{"id":3656,"date":"2022-03-30T10:31:26","date_gmt":"2022-03-30T19:01:26","guid":{"rendered":"http:\/\/10ccc.cc\/?p=3656"},"modified":"2026-01-02T20:36:59","modified_gmt":"2026-01-02T12:36:59","slug":"%e3%80%90flag%e3%80%91%e6%9c%ba%e5%99%a8%e5%ad%a6%e4%b9%a0100%e5%a4%a9-%e7%ac%ac1%e5%a4%a9-%e6%95%b0%e6%8d%ae%e6%b8%85%e6%b4%97","status":"publish","type":"post","link":"https:\/\/10ccc.cc\/?p=3656","title":{"rendered":"\u3010Flag\u3011\u673a\u5668\u5b66\u4e60100\u5929-[\u7b2c1\u5929]-\u6570\u636e\u6e05\u6d17"},"content":{"rendered":"

\u7b2c\u4e00\u5929\uff1a\u4e00\u5207\u5c31\u7eea<\/p>\n

\"\"<\/p>\n

\u6570\u636e\u9884\u5904\u7406\u7684\u57fa\u672c\u6b65\u9aa4<\/h2>\n

\u6839\u636e day_1 \u7684\u539f\u6587<\/a>\uff0c\u6211\u4eec\u53ef\u4ee5\u603b\u7ed3\u51fa\u6570\u636e\u9884\u5904\u7406\u7684\u57fa\u672c\u6d41\u7a0b\uff1a<\/p>\n

