{"id":615,"date":"2022-12-20T04:22:52","date_gmt":"2022-10-07T08:56:31","guid":{"rendered":"https:\/\/www.silicloud.com\/zh\/blog\/index.php\/2023\/11\/30\/python%e4%b8%ad%e7%9a%84%e6%8d%9f%e5%a4%b1%e5%87%bd%e6%95%b0-%e7%ae%80%e5%8d%95%e5%ae%9e%e7%8e%b0\/"},"modified":"2025-08-02T22:52:41","modified_gmt":"2025-08-02T14:52:41","slug":"python%e4%b8%ad%e7%9a%84%e6%8d%9f%e5%a4%b1%e5%87%bd%e6%95%b0-%e7%ae%80%e5%8d%95%e5%ae%9e%e7%8e%b0","status":"publish","type":"post","link":"https:\/\/www.silicloud.com\/zh\/blog\/python%e4%b8%ad%e7%9a%84%e6%8d%9f%e5%a4%b1%e5%87%bd%e6%95%b0-%e7%ae%80%e5%8d%95%e5%ae%9e%e7%8e%b0\/","title":{"rendered":"Python\u635f\u5931\u51fd\u6570\u5b8c\u5168\u6307\u5357\uff1a\u4ece\u7406\u8bba\u5230\u5b9e\u6218\u5b9e\u73b0"},"content":{"rendered":"

\u5728Python\u4e2d\uff0c\u635f\u5931\u51fd\u6570\u662f\u4efb\u4f55\u673a\u5668\u5b66\u4e60\u6a21\u578b\u4e2d\u4e0d\u53ef\u6216\u7f3a\u7684\u4e00\u90e8\u5206\u3002\u8fd9\u4e9b\u51fd\u6570\u544a\u8bc9\u6211\u4eec\u6a21\u578b\u7684\u9884\u6d4b\u8f93\u51fa\u4e0e\u5b9e\u9645\u8f93\u51fa\u7684\u5dee\u5f02\u7a0b\u5ea6\u6709\u591a\u5927\u3002<\/p>\n

\u6709\u591a\u79cd\u8ba1\u7b97\u8fd9\u4e2a\u5dee\u5f02\u7684\u65b9\u6cd5\u3002\u5728\u672c\u6559\u7a0b\u4e2d\uff0c\u6211\u4eec\u5c06\u4ecb\u7ecd\u4e00\u4e9b\u8f83\u4e3a\u5e38\u89c1\u7684\u635f\u5931\u51fd\u6570\u3002<\/p>\n

\u5728\u672c\u6559\u7a0b\u4e2d\uff0c\u6211\u4eec\u5c06\u8ba8\u8bba\u4ee5\u4e0b\u56db\u79cd\u635f\u5931\u51fd\u6570\uff1a<\/p>\n

    \n
  1. \u5747\u65b9\u5dee<\/li>\n
  2. \u5747\u65b9\u6839\u8bef\u5dee<\/li>\n
  3. \u5e73\u5747\u7edd\u5bf9\u8bef\u5dee<\/li>\n
  4. \u4ea4\u53c9\u71b5\u635f\u5931<\/li>\n<\/ol>\n

    \u5728\u8fd9\u56db\u79cd\u635f\u5931\u51fd\u6570\u4e2d\uff0c\u524d\u4e09\u79cd\u9002\u7528\u4e8e\u56de\u5f52\u6a21\u578b\uff0c\u800c\u6700\u540e\u4e00\u79cd\u9002\u7528\u4e8e\u5206\u7c7b\u6a21\u578b\u3002<\/p>\n

    \u5728Python\u4e2d\u5b9e\u65bd\u635f\u5931\u51fd\u6570<\/h2>\n

    \u8ba9\u6211\u4eec\u6765\u770b\u770b\u5982\u4f55\u5728Python\u4e2d\u5b9e\u73b0\u8fd9\u4e9b\u635f\u5931\u51fd\u6570\u3002<\/p>\n

