{"id":113593,"date":"2026-10-06T22:17:33","date_gmt":"2026-10-06T14:17:33","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/113593.html"},"modified":"2026-10-06T22:17:33","modified_gmt":"2026-10-06T14:17:33","slug":"%e6%9c%ba%e5%99%a8%e5%ad%a6%e4%b9%a0%e5%ad%a6%e4%b9%a0%e7%ac%94%e8%ae%b0%ef%bc%88%e4%ba%94%ef%bc%89%ef%bc%9a%e5%86%b3%e7%ad%96%e6%a0%91%ef%bc%8ccart-%e5%8e%9f%e7%90%86%e3%80%81%e6%b3%b0%e5%9d%a6","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/113593.html","title":{"rendered":"\u673a\u5668\u5b66\u4e60\u5b66\u4e60\u7b14\u8bb0\uff08\u4e94\uff09\uff1a\u51b3\u7b56\u6811\uff0cCART \u539f\u7406\u3001\u6cf0\u5766\u5c3c\u514b\u751f\u5b58\u9884\u6d4b\u4e0e\u526a\u679d"},"content":{"rendered":"

\u7cfb\u5217\u7b2c\u4e94\u7bc7\u3002\u51b3\u7b56\u6811\u662f\u53ef\u89e3\u91ca\u6027\u6700\u597d\u7684\u6a21\u578b（\u6bcf\u4e00\u5c42\u90fd\u662f\u4e00\u4e2a"\u662f\/\u5426"\u95ee\u9898），\u4e5f\u662f\u96c6\u6210\u5b66\u4e60（\u968f\u673a\u68ee\u6797\u3001GBDT\u3001XGBoost）\u7684\u57fa\u77f3，\u4e0b\u4e00\u7bc7\u5c31\u8981\u7528\u5b83\u3002\u8fd9\u7bc7\u7684\u91cd\u70b9\u6709\u4e24\u4e2a：CART \u6811\u7684\u5206\u88c2\u903b\u8f91（\u57fa\u5c3c\u7cfb\u6570），\u4ee5\u53ca\u6cf0\u5766\u5c3c\u514b\u6848\u4f8b\u91cc\u88ab\u5f88\u591a\u4eba\u5ffd\u7565\u7684\u6570\u636e\u9884\u5904\u7406\u548c\u8c03\u53c2\u7ec6\u8282\u3002<\/p>\n

\u672c\u7bc7\u770b\u5b8c\u8981\u56de\u7b54\u7684\u56db\u4e2a\u9762\u8bd5\u9898：<\/p>\n

  • \u51b3\u7b56\u6811\u600e\u4e48\u9009\u5206\u88c2\u7279\u5f81？\u57fa\u5c3c\u7cfb\u6570\u662f\u4ec0\u4e48？<\/li>\n
  • CART \u6811\u548c ID3\u3001C4.5 \u6709\u4ec0\u4e48\u533a\u522b？<\/li>\n
  • \u51b3\u7b56\u6811\u8fc7\u62df\u5408\u600e\u4e48\u529e？\u9884\u526a\u679d\u548c\u540e\u526a\u679d\u7684\u533a\u522b？<\/li>\n
  • \u56de\u5f52\u6811\u662f\u600e\u4e48\u8f93\u51fa\u8fde\u7eed\u503c\u7684？<\/li>\n
    \n

    \u4e00\u3001\u51b3\u7b56\u6811：\u4e00\u4e32"\u662f\/\u5426"\u95ee\u9898<\/h3>\n

    \u51b3\u7b56\u6811\u662f\u4e00\u79cd\u6811\u5f62\u7ed3\u6784：\u5185\u90e8\u8282\u70b9\u662f\u4e00\u4e2a\u7279\u5f81\u4e0a\u7684\u5224\u65ad，\u5206\u652f\u662f\u5224\u65ad\u7ed3\u679c\u7684\u8f93\u51fa，\u53f6\u5b50\u8282\u70b9\u662f\u6700\u7ec8\u7684\u5206\u7c7b\u7ed3\u679c\u3002<\/p>\n

    \u8bfe\u4ef6\u91cc\u7684\u4f8b\u5b50\u662f\u5973\u5b69\u76f8\u4eb2：\u7b2c\u4e00\u4e2a\u95ee\u9898\u95ee\u5e74\u9f84（\u5e74\u9f84 > \u7279\u5b9a\u503c？\u76f4\u63a5\u4e0d\u89c1），\u7b2c\u4e8c\u4e2a\u95ee\u957f\u76f8，\u7b2c\u4e09\u4e2a\u95ee\u6536\u5165，\u6700\u540e\u95ee\u662f\u5426\u516c\u52a1\u5458\u3002\u6bcf\u4e2a\u95ee\u9898\u90fd\u5728\u7f29\u5c0f\u8303\u56f4，\u8d70\u5230\u5e95\u5c31\u5f97\u51fa\u7ed3\u8bba\u3002<\/p>\n

    \u503c\u5f97\u6ce8\u610f\u7684\u662f\u95ee\u9898\u7684\u5148\u540e\u987a\u5e8f：\u5979\u628a\u5e74\u9f84\u653e\u5728\u7b2c\u4e00\u4e2a\u95ee\u3002\u5148\u95ee\u54ea\u4e2a\u7279\u5f81，\u672c\u8d28\u4e0a\u662f\u5728\u95ee"\u54ea\u4e2a\u7279\u5f81\u6700\u80fd\u628a\u6211\u5173\u5fc3\u7684\u4eba\u7fa4\u5206\u5f00"，\u8fd9\u6b63\u662f\u51b3\u7b56\u6811\u6784\u5efa\u7684\u6838\u5fc3\u95ee\u9898\u3002<\/p>\n

    \u51b3\u7b56\u6811\u7684\u5efa\u7acb\u8fc7\u7a0b\u5206\u4e09\u6b65：<\/p>\n

  • \u7279\u5f81\u9009\u62e9：\u9009\u53d6\u5206\u7c7b\u80fd\u529b\u5f3a\u7684\u7279\u5f81<\/li>\n
  • \u51b3\u7b56\u6811\u751f\u6210：\u6839\u636e\u9009\u62e9\u7684\u7279\u5f81\u9012\u5f52\u751f\u6210\u6811<\/li>\n
  • \u526a\u679d：\u51b3\u7b56\u6811\u5bb9\u6613\u8fc7\u62df\u5408，\u7528\u526a\u679d\u7f13\u89e3<\/li>\n

    \u4e8c\u3001CART \u6811：\u6240\u6709\u4e3b\u6d41\u6811\u6a21\u578b\u7684\u8001\u7956\u5b97<\/h3>\n

    CART \u5168\u79f0 Classification and Regression Tree（\u5206\u7c7b\u4e0e\u56de\u5f52\u6811），\u89c4\u5219\u53ef\u4ee5\u6982\u62ec\u6210\u4e00\u53e5\u8bdd：\u4e0d\u505c\u505a\u4e8c\u5206\u9009\u62e9\u9898，\u4e00\u5c42\u4e00\u5c42\u7b5b\u9009\u6570\u636e，\u5206\u5f97\u8d8a\u7eaf\u8d8a\u597d\u3002<\/p>\n

    2.1 \u4e3a\u4ec0\u4e48\u662f CART：\u524d\u8f88\u7684\u5c40\u9650<\/h4>\n

    \u5728 CART \u4e4b\u524d\u6709 ID3 \u548c C4.5 \u4e24\u4ee3\u7b97\u6cd5，\u5b83\u4eec\u6709\u4e09\u4e2a\u95ee\u9898：<\/p>\n\n\u5c40\u9650\u8bf4\u660e<\/tr>\n\n\n\n\n
    \u529f\u80fd\u5355\u4e00<\/td>\n\u53ea\u652f\u6301\u5206\u7c7b，\u65e0\u6cd5\u505a\u56de\u5f52<\/td>\n<\/tr>\n
    \u5bb9\u6613\u8fc7\u62df\u5408<\/td>\n\u5b66\u4e86\u592a\u591a\u8bad\u7ec3\u6570\u636e\u7684\u7ec6\u8282\u566a\u58f0<\/td>\n<\/tr>\n
    \u5206\u88c2\u89c4\u5219\u4e0d\u7edf\u4e00<\/td>\n\u65f6\u800c\u4e8c\u5206\u65f6\u800c\u591a\u5206，\u8ba1\u7b97\u903b\u8f91\u4e0d\u89c4\u8303<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

    CART \u7684\u89e3\u6cd5\u662f\u5168\u90e8\u7edf\u4e00：\u65e2\u80fd\u5206\u7c7b\u53c8\u80fd\u56de\u5f52，\u5f3a\u5236\u4e8c\u5206，\u81ea\u5e26\u526a\u679d\u673a\u5236\u3002<\/p>\n

    2.2 \u6838\u5fc3\u89c4\u5219\u4e00：\u6bcf\u6b21\u53ea\u505a\u4e8c\u5206<\/h4>\n

    \u65e0\u8bba\u9009\u54ea\u4e2a\u7279\u5f81\u5212\u5206，\u90fd\u53ea\u80fd\u628a\u6570\u636e\u5206\u6210\u4e24\u7ec4\u3002<\/p>\n

