{"id":68323,"date":"2026-01-30T01:41:26","date_gmt":"2026-01-29T17:41:26","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/68323.html"},"modified":"2026-01-30T01:41:26","modified_gmt":"2026-01-29T17:41:26","slug":"%e6%9c%ba%e5%99%a8%e5%ad%a6%e4%b9%a02-%e7%bb%8f%e5%85%b8%e7%ae%97%e6%b3%95knn%e7%ae%97%e6%b3%95","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/68323.html","title":{"rendered":"\u673a\u5668\u5b66\u4e602-\u7ecf\u5178\u7b97\u6cd5KNN\u7b97\u6cd5"},"content":{"rendered":"

1.KNN\u7b97\u6cd5\u7684\u4ecb\u7ecd\u4e0e\u6d41\u7a0b<\/h2>\n

\u00a0 \u00a0 \u00a0 \u00a0 knn\u7b97\u6cd5\u4e5f\u53ebK\u8fd1\u90bb\u7b97\u6cd5，\u987e\u540d\u601d\u4e49，\u662f\u5bfb\u627e\u8ddd\u79bb\u76ee\u6807\u9884\u6d4b\u70b9\u6700\u8fd1\u7684K\u4e2a\u70b9，\u5176\u53ef\u4ee5\u7528\u4e8e\u89e3\u51b3\u5206\u7c7b\u95ee\u9898\u4e0e\u56de\u5f52\u95ee\u9898，\u4e00\u822c\u7528\u4e8e\u5206\u7c7b\u95ee\u9898\u8f83\u591a\u3002<\/p>\n

\u00a0 \u00a0 \u00a0 \u00a0 knn\u7b97\u6cd5\u7684\u6d41\u7a0b：<\/p>\n

\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a01.\u8ba1\u7b97\u5404\u4e2a\u70b9\u5230\u9884\u6d4b\u70b9\u7684\u8ddd\u79bb（\u4e00\u822c\u4f7f\u7528\u6b27\u5f0f\u8ddd\u79bb）<\/p>\n

\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 2.\u5bf9\u8ddd\u79bb\u8fdb\u884c\u6392\u5e8f<\/p>\n

\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 3.\u627e\u51fa\u8ddd\u79bb\u6700\u8fd1\u7684k\u4e2a\u70b9（\u5373\u6392\u5e8f\u540e\u524dk\u4e2a\u70b9）<\/p>\n

\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 4.\u5bf9\u4e8e\u5206\u7c7b\u95ee\u9898，\u5c06k\u4e2a\u70b9\u4e2d\u6570\u91cf\u6700\u591a\u7684\u6807\u7b7e\u4f5c\u4e3a\u9884\u6d4b\u70b9\u7684\u6807\u7b7e；\u5bf9\u4e8e\u56de\u5f52\u95ee\u9898，\u5c06k\u4e2a\u70b9\u7684\u6807\u7b7e\u7684\u5e73\u5747\u503c\u4f5c\u4e3a\u9884\u6d4b\u503c\u3002<\/p>\n

\u00a0 \u00a0 \u00a0 \u00a0 \u5176\u6548\u679c\u5982\u4e0b\u56fe：<\/p>\n

\"\"<\/p>\n

2.\u76f8\u5173API\u4ecb\u7ecd（\u5373\u6240\u9700\u8981\u5bfc\u5165\u7684\u5305）<\/h2>\n

knn\u5206\u7c7b：from sklearn.neighbors import Kneighbors.Classifier<\/p>\n

knn\u56de\u5f52：from sklearn.neighbors import Kneighbors.Regressor<\/p>\n

3.\u76f8\u5173\u8ddd\u79bb\u7684\u4ecb\u7ecd<\/h2>\n

\u00a0 \u00a0 \u00a0 \u00a0 \u521a\u521a\u63d0\u5230，\u5728knn\u7b97\u6cd5\u4e2d\u6d89\u53ca\u5230\u8ddd\u79bb\u7684\u8ba1\u7b97，\u63a5\u4e0b\u6765\u6211\u4eec\u5c31\u6765\u5177\u4f53\u4ecb\u7ecd\u4e00\u4e0b\u51e0\u79cd\u5e38\u89c1\u7684\u8ddd\u79bb：<\/p>\n

\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u6b27\u6c0f\u8ddd\u79bb：\u4e5f\u53eb\u6b27\u51e0\u91cc\u5f97\u8ddd\u79bb，\u7528\u4e8e\u8ba1\u7b97\u7a7a\u95f4\u4e2d\u4e24\u70b9\u7684\u7ebf\u6bb5\u8ddd\u79bb，\u516c\u5f0f\u4e3a：<\/p>\n