    1. \u5747\u65b9\u8bef\u5dee\uff08Mean Square Error\uff0c\u7b80\u79f0MSE\uff09<\/h3>\n

    \u5747\u65b9\u8bef\u5dee\uff08MSE\uff09\u662f\u901a\u8fc7\u9884\u6d4b\u503c\u4e0e\u5b9e\u9645\u89c2\u5bdf\u503c\u4e4b\u5dee\u7684\u5e73\u65b9\u7684\u5e73\u5747\u503c\u6765\u8ba1\u7b97\u7684\u3002\u6570\u5b66\u4e0a\u53ef\u4ee5\u8868\u793a\u5982\u4e0b\uff1a<\/p>\n

    \n \"Python\u4e2d\u7684\u5747\u65b9\u8bef\u5dee\u635f\u5931\u51fd\u6570\"\n<\/div>\n

    \u4ee5\u4e0b\u662f\u7528Python\u5b9e\u73b0\u5747\u65b9\u8bef\u5dee\uff08MSE\uff09\u7684\u4ee3\u7801\uff1a<\/p>\n

    import<\/span> numpy as<\/span> np\r\ndef<\/span> mean_squared_error<\/span>(<\/span>act,<\/span> pred)<\/span>:<\/span>\r\n\r\n   diff =<\/span> pred -<\/span> act\r\n   differences_squared =<\/span> diff **<\/span> 2<\/span>\r\n   mean_diff =<\/span> differences_squared.<\/span>mean(<\/span>)<\/span>\r\n   \r\n   return<\/span> mean_diff\r\n\r\nact =<\/span> np.<\/span>array(<\/span>[<\/span>1.1<\/span>,<\/span>2<\/span>,<\/span>1.7<\/span>]<\/span>)<\/span>\r\npred =<\/span> np.<\/span>array(<\/span>[<\/span>1<\/span>,<\/span>1.7<\/span>,<\/span>1.5<\/span>]<\/span>)<\/span>\r\n\r\nprint<\/span>(<\/span>mean_squared_error(<\/span>act,<\/span>pred)<\/span>)<\/span>\r\n<\/code><\/pre>\n
    \u8f93\u51fa\uff1a<\/p>\n
    0.04666666666666667\r\n<\/code><\/pre>\n
    \u60a8\u8fd8\u53ef\u4ee5\u4f7f\u7528sklearn\u4e2d\u7684mean_squared_error\u6765\u8ba1\u7b97MSE\u3002\u8be5\u51fd\u6570\u7684\u5de5\u4f5c\u539f\u7406\u5982\u4e0b\uff1a<\/p>\n
    from<\/span> sklearn.<\/span>metrics import<\/span> mean_squared_error\r\nact =<\/span> np.<\/span>array(<\/span>[<\/span>1.1<\/span>,<\/span>2<\/span>,<\/span>1.7<\/span>]<\/span>)<\/span>\r\npred =<\/span> np.<\/span>array(<\/span>[<\/span>1<\/span>,<\/span>1.7<\/span>,<\/span>1.5<\/span>]<\/span>)<\/span>\r\nmean_squared_error(<\/span>act,<\/span> pred)<\/span>\r\n<\/code><\/pre>\n
    \u8f93\u51fa\u7ed3\u679c\uff1a<\/p>\n
    0.04666666666666667\r\n<\/code><\/pre>\n
    2. \u5747\u65b9\u6839\u8bef\u5dee\uff08Root Mean Square Error, RMSE\uff09<\/h3>\n
    \u5747\u65b9\u6839\u8bef\u5dee\uff08RMSE\uff09\u662f\u4ee5\u5747\u65b9\u8bef\u5dee\u7684\u5e73\u65b9\u6839\u8ba1\u7b97\u800c\u5f97\u3002\u4ece\u6570\u5b66\u4e0a\u8bb2\uff0c\u6211\u4eec\u53ef\u4ee5\u8868\u793a\u4e3a\u4ee5\u4e0b\u5f62\u5f0f\uff1a<\/p>\n