    \n
  1. \u5bfc\u5165\u6570\u636e\u5904\u7406\u76f8\u5173\u5e93<\/strong>\uff1a\u5176\u4e2d\u6700\u5e38\u89c1\u7684\u5e93\u662f numpy \u4ee5\u53ca pandas \u3002\u5176\u4ed6\u7684\u5e93\u53ef\u4ee5\u6839\u636e\u9700\u8981\u518d\u8fdb\u884c\u5bfc\u5165\uff1b<\/li>\n
  2. \u5bfc\u5165\u6570\u636e\u96c6<\/strong>\uff1a\u6570\u636e\u96c6\u4e00\u822c\u662f.csv\u6216.xlsx\u683c\u5f0f\u7684\u6587\u4ef6(\u5982\u679c\u4e0d\u662f\u8fd9\u7c7b\u578b\u7684\u6bd4\u5982sql\u7b49\uff0c\u5219\u53ef\u4e8b\u5148\u8f6c\u4e3a\u6b63\u786e\u7684\u683c\u5f0f)\u3002\u8fd9\u79cd\u6587\u4ef6\u6700\u5927\u7684\u6bcf\u4e00\u884c\u90fd\u662f\u4e00\u6761\u6570\u636e\u8bb0\u5f55\u3002\u5bfc\u5165\u6570\u636e\u96c6\u6211\u4eec\u4e00\u822c\u4f7f\u7528pandas\u7684read_csv()\u65b9\u6cd5\uff0c\u8bfb\u53d6\u540e\uff0c\u5c06\u5176\u8f6c\u4e3a\u4e00\u4e2a\u6570\u636e\u5e27\u3002\u4e4b\u540e\u6211\u4eec\u53ef\u4ee5\u518d\u6839\u636e\u5177\u4f53\u7684\u4e1a\u52a1\u9009\u53d6\u81ea\u53d8\u91cf\u77e9\u9635\u53ca\u56e0\u53d8\u91cf\u5411\u91cf\uff1b<\/li>\n
  3. \u5904\u7406\u4e22\u5931\u6570\u636e<\/strong>\uff1a\u5982\u9047\u5230\u6570\u636e\u96c6\u4e0d\u5b8c\u6574\u7684\u60c5\u51b5\uff0c\u4e3a\u4e0d\u5f71\u54cd\u6a21\u578b\u7684\u6027\u80fd\uff0c\u6216\u7eaf\u7cb9\u4e3a\u4e86\u6a21\u578b\u80fd\u591f\u8dd1\u8d77\u6765\uff0c\u6211\u4eec\u9700\u8981\u5904\u7406\u6570\u636e\u4e2d\u7684\u7a7a\u503c(\u6216\u5176\u4ed6\u7c7b\u578b\u7684\u975e\u6570\u503c)\uff0c\u5e76\u7528\u6574\u5217\u7684\u5747\u503c\u3001\u4e2d\u503c\u3001\u4f17\u6570\u6216\u7279\u5b9a\u7684\u5e38\u6570\u8fdb\u884c\u5904\u7406\u3002\u5b8c\u6210\u8fd9\u4e00\u6b65\u9aa4\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528 sklearn.impute \u4e2d\u5bfc\u5165SimpleImputer \u65b9\u6cd5\u6765\u8fdb\u884c\u5904\u7406\uff1b<\/li>\n
  4. \u89e3\u6790\u5206\u7c7b\u6570\u636e(Encoding categorical data)\uff1a\u5206\u7c7b\u6570\u636e\u6307\u7684\u662f\u8fd8\u6709\u6807\u7b7e\u503c\u800c\u4e0d\u662f\u6570\u5b57\u7684\u53d8\u91cf(\u5982\u5b9a\u5e8f\u3001\u5b9a\u7c7b\u4ee5\u53ca\u5b9a\u8ddd\u53d8\u91cf\u7b49\uff0c\u6216\u5176\u4ed6\u7684\u975e\u6570\u5b57\u6807\u7b7e)\u3002\u8fd9\u7c7b\u578b\u7684\u6570\u636e\u901a\u5e38\u662f\u56fa\u5b9a\u7684\uff0c\u5982\u539f\u59cb\u6570\u636e\u4e2d\u7684 purchased \u5411\u91cf\u4e2d\u7684 YES \u548c NO\u3002\u4f46\u8fd9\u4e9b\u6807\u7b7e\u662f\u65e0\u6cd5\u8fdb\u884c\u6570\u5b66\u8ba1\u7b97\u7684\uff0c\u56e0\u6b64\u6211\u4eec\u8981\u6839\u636e\u4e00\u5b9a\u7684\u89c4\u5219\uff0c\u5c06\u5176\u8f6c\u6362\u4e3a\u6570\u5b57\u3002\u901a\u5e38\uff0c\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7 sklearn.preprocessing \u5e93\u4e2d\u7684 LabelEncoder \u7c7b \u6765\u5b8c\u6210\u8fd9\u4e00\u6b65\u9aa4\u7684\u5904\u7406\uff1b<\/strong><\/li>\n
  5. \u5c06\u6570\u636e\u96c6\u62c6\u5206\u4e3a\u8bad\u7ec3\u96c6\u548c\u6d4b\u8bd5\u96c6<\/strong>\uff1a\u5176\u4e2d\u8bad\u7ec3\u96c6\u7528\u4e8e\u8bad\u7ec3\u6a21\u578b\u3002\u8bad\u7ec3\u597d\u540e\uff0c\u518d\u7528\u6d4b\u8bd5\u96c6\u6765\u9a8c\u8bc1\u6a21\u578b\u9884\u6d4b\u7684\u7ed3\u679c\u3002\u4e00\u822c\u6765\u8bf4\uff0c\u8fd9\u4e24\u8005\u7684\u6570\u636e\u91cf\u6bd4\u4e3a ----> \u8bad\u7ec3\u96c6:\u6d4b\u8bd5\u96c6 = 80:20 \u3002\u800c\u62c6\u5206\u6570\u636e\u96c6\u53ef\u4ee5\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7 sklearn.crossvalidation \u4e2d\u7684tran_test_split()\u65b9\u6cd5\u5b9e\u73b0\u3002<\/li>\n
  6. \u7279\u5f81\u7f29\u653e(Feature Scaling)<\/strong>\uff1a\u5927\u90e8\u5206\u6a21\u578b\u7b97\u6cd5\u5728\u8ba1\u7b97\u4e24\u70b9\u95f4\u7684\u8ddd\u79bb\u65f6\u4e00\u822c\u7528\u6b27\u5f0f\u8ddd\u79bb\u3002\u4f46\u6b64\u7279\u7279\u5f81\u5728\u5e45\u5ea6\u3001\u5355\u4f4d\u4ee5\u53ca\u8303\u56f4\u4e0a\u53d8\u5316\u5f88\u5927\u3002\u5728\u8ddd\u79bb\u8ba1\u7b97\u65f6\uff0c\u9ad8\u5e45\u5ea6\u7684\u7279\u5f81\u6bd4\u4f4e\u5e45\u5ea6\u7684\u7279\u5f81\u6743\u91cd\u66f4\u5927\u3002\u4e3a\u6b64\uff0c\u53ef\u4f7f\u7528\u7279\u5f81\u6807\u51c6\u6216Z\u503c\u5f52\u4e00\u5316<\/a>\u5c06\u6240\u6709\u6570\u636e\u6620\u5c04\u5230[0\uff0c1] \u6216 [-1, 1]\u533a\u95f4\u5185\uff0c\u907f\u514d\"\u8ddd\u79bb\"\u6570\u91cf\u7ea7\u5dee\u8ddd\u8fc7\u5927\u7684\u5f71\u54cd\u3002(\u53c2\u8003:\u8fd9\u91cc<\/a>)<\/li>\n<\/ol>\n