\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u200b\u200b\u200b\u200b\u200b\u200b\u200b\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u200b\u200b\u200b\u200b\u200b\u200b\u200b\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u200b\u200b\u200b\u200b\u200b\u200b\u200b\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u200b\u200b\u200b\u200b\u200b\u200b\u200b\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u200b\u200b\u200b\u200b\u200b\u200b\u200b\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\"\"<\/p>\n

\u00a0 \u00a0 \u00a0 \u00a0 \u66fc\u54c8\u987f\u8ddd\u79bb：\u4e5f\u53eb\u57ce\u5e02\u8857\u533a\u8ddd\u79bb，\u5176\u6838\u5fc3\u8981\u7d20\u662f\u6a2a\u5e73\u7ad6\u76f4，\u5373\u53ea\u80fd\u6a2a\u5411\u6216\u7eb5\u5411\u884c\u8d70，\u5176\u8ddd\u79bb\u516c\u5f0f\u4e3a：<\/p>\n

\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u200b\u200b\u200b\u200b\u200b\u200b\u200b\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u200b\u200b\u200b\u200b\u200b\u200b\u200b\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u200b\u200b\u200b\u200b\u200b\u200b\u200b\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u200b\u200b\u200b\u200b\u200b\u200b\u200b\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u200b\u200b\u200b\u200b\u200b\u200b\u200b\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u200b\u200b\u200b\u200b\u200b\u200b\u200b\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\"\"<\/p>\n

\u00a0 \u00a0 \u00a0 \u00a0 \u5207\u6bd4\u96ea\u592b\u8ddd\u79bb：\u7c7b\u4f3c\u56fd\u9645\u8c61\u68cb，\u53ea\u80fd\u6a2a\u7ad6\u6216\u5bf9\u89d2\u7ebf\u884c\u8d70，\u5176\u8ddd\u79bb\u8ba1\u7b97\u7684\u5176\u5b9e\u662f\u6b65\u6570，\u6a2a\u7ad6\u8d70\u4e0e\u5bf9\u89d2\u7ebf\u8d70，\u90fd\u7b97\u4e00\u6b65\u3002\u5176\u5b9e\u76f8\u5f53\u4e8e，\u6c42\u6700\u5927\u7684\u5750\u6807\u5dee\u3002\u5176\u8ba1\u7b97\u516c\u5f0f\u662f：<\/p>\n

\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u200b\u200b\u200b\u200b\u200b\u200b\u200b\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u200b\u200b\u200b\u200b\u200b\u200b\u200b\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u200b\u200b\u200b\u200b\u200b\u200b\u200b\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u200b\u200b\u200b\u200b\u200b\u200b\u200b\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u200b\u200b\u200b\u200b\u200b\u200b\u200b\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\"\"<\/p>\n

\u00a0 \u00a0 \u00a0 \u00a0 \u95f5\u6c0f\u8ddd\u79bb：\u4e5f\u53eb\u95f5\u53ef\u592b\u65af\u57fa\u8ddd\u79bb，\u662f\u5bf9\u4e8e\u4e0a\u8ff0\u8ddd\u79bb\u7684\u4e00\u4e2a\u5f52\u7eb3，\u5176\u516c\u5f0f\u4e3a：<\/p>\n

\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u200b\u200b\u200b\u200b\u200b\u200b\u200b\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u200b\u200b\u200b\u200b\u200b\u200b\u200b\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u200b\u200b\u200b\u200b\u200b\u200b\u200b\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u200b\u200b\u200b\u200b\u200b\u200b\u200b\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u200b\u200b\u200b\u200b\u200b\u200b\u200b\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u200b\u200b\u200b\u200b\u200b\u200b\u200b\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\"\"<\/p>\n

\u00a0 \u00a0 \u00a0 \u00a0 \u5f53p=1\u65f6，\u5c31\u53d8\u6210\u4e86\u66fc\u54c8\u987f\u8ddd\u79bb\u3002<\/p>\n

\u00a0 \u00a0 \u00a0 \u00a0 \u5f53p=2\u65f6，\u5c31\u53d8\u6210\u4e86\u6b27\u5f0f\u8ddd\u79bb\u3002<\/p>\n

\u00a0 \u00a0 \u00a0 \u00a0 \u5f53p=\u65e0\u7a77\u65f6，\u5c31\u53d8\u6210\u4e86\u5207\u6bd4\u96ea\u592b\u8ddd\u79bb\u3002<\/p>\n