    \n    \"Python\u4e2d\u7684\u5747\u65b9\u6839\u8bef\u5dee\u635f\u5931\u51fd\u6570\"\n<\/div>\n
    RMSE\u7684Python\u5b9e\u73b0\u5982\u4e0b\uff1a<\/p>\n
    \u8fd9\u662f\u6587\u7ae0\u300aPython\u4e2d\u7684\u635f\u5931\u51fd\u6570 – \u7b80\u5355\u5b9e\u73b0\u300b\u7684\u7b2c2\u90e8\u5206\uff08\u51714\u90e8\u5206\uff09\u3002<\/p>\n
    import<\/span> numpy as<\/span> np\r\ndef<\/span> root_mean_squared_error<\/span>(<\/span>act,<\/span> pred)<\/span>:<\/span>\r\n\r\n   diff =<\/span> pred -<\/span> act\r\n   differences_squared =<\/span> diff **<\/span> 2<\/span>\r\n   mean_diff =<\/span> differences_squared.<\/span>mean(<\/span>)<\/span>\r\n   rmse_val =<\/span> np.<\/span>sqrt(<\/span>mean_diff)<\/span>\r\n   return<\/span> rmse_val\r\n\r\nact =<\/span> np.<\/span>array(<\/span>[<\/span>1.1<\/span>,<\/span>2<\/span>,<\/span>1.7<\/span>]<\/span>)<\/span>\r\npred =<\/span> np.<\/span>array(<\/span>[<\/span>1<\/span>,<\/span>1.7<\/span>,<\/span>1.5<\/span>]<\/span>)<\/span>\r\n\r\nprint<\/span>(<\/span>root_mean_squared_error(<\/span>act,<\/span>pred)<\/span>)<\/span>\r\n<\/code><\/pre>\n
    \u8f93\u51fa\u7ed3\u679c\uff1a<\/p>\n
    0.21602468994692867\r\n<\/code><\/pre>\n
    \u4f60\u4e5f\u53ef\u4ee5\u4f7f\u7528sklearn\u5e93\u4e2d\u7684mean_squared_error\u51fd\u6570\u6765\u8ba1\u7b97\u5747\u65b9\u6839\u8bef\u5dee\uff08RMSE\uff09\u3002\u4e0b\u9762\u6211\u4eec\u6765\u770b\u770b\u5982\u4f55\u4f7f\u7528\u8be5\u51fd\u6570\u5b9e\u73b0RMSE\u8ba1\u7b97\u3002<\/p>\n
    from<\/span> sklearn.<\/span>metrics import<\/span> mean_squared_error\r\nact =<\/span> np.<\/span>array(<\/span>[<\/span>1.1<\/span>,<\/span>2<\/span>,<\/span>1.7<\/span>]<\/span>)<\/span>\r\npred =<\/span> np.<\/span>array(<\/span>[<\/span>1<\/span>,<\/span>1.7<\/span>,<\/span>1.5<\/span>]<\/span>)<\/span>\r\nmean_squared_error(<\/span>act,<\/span> pred,<\/span> squared =<\/span> False<\/span>)<\/span>\r\n<\/code><\/pre>\n
    \u8f93\u51fa\u7ed3\u679c\uff1a<\/p>\n
    0.21602468994692867\r\n<\/code><\/pre>\n
