{"id":982968,"date":"2024-12-27T07:15:23","date_gmt":"2024-12-26T23:15:23","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/982968.html"},"modified":"2024-12-27T07:15:25","modified_gmt":"2024-12-26T23:15:25","slug":"python%e5%a6%82%e4%bd%95%e5%bb%ba%e7%ab%8b%e5%9b%9e%e5%bd%92%e6%a8%a1%e5%9e%8b","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/982968.html","title":{"rendered":"python\u5982\u4f55\u5efa\u7acb\u56de\u5f52\u6a21\u578b"},"content":{"rendered":"

\"python\u5982\u4f55\u5efa\u7acb\u56de\u5f52\u6a21\u578b\"<\/p>\n

Python\u53ef\u4ee5\u901a\u8fc7\u591a\u79cd\u65b9\u5f0f\u5efa\u7acb\u56de\u5f52\u6a21\u578b\uff0c\u5305\u62ec\u4f7f\u7528\u5e93\u5982scikit-learn\u3001statsmodels\u548cTensorFlow\u7b49\u3002\u5173\u952e\u6b65\u9aa4\u5305\u62ec\u6570\u636e\u51c6\u5907\u3001\u9009\u62e9\u5408\u9002\u7684\u56de\u5f52\u6a21\u578b\u3001\u8bad\u7ec3\u6a21\u578b\u3001\u8bc4\u4f30\u6a21\u578b\u6027\u80fd\u548c\u8fdb\u884c\u9884\u6d4b\u3002scikit-learn\u662f\u4e00\u4e2a\u975e\u5e38\u6d41\u884c\u7684\u673a\u5668\u5b66\u4e60<\/a>\u5e93\uff0c\u9002\u7528\u4e8e\u6784\u5efa\u548c\u8bc4\u4f30\u56de\u5f52\u6a21\u578b\u3001\u5b83\u63d0\u4f9b\u4e86\u7b80\u5355\u6613\u7528\u7684API\u3001\u4e30\u5bcc\u7684\u5de5\u5177\u548c\u7b97\u6cd5\u3002<\/strong><\/p>\n<\/p>\n

\u4e00\u3001\u6570\u636e\u51c6\u5907<\/p>\n<\/p>\n

\u5728\u5efa\u7acb\u56de\u5f52\u6a21\u578b\u4e4b\u524d\uff0c\u6570\u636e\u51c6\u5907\u662f\u4e00\u4e2a\u5173\u952e\u6b65\u9aa4\u3002\u6570\u636e\u51c6\u5907\u5305\u62ec\u6570\u636e\u6536\u96c6\u3001\u6570\u636e\u6e05\u6d17\u548c\u7279\u5f81\u9009\u62e9\u3002<\/p>\n<\/p>\n

    \n
  1. \n

    \u6570\u636e\u6536\u96c6<\/strong>\uff1a\u5728\u5f00\u59cb\u5efa\u6a21\u4e4b\u524d\uff0c\u9700\u8981\u6536\u96c6\u76f8\u5173\u7684\u6570\u636e\u96c6\u3002\u6570\u636e\u96c6\u53ef\u4ee5\u6765\u81ea\u516c\u5f00\u7684\u6570\u636e\u6e90\uff0c\u4e5f\u53ef\u4ee5\u662f\u901a\u8fc7\u5b9e\u9a8c\u6216\u8c03\u67e5\u6536\u96c6\u7684\u6570\u636e\u3002<\/p>\n<\/p>\n<\/li>\n

  2. \n

    \u6570\u636e\u6e05\u6d17<\/strong>\uff1a\u6570\u636e\u96c6\u901a\u5e38\u5305\u542b\u7f3a\u5931\u503c\u3001\u5f02\u5e38\u503c\u6216\u4e0d\u4e00\u81f4\u7684\u6570\u636e\uff0c\u9700\u8981\u8fdb\u884c\u6e05\u6d17\u3002\u5e38\u7528\u7684\u65b9\u6cd5\u5305\u62ec\u5220\u9664\u7f3a\u5931\u503c\u3001\u586b\u8865\u7f3a\u5931\u503c\u3001\u53bb\u9664\u5f02\u5e38\u503c\u7b49\u3002<\/p>\n<\/p>\n<\/li>\n