\u00a0 \u00a0 3.\u7279\u5f81\u89c4\u8303\u5316\u64cd\u4f5c<\/h2>\n

\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u8fd9\u4e00\u822c\u653e\u5728\u7279\u5f81\u5de5\u7a0b\u7684\u7279\u5f81\u9884\u5904\u7406\u4e2d，\u5bf9\u4e8e\u7279\u5f81\u6570\u636e，\u53ef\u80fd\u51fa\u73b0\u6709\u7684\u7279\u5f81\u6570\u503c\u5f88\u5927，\u6709\u7684\u7279\u5f81\u6570\u503c\u5f88\u5c0f，\u5b58\u5728\u660e\u663e\u91cf\u7ea7\u5dee\u5f02，\u8fd9\u65f6\u5019，\u5927\u6570\u503c\u5bf9\u6a21\u578b\u5f71\u54cd\u8fc7\u5927，\u53ef\u80fd\u4f1a\u4f7f\u5f97\u6a21\u578b\u504f\u5411\u8fd9\u4e2a\u7279\u5f81，\u4e3a\u4e86\u6d88\u9664\u91cf\u7eb2\u5bf9\u4e8e\u6a21\u578b\u7684\u5f71\u54cd，\u6211\u4eec\u901a\u5e38\u8981\u5bf9\u6570\u636e\u8fdb\u884c\u653e\u7f29，\u5e38\u7528\u7684\u65b9\u6cd5\u6709\u4e24\u79cd：\u5f52\u4e00\u5316\u4e0e\u6807\u51c6\u5316\u3002<\/p>\n

\u00a0 \u00a0 \u00a0 \u00a0 \u5f52\u4e00\u5316：\"\"<\/p>\n

\u00a0 \u00a0 \u00a0 \u00a0\u5f52\u4e00\u5316API:from sklearn.preprocessing import MinMaxScaler<\/p>\n

\u00a0 \u00a0 \u00a0 \u00a0 \u6807\u51c6\u5316：\"\"<\/p>\n

\u00a0 \u00a0 \u00a0 \u00a0 \u6807\u51c6\u5316API:from sklearn.preprocessing import StandardScaler<\/p>\n

\u00a0 \u00a0 \u00a0 \u00a0 \u6ce8\u610f，\u6211\u4eec\u5e73\u5e38\u4e00\u822c\u4f7f\u7528\u6807\u51c6\u5316，\u56e0\u4e3a\u5f52\u4e00\u5316\u5bb9\u6613\u53d7\u6781\u503c\u5f02\u5e38\u503c\u7684\u5f71\u54cd\u3002<\/p>\n

4.KNN\u5206\u7c7b\u7b97\u6cd5\u5bf9\u9e22\u5c3e\u82b1\u5206\u7c7b\u6848\u4f8b<\/h2>\n

\u00a0 \u00a0 \u00a0 \u00a0 \u4e0b\u9762\u662f\u5bf9\u4e0a\u8ff0\u5185\u5bb9\u7684\u5e94\u7528，\u4e00\u4e2a\u7ecf\u5178\u7684\u9e22\u5c3e\u82b1\u6848\u4f8b\u4ee3\u7801：<\/p>\n

from sklearn.datasets import load_iris
\nimport seaborn as sns
\nimport pandas as pd
\nimport matplotlib.pyplot as plt
\nfrom sklearn.model_selection import train_test_split #\u7528\u4e8e\u5206\u9694\u8bad\u7ec3\u96c6\u4e0e\u6570\u636e\u96c6
\nfrom sklearn.preprocessing import StandardScaler #\u7528\u4e8e\u6570\u636e\u6807\u51c6\u5316
\nfrom sklearn.neighbors import KNeighborsClassifier #KNN\u7b97\u6cd5\u5206\u7c7b\u5bf9\u8c61
\nfrom sklearn.neighbors import KNeighborsRegressor #KNN\u7b97\u6cd5\u56de\u5f52\u5bf9\u8c61
\nfrom sklearn.metrics import accuracy_score #\u7528\u4e8e\u6a21\u578b\u9884\u6d4b
\n# def loadIris():
\n# #\u52a0\u8f7d\u9e22\u5c3e\u82b1\u6570\u636e\u96c6
\n# iris_data=load_iris()
\n# return iris_data
\n# def showIris():
\n# iris_data=load_iris()
\n# #\u5c06\u9e22\u5c3e\u82b1\u6570\u636e\u5c01\u88c5\u6210dataframe\u5bf9\u8c61
\n# iris_df=pd.DataFrame(iris_data.data,columns=iris_data.feature_names)
\n# #\u4e3adf\u5bf9\u8c61\u6dfb\u52a0\u6807\u7b7e\u5217
\n# iris_df['label']=iris_data.target
\n# #\u901a\u8fc7Seaborn\u7ed8\u5236\u6563\u70b9\u56fe
\n# sns.lmplot(data=iris_df,x='sepal length (cm)',y='sepal width (cm)',hue='label',fit_reg=True)
\n# #\u8bbe\u7f6e\u6807\u9898\u663e\u5f0f（\u7d27\u51d1\u6a21\u5f0f）
\n# plt.title('iris_data')
\n# plt.show()
\n# def split_train_test():
\n# iris_data=load_iris()
\n# x_train,x_test,y_train,y_test=train_test_split(iris_data.data,iris_data.target,test_size=0.2,random_state=19)
\n# print('\u8bad\u7ec3\u96c6\u7279\u5f81：',x_train,'\u8bad\u7ec3\u96c6\u6807\u7b7e：',y_train)<\/p>\n