    \u5982\u679c\u5c06\u53c2\u6570’squared’\u8bbe\u7f6e\u4e3aTrue\uff0c\u5219\u8be5\u51fd\u6570\u8fd4\u56de\u5747\u65b9\u8bef\u5dee\uff08MSE\uff09\u503c\u3002\u5982\u679c\u8bbe\u7f6e\u4e3aFalse\uff0c\u5219\u8be5\u51fd\u6570\u8fd4\u56de\u5747\u65b9\u6839\u8bef\u5dee\uff08RMSE\uff09\u503c\u3002<\/p>\n
    3. \u5e73\u5747\u7edd\u5bf9\u8bef\u5dee\uff08MAE\uff09<\/h3>\n
    \u5e73\u5747\u7edd\u5bf9\u8bef\u5dee\uff08MAE\uff09\u662f\u9884\u6d4b\u503c\u4e0e\u5b9e\u9645\u89c2\u6d4b\u503c\u4e4b\u95f4\u7edd\u5bf9\u5dee\u5f02\u7684\u5e73\u5747\u503c\u3002\u4ece\u6570\u5b66\u89d2\u5ea6\u53ef\u4ee5\u8868\u793a\u4e3a\uff1a<\/p>\n
    \n    \"Python\u4e2d\u7684MAE\u635f\u5931\u51fd\u6570\"\n<\/div>\n
    \u4e0b\u9762\u662fMAE\u7684Python\u5b9e\u73b0\uff1a<\/p>\n
    import<\/span> numpy as<\/span> np\r\ndef<\/span> mean_absolute_error<\/span>(<\/span>act,<\/span> pred)<\/span>:<\/span>\r\n    diff =<\/span> pred -<\/span> act\r\n    abs_diff =<\/span> np.<\/span>absolute(<\/span>diff)<\/span>\r\n    mean_diff =<\/span> abs_diff.<\/span>mean(<\/span>)<\/span>\r\n    return<\/span> mean_diff\r\n\r\nact =<\/span> np.<\/span>array(<\/span>[<\/span>1.1<\/span>,<\/span>2<\/span>,<\/span>1.7<\/span>]<\/span>)<\/span>\r\npred =<\/span> np.<\/span>array(<\/span>[<\/span>1<\/span>,<\/span>1.7<\/span>,<\/span>1.5<\/span>]<\/span>)<\/span>\r\nmean_absolute_error(<\/span>act,<\/span>pred)<\/span>\r\n<\/code><\/pre>\n
    \u8f93\u51fa\u7ed3\u679c\uff1a<\/p>\n
    0.20000000000000004\r\n<\/code><\/pre>\n
    \u60a8\u8fd8\u53ef\u4ee5\u4f7f\u7528sklearn\u5e93\u4e2d\u7684mean_absolute_error\u51fd\u6570\uff08\u5e73\u5747\u7edd\u5bf9\u8bef\u5dee\u51fd\u6570\uff09\u6765\u8ba1\u7b97MAE\u3002<\/p>\n
    from<\/span> sklearn.<\/span>metrics import<\/span> mean_absolute_error\r\nact =<\/span> np.<\/span>array(<\/span>[<\/span>1.1<\/span>,<\/span>2<\/span>,<\/span>1.7<\/span>]<\/span>)<\/span>\r\npred =<\/span> np.<\/span>array(<\/span>[<\/span>1<\/span>,<\/span>1.7<\/span>,<\/span>1.5<\/span>]<\/span>)<\/span>\r\nmean_absolute_error(<\/span>act,<\/span> pred)<\/span>\r\n<\/code><\/pre>\n
    \u8f93\u51fa\u7ed3\u679c\uff1a<\/p>\n
    0.20000000000000004\r\n<\/code><\/pre>\n
    4. Python \u4e2d\u7684\u4ea4\u53c9\u71b5\u635f\u5931\u51fd\u6570<\/h3>\n