      \n
    • \u7c7b\u522b\u7279\u5f81"\u8272\u6cfd"：\u4e0d\u5141\u8bb8\u4e00\u6b21\u5206\u51fa"\u9752\u7eff\u3001\u4e4c\u9ed1\u3001\u6d45\u767d"\u4e09\u7ec4，\u53ea\u80fd\u95ee"\u662f\u9752\u7eff\u5417"\u6216"\u662f\u4e4c\u9ed1\u5417"<\/li>\n
    • \u6570\u503c\u7279\u5f81"\u91cd\u91cf"：\u53ea\u80fd\u5728\u6570\u8f74\u4e0a\u5207\u4e00\u5200，\u201c\u5927\u4e8e 5 \u65a4"\u548c"\u5c0f\u4e8e\u7b49\u4e8e 5 \u65a4\u201d<\/li>\n<\/ul>\n

      2.3 \u6838\u5fc3\u89c4\u5219\u4e8c：\u9009"\u5206\u5f97\u6700\u7eaf"\u7684\u7279\u5f81<\/h4>\n

      \u5212\u5206\u7684\u76ee\u6807\u662f\u8ba9\u5206\u5f00\u540e\u7684\u4e24\u7ec4\u6570\u636e\u5185\u90e8\u5c3d\u53ef\u80fd\u4e00\u81f4\u3002\u4e00\u5806\u91cc\u7edd\u5927\u591a\u6570\u662f\u597d\u74dc\u3001\u53e6\u4e00\u5806\u7edd\u5927\u591a\u6570\u662f\u574f\u74dc，\u8bf4\u660e\u8fd9\u4e2a\u7279\u5f81\u6709\u7528\u3002<\/p>\n

      \u8861\u91cf\u7eaf\u5ea6\u7684\u6307\u6807\u662f\u57fa\u5c3c\u7cfb\u6570：<\/p>\n

      Gini(D)=1\u2212\u2211k=1Kpk2Gini(D) = 1 – \\\\sum_{k=1}^{K} p_k^2<\/span><\/span>G<\/span>ini<\/span>(<\/span>D<\/span>)<\/span><\/span>=<\/span><\/span><\/span><\/span>1<\/span><\/span>\u2212<\/span><\/span><\/span><\/span><\/span>k<\/span>=<\/span>1<\/span><\/span><\/span><\/span><\/span>\u2211<\/span><\/span><\/span><\/span>K<\/span><\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>p<\/span><\/span>k<\/span><\/span><\/span><\/span>2<\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/p>\n

      \u5176\u4e2d pkp_k<\/span><\/span>p<\/span><\/span>k<\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span> \u662f\u7b2c k \u7c7b\u6837\u672c\u7684\u5360\u6bd4\u3002\u57fa\u5c3c\u7cfb\u6570\u8d8a\u5c0f，\u6570\u636e\u8d8a\u7eaf\u3002\u4e24\u7c7b\u6570\u636e\u7684\u6781\u7aef\u60c5\u51b5\u5f88\u597d\u7b97：\u4e94\u4e94\u5f00\u65f6 Gini = 0.5（\u6700\u6df7\u4e71），\u5168\u662f\u540c\u4e00\u7c7b\u65f6 Gini = 0（\u6700\u7eaf）\u3002<\/p>\n

      \u7528\u6cf0\u5766\u5c3c\u514b\u6570\u636e\u7684\u6839\u8282\u70b9\u7b97\u4e00\u4e0b：712 \u4e2a\u8bad\u7ec3\u6837\u672c，442 \u4eba\u9047\u96be\u3001270 \u4eba\u751f\u5b58\u3002<\/p>\n

      Gini=1\u2212(442712)2\u2212(270712)2=0.471Gini = 1 – \\\\left(\\\\frac{442}{712}\\\\right)^2 – \\\\left(\\\\frac{270}{712}\\\\right)^2 = 0.471<\/span><\/span>G<\/span>ini<\/span><\/span>=<\/span><\/span><\/span><\/span>1<\/span><\/span>\u2212<\/span><\/span><\/span><\/span>(<\/span><\/span><\/span><\/span>712<\/span><\/span><\/span><\/span><\/span><\/span><\/span>442<\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>)<\/span><\/span><\/span><\/span>2<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>\u2212<\/span><\/span><\/span><\/span>(<\/span><\/span><\/span><\/span>712<\/span><\/span><\/span><\/span><\/span><\/span><\/span>270<\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>)<\/span><\/span><\/span><\/span>2<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>=<\/span><\/span><\/span><\/span>0.471<\/span><\/span><\/span><\/span><\/span><\/span><\/p>\n

      \u63a5\u8fd1 0.5，\u8bf4\u660e\u6839\u8282\u70b9\u5904\u4eba\u7fa4\u51e0\u4e4e\u5bf9\u534a\u5f00，\u975e\u5e38\u6df7\u4e71\u3002<\/p>\n

      2.4 \u6838\u5fc3\u89c4\u5219\u4e09：\u9010\u5c42\u9012\u5f52\u5206\u88c2<\/h4>\n

      \u5bf9\u6bcf\u4e2a\u5019\u9009\u7279\u5f81\u90fd\u5c1d\u8bd5\u4e8c\u5206，\u8ba1\u7b97"\u5212\u5206\u540e\u4e24\u7ec4\u7684\u52a0\u6743\u57fa\u5c3c\u7cfb\u6570"，\u9009\u8ba9\u7eaf\u5ea6\u63d0\u5347\u6700\u591a\u7684\u90a3\u4e2a\u3002\u4ee5\u6cf0\u5766\u5c3c\u514b\u7684\u7b2c\u4e00\u5c42\u4e3a\u4f8b（\u5b9e\u6d4b\u6570\u636e，\u4e0b\u4e00\u8282\u4f1a\u8bb2\u600e\u4e48\u6765\u7684）：<\/p>\n

      \u6309"\u662f\u5426\u7537\u6027"\u5212\u5206\u540e：\u7537\u6027\u7ec4 455 \u4eba（377 \u9047\u96be，88 \u751f\u5b58），Gini = 0.307；\u5973\u6027\u7ec4 247 \u4eba（65 \u9047\u96be，182 \u751f\u5b58），Gini = 0.388\u3002\u52a0\u6743\u5e73\u5747：<\/p>\n

      455\u00d70.307+247\u00d70.388712=0.331\\\\frac{455 \\\\times 0.307 + 247 \\\\times 0.388}{712} = 0.331<\/span><\/span><\/span><\/span>712<\/span><\/span><\/span><\/span><\/span><\/span><\/span>455<\/span><\/span>\u00d7<\/span><\/span>0.307<\/span><\/span>+<\/span><\/span>247<\/span><\/span>\u00d7<\/span><\/span>0.388<\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>=<\/span><\/span><\/span><\/span>0.331<\/span><\/span><\/span><\/span><\/span><\/span><\/p>\n

      \u4ece 0.471 \u964d\u5230 0.331，\u7eaf\u5ea6\u660e\u663e\u63d0\u5347，\u6240\u4ee5\u6839\u8282\u70b9\u9009\u4e86\u8fd9\u4e2a\u7279\u5f81\u3002\u5206\u51fa\u4e24\u7ec4\u540e，\u5728\u6bcf\u7ec4\u5185\u90e8\u91cd\u590d\u540c\u6837\u7684\u64cd\u4f5c，\u76f4\u5230\u53f6\u5b50\u8db3\u591f\u7eaf\u6216\u8fbe\u5230\u505c\u6b62\u6761\u4ef6\u3002<\/p>\n

      2.5 \u6838\u5fc3\u89c4\u5219\u56db：\u56de\u5f52\u95ee\u9898\u600e\u4e48\u529e<\/h4>\n

      \u5206\u7c7b\u6811\u7684\u53f6\u5b50\u8f93\u51fa\u7c7b\u522b（\u53f6\u5b50\u4e2d\u591a\u6570\u7c7b），\u56de\u5f52\u6811\u7684\u53f6\u5b50\u8f93\u51fa\u6570\u503c（\u53f6\u5b50\u4e2d\u6240\u6709\u6837\u672c\u76ee\u6807\u503c\u7684\u5747\u503c）\u3002\u5206\u88c2\u903b\u8f91\u4e0d\u53d8，\u53ea\u662f"\u7eaf\u5ea6"\u7684\u8861\u91cf\u4ece\u57fa\u5c3c\u7cfb\u6570\u6362\u6210\u4e86\u5e73\u65b9\u8bef\u5dee：\u8ba9\u5207\u51fa\u6765\u7684\u4e24\u5806，\u5404\u81ea\u5185\u90e8\u7684\u623f\u4ef7（\u6216\u5176\u4ed6\u76ee\u6807\u503c）\u5dee\u5f02\u5c3d\u91cf\u5c0f\u3002<\/p>\n

      2.6 \u4f18\u7f3a\u70b9<\/h4>\n\n\u4f18\u70b9\u7f3a\u70b9<\/tr>\n\n\n\n\n\n
      \u7b80\u5355\u6613\u61c2，\u51b3\u7b56\u8fc7\u7a0b\u6e05\u6670\u53ef\u89c1，\u53ef\u89e3\u91ca\u6027\u5f3a<\/td>\n\u5bb9\u6613\u8fc7\u62df\u5408（\u53c2\u6570\u4e0d\u5f53\u65f6\u5355\u68f5\u6811\u975e\u5e38\u660e\u663e）<\/td>\n<\/tr>\n
      \u5206\u7c7b\u56de\u5f52\u90fd\u80fd\u505a，\u529f\u80fd\u5168\u9762<\/td>\n\u7ed3\u679c\u4e0d\u7a33\u5b9a：\u6570\u636e\u5fae\u5c0f\u6270\u52a8\u53ef\u80fd\u8ba9\u6574\u68f5\u6811\u7ed3\u6784\u5267\u53d8<\/td>\n<\/tr>\n
      \u5f3a\u5236\u4e8c\u5206，\u8bad\u7ec3\u548c\u9884\u6d4b\u901f\u5ea6\u5feb<\/td>\n\u8d2a\u5fc3\u7b56\u7565，\u6bcf\u6b65\u53ea\u627e\u5c40\u90e8\u6700\u4f18，\u4e0d\u4fdd\u8bc1\u5168\u5c40\u6700\u4f18<\/td>\n<\/tr>\n
      \u81ea\u5e26\u526a\u679d，\u4e5f\u662f\u968f\u673a\u68ee\u6797\u3001GBDT\u3001XGBoost \u7684\u57fa\u7840\u6a21\u5757<\/td>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

