案例来源，《Python机器学习实战》
from sklearn import datasets from sklearn.neighbors import KNeighborsClassifier # from sklearn import cross_validation from sklearn import model_selection from sklearn.metrics import classification_report from sklearn.metrics import confusion_matrix from sklearn.metrics import accuracy_score #准备数据集，并分离训练集和验证集 iris = datasets.load_iris() #导入自带的Iris数据集 X = iris.data Y = iris.target validation_size = 0.20 seed = 1 X_train,X_validation,Y_train,Y_validation = model_selection.train_test_split( X,Y,test_size=validation_size,random_state=seed ) #将数据集中随机20%的内容作为验证集 #创建KNN分类器，并拟合数据集 knn = KNeighborsClassifier() knn.fit(X_train,Y_train) #在验证集上进行预测，并输出accuracy score,混淆矩阵和分类报告 predictions = knn.predict(X_validation) print(accuracy_score(Y_validation,predictions)) print(confusion_matrix(Y_validation,predictions)) print(classification_report(Y_validation,predictions))原书中调用的是cross_validation模块，实际中此模块已逐渐被弃用，不建议继续使用。可用其下方的sklearn.model_selection模块代替
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