    \u4ea4\u53c9\u71b5\u635f\u5931\u51fd\u6570\u4e5f\u88ab\u79f0\u4e3a\u8d1f\u5bf9\u6570\u4f3c\u7136\u51fd\u6570\u3002\u5b83\u6700\u5e38\u7528\u4e8e\u5206\u7c7b\u95ee\u9898\u3002\u5206\u7c7b\u95ee\u9898\u662f\u6307\u5c06\u4e00\u4e2a\u793a\u4f8b\u5f52\u7c7b\u4e3a\u4e24\u7c7b\u4ee5\u4e0a\u4e4b\u4e00\u7684\u95ee\u9898\u3002<\/p>\n
    \u8ba9\u6211\u4eec\u6765\u770b\u770b\u5982\u4f55\u8ba1\u7b97\u5728\u4e8c\u5143\u5206\u7c7b\u95ee\u9898\u4e2d\u7684\u9519\u8bef\u7387\u3002<\/p>\n
    \u8ba9\u6211\u4eec\u8003\u8651\u4e00\u4e2a\u5206\u7c7b\u95ee\u9898\uff0c\u6a21\u578b\u8bd5\u56fe\u5bf9\u72d7\u548c\u732b\u8fdb\u884c\u5206\u7c7b\u3002<\/p>\n
    \u4ee5\u4e0b\u662f\u7528Python\u7f16\u5199\u7684\u67e5\u627e\u9519\u8bef\u7684\u4ee3\u7801\u3002<\/p>\n
    from<\/span> sklearn.<\/span>metrics import<\/span> log_loss\r\nlog_loss(<\/span>[<\/span>\"\u72d7\"<\/span>,<\/span> \"\u732b\"<\/span>,<\/span> \"\u732b\"<\/span>,<\/span> \"\u72d7\"<\/span>]<\/span>,<\/span>[<\/span>[<\/span>.1<\/span>,<\/span> .9<\/span>]<\/span>,<\/span> [<\/span>.9<\/span>,<\/span> .1<\/span>]<\/span>,<\/span> [<\/span>.8<\/span>,<\/span> .2<\/span>]<\/span>,<\/span> [<\/span>.35<\/span>,<\/span> .65<\/span>]<\/span>]<\/span>)<\/span>\r\n<\/code><\/pre>\n
    \u8f93\u51fa\uff1a<\/p>\n
    0.21616187468057912\r\n<\/code><\/pre>\n
    \u6211\u4eec\u6b63\u5728\u4f7f\u7528scikit-learn\u5e93\u4e2d\u7684log_loss\u65b9\u6cd5\u3002<\/p>\n
    \u51fd\u6570\u8c03\u7528\u4e2d\u7684\u7b2c\u4e00\u4e2a\u53c2\u6570\u662f\u6bcf\u4e2a\u8f93\u5165\u6837\u672c\u7684\u6b63\u786e\u7c7b\u522b\u6807\u7b7e\u5217\u8868\u3002\u7b2c\u4e8c\u4e2a\u53c2\u6570\u662f\u6a21\u578b\u9884\u6d4b\u7684\u6982\u7387\u5206\u5e03\u5217\u8868\u3002<\/p>\n
    \u6982\u7387\u5206\u5e03\u7684\u683c\u5f0f\u5982\u4e0b\uff1a<\/p>\n
    [P(\u72d7), P(\u732b)]\r\n<\/code><\/pre>\n
    \u7ed3\u8bba<\/h2>\n
    \u672c\u6559\u7a0b\u4ecb\u7ecd\u4e86Python\u4e2d\u7684\u635f\u5931\u51fd\u6570\u76f8\u5173\u77e5\u8bc6\u3002\u6211\u4eec\u8be6\u7ec6\u8bb2\u89e3\u4e86\u7528\u4e8e\u56de\u5f52\u548c\u5206\u7c7b\u95ee\u9898\u7684\u5404\u79cd\u635f\u5931\u51fd\u6570\u3002\u5e0c\u671b\u60a8\u5728\u5b66\u4e60\u8fc7\u7a0b\u4e2d\u6709\u6240\u6536\u83b7\uff01<\/p>\n","protected":false},"excerpt":{"rendered":"
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