{"id":477828,"date":"2023-08-09T09:21:11","date_gmt":"2023-08-09T09:21:11","guid":{"rendered":""},"modified":"2023-09-05T11:15:32","modified_gmt":"2023-09-05T11:15:32","slug":"lightgbm","status":"publish","type":"wiki","link":"https:\/\/oneproxy.pro\/cn\/wiki\/lightgbm\/","title":{"rendered":"\u5149GBM"},"content":{"rendered":"

LightGBM \u662f\u4e00\u4e2a\u529f\u80fd\u5f3a\u5927\u4e14\u9ad8\u6548\u7684\u5f00\u6e90\u673a\u5668\u5b66\u4e60\u5e93\uff0c\u4e13\u4e3a\u68af\u5ea6\u63d0\u5347\u800c\u8bbe\u8ba1\u3002\u5b83\u7531 Microsoft \u5f00\u53d1\uff0c\u56e0\u5176\u5904\u7406\u5927\u89c4\u6a21\u6570\u636e\u96c6\u7684\u901f\u5ea6\u548c\u9ad8\u6027\u80fd\u800c\u5728\u6570\u636e\u79d1\u5b66\u5bb6\u548c\u7814\u7a76\u4eba\u5458\u4e2d\u5e7f\u53d7\u6b22\u8fce\u3002LightGBM \u57fa\u4e8e\u68af\u5ea6\u63d0\u5347\u6846\u67b6\uff0c\u8fd9\u662f\u4e00\u79cd\u673a\u5668\u5b66\u4e60\u6280\u672f\uff0c\u5b83\u7ed3\u5408\u4e86\u5f31\u5b66\u4e60\u5668\uff08\u901a\u5e38\u662f\u51b3\u7b56\u6811\uff09\u6765\u521b\u5efa\u5f3a\u5927\u7684\u9884\u6d4b\u6a21\u578b\u3002\u5b83\u80fd\u591f\u4ee5\u51fa\u8272\u7684\u51c6\u786e\u6027\u5904\u7406\u5927\u6570\u636e\uff0c\u4f7f\u5176\u6210\u4e3a\u81ea\u7136\u8bed\u8a00\u5904\u7406\u3001\u8ba1\u7b97\u673a\u89c6\u89c9\u548c\u91d1\u878d\u5efa\u6a21\u7b49\u5404\u4e2a\u9886\u57df\u7684\u9996\u9009\u3002<\/p>\n

LightGBM \u7684\u8d77\u6e90\u5386\u53f2\u4ee5\u53ca\u9996\u6b21\u63d0\u53ca<\/h2>\n

LightGBM \u4e8e 2017 \u5e74\u7531\u5fae\u8f6f\u7814\u7a76\u4eba\u5458\u5728\u4e00\u7bc7\u9898\u4e3a\u201cLightGBM\uff1a\u4e00\u79cd\u9ad8\u6548\u7684\u68af\u5ea6\u63d0\u5347\u51b3\u7b56\u6811\u201d\u7684\u8bba\u6587\u4e2d\u9996\u6b21\u63d0\u51fa\u3002\u8be5\u8bba\u6587\u7684\u4f5c\u8005\u5305\u62ec\u90ed\u6797\u67ef\u3001\u5b5f\u5947\u3001Thomas Finley\u3001\u738b\u6cf0\u5cf0\u3001\u9648\u4f1f\u3001\u9a6c\u4f1f\u4e1c\u3001\u53f6\u5947\u4f1f\u548c\u5218\u94c1\u71d5\u3002\u8fd9\u9879\u5177\u6709\u91cc\u7a0b\u7891\u610f\u4e49\u7684\u7814\u7a76\u5c06 LightGBM \u4f5c\u4e3a\u4e00\u79cd\u65b0\u65b9\u6cd5\uff0c\u7528\u4e8e\u63d0\u9ad8\u68af\u5ea6\u63d0\u5347\u7b97\u6cd5\u7684\u6548\u7387\uff0c\u540c\u65f6\u4fdd\u6301\u5177\u6709\u7ade\u4e89\u529b\u7684\u51c6\u786e\u6027\u3002<\/p>\n