      \u7f3a\u70b9\u91cc\u7684"\u4e0d\u7a33\u5b9a"\u548c"\u8d2a\u5fc3"\u4e0d\u662f\u80cc\u4e66\u7528\u7684，\u7b2c\u56db\u8282\u5b9e\u6d4b\u4f1a\u4eb2\u773c\u770b\u5230：\u6570\u636e\u53ea\u52a8\u4e00\u70b9，\u6700\u4f18\u6df1\u5ea6\u548c\u51c6\u786e\u7387\u5c31\u8ddf\u7740\u8df3\u3002<\/p>\n

      \u4e09\u3001\u6cf0\u5766\u5c3c\u514b\u751f\u5b58\u9884\u6d4b：\u91cd\u70b9\u5728\u6570\u636e\u9884\u5904\u7406<\/h3>\n

      \u6848\u4f8b\u80cc\u666f：1912 \u5e74\u6cf0\u5766\u5c3c\u514b\u53f7\u6c89\u6ca1，2224 \u4eba\u4e2d 1502 \u4eba\u9047\u96be\u3002\u6551\u751f\u8247\u4e0d\u591f，\u4f46"\u5987\u5973\u3001\u513f\u7ae5\u548c\u4e0a\u6d41\u793e\u4f1a"\u66f4\u5bb9\u6613\u751f\u5b58\u3002\u6211\u4eec\u8981\u7528\u4e58\u5ba2\u6570\u636e\u9884\u6d4b\u8c01\u80fd\u5e78\u5b58\u3002<\/p>\n

      \u6570\u636e\u96c6 891 \u884c，\u7528\u4e09\u4e2a\u7279\u5f81：Pclass（\u8239\u7968\u7b49\u7ea7 1\/2\/3）\u3001Age（\u5e74\u9f84）\u3001Sex（\u6027\u522b），\u6807\u7b7e\u662f Survived（0 \u9047\u96be，1 \u751f\u5b58）\u3002<\/p>\n

      \u8fd9\u4e2a\u6848\u4f8b\u7684\u91cd\u70b9\u4e0d\u662f\u51b3\u7b56\u6811\u672c\u8eab（API \u4e00\u884c\u5c31\u5b8c），\u800c\u662f\u4e2d\u95f4\u7684\u6570\u636e\u9884\u5904\u7406，\u4e00\u5171\u4e09\u4ef6\u4e8b：<\/p>\n

    • \u7f3a\u5931\u503c\u5904\u7406：Age \u5217\u6709 177 \u4e2a\u7f3a\u5931，\u7528\u5747\u503c\u586b\u5145<\/li>\n
    • \u7c7b\u522b\u7279\u5f81\u8f6c\u6570\u503c：Sex \u662f\u5b57\u7b26\u4e32，\u6a21\u578b\u4e0d\u8ba4\u8bc6，\u7528 pd.get_dummies \u505a one-hot \u7f16\u7801，\u5c55\u5f00\u6210 Sex_female \u548c Sex_male \u4e24\u5217 0\/1<\/li>\n
    • \u6570\u636e\u96c6\u5212\u5206：8 : 2 \u5207\u5206\u8bad\u7ec3\u96c6\u548c\u6d4b\u8bd5\u96c6<\/li>\n

      3.1 \u7b2c\u4e00\u8bfe：\u4e0d\u9650\u6df1\u5ea6\u7684\u6811\u957f\u4ec0\u4e48\u6837<\/h4>\n

      \u5148\u6309\u8bfe\u4ef6\u539f\u6837\u8bad\u7ec3\u4e00\u68f5\u4e0d\u505a\u4efb\u4f55\u9650\u5236\u7684\u51b3\u7b56\u6811：<\/p>\n

      import<\/span> pandas as<\/span> pd
      \nfrom<\/span> sklearn.<\/span>model_selection import<\/span> train_test_split
      \nfrom<\/span> sklearn.<\/span>tree import<\/span> DecisionTreeClassifier
      \nfrom<\/span> sklearn.<\/span>metrics import<\/span> classification_report<\/p>\n

      titan_df =<\/span> pd.<\/span>read_csv(<\/span>".\/data\/titanic\/train.csv"<\/span>)<\/span><\/p>\n

      x =<\/span> titan_df[<\/span>[<\/span>"Pclass"<\/span>,<\/span> "Age"<\/span>,<\/span> "Sex"<\/span>]<\/span>]<\/span>
      \ny =<\/span> titan_df[<\/span>"Survived"<\/span>]<\/span><\/p>\n

      x[<\/span>"Age"<\/span>]<\/span> =<\/span> x[<\/span>"Age"<\/span>]<\/span>.<\/span>fillna(<\/span>x[<\/span>"Age"<\/span>]<\/span>.<\/span>mean(<\/span>)<\/span>)<\/span>
      \nx =<\/span> pd.<\/span>get_dummies(<\/span>x)<\/span><\/p>\n

      x_train,<\/span> x_test,<\/span> y_train,<\/span> y_test =<\/span> train_test_split(<\/span>x,<\/span> y,<\/span> test_size=<\/span>0.20<\/span>,<\/span> random_state=<\/span>33<\/span>)<\/span><\/p>\n

      estimator =<\/span> DecisionTreeClassifier(<\/span>random_state=<\/span>33<\/span>)<\/span>
      \nestimator.<\/span>fit(<\/span>x_train,<\/span> y_train)<\/span><\/p>\n

      \u7ed3\u679c：<\/p>\n\n\u6307\u6807\u6570\u503c<\/tr>\n\n\n\n\n\n
      \u8bad\u7ec3\u96c6\u51c6\u786e\u7387<\/td>\n0.8750<\/td>\n<\/tr>\n
      \u6d4b\u8bd5\u96c6\u51c6\u786e\u7387<\/td>\n0.8492<\/td>\n<\/tr>\n
      \u6811\u7684\u6df1\u5ea6<\/td>\n16<\/td>\n<\/tr>\n
      \u53f6\u5b50\u6570<\/td>\n115<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

      712 \u4e2a\u8bad\u7ec3\u6837\u672c\u88ab\u5207\u6210 115 \u4e2a\u53f6\u5b50，\u5f88\u591a\u53f6\u5b50\u91cc\u53ea\u5269\u4e00\u4e24\u4e2a\u4eba\u3002\u8fd9\u5c31\u662f\u8fc7\u62df\u5408\u7684\u5f62\u6001：\u6811\u628a\u8bad\u7ec3\u96c6\u91cc\u5927\u91cf\u5076\u7136\u7ec6\u8282\u90fd\u5f53\u6210\u4e86\u89c4\u5219\u3002\u6d4b\u8bd5\u96c6\u51c6\u786e\u7387\u770b\u8d77\u6765\u8fd8\u884c，\u4f46\u8bad\u7ec3\u96c6\u548c\u6d4b\u8bd5\u96c6\u7684\u5dee\u8ddd\u3001\u4ee5\u53ca\u5938\u5f20\u7684\u53f6\u5b50\u6570\u90fd\u8bf4\u660e\u5b83\u5b66\u4e86\u4e0d\u8be5\u5b66\u7684\u4e1c\u897f\u3002<\/p>\n

      3.2 \u7b2c\u4e8c\u8bfe：\u9650\u6df1\u4e4b\u540e\u53d1\u751f\u4e86\u4ec0\u4e48<\/h4>\n

      \u9650\u5236 max_depth，\u4ece 2 \u5230 10 \u9010\u4e2a\u8bd5（\u6bcf\u7ec4\u90fd\u56fa\u5b9a random_state=33）：<\/p>\n\nmax_depth\u8bad\u7ec3\u96c6\u6d4b\u8bd5\u96c6<\/tr>\n\n\n\n\n\n\n\n\n\n\n
      2<\/td>\n0.7978<\/td>\n0.7821<\/td>\n<\/tr>\n
      3<\/td>\n0.8076<\/td>\n0.8156<\/td>\n<\/tr>\n
      4<\/td>\n0.8104<\/td>\n0.8156<\/td>\n<\/tr>\n
      5<\/td>\n0.8230<\/td>\n0.8045<\/td>\n<\/tr>\n
      6<\/td>\n0.8272<\/td>\n0.8101<\/td>\n<\/tr>\n
      7<\/td>\n0.8441<\/td>\n0.8436<\/td>\n<\/tr>\n
      8<\/td>\n0.8539<\/td>\n0.8492<\/td>\n<\/tr>\n
      9<\/td>\n0.8553<\/td>\n0.8547<\/td>\n<\/tr>\n
      10<\/td>\n0.8610<\/td>\n0.8268<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