     <\/p>\n

    \u521b\u5efa\u9879\u76ee\u76ee\u5f55<\/h2>\n

    \u8fdb\u5165 Jupiter Notebook \u540e\uff0c\u6211\u4eec\u9700\u8981\u505a\u7684\u7b2c\u4e00\u4ef6\u4e8b\u5c31\u662f\u5728\u81ea\u5df1\u559c\u6b22\u7684\u4f4d\u7f6e\u65b0\u5efa\u4e00\u4e2a\u6587\u4ef6\u5939\uff0c\u4f5c\u4e3a\u6211\u4eec\u9879\u76ee\u7684\u5de5\u4f5c\u76ee\u5f55\u3002\u540e\u7eed\u4e2d\uff0c\u6211\u4eec\u7684\u4ee3\u7801\u3001\u6570\u636e\u96c6\u90fd\u5c06\u653e\u7f6e\u5728\u8fd9\u4e2a\u76ee\u5f55\u4e2d\u3002\u4e00\u4e2a\u6574\u6d01\u3001\u6e05\u6670\u7684\u9879\u76ee\u9879\u76ee\u6587\u4ef6\u7ed3\u6784\uff0c\u53ef\u4ee5\u5728\u5f88\u5927\u7a0b\u5ea6\u4e0a\u63d0\u9ad8\u6211\u4eec\u7684\u6548\u7387\u5e76\u4e14\u51cf\u5c11\u72af\u9519\u7684\u51e0\u7387\u3002<\/p>\n

     <\/p>\n

    \u5728\u8fd9\u6b21\u9879\u76ee\u4e2d\uff0c\u9879\u76ee\u7684\u6587\u4ef6\u7ed3\u6784\u5982\u4e0b\u56fe\u6240\u793a\uff1a<\/p>\n

    \"\"<\/p>\n

    \u5f53\u7136\uff0c\u4e5f\u53ef\u4ee5\u6709\u5176\u4ed6\u7684\u6587\u4ef6\u5f52\u7eb3\u65b9\u5f0f\u3002\u4e4b\u6240\u4ee5\u8fd9\u4e48\u5f52\u7eb3\u7684\u539f\u56e0\u5728\u4e8e\uff0c\u5982\u7b2c0\u5929\u4e2d\u6240\u8bf4\uff0c100-Days-Of-ML-Code \u8fd9\u4e2a\u9879\u76ee\u6bcf\u5929\u7684\u5185\u5bb9\u5dee\u5f02\u5de8\u5927\uff0c\u4e14\u6240\u7528\u5230\u6570\u636e\u96c6\u4e5f\u5927\u76f8\u5f84\u5ead\u3002\u66f4\u91cd\u8981\u7684\u662f\uff0c\u6211\u5e0c\u671b\u53ef\u4ee5\u8fbe\u5230\uff0c\u6253\u5f00\u67d0\u5929\u7684\u7b14\u8bb0\uff0c\u5373\u53ef\u5c06\u76f8\u5173\u5185\u5bb9\u5c3d\u6536\u773c\u5e95\uff0c\u6ca1\u6709\u9057\u6f0f\u3002\u5f53\u7136\uff0c\u4e5f\u4e0d\u9700\u8981\u5728\u81ea\u5df1\u60f3\u8981\u67e5\u770b\u6570\u636e\u7684\u539f\u59cb\u6587\u4ef6\u65f6\uff0c\u5728\u65e0\u6570\u7684\u6587\u4ef6\u5939\u4e2d\u5207\u6362\u67e5\u770b\u3002<\/p>\n

     <\/p>\n

    \u8fd9\u6837\u7684\u601d\u7ef4\u65b9\u5f0f\u5728\u9879\u76ee\u4e2d\u7684\u5177\u4f53\u4f53\u73b0\u53ef\u4ee5\u662f\u8fd9\u6837\u7684\uff1a<\/p>\n