def iris_evaluate_test():
\n #\u52a0\u8f7d\u6570\u636e，\u6570\u636e\u6765\u81ea\u4e8esklearn.datasets\u81ea\u5e26\u7684load_iris
\n iris_data=load_iris()
\n #\u6570\u636e\u9884\u5904\u7406，\u5212\u5206\u8bad\u7ec3\u96c6\u4e0e\u6d4b\u8bd5\u96c6，\u6bd4\u4f8b\u4e3a2:8
\n x_train,x_test,y_train,y_test=train_test_split(iris_data.data,iris_data.target,test_size=0.2,random_state=19)
\n #\u7279\u5f81\u5de5\u7a0b，\u7279\u5f81\u9884\u5904\u7406-\u6807\u51c6\u5316
\n #\u521b\u5efa\u6807\u51c6\u5316\u5bf9\u8c61
\n std=StandardScaler()
\n #fit_transform\u7528\u4e8e\u8bad\u7ec3\u5e76\u8f6c\u6362，fit\u7528\u4e8e\u8bad\u7ec3，transform\u7528\u4e8e\u8f6c\u6362,\u53ea\u6709\u7b2c\u4e00\u6b21\u9700\u8981\u8bad\u7ec3
\n x_train=std.fit_transform(x_train)
\n x_test=std.transform(x_test)
\n #\u521b\u5efaKNN\u5206\u7c7b\u5bf9\u8c61
\n estimator=KNeighborsClassifier(n_neighbors=3)
\n #\u8bad\u7ec3\u6a21\u578b
\n estimator.fit(x_train,y_train)
\n #\u6a21\u578b\u9884\u6d4b
\n y_pre=estimator.predict(x_test)
\n print('\u9884\u6d4b\u7ed3\u679c：',y_pre)
\n #\u81ea\u5b9a\u4e49\u6570\u636e\u96c6\u8fdb\u884c\u6d4b\u8bd5
\n my_data=[[5.1,3.5,1.4,0.2]]
\n my_data=std.transform(my_data)
\n print('\u9884\u6d4b\u7ed3\u679c：',estimator.predict(my_data))
\n #\u67e5\u770b\u4e0a\u8ff0\u6570\u636e\u96c6，\u6bcf\u79cd\u5206\u7c7b\u7684\u9884\u6d4b\u6982\u7387
\n print('\u9884\u6d4b\u6982\u7387：',estimator.predict_proba(my_data))
\n # print('\u51c6\u786e\u7387：',accuracy_score(y_test,y_pre))
\n #\u76f4\u63a5\u8fdb\u884c\u8bc4\u5206，\u57fa\u4e8e\u8bad\u7ec3\u96c6\u7279\u5f81\u4e0e\u8bad\u7ec3\u96c6\u6807\u7b7e
\n print('\u51c6\u786e\u7387：',estimator.score(x_train,y_train))
\n #\u57fa\u4e8e\u8bad\u7ec3\u96c6\u6807\u7b7e\u4e0e\u9884\u6d4b\u7ed3\u679c\u8fdb\u884c\u8bc4\u5206
\n print('\u51c6\u786e\u7387：',accuracy_score(y_test,y_pre))
\nif __name__ == '__main__':
\n # showIris()
\n # split_train_test()
\n iris_evaluate_test()<\/p>\n","protected":false},"excerpt":{"rendered":"

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