      \u4e24\u4e2a\u89c2\u5bdf：<\/p>\n

    • \u8bad\u7ec3\u96c6\u51c6\u786e\u7387\u968f\u6df1\u5ea6\u5355\u8c03\u4e0a\u5347，\u6d4b\u8bd5\u96c6\u4e0d\u662f\u3002\u8fd9\u5c31\u662f\u6b20\u62df\u5408\/\u8fc7\u62df\u5408\u7684\u5b8c\u6574\u66f2\u7ebf：\u6d45\u4e86\u6b20\u62df\u5408（\u6df1\u5ea6 2 \u53ea\u6709 0.78），\u6df1\u4e86\u8fc7\u62df\u5408（\u6df1\u5ea6 10 \u53cd\u800c\u6389\u5230 0.8268）<\/li>\n
    • \u6d4b\u8bd5\u96c6\u5206\u6570\u4ece\u6df1\u5ea6 9 \u5230 10 \u76f4\u63a5\u6389\u4e86\u8fd1 3 \u4e2a\u767e\u5206\u70b9，\u8fd9\u5c31\u662f\u7b2c\u4e8c\u8282\u8bf4\u7684"\u7ed3\u679c\u4e0d\u7a33\u5b9a"：\u6811\u7684\u5206\u88c2\u8def\u5f84\u5bf9\u6570\u636e\u548c\u6df1\u5ea6\u6781\u5176\u654f\u611f<\/li>\n

      3.3 \u7b2c\u4e09\u8bfe：\u6211\u5dee\u70b9\u72af\u7684\u6570\u636e\u6cc4\u6f0f\u9519\u8bef<\/h4>\n

      \u770b\u5230\u4e0a\u8868，\u5f88\u81ea\u7136\u7684\u53cd\u5e94\u662f"\u90a3\u9009\u6d4b\u8bd5\u96c6\u5206\u6570\u6700\u9ad8\u7684\u6df1\u5ea6 9 \u5c31\u5b8c\u4e8b\u4e86"\u3002\u9519，\u8fd9\u662f\u6570\u636e\u6cc4\u6f0f\u3002<\/p>\n

      \u6d4b\u8bd5\u96c6\u7684\u4f5c\u7528\u662f\u6700\u7ec8\u8bc4\u4f30，\u4e0d\u662f\u9009\u53c2\u6570\u3002\u7528\u6d4b\u8bd5\u96c6\u9009\u8d85\u53c2\u6570，\u7b49\u4e8e\u8ba9\u6a21\u578b\u63d0\u524d"\u770b\u8fc7"\u8003\u8bd5\u9898\u518d\u6311\u7b54\u6848，\u9009\u51fa\u6765\u7684\u5206\u6570\u662f\u865a\u9ad8\u7684（\u6df1\u5ea6 9 \u7684 0.8547 \u5c31\u6709\u8fd9\u4e2a\u6210\u5206）\u3002\u6b63\u786e\u505a\u6cd5\u662f\u5728\u8bad\u7ec3\u96c6\u5185\u90e8\u505a\u4ea4\u53c9\u9a8c\u8bc1\u6765\u9009\u53c2\u6570：<\/p>\n

      from<\/span> sklearn.<\/span>model_selection import<\/span> cross_val_score<\/p>\n

      for<\/span> depth in<\/span> range<\/span>(<\/span>2<\/span>,<\/span> 11<\/span>)<\/span>:<\/span>
      \n m =<\/span> DecisionTreeClassifier(<\/span>max_depth=<\/span>depth,<\/span> random_state=<\/span>33<\/span>)<\/span>
      \n scores =<\/span> cross_val_score(<\/span>m,<\/span> x_train,<\/span> y_train,<\/span> cv=<\/span>5<\/span>)<\/span>
      \n print<\/span>(<\/span>f"max_depth=<\/span>{<\/span>depth}<\/span><\/span> 5\u6298\u4ea4\u53c9\u9a8c\u8bc1=<\/span>{<\/span>scores.<\/span>mean(<\/span>)<\/span>:<\/span>.4f<\/span>}<\/span><\/span>"<\/span><\/span>)<\/span><\/p>\n\nmax_depth5 \u6298\u4ea4\u53c9\u9a8c\u8bc1<\/tr>\n\n\n\n\n\n\n\n\n\n\n
      2<\/td>\n0.7823<\/td>\n<\/tr>\n
      3<\/td>\n0.7978<\/td>\n<\/tr>\n
      4<\/td>\n0.7879<\/td>\n<\/tr>\n
      5<\/td>\n0.7782<\/td>\n<\/tr>\n
      6<\/td>\n0.7767<\/td>\n<\/tr>\n
      7<\/td>\n0.7852<\/td>\n<\/tr>\n
      8<\/td>\n0.7838<\/td>\n<\/tr>\n
      9<\/td>\n0.7852<\/td>\n<\/tr>\n
      10<\/td>\n0.7852<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

      \u4ea4\u53c9\u9a8c\u8bc1\u9009\u51fa\u7684\u6700\u4f18\u6df1\u5ea6\u662f 3（0.7978），\u800c\u4e0d\u662f\u5077\u770b\u6d4b\u8bd5\u96c6\u5f97\u5230\u7684 9\u3002\u5b83\u7684\u771f\u5b9e\u6d4b\u8bd5\u96c6\u6210\u7ee9：<\/p>\n

      best =<\/span> DecisionTreeClassifier(<\/span>max_depth=<\/span>3<\/span>,<\/span> random_state=<\/span>33<\/span>)<\/span>
      \nbest.<\/span>fit(<\/span>x_train,<\/span> y_train)<\/span>
      \ny_pred =<\/span> best.<\/span>predict(<\/span>x_test)<\/span>
      \nprint<\/span>(<\/span>classification_report(<\/span>y_test,<\/span> y_pred,<\/span> target_names=<\/span>[<\/span>"died"<\/span>,<\/span> "survived"<\/span>]<\/span>)<\/span>)<\/span><\/p>\n\n\u6307\u6807\u6570\u503c<\/tr>\n\n\n\n\n
      \u6d4b\u8bd5\u96c6\u51c6\u786e\u7387<\/td>\n0.8156<\/td>\n<\/tr>\n
      \u6811\u7684\u6df1\u5ea6<\/td>\n3<\/td>\n<\/tr>\n
      \u53f6\u5b50\u6570<\/td>\n8<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

      precision recall f1-score support<\/p>\n

      died 0.82 0.88 0.85 107
      \n survived 0.80 0.72 0.76 72<\/p>\n

      accuracy 0.82 179<\/p>\n

      \u8bda\u5b9e\u5730\u8bf4：\u4ea4\u53c9\u9a8c\u8bc1\u9009\u51fa\u7684\u6a21\u578b（0.8156）\u5728\u8fd9\u4e00\u6b21\u6d4b\u8bd5\u4e0a\u786e\u5b9e\u4f4e\u4e8e"\u4f5c\u5f0a"\u9009\u51fa\u7684 0.8547\u3002\u4f46\u8fd9\u4e0d\u77db\u76fe：0.8547 \u662f\u63d0\u524d\u770b\u8fc7\u8003\u9898\u6311\u51fa\u6765\u7684\u6570\u5b57，\u6362\u4e00\u6279\u6d4b\u8bd5\u96c6\u5927\u6982\u7387\u6ca1\u8fd9\u4e48\u597d；0.8156 \u624d\u662f\u6a21\u578b\u771f\u5b9e\u6cdb\u5316\u80fd\u529b\u7684\u5408\u7406\u4f30\u8ba1\u3002\u6df1\u5ea6 3 \u7684\u6811\u53ea\u6709 8 \u4e2a\u53f6\u5b50，\u5728\u53ef\u89e3\u91ca\u6027\u548c\u6cdb\u5316\u4e4b\u95f4\u53d6\u5f97\u4e86\u597d\u5e73\u8861，\u9762\u8bd5\u91cc\u8bb2\u6e05\u695a\u8fd9\u4e2a\u53d6\u820d\u662f\u52a0\u5206\u9879\u3002<\/p>\n

      3.4 \u8fd9\u68f5\u6811\u5230\u5e95\u5b66\u5230\u4e86\u4ec0\u4e48<\/h4>\n

      \u7528 plot_tree \u753b\u51fa\u6df1\u5ea6 3 \u7684\u6811（\u5b8c\u6574\u56fe\u89c1\u9879\u76ee\u4ed3\u5e93，\u8fd9\u91cc\u8bf4\u7ed3\u8bba）：<\/p>\n

        \n
      • \u6839\u8282\u70b9\u95ee\u7684\u7b2c\u4e00\u4e2a\u95ee\u9898\u662f"\u662f\u5426\u7537\u6027"（Sex_female <= 0.5）\u3002\u7b97\u6cd5\u5728\u6240\u6709\u7279\u5f81\u91cc\u81ea\u52a8\u628a\u6027\u522b\u6392\u5728\u4e86\u7b2c\u4e00\u4f18\u5148\u7ea7<\/li>\n
      • \u7537\u6027\u5206\u652f\u7b2c\u4e8c\u4e2a\u95ee\u9898\u95ee\u5e74\u9f84（Age <= 5.5）：\u7537\u5b69\u91cc 5 \u5c81\u4ee5\u4e0b\u4f18\u5148\u83b7\u6551<\/li>\n
      • \u5973\u6027\u5206\u652f\u7b2c\u4e8c\u4e2a\u95ee\u9898\u95ee\u8239\u7968\u7b49\u7ea7（Pclass <= 2.5）：\u4e00\u4e8c\u7b49\u8231\u7684\u5973\u6027\u4f18\u5148\u83b7\u6551<\/li>\n<\/ul>\n

        "\u5987\u5973\u513f\u7ae5\u4f18\u5148 + \u8231\u4f4d\u5dee\u5f02"\u8fd9\u4e2a\u5386\u53f2\u53d9\u8ff0，\u88ab\u6570\u636e\u5b8c\u6574\u590d\u73b0\u4e86\u3002\u770b\u7279\u5f81\u91cd\u8981\u6027\u66f4\u76f4\u63a5：<\/p>\n\n\u7279\u5f81\u91cd\u8981\u6027<\/tr>\n\n\n\n\n\n
        Sex_female<\/td>\n0.6621<\/td>\n<\/tr>\n
        Pclass<\/td>\n0.2431<\/td>\n<\/tr>\n
        Age<\/td>\n0.0947<\/td>\n<\/tr>\n
        Sex_male<\/td>\n0.0000<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