    \"\u6574\u4f53\u7ed3\u6784\"
    \u6574\u4f53\u7ed3\u6784<\/figcaption><\/figure>\n

     <\/p>\n

    \"day_1\u6587\u4ef6\u76ee\u5f55\"
    day_1\u6587\u4ef6\u76ee\u5f55<\/figcaption><\/figure>\n

     <\/p>\n

    \u6211\u4eec\u8fdb\u5165\u5230 day_1 \u6587\u4ef6\u5939\uff0c\u8fdb\u5165 day_1.ipynb \u6587\u4ef6\uff0c\u5373\u53ef\u5f00\u59cb\u7f16\u5199day_1\u7684\u76f8\u5173\u4ee3\u7801\u3002<\/p>\n

     <\/p>\n

    \u67e5\u770b\u539f\u59cb\u6570\u636e<\/h2>\n

    \"\u539f\u59cb\u6570\u636e\"<\/p>\n

    \u53ef\u4ee5\u770b\u5230\uff0c\u8fd9\u662f\u4e00\u7ec4\u6bd4\u8f83\u7b80\u5355\u7684\u6570\u636e\u3002\u8fd9\u7ec4\u6570\u7ec4\u4e2d\u5305\u542b country(\u56fd\u7c4d)\u3001age(\u5e74\u9f84)\u3001salary(\u6536\u5165)\u3001 purchased(\u662f\u5426\u8d2d\u4e70)\u51e0\u4e2a\u7ef4\u5ea6\u3002<\/p>\n

    \u9700\u8981\u6ce8\u610f\u7684\u662f\uff0c\u5176\u4e2d\u7684Age \u4ee5\u53ca Salary \u8fd9\u4e24\u5217\u4e2d\u5b58\u5728\u7a7a\u503c\u3002\u4f46\u4e0d\u7528\u62c5\u5fc3\uff0c\u8fd9\u70b9\u6211\u4eec\u5728\u6570\u636e\u9884\u5904\u7406\/\u6570\u636e\u6e05\u6d17\u9636\u6bb5\u4f1a\u89e3\u51b3\u8fd9\u4e2a\u95ee\u9898\u3002<\/p>\n

    \u6b64\u5916\uff0c\u5728\u8fd9\u91cc\uff0c\u6211\u4eec\u53ef\u4ee5\u731c\u6d4b\uff1a<\/p>\n

    \u4f3c\u4e4e\u5c1d\u8bd5\u901a\u8fc7\u56fd\u7c4d\u3001\u5e74\u9f84\u3001\u4ee5\u53ca\u6536\u5165\u7b49\u51e0\u4e2a\u81ea\u53d8\u91cf\u53d8\u91cf\u53bb\u9884\u6d4b\u8d2d\u4e70\u7ed3\u679c\u7684\u56e0\u53d8\u91cf<\/p><\/blockquote>\n

