iris.data
y = iris.target
idx = np.arange(X.shape[0])
np.random.seed(13)
np.random.shuffle(idx)  #将idx打乱
X=X[idx]
y=y[idx]
#划分训练集与测试集
X_train = X[:int(X.shape[0]*0.75)]
X_test = X[int(X.shape[0]*0.75):]
y_train = y[:int(X.shape[0]*0.75)]
y_test = y[int(X.shape[0]*0.75):]
#搭建决策树模型
clf = tree.DecisionTreeClassifier()
#模型拟合
clf.fit(X_train,y_train)
#对测试集做出预测
y_predict = clf.predict(X_test)
result = metrics.classification_report(y_test,y_predict,target_names=iris.target_names)
print(result)