\u5173\u4e8eLightGBM\u7684\u8be6\u7ec6\u4fe1\u606f<\/h2>\n

LightGBM \u4ee5\u5176\u72ec\u7279\u7684\u529f\u80fd\u5f7b\u5e95\u6539\u53d8\u4e86\u68af\u5ea6\u63d0\u5347\u9886\u57df\u3002\u4e0e\u4f7f\u7528\u6df1\u5ea6\u6811\u589e\u957f\u7684\u4f20\u7edf\u68af\u5ea6\u63d0\u5347\u6846\u67b6\u4e0d\u540c\uff0cLightGBM \u91c7\u7528\u53f6\u5b50\u6811\u589e\u957f\u7b56\u7565\u3002\u8fd9\u79cd\u65b9\u6cd5\u5728\u6bcf\u6b21\u6811\u6269\u5c55\u671f\u95f4\u9009\u62e9\u635f\u5931\u51cf\u5c11\u6700\u591a\u7684\u53f6\u5b50\u8282\u70b9\uff0c\u4ece\u800c\u4ee5\u66f4\u5c11\u7684\u53f6\u5b50\u83b7\u5f97\u66f4\u51c6\u786e\u7684\u6a21\u578b\u3002<\/p>\n

\u6b64\u5916\uff0cLightGBM \u901a\u8fc7\u4e24\u79cd\u6280\u672f\u4f18\u5316\u5185\u5b58\u4f7f\u7528\uff1a\u57fa\u4e8e\u68af\u5ea6\u7684\u5355\u8fb9\u91c7\u6837 (GOSS) \u548c\u72ec\u5360\u7279\u5f81\u6346\u7ed1 (EFB)\u3002GOSS \u5728\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\u4ec5\u9009\u62e9\u663e\u8457\u7684\u68af\u5ea6\uff0c\u5728\u4fdd\u6301\u6a21\u578b\u7cbe\u5ea6\u7684\u540c\u65f6\u51cf\u5c11\u6570\u636e\u5b9e\u4f8b\u6570\u91cf\u3002EFB \u5c06\u72ec\u5360\u7279\u5f81\u5206\u7ec4\u4ee5\u538b\u7f29\u5185\u5b58\u5e76\u63d0\u9ad8\u6548\u7387\u3002<\/p>\n

\u8be5\u5e93\u8fd8\u652f\u6301\u5404\u79cd\u673a\u5668\u5b66\u4e60\u4efb\u52a1\uff0c\u4f8b\u5982\u56de\u5f52\u3001\u5206\u7c7b\u3001\u6392\u540d\u548c\u63a8\u8350\u7cfb\u7edf\u3002\u5b83\u4ee5 Python\u3001R \u548c C++ \u7b49\u591a\u79cd\u7f16\u7a0b\u8bed\u8a00\u63d0\u4f9b\u7075\u6d3b\u7684 API\uff0c\u4f7f\u4e0d\u540c\u5e73\u53f0\u7684\u5f00\u53d1\u4eba\u5458\u53ef\u4ee5\u8f7b\u677e\u8bbf\u95ee\u5b83\u3002<\/p>\n

LightGBM\u7684\u5185\u90e8\u7ed3\u6784\uff1aLightGBM\u7684\u5de5\u4f5c\u539f\u7406<\/h2>\n

LightGBM \u7684\u6838\u5fc3\u662f\u57fa\u4e8e\u68af\u5ea6\u63d0\u5347\u6280\u672f\uff0c\u8fd9\u662f\u4e00\u79cd\u96c6\u6210\u5b66\u4e60\u65b9\u6cd5\uff0c\u5176\u4e2d\u591a\u4e2a\u5f31\u5b66\u4e60\u5668\u7ec4\u5408\u5728\u4e00\u8d77\u5f62\u6210\u5f3a\u5927\u7684\u9884\u6d4b\u6a21\u578b\u3002LightGBM \u7684\u5185\u90e8\u7ed3\u6784\u53ef\u4ee5\u6982\u62ec\u4e3a\u4ee5\u4e0b\u6b65\u9aa4\uff1a<\/p>\n

    \n
  1. \n

    \u6570\u636e\u51c6\u5907<\/strong>\uff1aLightGBM\u9700\u8981\u5c06\u6570\u636e\u7ec4\u7ec7\u6210\u7279\u5b9a\u7684\u683c\u5f0f\uff0c\u6bd4\u5982Dataset\u6216\u8005DMatrix\uff0c\u4ee5\u589e\u5f3a\u6027\u80fd\u5e76\u51cf\u5c11\u5185\u5b58\u4f7f\u7528\u3002<\/p>\n<\/li>\n