        Sex_male \u7684\u91cd\u8981\u6027\u662f 0：one-hot \u7f16\u7801\u4ea7\u751f\u4e86\u4e24\u5217\u5b8c\u5168\u4e92\u65a5\u7684\u7279\u5f81（\u4e00\u5217\u662f 1 \u53e6\u4e00\u5217\u5fc5\u662f 0），\u6811\u53ea\u9700\u8981\u7528\u5176\u4e2d\u4e00\u5217\u5c31\u591f，\u53e6\u4e00\u5217\u7684\u4fe1\u606f\u91cf\u4e3a\u96f6\u3002one-hot \u540e\u51fa\u73b0\u67d0\u4e2a\u54d1\u53d8\u91cf\u91cd\u8981\u6027\u4e3a 0 \u662f\u6b63\u5e38\u73b0\u8c61，\u4e0d\u662f bug\u3002<\/p>\n

        \u56db\u3001\u56de\u5f52\u6811\u5b9e\u6218：\u52a0\u5dde\u623f\u4ef7<\/h3>\n

        \u8bfe\u4ef6\u7684\u56de\u5f52\u6811\u6848\u4f8b\u8fd8\u5728\u7528\u6ce2\u58eb\u987f\u623f\u4ef7，\u4f46 load_boston \u4ece sklearn 1.2 \u8d77\u5df2\u88ab\u79fb\u9664（\u7ebf\u6027\u56de\u5f52\u7bc7\u8bb2\u8fc7\u7684\u8001\u5751），\u8fd9\u91cc\u540c\u6837\u6362 fetch_california_housing\u3002\u56de\u5f52\u6811 API \u662f DecisionTreeRegressor，\u6d41\u7a0b\u548c\u5206\u7c7b\u6811\u4e00\u81f4：<\/p>\n

        from<\/span> sklearn.<\/span>datasets import<\/span> fetch_california_housing
        \nfrom<\/span> sklearn.<\/span>model_selection import<\/span> train_test_split
        \nfrom<\/span> sklearn.<\/span>preprocessing import<\/span> StandardScaler
        \nfrom<\/span> sklearn.<\/span>tree import<\/span> DecisionTreeRegressor
        \nfrom<\/span> sklearn.<\/span>metrics import<\/span> mean_squared_error,<\/span> mean_absolute_error
        \nimport<\/span> numpy as<\/span> np<\/p>\n

        data =<\/span> fetch_california_housing(<\/span>)<\/span>
        \nx_train,<\/span> x_test,<\/span> y_train,<\/span> y_test =<\/span> train_test_split(<\/span>data.<\/span>data,<\/span> data.<\/span>target,<\/span> random_state=<\/span>22<\/span>)<\/span>
        \nscaler =<\/span> StandardScaler(<\/span>)<\/span>
        \nx_train =<\/span> scaler.<\/span>fit_transform(<\/span>x_train)<\/span>
        \nx_test =<\/span> scaler.<\/span>transform(<\/span>x_test)<\/span><\/p>\n

        m =<\/span> DecisionTreeRegressor(<\/span>max_depth=<\/span>8<\/span>,<\/span> random_state=<\/span>22<\/span>)<\/span>
        \nm.<\/span>fit(<\/span>x_train,<\/span> y_train)<\/span>
        \ny_pred =<\/span> m.<\/span>predict(<\/span>x_test)<\/span>
        \nmse =<\/span> mean_squared_error(<\/span>y_test,<\/span> y_pred)<\/span>
        \nprint<\/span>(<\/span>np.<\/span>sqrt(<\/span>mse)<\/span>,<\/span> mean_absolute_error(<\/span>y_test,<\/span> y_pred)<\/span>)<\/span><\/p>\n

        \u6df1\u5ea6 8 \u65f6：MSE = 0.4196，RMSE = 0.6478，MAE = 0.4525\u3002\u5bf9\u6bd4\u7ebf\u6027\u56de\u5f52\u7bc7\u6b63\u89c4\u65b9\u7a0b\u7684 MSE = 0.4918，\u5355\u68f5\u56de\u5f52\u6811\u5728\u8fd9\u4e2a\u6570\u636e\u4e0a\u6bd4\u7ebf\u6027\u56de\u5f52\u8fd8\u51c6（\u7279\u5f81\u5173\u7cfb\u6709\u975e\u7ebf\u6027\u6210\u5206，\u6811\u80fd\u62df\u5408\u51fa\u6765）\u3002<\/p>\n

        \u66f4\u6709\u4ef7\u503c\u7684\u662f\u6df1\u5ea6\u5bf9\u7167\u5b9e\u9a8c：<\/p>\n\nmax_depth\u8bad\u7ec3\u96c6 MSE\u6d4b\u8bd5\u96c6 MSE<\/tr>\n\n\n\n\n\n
        4<\/td>\n0.5581<\/td>\n0.5395<\/td>\n<\/tr>\n
        6<\/td>\n0.4455<\/td>\n0.4464<\/td>\n<\/tr>\n
        10<\/td>\n0.2276<\/td>\n0.4142<\/td>\n<\/tr>\n
        \u4e0d\u9650<\/td>\n0.0000<\/td>\n0.5274<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

        \u4e0d\u9650\u6df1\u5ea6\u65f6\u8bad\u7ec3\u96c6 MSE \u6b63\u597d\u662f 0：\u6811\u628a\u6bcf\u4e2a\u8bad\u7ec3\u6837\u672c\u90fd\u5206\u5230\u4e86\u72ec\u4eab\u7684\u53f6\u5b50\u91cc，\u201c\u9884\u6d4b"\u9000\u5316\u6210"\u80cc\u7b54\u6848\u201d\u3002\u6d4b\u8bd5\u96c6 MSE \u968f\u4e4b\u4ece 0.4142（\u6df1\u5ea6 10 \u7684\u6700\u4f18\u9644\u8fd1）\u6076\u5316\u5230 0.5274\u3002\u8bad\u7ec3\u8bef\u5dee 0 \u6c38\u8fdc\u4e0d\u662f\u597d\u4e8b，\u5b83\u662f\u8fc7\u62df\u5408\u6700\u6781\u7aef\u7684\u6837\u5b50\u3002<\/p>\n

        \u53e6\u5916\u6ce8\u610f：\u56de\u5f52\u6811\u5bf9\u7279\u5f81\u505a\u4e86\u6807\u51c6\u5316，\u4f46\u4e25\u683c\u8bf4\u6811\u6a21\u578b\u5bf9\u5355\u8c03\u7f29\u653e\u4e0d\u654f\u611f（\u5206\u88c2\u53ea\u770b\u76f8\u5bf9\u5927\u5c0f），\u8fd9\u91cc\u505a\u6807\u51c6\u5316\u662f\u6cbf\u7528\u6d41\u7a0b\u4e60\u60ef，\u4e0d\u5f71\u54cd\u7ed3\u8bba\u3002<\/p>\n

        \u4e94\u3001\u526a\u679d：\u7ed9\u6811"\u51cf\u8d1f"<\/h3>\n

        \u526a\u679d\u662f\u4e00\u79cd\u9632\u6b62\u51b3\u7b56\u6811\u8fc7\u62df\u5408\u7684\u6b63\u5219\u5316\u65b9\u6cd5，\u601d\u8def\u662f\u628a\u5b50\u6811\u5220\u6389\u3001\u7528\u53f6\u5b50\u8282\u70b9\u66ff\u6362，\u727a\u7272\u4e00\u70b9\u8bad\u7ec3\u96c6\u51c6\u786e\u7387\u6362\u6cdb\u5316\u80fd\u529b\u3002\u4e24\u79cd\u65b9\u5f0f：<\/p>\n

        \u9884\u526a\u679d：\u8fb9\u751f\u6210\u8fb9\u5224\u65ad\u3002\u6bcf\u4e2a\u8282\u70b9\u5206\u88c2\u524d\u5148\u4f30\u8ba1，\u5982\u679c\u8fd9\u6b21\u5212\u5206\u4e0d\u80fd\u5e26\u6765\u6cdb\u5316\u6027\u80fd\u63d0\u5347，\u5c31\u505c\u6b62\u5212\u5206\u3001\u76f4\u63a5\u6807\u8bb0\u4e3a\u53f6\u8282\u70b9\u3002\u4e0a\u6587\u7528 max_depth \u9650\u6df1\u5c31\u662f\u6700\u5e38\u7528\u7684\u9884\u526a\u679d\u624b\u6bb5\u3002<\/p>\n