     <\/p>\n

    \u5f00\u59cb\u7f16\u7801<\/h2>\n

    \u5bfc\u5165\u5e93\u548c\u6570\u636e\u96c6<\/strong><\/h3>\n
    \n\t\t\t
    \n
    \n
    \n
    <\/div>\n
    <\/div>\n
    <\/div>\n <\/div>\n
    \n \n Copy Code<\/span>\n Copied<\/span>\n Use a different Browser<\/span><\/a>\n <\/div>\n <\/div>\n 
    <\/p>\n
    import numpy as np\r\nimport pandas as pd\r\n\r\ndataset = pd.read_csv('Data.csv')\r\nX = dataset.iloc[ : , :-1].values\r\nY = dataset.iloc[ : , 3].values<\/pre>\n
    <\/code><\/pre>\n\t\t\t<\/div>\n        <\/div>\n
     <\/p>\n
    \u5904\u7406\u4e22\u5931\u6570\u636e<\/strong><\/h3>\n
    \n\t\t\t
    \n                
    \n                    
    \n                        
    <\/div>\n                        
    <\/div>\n                        
    <\/div>\n                    <\/div>\n                    
    \n                        \n                        Copy Code<\/span>\n                        Copied<\/span>\n                        Use a different Browser<\/span><\/a>\n                    <\/div>\n                <\/div>\n                
    <\/p>\n
    from sklearn.impute import SimpleImputer as Imputer\r\n\r\nimputer = Imputer(missing_values = np.NAN, strategy = \"mean\")\r\nimputer = imputer.fit(X[ : , 1:3])\r\nX[ : , 1:3] = imputer.transform(X[ : , 1:3])<\/pre>\n
    <\/code><\/pre>\n\t\t\t<\/div>\n        <\/div>\n
    \u6ce8\u610f\uff0c\u6b64\u5904\u4e0d\u80fd\u6309\u7167github\u4e0a\u7684\u6e90\u4ee3\u7801\u8fdb\u884c\u5904\u7406\uff0c\u56e0\u4e3a\u65b0\u7248\u7684sklearn\u5df2\u7ecf\u6ca1\u6709imputer\u51fd\u6570\u3002\u9700\u6309\u7167\u672c\u6587\u4e2d\u7684\u65b9\u6cd5\u8fdb\u884c\u5904\u7406\u3002<\/p>\n
    \"\u6570\u636e\u7a7a\u503c\u5904\u7406\u524d\"
    \u6570\u636e\u7a7a\u503c\u5904\u7406\u524d<\/figcaption><\/figure>\n
    \"\u6570\u636e\u7a7a\u503c\u5904\u7406\"
    \u6570\u636e\u7a7a\u503c\u5904\u7406\u540e<\/figcaption><\/figure>\n
    \u4f7f\u7528 SimpleImuter \u65b9\u6cd5\u5904\u7406\u7a7a\u503c\u65f6\uff0c\u6211\u4eec\u6ce8\u610fstategy\u53c2\u6570\uff0c\u6b64\u5904\u6211\u4eec\u9009\u62e9\u201cmean\u201d\u5373\u5747\u503c\uff0c\u610f\u601d\u4e3a\u5c06\u8be5\u5217\u6240\u6709\u7684\u7a7a\u503c\u66ff\u6362\u4e3a\u8be5\u5217\u6240\u6709\u975e\u7a7a\u503c\u7684\u5747\u503c\u3002<\/span>\u53c2\u6570<\/span>\u5176\u4ed6\u53ef\u9009\u7684\u503c\u4e3a\uff1a<\/span><\/p>\n
    The imputation strategy.
    \nIf \u201cmean\u201d, then replace missing values using the mean along the axis. \u4f7f\u7528\u5e73\u5747\u503c\u4ee3\u66ff<\/p>\n
    If \u201cmedian\u201d, then replace missing values using the median along the axis.\u4f7f\u7528\u4e2d\u503c\u4ee3\u66ff<\/p>\n
    If \u201cmost_frequent\u201d, then replace missing using the most frequent value along the axis.\u4f7f\u7528\u4f17\u6570\u4ee3\u66ff\uff0c\u4e5f\u5c31\u662f\u51fa\u73b0\u6b21\u6570\u6700\u591a\u7684\u6570<\/p><\/blockquote>\n
     <\/p>\n
    \u6211\u4eec\u53ef\u4ee5\u9a8c\u8bc1\u4e00\u4e0bage\u5217\uff1a<\/p>\n
    \"\"<\/p>\n
    \u624b\u52a8\u8ba1\u7b97\u503c\u4e0e SimpleImputer \u7ed9\u51fa\u7684\u7ed3\u679c\u4e00\u81f4\uff08\u6ce8\u610f\u8be5\u5217\u975e\u7a7a\u503c\u67099\u4e2a\u6240\u4ee5\u9664\u6570\u4e3a9\uff09\u3002<\/p>\n
     <\/p>\n
    \u53e6\u5916\uff0cImputer\u548cSimpleImputer\u8fd9\u4e24\u4e2a\u65b9\u6cd5\u7684\u533a\u522b\u4e3a\uff1a<\/p>\n
    \n\t\t\t
    \n                
    \n                    
    \n                        
    <\/div>\n                        
    <\/div>\n                        
    <\/div>\n                    <\/div>\n                    