  2. \n

    \u6811\u7684\u6784\u9020<\/strong>\uff1a\u5728\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\uff0cLightGBM \u91c7\u7528\u9010\u53f6\u6811\u751f\u957f\u7b56\u7565\u3002\u5b83\u4ece\u5355\u4e2a\u53f6\u5b50\u5f00\u59cb\u4f5c\u4e3a\u6839\u8282\u70b9\uff0c\u7136\u540e\u901a\u8fc7\u5206\u88c2\u53f6\u5b50\u8282\u70b9\u8fed\u4ee3\u6269\u5c55\u6811\uff0c\u4ee5\u6700\u5c0f\u5316\u635f\u5931\u51fd\u6570\u3002<\/p>\n<\/li>\n

  3. \n

    \u53f6\u5b50\u751f\u957f<\/strong>\uff1aLightGBM \u9009\u62e9\u63d0\u4f9b\u6700\u663e\u8457\u635f\u5931\u51cf\u5c11\u7684\u53f6\u8282\u70b9\uff0c\u4ece\u800c\u7528\u66f4\u5c11\u7684\u53f6\u5b50\u8282\u70b9\u5b9e\u73b0\u66f4\u7cbe\u786e\u7684\u6a21\u578b\u3002<\/p>\n<\/li>\n

  4. \n

    \u57fa\u4e8e\u68af\u5ea6\u7684\u5355\u4fa7\u91c7\u6837\uff08GOSS\uff09<\/strong>\uff1a\u5728\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\uff0cGOSS \u4ec5\u9009\u62e9\u91cd\u8981\u7684\u68af\u5ea6\u8fdb\u884c\u8fdb\u4e00\u6b65\u4f18\u5316\uff0c\u4ece\u800c\u5b9e\u73b0\u66f4\u5feb\u7684\u6536\u655b\u5e76\u51cf\u5c11\u8fc7\u5ea6\u62df\u5408\u3002<\/p>\n<\/li>\n

  5. \n

    \u72ec\u5bb6\u529f\u80fd\u6346\u7ed1 (EFB)<\/strong>\uff1aEFB \u7ec4\u72ec\u6709\u7684\u529f\u80fd\u4ee5\u8282\u7701\u5185\u5b58\u5e76\u52a0\u5feb\u8bad\u7ec3\u8fc7\u7a0b\u3002<\/p>\n<\/li>\n

  6. \n

    \u63d0\u5347<\/strong>\uff1a\u5f31\u5b66\u4e60\u8005\uff08\u51b3\u7b56\u6811\uff09\u6309\u987a\u5e8f\u6dfb\u52a0\u5230\u6a21\u578b\u4e2d\uff0c\u6bcf\u68f5\u65b0\u6811\u90fd\u4f1a\u7ea0\u6b63\u5176\u524d\u8f88\u7684\u9519\u8bef\u3002<\/p>\n<\/li>\n

  7. \n

    \u6b63\u5219\u5316<\/strong>\uff1aLightGBM \u91c7\u7528 L1 \u548c L2 \u6b63\u5219\u5316\u6280\u672f\u6765\u9632\u6b62\u8fc7\u5ea6\u62df\u5408\u5e76\u63d0\u9ad8\u6cdb\u5316\u80fd\u529b\u3002<\/p>\n<\/li>\n

  8. \n

    \u9884\u8a00<\/strong>\uff1a\u4e00\u65e6\u6a21\u578b\u8bad\u7ec3\u5b8c\u6210\uff0cLightGBM \u5c31\u53ef\u4ee5\u6709\u6548\u5730\u9884\u6d4b\u65b0\u6570\u636e\u7684\u7ed3\u679c\u3002<\/p>\n<\/li>\n<\/ol>\n

    LightGBM\u5173\u952e\u7279\u6027\u5206\u6790<\/h2>\n

    LightGBM \u62e5\u6709\u51e0\u4e2a\u5173\u952e\u7279\u6027\uff0c\u8fd9\u4e9b\u7279\u6027\u4f7f\u5176\u5f97\u5230\u5e7f\u6cdb\u91c7\u7528\u5e76\u53d1\u6325\u5176\u6709\u6548\u6027\uff1a<\/p>\n