        \u540e\u526a\u679d：\u5148\u8ba9\u6811\u5b8c\u6574\u751f\u6210，\u518d\u81ea\u5e95\u5411\u4e0a\u8003\u5bdf\u6bcf\u4e2a\u975e\u53f6\u8282\u70b9，\u628a\u67d0\u8282\u70b9\u5bf9\u5e94\u7684\u5b50\u6811\u66ff\u6362\u4e3a\u53f6\u8282\u70b9\u540e\u5982\u679c\u6cdb\u5316\u6027\u80fd\u63d0\u5347（\u901a\u5e38\u7528\u9a8c\u8bc1\u96c6\u5224\u65ad），\u5c31\u526a\u6389\u3002<\/p>\n\n\u4f18\u70b9\u7f3a\u70b9<\/tr>\n\n\n\n
        \u9884\u526a\u679d<\/td>\n\u5206\u652f\u5c11，\u8bad\u7ec3\u548c\u6d4b\u8bd5\u7684\u65f6\u95f4\u5f00\u9500\u663e\u8457\u964d\u4f4e<\/td>\n\u6709\u4e9b\u5206\u652f\u5f53\u524d\u770b\u7740\u6ca1\u7528\u3001\u540e\u7eed\u5206\u88c2\u53ef\u80fd\u663e\u8457\u63d0\u5347\u6027\u80fd，\u63d0\u524d\u505c\u6709\u6b20\u62df\u5408\u98ce\u9669<\/td>\n<\/tr>\n
        \u540e\u526a\u679d<\/td>\n\u4fdd\u7559\u66f4\u591a\u5206\u652f，\u6b20\u62df\u5408\u98ce\u9669\u5c0f，\u6cdb\u5316\u6027\u80fd\u901a\u5e38\u4f18\u4e8e\u9884\u526a\u679d<\/td>\n\u5148\u751f\u6210\u518d\u904d\u5386，\u8bad\u7ec3\u5f00\u9500\u5927<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

        sklearn \u7684 DecisionTreeClassifier \u91cc，max_depth\u3001min_samples_split（\u8282\u70b9\u518d\u5212\u5206\u6240\u9700\u6700\u5c0f\u6837\u672c\u6570）\u3001min_samples_leaf（\u53f6\u5b50\u8282\u70b9\u6700\u5c11\u6837\u672c\u6570）\u8fd9\u4e9b\u90fd\u662f\u9884\u526a\u679d\u53c2\u6570；\u540e\u526a\u679d\u53ef\u7528 ccp_alpha（\u4ee3\u4ef7\u590d\u6742\u5ea6\u526a\u679d）\u5b9e\u73b0，\u672c\u6587\u5b9e\u9a8c\u4ee5\u9884\u526a\u679d\u4e3a\u4e3b\u3002<\/p>\n

        \u8bfe\u4ef6\u91cc\u90a3\u9053\u591a\u9009\u9898\u503c\u5f97\u8bb0\u4e00\u4e0b：\u526a\u679d\u9632\u8fc7\u62df\u5408\u3001\u5206\u9884\u526a\u679d\u548c\u540e\u526a\u679d\u3001\u9884\u526a\u679d\u662f\u8fb9\u751f\u6210\u8fb9\u9a8c\u8bc1\u3001\u540e\u526a\u679d\u662f\u6784\u5efa\u5b8c\u518d\u904d\u5386，\u56db\u4e2a\u8bf4\u6cd5\u5168\u5bf9\u3002<\/p>\n

        \u516d\u3001\u6613\u9519\u70b9<\/h3>\n
          \n
        • \u7528\u6d4b\u8bd5\u96c6\u8c03\u8d85\u53c2\u6570\u5c31\u662f\u6570\u636e\u6cc4\u6f0f：3.3 \u8282\u7684\u5b8c\u6574\u6559\u8bad\u3002\u8c03\u53c2\u53ea\u80fd\u7528\u8bad\u7ec3\u96c6\u5185\u90e8\u7684\u4ea4\u53c9\u9a8c\u8bc1，\u6d4b\u8bd5\u96c6\u53ea\u5728\u6700\u7ec8\u8bc4\u4f30\u65f6\u78b0\u4e00\u6b21<\/li>\n
        • one-hot \u4e4b\u524d\u5fc5\u987b\u5148\u5904\u7406\u7f3a\u5931\u503c：\u5982\u679c\u5148 get_dummies \u518d\u586b\u5145，\u7f3a\u5931\u503c\u4f1a\u88ab\u5f53\u6210\u4e00\u4e2a\u72ec\u7acb\u7c7b\u522b\u5904\u7406，\u903b\u8f91\u4e0a\u5c31\u9519\u4e86\u3002\u672c\u6848\u4f8b\u987a\u5e8f\u662f\u5148 fillna \u518d get_dummies<\/li>\n
        • pandas \u7684 inplace=True \u94fe\u5f0f\u8d4b\u503c\u5728\u65b0\u7248\u672c\u4f1a\u62a5 FutureWarning \u751a\u81f3\u5931\u6548：\u8bfe\u4ef6\u91cc\u7684 x['Age'].fillna(…, inplace=True) \u5199\u6cd5\u5df2\u7ecf\u8fc7\u65f6，\u63a8\u8350 x['Age'] = x['Age'].fillna(…)（\u672c\u6587\u4ee3\u7801\u5df2\u7528\u65b0\u5199\u6cd5）<\/li>\n
        • \u4e0d\u9650\u6df1\u5ea6\u7684\u51b3\u7b56\u6811\u5728\u8bad\u7ec3\u96c6\u4e0a MSE \u53ef\u4ee5\u662f 0：\u770b\u5230\u8bad\u7ec3\u8bef\u5dee\u4e3a 0 \u4e0d\u8981\u4ee5\u4e3a\u662f\u6a21\u578b\u5f88\u5f3a，\u5148\u770b\u6d4b\u8bd5\u96c6<\/li>\n
        • classification_report \u7684\u53c2\u6570\u987a\u5e8f：classification_report(y_test, y_pred)，\u771f\u5b9e\u503c\u5728\u524d\u9884\u6d4b\u503c\u5728\u540e\u3002\u4f20\u53cd\u4e86\u62a5\u544a\u7167\u6837\u80fd\u751f\u6210，\u4f46\u7cbe\u786e\u7387\u548c\u53ec\u56de\u7387\u5c31\u5168\u90e8\u9519\u4f4d，\u8fd9\u4e2a bug \u5f88\u9690\u853d<\/li>\n
        • \u6811\u6a21\u578b\u4e0d\u9700\u8981\u6807\u51c6\u5316（\u5bf9\u5355\u8c03\u7f29\u653e\u4e0d\u654f\u611f），\u4f46\u9700\u8981\u505a\u7c7b\u522b\u7279\u5f81\u7f16\u7801；\u6807\u51c6\u5316\u5728\u6811\u6a21\u578b\u6d41\u7a0b\u91cc\u662f"\u53ef\u505a\u53ef\u4e0d\u505a"，\u5728 KNN\u3001\u7ebf\u6027\u56de\u5f52\u6d41\u7a0b\u91cc\u662f"\u5fc5\u987b\u505a"<\/li>\n<\/ul>\n

          \u4e03\u3001\u77e5\u8bc6\u6e05\u5355<\/h3>\n
            \n
          • \u51b3\u7b56\u6811\u7ed3\u6784：\u6839\u8282\u70b9\u3001\u5185\u90e8\u8282\u70b9\u3001\u5206\u652f\u3001\u53f6\u5b50\u8282\u70b9<\/li>\n
          • \u5efa\u6811\u4e09\u6b65：\u7279\u5f81\u9009\u62e9\u3001\u751f\u6210\u3001\u526a\u679d<\/li>\n
          • CART \u4e94\u6761\u6838\u5fc3\u89c4\u5219：\u5f3a\u5236\u4e8c\u5206\u3001\u9009\u6700\u7eaf\u5206\u88c2\u3001\u9010\u5c42\u9012\u5f52\u3001\u53f6\u5b50\u8f93\u51fa（\u7c7b\u522b \/ \u5747\u503c）\u3001\u526a\u679d<\/li>\n
          • \u57fa\u5c3c\u7cfb\u6570：Gini=1\u2212\u2211pk2Gini = 1 – \\\\sum p_k^2<\/span><\/span>G<\/span>ini<\/span><\/span>=<\/span><\/span><\/span><\/span>1<\/span><\/span>\u2212<\/span><\/span><\/span><\/span>\u2211<\/span><\/span>p<\/span><\/span>k<\/span><\/span><\/span><\/span>2<\/span><\/span><\/span><\/span>\u200b<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>，\u8d8a\u5c0f\u8d8a\u7eaf；\u4e94\u4e94\u5f00 0.5，\u7eaf\u4e00\u7c7b 0<\/li>\n
          • ID3 \/ C4.5 \u5c40\u9650：\u4e0d\u80fd\u56de\u5f52\u3001\u6613\u8fc7\u62df\u5408\u3001\u5206\u88c2\u89c4\u5219\u4e0d\u7edf\u4e00<\/li>\n
          • \u9884\u526a\u679d\u53c2\u6570：max_depth\u3001min_samples_split\u3001min_samples_leaf；\u540e\u526a\u679d：ccp_alpha<\/li>\n
          • \u56de\u5f52\u6811 API：DecisionTreeRegressor，\u8bc4\u4f30\u7528 MSE \/ RMSE \/ MAE<\/li>\n
          • \u8c03\u53c2\u7eaa\u5f8b：\u4ea4\u53c9\u9a8c\u8bc1\u9009\u53c2，\u6d4b\u8bd5\u96c6\u53ea\u505a\u6700\u7ec8\u8bc4\u4f30<\/li>\n<\/ul>\n

            \u516b\u3001\u9762\u8bd5\u9ad8\u9891\u95ee\u7b54<\/h3>\n

            Q1：\u51b3\u7b56\u6811\u5982\u4f55\u9009\u62e9\u5206\u88c2\u7279\u5f81？<\/p>\n

            \u8ba1\u7b97\u6bcf\u4e2a\u5019\u9009\u7279\u5f81\u5212\u5206\u540e\u7684\u4e0d\u7eaf\u5ea6（\u5206\u7c7b\u7528\u57fa\u5c3c\u7cfb\u6570\u6216\u4fe1\u606f\u71b5，\u56de\u5f52\u7528\u5e73\u65b9\u8bef\u5dee），\u9009\u4f7f\u4e0d\u7eaf\u5ea6\u4e0b\u964d\u6700\u591a\u7684\u7279\u5f81\u548c\u5206\u88c2\u70b9\u3002CART \u5f3a\u5236\u6bcf\u6b21\u4e8c\u5206，\u9010\u5c42\u9012\u5f52\u76f4\u5230\u6ee1\u8db3\u505c\u6b62\u6761\u4ef6\u3002<\/p>\n