  3. \n

    \u7279\u5f81\u9009\u62e9<\/strong>\uff1a\u7279\u5f81\u9009\u62e9\u662f\u6307\u9009\u62e9\u5bf9\u6a21\u578b\u9884\u6d4b\u6700\u6709\u5e2e\u52a9\u7684\u53d8\u91cf\u3002\u7279\u5f81\u9009\u62e9\u53ef\u4ee5\u901a\u8fc7\u7edf\u8ba1\u5206\u6790\u3001\u76f8\u5173\u6027\u5206\u6790\u6216\u5176\u4ed6\u65b9\u6cd5\u6765\u5b8c\u6210\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\n

    \u4e8c\u3001\u9009\u62e9\u5408\u9002\u7684\u56de\u5f52\u6a21\u578b<\/p>\n<\/p>\n

    Python\u4e2d\u6709\u591a\u79cd\u56de\u5f52\u6a21\u578b\u53ef\u4f9b\u9009\u62e9\uff0c\u9009\u62e9\u5408\u9002\u7684\u6a21\u578b\u53d6\u51b3\u4e8e\u6570\u636e\u7684\u6027\u8d28\u548c\u95ee\u9898\u7684\u9700\u6c42\u3002<\/p>\n<\/p>\n

      \n
    1. \n

      \u7ebf\u6027\u56de\u5f52<\/strong>\uff1a\u7ebf\u6027\u56de\u5f52\u662f\u6700\u7b80\u5355\u7684\u56de\u5f52\u6a21\u578b\uff0c\u9002\u7528\u4e8e\u9884\u6d4b\u7ebf\u6027\u5173\u7cfb\u7684\u6570\u636e\u3002\u53ef\u4ee5\u4f7f\u7528scikit-learn\u5e93\u4e2d\u7684LinearRegression<\/code>\u7c7b\u6765\u5b9e\u73b0\u3002<\/p>\n<\/p>\n<\/li>\n

    2. \n

      \u591a\u9879\u5f0f\u56de\u5f52<\/strong>\uff1a\u5f53\u6570\u636e\u5448\u73b0\u975e\u7ebf\u6027\u5173\u7cfb\u65f6\uff0c\u53ef\u4ee5\u4f7f\u7528\u591a\u9879\u5f0f\u56de\u5f52\u3002\u53ef\u4ee5\u901a\u8fc7scikit-learn\u7684PolynomialFeatures<\/code>\u7c7b\u6765\u6269\u5c55\u7279\u5f81\uff0c\u7136\u540e\u4f7f\u7528\u7ebf\u6027\u56de\u5f52\u8fdb\u884c\u62df\u5408\u3002<\/p>\n<\/p>\n<\/li>\n

    3. \n

      \u5cad\u56de\u5f52\u548cLasso\u56de\u5f52<\/strong>\uff1a\u8fd9\u4e24\u79cd\u662f\u5e26\u6b63\u5219\u5316\u7684\u7ebf\u6027\u56de\u5f52\u6a21\u578b\uff0c\u7528\u4e8e\u5904\u7406\u591a\u91cd\u5171\u7ebf\u6027\u95ee\u9898\u3002scikit-learn\u63d0\u4f9b\u4e86Ridge<\/code>\u548cLasso<\/code>\u7c7b\u6765\u5b9e\u73b0\u3002<\/p>\n<\/p>\n<\/li>\n

    4. \n

      \u652f\u6301\u5411\u91cf\u56de\u5f52\uff08SVR\uff09<\/strong>\uff1a\u9002\u7528\u4e8e\u5904\u7406\u975e\u7ebf\u6027\u6570\u636e\u7684\u56de\u5f52\u95ee\u9898\u3002scikit-learn\u63d0\u4f9b\u4e86SVR<\/code>\u7c7b\u6765\u5b9e\u73b0\u3002<\/p>\n<\/p>\n<\/li>\n

    5. \n

      \u51b3\u7b56\u6811\u56de\u5f52<\/strong>\uff1a\u9002\u7528\u4e8e\u5904\u7406\u590d\u6742\u6570\u636e\u96c6\uff0c\u80fd\u591f\u6355\u83b7\u975e\u7ebf\u6027\u5173\u7cfb\u3002\u53ef\u4ee5\u4f7f\u7528scikit-learn\u7684DecisionTreeRegressor<\/code>\u7c7b\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\n