      \n
    1. \n

      \u9ad8\u901f<\/strong>\uff1a\u53f6\u5b50\u6811\u751f\u957f\u548c GOSS \u4f18\u5316\u6280\u672f\u4f7f LightGBM \u6bd4\u5176\u4ed6\u68af\u5ea6\u63d0\u5347\u6846\u67b6\u5feb\u5f97\u591a\u3002<\/p>\n<\/li>\n

    2. \n

      \u5185\u5b58\u6548\u7387<\/strong>\uff1aEFB \u65b9\u6cd5\u51cf\u5c11\u4e86\u5185\u5b58\u6d88\u8017\uff0c\u4f7f\u5f97 LightGBM \u80fd\u591f\u5904\u7406\u4f7f\u7528\u4f20\u7edf\u7b97\u6cd5\u53ef\u80fd\u65e0\u6cd5\u653e\u5165\u5185\u5b58\u7684\u5927\u578b\u6570\u636e\u96c6\u3002<\/p>\n<\/li>\n

    3. \n

      \u53ef\u6269\u5c55\u6027<\/strong>\uff1aLightGBM \u53ef\u4ee5\u6709\u6548\u6269\u5c55\u4ee5\u5904\u7406\u5177\u6709\u6570\u767e\u4e07\u4e2a\u5b9e\u4f8b\u548c\u7279\u5f81\u7684\u5927\u89c4\u6a21\u6570\u636e\u96c6\u3002<\/p>\n<\/li>\n

    4. \n

      \u7075\u6d3b\u6027<\/strong>\uff1aLightGBM \u652f\u6301\u5404\u79cd\u673a\u5668\u5b66\u4e60\u4efb\u52a1\uff0c\u4f7f\u5176\u9002\u7528\u4e8e\u56de\u5f52\u3001\u5206\u7c7b\u3001\u6392\u540d\u548c\u63a8\u8350\u7cfb\u7edf\u3002<\/p>\n<\/li>\n

    5. \n

      \u51c6\u786e\u9884\u6d4b<\/strong>\uff1a\u9010\u53f6\u6811\u751f\u957f\u7b56\u7565\u901a\u8fc7\u4f7f\u7528\u66f4\u5c11\u7684\u53f6\u5b50\u6765\u63d0\u9ad8\u6a21\u578b\u7684\u9884\u6d4b\u51c6\u786e\u6027\u3002<\/p>\n<\/li>\n

    6. \n

      \u5bf9\u5206\u7c7b\u7279\u5f81\u7684\u652f\u6301<\/strong>\uff1aLightGBM \u6709\u6548\u5730\u5904\u7406\u5206\u7c7b\u7279\u5f81\uff0c\u800c\u65e0\u9700\u8fdb\u884c\u5927\u91cf\u7684\u9884\u5904\u7406\u3002<\/p>\n<\/li>\n

    7. \n

      \u5e73\u884c\u5b66\u4e60<\/strong>\uff1aLightGBM\u652f\u6301\u5e76\u884c\u8bad\u7ec3\uff0c\u5229\u7528\u591a\u6838CPU\u8fdb\u4e00\u6b65\u63d0\u5347\u5176\u6027\u80fd\u3002<\/p>\n<\/li>\n<\/ol>\n

      LightGBM \u7684\u7c7b\u578b<\/h2>\n

      \u6839\u636e\u6240\u4f7f\u7528\u7684\u589e\u5f3a\u7c7b\u578b\uff0cLightGBM \u63d0\u4f9b\u4e24\u79cd\u4e3b\u8981\u7c7b\u578b\uff1a<\/p>\n

        \n
      1. \n

        \u68af\u5ea6\u63d0\u5347\u673a\uff08GBM\uff09<\/strong>\uff1a\u8fd9\u662f LightGBM \u7684\u6807\u51c6\u5f62\u5f0f\uff0c\u4f7f\u7528\u68af\u5ea6\u63d0\u5347\u548c\u53f6\u5b50\u6811\u751f\u957f\u7b56\u7565\u3002<\/p>\n<\/li>\n