    \n                        \n                        Copy Code<\/span>\n                        Copied<\/span>\n                        Use a different Browser<\/span><\/a>\n                    <\/div>\n                <\/div>\n                
    <\/p>\n
    SI = SimpleImputer(missing_values=np.NAN, strategy=\u2019mean\u2019)<\/p>\n
    I = Imputer(missing_values=\u2019NaN\u2019, strategy=\u2019mean\u2019, axis=0, verbose=0, copy=True)<\/p>\n
    <\/code><\/pre>\n\t\t\t<\/div>\n        <\/div>\n
    \u5176\u4e2d\u6700\u5927\u7684\u533a\u522b\u4e3a SimpleImputer\u6ca1\u6709axis\u53c2\u6570\uff0c\u9ed8\u8ba4\u4e3a\u6309\u5217\u5904\u7406\u3002\u8fd9\u4e5f\u8981\u6c42\u6211\u4eec\u5728\u641c\u96c6\u5904\u7406\u6570\u636e\u96c6\u65f6\uff0c\u9700\u8981\u4e25\u683c\u6309\u7167\u6807\u51c6\u3002<\/p>\n
     <\/p>\n
    <\/div>\n
    \u89e3\u6790\u5206\u7c7b\u6570\u636e<\/strong><\/h3>\n
    \n\t\t\t
    \n                
    \n                    
    \n                        
    <\/div>\n                        
    <\/div>\n                        
    <\/div>\n                    <\/div>\n                    
    \n                        \n                        Copy Code<\/span>\n                        Copied<\/span>\n                        Use a different Browser<\/span><\/a>\n                    <\/div>\n                <\/div>\n                
    <\/p>\n
    from sklearn.preprocessing import LabelEncoder, OneHotEncoder
    \nlabelencoder_X = LabelEncoder()
    \nX[ : , 0] = labelencoder_X.fit_transform(X[ : , 0])<\/p>\n
    print(X[ : , 0])<\/p>\n
    <\/code><\/pre>\n\t\t\t<\/div>\n        <\/div>\n
    \u8fd0\u884c\u7ed3\u679c\u4e3a\uff1a[0 2 1 2 1 0 2 0 1 0]<\/p><\/blockquote>\n
     <\/p>\n
    \u8fd9\u6bb5\u4ee3\u7801\u505a\u4e86\u4ec0\u4e48\u5462\uff1f\u6211\u4eec\u770b\u4e00\u4e0b\u4e0b\u56fe\u3002\u8fd9\u6bb5\u4ee3\u7801\u5c06\u6570\u636e\u96c6\u4e2d\u7684 country \u4e00\u5217\u6570\u636e\u7531\u6587\u5b57\u8f6c\u4e3a\u4e86\u6570\u5b57\u3002\u6709\u610f\u601d\u7684\u662f\uff0cX[ : , 0]\u5177\u6709\u4e0einnerHTML\u7c7b\u4f3c\u7684\u529f\u80fd\uff1a\u65e2\u80fd\u53d6\u503c\uff0c\u4e5f\u53ef\u8d4b\u503c\u3002<\/p>\n
    \"\"<\/p>\n
     <\/p>\n
    \u521b\u5efa\u54d1\u53d8\u91cf<\/strong><\/h3>\n
    \"\"<\/p>\n
    \n\t\t\t
    \n                
    \n                    
    \n                        
    <\/div>\n                        
    <\/div>\n                        
    <\/div>\n                    <\/div>\n                    
    \n                        \n                        Copy Code<\/span>\n                        Copied<\/span>\n                        Use a different Browser<\/span><\/a>\n                    <\/div>\n                <\/div>\n                
    <\/p>\n
    onehotencoder = OneHotEncoder(categorical_features = [0])
    \nX = onehotencoder.fit_transform(X).toarray()
    \nlabelencoder_Y = LabelEncoder()
    \nY = labelencoder_Y.fit_transform(Y)
    \nprint(\"\u53d8\u91cfX\", X)
    \nprint(\"\u53d8\u91cfY\",Y)<\/p>\n
    <\/code><\/pre>\n\t\t\t<\/div>\n        <\/div>\n
     <\/p>\n
    \u62c6\u5206\u6570\u636e\u96c6\u4e3a\u8bad\u7ec3\u96c6\u5408\u548c\u6d4b\u8bd5\u96c6\u5408<\/h3>\n
    \"\"<\/p>\n
    \n\t\t\t
    \n                
    \n                    
    \n                        
    <\/div>\n                        
    <\/div>\n                        
    <\/div>\n                    <\/div>\n                    
    \n                        \n                        Copy Code<\/span>\n                        Copied<\/span>\n                        Use a different Browser<\/span><\/a>\n                    <\/div>\n                <\/div>\n                
    <\/p>\n
    from sklearn.model_selection import train_test_split<\/p>\n