            Q2：CART \u548c ID3\u3001C4.5 \u7684\u533a\u522b？<\/p>\n

            ID3 \u7528\u4fe1\u606f\u589e\u76ca\u3001\u53ea\u80fd\u5904\u7406\u79bb\u6563\u7279\u5f81\u3001\u4e0d\u80fd\u56de\u5f52；C4.5 \u7528\u4fe1\u606f\u589e\u76ca\u6bd4\u3001\u80fd\u5904\u7406\u8fde\u7eed\u7279\u5f81\u3001\u4ecd\u4e0d\u80fd\u56de\u5f52；CART \u7528\u57fa\u5c3c\u7cfb\u6570（\u5206\u7c7b）\u6216\u5e73\u65b9\u8bef\u5dee（\u56de\u5f52）\u3001\u5f3a\u5236\u4e8c\u5206\u3001\u5206\u7c7b\u56de\u5f52\u901a\u5403，\u4e14\u662f\u968f\u673a\u68ee\u6797\u548c GBDT \u7684\u57fa\u7840\u6a21\u5757\u3002<\/p>\n

            Q3：\u51b3\u7b56\u6811\u8fc7\u62df\u5408\u600e\u4e48\u89e3\u51b3？<\/p>\n

            \u526a\u679d（\u9884\u526a\u679d\u9650\u5236\u751f\u957f，\u5982 max_depth\u3001min_samples_leaf；\u540e\u526a\u679d\u5148\u751f\u6210\u518d\u81ea\u5e95\u5411\u4e0a\u88c1\u526a）\u3001\u96c6\u6210\u5b66\u4e60（\u628a\u5355\u68f5\u4e0d\u7a33\u5b9a\u5927\u6811\u6362\u6210\u591a\u68f5\u5c0f\u6811\u7684\u968f\u673a\u68ee\u6797，\u4e0b\u4e00\u7bc7\u7684\u4e3b\u9898）\u3001\u4ee5\u53ca\u589e\u52a0\u8bad\u7ec3\u6570\u636e\u3002<\/p>\n

            Q4：\u9884\u526a\u679d\u548c\u540e\u526a\u679d\u7684\u533a\u522b\u4e0e\u53d6\u820d？<\/p>\n

            \u9884\u526a\u679d\u8fb9\u751f\u6210\u8fb9\u5224\u65ad，\u7701\u65f6\u95f4\u4f46\u53ef\u80fd\u63d0\u524d\u780d\u6389\u540e\u671f\u6709\u4ef7\u503c\u7684\u5206\u652f，\u6709\u6b20\u62df\u5408\u98ce\u9669；\u540e\u526a\u679d\u5148\u751f\u6210\u5b8c\u6574\u6811\u518d\u81ea\u5e95\u5411\u4e0a\u8003\u5bdf，\u6548\u679c\u901a\u5e38\u66f4\u597d\u4f46\u8bad\u7ec3\u5f00\u9500\u5927\u3002\u5de5\u7a0b\u4e0a sklearn \u9ed8\u8ba4\u8d70\u9884\u526a\u679d\u53c2\u6570，\u8ffd\u6c42\u6027\u80fd\u65f6\u914d\u5408\u4ea4\u53c9\u9a8c\u8bc1\u8c03\u53c2\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"