      \u4e09\u3001\u8bad\u7ec3\u6a21\u578b<\/p>\n<\/p>\n

      \u5728\u9009\u62e9\u5408\u9002\u7684\u56de\u5f52\u6a21\u578b\u540e\uff0c\u4e0b\u4e00\u6b65\u662f\u8bad\u7ec3\u6a21\u578b\u3002\u8bad\u7ec3\u6a21\u578b\u9700\u8981\u5c06\u6570\u636e\u96c6\u5212\u5206\u4e3a\u8bad\u7ec3\u96c6\u548c\u6d4b\u8bd5\u96c6\uff0c\u7136\u540e\u4f7f\u7528\u8bad\u7ec3\u96c6\u62df\u5408\u6a21\u578b\u3002<\/p>\n<\/p>\n

        \n
      1. \n

        \u6570\u636e\u96c6\u5212\u5206<\/strong>\uff1a\u53ef\u4ee5\u4f7f\u7528scikit-learn\u7684trAI<\/a>n_test_split<\/code>\u51fd\u6570\u5c06\u6570\u636e\u96c6\u5212\u5206\u4e3a\u8bad\u7ec3\u96c6\u548c\u6d4b\u8bd5\u96c6\u3002\u901a\u5e38\uff0c\u8bad\u7ec3\u96c6\u536070%\u523080%\uff0c\u6d4b\u8bd5\u96c6\u536020%\u523030%\u3002<\/p>\n<\/p>\n<\/li>\n

      2. \n

        \u6a21\u578b\u8bad\u7ec3<\/strong>\uff1a\u4f7f\u7528\u8bad\u7ec3\u96c6\u7684\u6570\u636e\u62df\u5408\u56de\u5f52\u6a21\u578b\u3002\u4f8b\u5982\uff0c\u4f7f\u7528\u7ebf\u6027\u56de\u5f52\u65f6\uff0c\u53ef\u4ee5\u8c03\u7528fit<\/code>\u65b9\u6cd5\u3002<\/p>\n<\/p>\n<\/li>\n

      3. \n

        \u4ea4\u53c9\u9a8c\u8bc1<\/strong>\uff1a\u4ea4\u53c9\u9a8c\u8bc1\u662f\u8bc4\u4f30\u6a21\u578b\u6027\u80fd\u7684\u4e00\u79cd\u65b9\u6cd5\uff0c\u901a\u8fc7\u5c06\u6570\u636e\u96c6\u5212\u5206\u4e3a\u591a\u4e2a\u5b50\u96c6\uff0c\u4f9d\u6b21\u4f7f\u7528\u6bcf\u4e2a\u5b50\u96c6\u8fdb\u884c\u9a8c\u8bc1\u3002scikit-learn\u7684cross_val_score<\/code>\u51fd\u6570\u53ef\u4ee5\u5b9e\u73b0\u4ea4\u53c9\u9a8c\u8bc1\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\n

        \u56db\u3001\u8bc4\u4f30\u6a21\u578b\u6027\u80fd<\/p>\n<\/p>\n

        \u8bc4\u4f30\u6a21\u578b\u6027\u80fd\u662f\u5efa\u7acb\u56de\u5f52\u6a21\u578b\u7684\u91cd\u8981\u6b65\u9aa4\u3002\u5e38\u7528\u7684\u8bc4\u4f30\u6307\u6807\u5305\u62ec\u5747\u65b9\u8bef\u5dee\uff08MSE\uff09\u3001\u5747\u65b9\u6839\u8bef\u5dee\uff08RMSE\uff09\u3001\u51b3\u5b9a\u7cfb\u6570\uff08R\u00b2\uff09\u7b49\u3002<\/p>\n<\/p>\n

          \n
        1. \n

          \u5747\u65b9\u8bef\u5dee\uff08MSE\uff09<\/strong>\uff1aMSE\u662f\u9884\u6d4b\u503c\u4e0e\u5b9e\u9645\u503c\u4e4b\u95f4\u5dee\u5f02\u7684\u5e73\u65b9\u548c\u7684\u5e73\u5747\u503c\u3002\u53ef\u4ee5\u4f7f\u7528scikit-learn\u7684mean_squared_error<\/code>\u51fd\u6570\u8ba1\u7b97\u3002<\/p>\n<\/p>\n<\/li>\n

        2. \n

          \u5747\u65b9\u6839\u8bef\u5dee\uff08RMSE\uff09<\/strong>\uff1aRMSE\u662fMSE\u7684\u5e73\u65b9\u6839\uff0c\u8868\u793a\u9884\u6d4b\u503c\u4e0e\u5b9e\u9645\u503c\u4e4b\u95f4\u7684\u6807\u51c6\u5dee\u3002<\/p>\n<\/p>\n<\/li>\n