    X_train, X_test, Y_train, Y_test = train_test_split( X , Y , test_size = 0.2, random_state = 0)<\/p>\n
    <\/code><\/pre>\n\t\t\t<\/div>\n        <\/div>\n
    \u6ce8\u610f\uff0c\u539f\u6587\u4e2d\u7684from<\/span> sklearn<\/span>.cross_validation<\/span> import<\/span> train_test_split \u5df2\u5931\u6548\uff0c\u9700\u66ff\u6362\u4e3a\u4e0a\u6587\u4ee3\u7801\u4e2d\u7684\u8bed\u53e5\u3002 <\/span><\/p>\n
     <\/p>\n
     <\/p>\n
    \u7279\u5f81\u7f29\u653e<\/h3>\n
    \"\"<\/p>\n
    \n\t\t\t
    \n                
    \n                    
    \n                        
    <\/div>\n                        
    <\/div>\n                        
    <\/div>\n                    <\/div>\n                    
    \n                        \n                        Copy Code<\/span>\n                        Copied<\/span>\n                        Use a different Browser<\/span><\/a>\n                    <\/div>\n                <\/div>\n                
    <\/p>\n
    from sklearn.preprocessing import StandardScaler<\/p>\n
    sc_X = StandardScaler()
    \nX_train = sc_X.fit_transform(X_train)
    \nX_test = sc_X.fit_transform(X_test)
    \nprint(X_train, X_test)<\/p>\n
    <\/code><\/pre>\n\t\t\t<\/div>\n        <\/div>\n
    \u76f8\u5173\u77e5\u8bc6\u8865\u5145<\/h2>\n
    \u81f3\u4e8efit_transform()\u76f8\u5173\u7684<\/p>\n
      \n
    1. \u8fdb\u884c\u5f52\u4e00\u5316\u64cd\u4f5c\u7684\u539f\u56e0\uff0c\u4ee5\u53ca\u5e38\u7528\u7684\u5f52\u4e00\u5316\u65b9\u6cd5\u53ef\u53c2\u8003\u672c\u6587<\/a><\/li>\n
    2. fit(), fit_transform(), transform()\u5728\u4e0d\u540c\u65b9\u6cd5\u4e0b\u7684\u5f02\u540c\u53ef\u53c2\u8003\u672c\u6587<\/a><\/li>\n<\/ol>\n
       <\/p>\n
      \u7b14\u8bb0\u4e0b\u8f7d<\/h2>\n
       <\/p>\nData<\/a>, day_1.ipynb<\/a>\" rel=\"external\"\r\ntarget=\"_blank\" title=\"\u4e0b\u8f7d\u5730\u5740\">\r\n<\/i>Download<\/span><\/a>\n","protected":false},"excerpt":{"rendered":"
      \u7b2c\u4e00\u5929\uff1a\u4e00\u5207\u5c31\u7eea \u6570\u636e\u9884\u5904\u7406\u7684\u57fa\u672c\u6b65\u9aa4 \u6839\u636e day_1 \u7684\u539f\u6587\uff0c\u6211\u4eec\u53ef\u4ee5\u603b\u7ed3\u51fa\u6570\u636e\u9884\u5904\u7406\u7684\u57fa\u672c\u6d41\u7a0b\uff1a \u5bfc\u5165\u6570\u636e\u5904\u7406\u76f8\u5173\u5e93\uff1a\u5176\u4e2d\u6700 …<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"emotion":"","emotion_color":"","title_style":"","license":"","footnotes":""},"categories":[82],"tags":[105,107,106],"class_list":["post-3656","post","type-post","status-publish","format-standard","hentry","category-82","tag-105","tag-107","tag-106"],"_links":{"self":[{"href":"https:\/\/10ccc.cc\/index.php?rest_route=\/wp\/v2\/posts\/3656","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/10ccc.cc\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/10ccc.cc\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/10ccc.cc\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/10ccc.cc\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=3656"}],"version-history":[{"count":3,"href":"https:\/\/10ccc.cc\/index.php?rest_route=\/wp\/v2\/posts\/3656\/revisions"}],"predecessor-version":[{"id":4519,"href":"https:\/\/10ccc.cc\/index.php?rest_route=\/wp\/v2\/posts\/3656\/revisions\/4519"}],"wp:attachment":[{"href":"https:\/\/10ccc.cc\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=3656"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/10ccc.cc\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=3656"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/10ccc.cc\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=3656"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}