            \u7cfb\u5217\u7b2c\u4e94\u7bc7\u3002\u51b3\u7b56\u6811\u662f\u53ef\u89e3\u91ca\u6027\u6700\u597d\u7684\u6a21\u578b（\u6bcf\u4e00\u5c42\u90fd\u662f\u4e00\u4e2a\\”\u662f\/\u5426\\”\u95ee\u9898），\u4e5f\u662f\u96c6\u6210\u5b66\u4e60（\u968f\u673a\u68ee\u6797\u3001GBDT\u3001XGBoost）\u7684\u57fa\u77f3，\u4e0b\u4e00\u7bc7\u5c31\u8981\u7528\u5b83\u3002\u8fd9\u7bc7\u7684\u91cd\u70b9\u6709\u4e24\u4e2a：CART \u6811\u7684\u5206\u88c2\u903b\u8f91（\u57fa\u5c3c\u7cfb\u6570），\u4ee5\u53ca\u6cf0\u5766\u5c3c\u514b\u6848\u4f8b\u91cc\u88ab\u5f88\u591a\u4eba\u5ffd\u7565\u7684\u6570\u636e\u9884\u5904\u7406\u548c\u8c03\u53c2\u7ec6\u8282\u3002\u672c\u7bc7\u770b\u5b8c\u8981\u56de\u7b54\u7684\u56db\u4e2a\u9762\u8bd5\u9898：
            \n\u51b3\u7b56\u6811\u600e\u4e48\u9009\u5206\u88c2\u7279\u5f81？\u57fa\u5c3c\u7cfb<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[371,207,305],"topic":[],"class_list":["post-113593","post","type-post","status-publish","format-standard","hentry","category-server","tag-371","tag-207","tag-305"],"yoast_head":"\n\u673a\u5668\u5b66\u4e60\u5b66\u4e60\u7b14\u8bb0\uff08\u4e94\uff09\uff1a\u51b3\u7b56\u6811\uff0cCART \u539f\u7406\u3001\u6cf0\u5766\u5c3c\u514b\u751f\u5b58\u9884\u6d4b\u4e0e\u526a\u679d - \u7f51\u7855\u4e92\u8054\u5e2e\u52a9\u4e2d\u5fc3<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.wsisp.com\/helps\/113593.html\" \/>\n<meta property=\"og:locale\" content=\"zh_CN\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"\u673a\u5668\u5b66\u4e60\u5b66\u4e60\u7b14\u8bb0\uff08\u4e94\uff09\uff1a\u51b3\u7b56\u6811\uff0cCART \u539f\u7406\u3001\u6cf0\u5766\u5c3c\u514b\u751f\u5b58\u9884\u6d4b\u4e0e\u526a\u679d - \u7f51\u7855\u4e92\u8054\u5e2e\u52a9\u4e2d\u5fc3\" \/>\n<meta property=\"og:description\" content=\"\u7cfb\u5217\u7b2c\u4e94\u7bc7\u3002\u51b3\u7b56\u6811\u662f\u53ef\u89e3\u91ca\u6027\u6700\u597d\u7684\u6a21\u578b（\u6bcf\u4e00\u5c42\u90fd\u662f\u4e00\u4e2a"\u662f\/\u5426"\u95ee\u9898），\u4e5f\u662f\u96c6\u6210\u5b66\u4e60（\u968f\u673a\u68ee\u6797\u3001GBDT\u3001XGBoost）\u7684\u57fa\u77f3，\u4e0b\u4e00\u7bc7\u5c31\u8981\u7528\u5b83\u3002\u8fd9\u7bc7\u7684\u91cd\u70b9\u6709\u4e24\u4e2a：CART \u6811\u7684\u5206\u88c2\u903b\u8f91（\u57fa\u5c3c\u7cfb\u6570），\u4ee5\u53ca\u6cf0\u5766\u5c3c\u514b\u6848\u4f8b\u91cc\u88ab\u5f88\u591a\u4eba\u5ffd\u7565\u7684\u6570\u636e\u9884\u5904\u7406\u548c\u8c03\u53c2\u7ec6\u8282\u3002\u672c\u7bc7\u770b\u5b8c\u8981\u56de\u7b54\u7684\u56db\u4e2a\u9762\u8bd5\u9898： \u51b3\u7b56\u6811\u600e\u4e48\u9009\u5206\u88c2\u7279\u5f81？\u57fa\u5c3c\u7cfb\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.wsisp.com\/helps\/113593.html\" \/>\n<meta property=\"og:site_name\" content=\"\u7f51\u7855\u4e92\u8054\u5e2e\u52a9\u4e2d\u5fc3\" \/>\n<meta property=\"article:published_time\" content=\"2026-10-06T14:17:33+00:00\" \/>\n<meta name=\"author\" content=\"admin\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"\u4f5c\u8005\" \/>\n\t<meta name=\"twitter:data1\" content=\"admin\" \/>\n\t<meta name=\"twitter:label2\" content=\"\u9884\u8ba1\u9605\u8bfb\u65f6\u95f4\" \/>\n\t<meta name=\"twitter:data2\" content=\"5 \u5206\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/www.wsisp.com\/helps\/113593.html\",\"url\":\"https:\/\/www.wsisp.com\/helps\/113593.html\",\"name\":\"\u673a\u5668\u5b66\u4e60\u5b66\u4e60\u7b14\u8bb0\uff08\u4e94\uff09\uff1a\u51b3\u7b56\u6811\uff0cCART \u539f\u7406\u3001\u6cf0\u5766\u5c3c\u514b\u751f\u5b58\u9884\u6d4b\u4e0e\u526a\u679d - \u7f51\u7855\u4e92\u8054\u5e2e\u52a9\u4e2d\u5fc3\",\"isPartOf\":{\"@id\":\"https:\/\/www.wsisp.com\/helps\/#website\"},\"datePublished\":\"2026-10-06T14:17:33+00:00\",\"dateModified\":\"2026-10-06T14:17:33+00:00\",\"author\":{\"@id\":\"https:\/\/www.wsisp.com\/helps\/#\/schema\/person\/358e386c577a3ab51c4493330a20ad41\"},\"breadcrumb\":{\"@id\":\"https:\/\/www.wsisp.com\/helps\/113593.html#breadcrumb\"},\"inLanguage\":\"zh-Hans\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/www.wsisp.com\/helps\/113593.html\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/www.wsisp.com\/helps\/113593.html#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"\u9996\u9875\",\"item\":\"https:\/\/www.wsisp.com\/helps\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"\u673a\u5668\u5b66\u4e60\u5b66\u4e60\u7b14\u8bb0\uff08\u4e94\uff09\uff1a\u51b3\u7b56\u6811\uff0cCART \u539f\u7406\u3001\u6cf0\u5766\u5c3c\u514b\u751f\u5b58\u9884\u6d4b\u4e0e\u526a\u679d\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/www.wsisp.com\/helps\/#website\",\"url\":\"https:\/\/www.wsisp.com\/helps\/\",\"name\":\"\u7f51\u7855\u4e92\u8054\u5e2e\u52a9\u4e2d\u5fc3\",\"description\":\"\u9999\u6e2f\u670d\u52a1\u5668_\u9999\u6e2f\u4e91\u670d\u52a1\u5668\u8d44\u8baf_\u670d\u52a1\u5668\u5e2e\u52a9\u6587\u6863_\u670d\u52a1\u5668\u6559\u7a0b\",\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/www.wsisp.com\/helps\/?s={search_term_string}\"},\"query-input\":\"required name=search_term_string\"}],\"inLanguage\":\"zh-Hans\"},{\"@type\":\"Person\",\"@id\":\"https:\/\/www.wsisp.com\/helps\/#\/schema\/person\/358e386c577a3ab51c4493330a20ad41\",\"name\":\"admin\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"zh-Hans\",\"@id\":\"https:\/\/www.wsisp.com\/helps\/#\/schema\/person\/image\/\",\"url\":\"https:\/\/gravatar.wp-china-yes.net\/avatar\/?s=96&d=mystery\",\"contentUrl\":\"https:\/\/gravatar.wp-china-yes.net\/avatar\/?s=96&d=mystery\",\"caption\":\"admin\"},\"sameAs\":[\"http:\/\/wp.wsisp.com\"],\"url\":\"https:\/\/www.wsisp.com\/helps\/author\/admin\"}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"\u673a\u5668\u5b66\u4e60\u5b66\u4e60\u7b14\u8bb0\uff08\u4e94\uff09\uff1a\u51b3\u7b56\u6811\uff0cCART \u539f\u7406\u3001\u6cf0\u5766\u5c3c\u514b\u751f\u5b58\u9884\u6d4b\u4e0e\u526a\u679d - \u7f51\u7855\u4e92\u8054\u5e2e\u52a9\u4e2d\u5fc3","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/www.wsisp.com\/helps\/113593.html","og_locale":"zh_CN","og_type":"article","og_title":"\u673a\u5668\u5b66\u4e60\u5b66\u4e60\u7b14\u8bb0\uff08\u4e94\uff09\uff1a\u51b3\u7b56\u6811\uff0cCART \u539f\u7406\u3001\u6cf0\u5766\u5c3c\u514b\u751f\u5b58\u9884\u6d4b\u4e0e\u526a\u679d - \u7f51\u7855\u4e92\u8054\u5e2e\u52a9\u4e2d\u5fc3","og_description":"\u7cfb\u5217\u7b2c\u4e94\u7bc7\u3002\u51b3\u7b56\u6811\u662f\u53ef\u89e3\u91ca\u6027\u6700\u597d\u7684\u6a21\u578b（\u6bcf\u4e00\u5c42\u90fd\u662f\u4e00\u4e2a\"\u662f\/\u5426\"\u95ee\u9898），\u4e5f\u662f\u96c6\u6210\u5b66\u4e60（\u968f\u673a\u68ee\u6797\u3001GBDT\u3001XGBoost）\u7684\u57fa\u77f3，\u4e0b\u4e00\u7bc7\u5c31\u8981\u7528\u5b83\u3002\u8fd9\u7bc7\u7684\u91cd\u70b9\u6709\u4e24\u4e2a：CART \u6811\u7684\u5206\u88c2\u903b\u8f91（\u57fa\u5c3c\u7cfb\u6570），\u4ee5\u53ca\u6cf0\u5766\u5c3c\u514b\u6848\u4f8b\u91cc\u88ab\u5f88\u591a\u4eba\u5ffd\u7565\u7684\u6570\u636e\u9884\u5904\u7406\u548c\u8c03\u53c2\u7ec6\u8282\u3002\u672c\u7bc7\u770b\u5b8c\u8981\u56de\u7b54\u7684\u56db\u4e2a\u9762\u8bd5\u9898： \u51b3\u7b56\u6811\u600e\u4e48\u9009\u5206\u88c2\u7279\u5f81？\u57fa\u5c3c\u7cfb","og_url":"https:\/\/www.wsisp.com\/helps\/113593.html","og_site_name":"\u7f51\u7855\u4e92\u8054\u5e2e\u52a9\u4e2d\u5fc3","article_published_time":"2026-10-06T14:17:33+00:00","author":"admin","twitter_card":"summary_large_image","twitter_misc":{"\u4f5c\u8005":"admin","\u9884\u8ba1\u9605\u8bfb\u65f6\u95f4":"5 \u5206"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"WebPage","@id":"https:\/\/www.wsisp.com\/helps\/113593.html","url":"https:\/\/www.wsisp.com\/helps\/113593.html","name":"\u673a\u5668\u5b66\u4e60\u5b66\u4e60\u7b14\u8bb0\uff08\u4e94\uff09\uff1a\u51b3\u7b56\u6811\uff0cCART \u539f\u7406\u3001\u6cf0\u5766\u5c3c\u514b\u751f\u5b58\u9884\u6d4b\u4e0e\u526a\u679d - \u7f51\u7855\u4e92\u8054\u5e2e\u52a9\u4e2d\u5fc3","isPartOf":{"@id":"https:\/\/www.wsisp.com\/helps\/#website"},"datePublished":"2026-10-06T14:17:33+00:00","dateModified":"2026-10-06T14:17:33+00:00","author":{"@id":"https:\/\/www.wsisp.com\/helps\/#\/schema\/person\/358e386c577a3ab51c4493330a20ad41"},"breadcrumb":{"@id":"https:\/\/www.wsisp.com\/helps\/113593.html#breadcrumb"},"inLanguage":"zh-Hans","potentialAction":[{"@type":"ReadAction","target":["https:\/\/www.wsisp.com\/helps\/113593.html"]}]},{"@type":"BreadcrumbList","@id":"https:\/\/www.wsisp.com\/helps\/113593.html#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"\u9996\u9875","item":"https:\/\/www.wsisp.com\/helps"},{"@type":"ListItem","position":2,"name":"\u673a\u5668\u5b66\u4e60\u5b66\u4e60\u7b14\u8bb0\uff08\u4e94\uff09\uff1a\u51b3\u7b56\u6811\uff0cCART \u539f\u7406\u3001\u6cf0\u5766\u5c3c\u514b\u751f\u5b58\u9884\u6d4b\u4e0e\u526a\u679d"}]},{"@type":"WebSite","@id":"https:\/\/www.wsisp.com\/helps\/#website","url":"https:\/\/www.wsisp.com\/helps\/","name":"\u7f51\u7855\u4e92\u8054\u5e2e\u52a9\u4e2d\u5fc3","description":"\u9999\u6e2f\u670d\u52a1\u5668_\u9999\u6e2f\u4e91\u670d\u52a1\u5668\u8d44\u8baf_\u670d\u52a1\u5668\u5e2e\u52a9\u6587\u6863_\u670d\u52a1\u5668\u6559\u7a0b","potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/www.wsisp.com\/helps\/?s={search_term_string}"},"query-input":"required name=search_term_string"}],"inLanguage":"zh-Hans"},{"@type":"Person","@id":"https:\/\/www.wsisp.com\/helps\/#\/schema\/person\/358e386c577a3ab51c4493330a20ad41","name":"admin","image":{"@type":"ImageObject","inLanguage":"zh-Hans","@id":"https:\/\/www.wsisp.com\/helps\/#\/schema\/person\/image\/","url":"https:\/\/gravatar.wp-china-yes.net\/avatar\/?s=96&d=mystery","contentUrl":"https:\/\/gravatar.wp-china-yes.net\/avatar\/?s=96&d=mystery","caption":"admin"},"sameAs":["http:\/\/wp.wsisp.com"],"url":"https:\/\/www.wsisp.com\/helps\/author\/admin"}]}},"_links":{"self":[{"href":"https:\/\/www.wsisp.com\/helps\/wp-json\/wp\/v2\/posts\/113593","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.wsisp.com\/helps\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.wsisp.com\/helps\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.wsisp.com\/helps\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.wsisp.com\/helps\/wp-json\/wp\/v2\/comments?post=113593"}],"version-history":[{"count":0,"href":"https:\/\/www.wsisp.com\/helps\/wp-json\/wp\/v2\/posts\/113593\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.wsisp.com\/helps\/wp-json\/wp\/v2\/media?parent=113593"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.wsisp.com\/helps\/wp-json\/wp\/v2\/categories?post=113593"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.wsisp.com\/helps\/wp-json\/wp\/v2\/tags?post=113593"},{"taxonomy":"topic","embeddable":true,"href":"https:\/\/www.wsisp.com\/helps\/wp-json\/wp\/v2\/topic?post=113593"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}