        3. \n

          \u51b3\u5b9a\u7cfb\u6570\uff08R\u00b2\uff09<\/strong>\uff1aR\u00b2\u8868\u793a\u6a21\u578b\u5bf9\u6570\u636e\u7684\u89e3\u91ca\u80fd\u529b\uff0c\u53d6\u503c\u8303\u56f4\u4e3a0\u52301\u3002scikit-learn\u7684r2_score<\/code>\u51fd\u6570\u53ef\u4ee5\u8ba1\u7b97R\u00b2\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\n

          \u4e94\u3001\u8fdb\u884c\u9884\u6d4b<\/p>\n<\/p>\n

          \u5728\u6a21\u578b\u8bad\u7ec3\u548c\u8bc4\u4f30\u5b8c\u6210\u540e\uff0c\u53ef\u4ee5\u4f7f\u7528\u6a21\u578b\u8fdb\u884c\u9884\u6d4b\u3002\u9884\u6d4b\u65b0\u6570\u636e\u65f6\uff0c\u9700\u8981\u5bf9\u6570\u636e\u8fdb\u884c\u4e0e\u8bad\u7ec3\u6570\u636e\u76f8\u540c\u7684\u9884\u5904\u7406\u3002<\/p>\n<\/p>\n

            \n
          1. \n

            \u6570\u636e\u9884\u5904\u7406<\/strong>\uff1a\u5bf9\u65b0\u6570\u636e\u8fdb\u884c\u4e0e\u8bad\u7ec3\u6570\u636e\u76f8\u540c\u7684\u9884\u5904\u7406\u6b65\u9aa4\uff0c\u5305\u62ec\u7279\u5f81\u7f29\u653e\u3001\u7279\u5f81\u9009\u62e9\u7b49\u3002<\/p>\n<\/p>\n<\/li>\n

          2. \n

            \u6a21\u578b\u9884\u6d4b<\/strong>\uff1a\u4f7f\u7528\u8bad\u7ec3\u597d\u7684\u6a21\u578b\u5bf9\u65b0\u6570\u636e\u8fdb\u884c\u9884\u6d4b\u3002\u4f8b\u5982\uff0c\u4f7f\u7528\u7ebf\u6027\u56de\u5f52\u6a21\u578b\u65f6\uff0c\u53ef\u4ee5\u8c03\u7528predict<\/code>\u65b9\u6cd5\u3002<\/p>\n<\/p>\n<\/li>\n

          3. \n

            \u7ed3\u679c\u5206\u6790<\/strong>\uff1a\u5206\u6790\u9884\u6d4b\u7ed3\u679c\uff0c\u5224\u65ad\u6a21\u578b\u7684\u9884\u6d4b\u80fd\u529b\u662f\u5426\u6ee1\u8db3\u9700\u6c42\u3002\u5982\u679c\u6a21\u578b\u6027\u80fd\u4e0d\u4f73\uff0c\u53ef\u80fd\u9700\u8981\u91cd\u65b0\u9009\u62e9\u6a21\u578b\u3001\u8c03\u6574\u8d85\u53c2\u6570\u6216\u589e\u52a0\u66f4\u591a\u7684\u7279\u5f81\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\n

            \u901a\u8fc7\u4ee5\u4e0a\u6b65\u9aa4\uff0c\u53ef\u4ee5\u4f7f\u7528Python\u6784\u5efa\u4e00\u4e2a\u6709\u6548\u7684\u56de\u5f52\u6a21\u578b\u3002\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u9009\u62e9\u5408\u9002\u7684\u6a21\u578b\u3001\u6570\u636e\u51c6\u5907\u548c\u6a21\u578b\u8bc4\u4f30\u90fd\u662f\u5173\u952e\u6b65\u9aa4\uff0c\u9700\u8981\u6839\u636e\u5177\u4f53\u95ee\u9898\u7075\u6d3b\u8c03\u6574\u3002Python\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u5de5\u5177\u548c\u5e93\uff0c\u80fd\u591f\u5e2e\u52a9\u6211\u4eec\u9ad8\u6548\u5730\u8fdb\u884c\u56de\u5f52\u5efa\u6a21\u3002<\/p>\n<\/p>\n

