- #从 sklearn.datasets 导入 iris 数据加载器
- from sklearn.datasets import load_iris
- iris=load_iris()
- print(iris.data.shape)#(150, 4)
- # 查看数据说明
- print(iris.DESCR)
- # 对 iris 数据集进行分割
- from sklearn.cross_validation import train_test_split
- # 随机采样 25% 的数据用于测试, 剩下的 75% 用于构建训练集合
- X_train,X_test,y_train,y_test=train_test_split(iris.data,iris.target,test_size=0.25,random_state=33)
- # 使用 k 近邻分类器对 iris 数据进行类别预测
- # 从 sklearn.preprocessing 里导入 StandardScaler 数据标准化模块
- from sklearn.preprocessing import StandardScaler
- from sklearn.neighbors import KNeighborsClassifier
- ss=StandardScaler()
- X_train=ss.fit_transform(X_train)
- X_test=ss.fit_transform(X_test)
- knc=KNeighborsClassifier()
- knc.fit(X_train,y_train)
- y_predict=knc.predict(X_test)
- print('The Accuracy of K-Nearest Neighbor Classifier is',knc.score(X_test,y_test))
- # 从 sklearn.metrics 里导入 classification_report 模块
- from sklearn.metrics import classification_report
- print(classification_report(y_test,y_predict,target_names=iris.target_names))
来源: http://www.bubuko.com/infodetail-2993118.html