            \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n

            \u56de\u5f52\u6a21\u578b\u7684\u57fa\u7840\u77e5\u8bc6\u662f\u4ec0\u4e48\uff1f<\/strong>
            \u56de\u5f52\u6a21\u578b\u662f\u4e00\u79cd\u7edf\u8ba1\u5206\u6790\u65b9\u6cd5\uff0c\u7528\u4e8e\u9884\u6d4b\u4e00\u4e2a\u53d8\u91cf\uff08\u56e0\u53d8\u91cf\uff09\u4e0e\u4e00\u4e2a\u6216\u591a\u4e2a\u5176\u4ed6\u53d8\u91cf\uff08\u81ea\u53d8\u91cf\uff09\u4e4b\u95f4\u7684\u5173\u7cfb\u3002\u4e86\u89e3\u56de\u5f52\u6a21\u578b\u7684\u57fa\u672c\u6982\u5ff5\uff0c\u5305\u62ec\u7ebf\u6027\u56de\u5f52\u3001\u903b\u8f91\u56de\u5f52\u7b49\u7c7b\u578b\uff0c\u5bf9\u4e8e\u5efa\u7acb\u6709\u6548\u7684\u6a21\u578b\u81f3\u5173\u91cd\u8981\u3002<\/p>\n

            \u5982\u4f55\u9009\u62e9\u5408\u9002\u7684\u56de\u5f52\u6a21\u578b\uff1f<\/strong>
            \u9009\u62e9\u5408\u9002\u7684\u56de\u5f52\u6a21\u578b\u53d6\u51b3\u4e8e\u6570\u636e\u7684\u6027\u8d28\u548c\u5206\u6790\u76ee\u6807\u3002\u5bf9\u4e8e\u7ebf\u6027\u5173\u7cfb\u7684\u6570\u636e\uff0c\u7ebf\u6027\u56de\u5f52\u53ef\u80fd\u662f\u6700\u6709\u6548\u7684\u9009\u62e9\uff1b\u5982\u679c\u6570\u636e\u5b58\u5728\u975e\u7ebf\u6027\u5173\u7cfb\uff0c\u53ef\u80fd\u9700\u8981\u8003\u8651\u591a\u9879\u5f0f\u56de\u5f52\u6216\u5176\u4ed6\u590d\u6742\u6a21\u578b\u3002\u8bc4\u4f30\u6a21\u578b\u6027\u80fd\u7684\u65b9\u6cd5\uff0c\u5982\u5747\u65b9\u8bef\u5dee\uff08MSE\uff09\u548c\u51b3\u5b9a\u7cfb\u6570\uff08R\u00b2\uff09\uff0c\u4e5f\u5728\u9009\u62e9\u8fc7\u7a0b\u4e2d\u8d77\u5230\u91cd\u8981\u4f5c\u7528\u3002<\/p>\n

            \u5728Python\u4e2d\u5982\u4f55\u5904\u7406\u7f3a\u5931\u6570\u636e\u4ee5\u5efa\u7acb\u56de\u5f52\u6a21\u578b\uff1f<\/strong>
            \u7f3a\u5931\u6570\u636e\u7684\u5904\u7406\u662f\u5efa\u7acb\u56de\u5f52\u6a21\u578b\u7684\u91cd\u8981\u6b65\u9aa4\u3002\u53ef\u4ee5\u901a\u8fc7\u5220\u9664\u7f3a\u5931\u503c\u3001\u7528\u5747\u503c\u6216\u4e2d\u4f4d\u6570\u586b\u5145\u7f3a\u5931\u503c\uff0c\u6216\u8005\u4f7f\u7528\u63d2\u503c\u65b9\u6cd5\u6765\u5904\u7406\u7f3a\u5931\u6570\u636e\u3002\u4f7f\u7528Pandas\u5e93\u4e2d\u7684fillna()<\/code>\u51fd\u6570\u6216dropna()<\/code>\u51fd\u6570\uff0c\u53ef\u4ee5\u8f7b\u677e\u5904\u7406\u6570\u636e\u96c6\u4e2d\u7684\u7f3a\u5931\u503c\uff0c\u786e\u4fdd\u6a21\u578b\u7684\u51c6\u786e\u6027\u548c\u53ef\u9760\u6027\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"Python\u53ef\u4ee5\u901a\u8fc7\u591a\u79cd\u65b9\u5f0f\u5efa\u7acb\u56de\u5f52\u6a21\u578b\uff0c\u5305\u62ec\u4f7f\u7528\u5e93\u5982scikit-learn\u3001statsmodels\u548cTen […]","protected":false},"author":3,"featured_media":982973,"comment_status":"closed","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[37],"tags":[],"acf":[],"_links":{"self":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/982968"}],"collection":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/comments?post=982968"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/982968\/revisions"}],"predecessor-version":[{"id":982976,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/982968\/revisions\/982976"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/982973"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=982968"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=982968"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=982968"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}