      2. \n

        \u9556<\/strong>\uff1aDart \u662f LightGBM \u7684\u4e00\u4e2a\u53d8\u4f53\uff0c\u5b83\u5728\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\u5229\u7528\u57fa\u4e8e dropout \u7684\u6b63\u5219\u5316\u3002\u5b83\u901a\u8fc7\u5728\u6bcf\u6b21\u8fed\u4ee3\u4e2d\u968f\u673a\u5220\u9664\u4e00\u4e9b\u6811\u6765\u5e2e\u52a9\u9632\u6b62\u8fc7\u5ea6\u62df\u5408\u3002<\/p>\n<\/li>\n<\/ol>\n

        \u4e0b\u9762\u662f\u4e00\u4e2a\u6bd4\u8f83\u8868\uff0c\u91cd\u70b9\u4ecb\u7ecd\u4e86 GBM \u548c Dart \u4e4b\u95f4\u7684\u4e3b\u8981\u533a\u522b\uff1a<\/p>\n\n\n\n\n\n\n\n\n
        \u65b9\u9762<\/th>\n\u68af\u5ea6\u63d0\u5347\u673a\uff08GBM\uff09<\/th>\n\u9556<\/th>\n<\/tr>\n<\/thead>\n
        \u63d0\u5347\u7b97\u6cd5<\/td>\n\u68af\u5ea6\u63d0\u5347<\/td>\n\u4f7f\u7528 Dart \u8fdb\u884c\u68af\u5ea6\u63d0\u5347<\/td>\n<\/tr>\n
        \u6b63\u5219\u5316\u6280\u672f<\/td>\nL1 \u548c L2<\/td>\n\u5e26 Dropout \u7684 L1 \u548c L2<\/td>\n<\/tr>\n
        \u9884\u9632\u8fc7\u5ea6\u62df\u5408<\/td>\n\u7f13\u548c<\/td>\n\u4f7f\u7528 Dropout \u8fdb\u884c\u6539\u8fdb<\/td>\n<\/tr>\n
        \u6811\u6728\u4fee\u526a<\/td>\n\u65e0\u9700\u4fee\u526a<\/td>\n\u57fa\u4e8e Dropout \u7684\u526a\u679d<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

        LightGBM\u7684\u4f7f\u7528\u65b9\u6cd5\u3001\u4f7f\u7528\u4e2d\u9047\u5230\u7684\u95ee\u9898\u53ca\u89e3\u51b3\u65b9\u6cd5<\/h2>\n

        LightGBM \u53ef\u4ee5\u901a\u8fc7\u591a\u79cd\u65b9\u5f0f\u7528\u4e8e\u89e3\u51b3\u4e0d\u540c\u7684\u673a\u5668\u5b66\u4e60\u4efb\u52a1\uff1a<\/p>\n

          \n
        1. \n

          \u5206\u7c7b<\/strong>\uff1a\u4f7f\u7528LightGBM\u8fdb\u884c\u4e8c\u5143\u6216\u591a\u7c7b\u5206\u7c7b\u95ee\u9898\uff0c\u4f8b\u5982\u5783\u573e\u90ae\u4ef6\u68c0\u6d4b\u3001\u60c5\u611f\u5206\u6790\u548c\u56fe\u50cf\u8bc6\u522b\u3002<\/p>\n<\/li>\n

        2. \n

          \u56de\u5f52<\/strong>\uff1a\u5c06 LightGBM \u5e94\u7528\u4e8e\u56de\u5f52\u4efb\u52a1\uff0c\u4f8b\u5982\u9884\u6d4b\u623f\u4ef7\u3001\u80a1\u7968\u5e02\u573a\u4ef7\u503c\u6216\u6e29\u5ea6\u9884\u6d4b\u3002<\/p>\n<\/li>\n

        3. \n

          \u6392\u884c<\/strong>\uff1a\u5229\u7528LightGBM\u6784\u5efa\u6392\u540d\u7cfb\u7edf\uff0c\u4f8b\u5982\u641c\u7d22\u5f15\u64ce\u7ed3\u679c\u6392\u540d\u6216\u63a8\u8350\u7cfb\u7edf\u3002<\/p>\n<\/li>\n

        4. \n

          \u63a8\u8350\u7cfb\u7edf<\/strong>\uff1aLightGBM \u53ef\u4ee5\u4e3a\u4e2a\u6027\u5316\u63a8\u8350\u5f15\u64ce\u63d0\u4f9b\u652f\u6301\uff0c\u5411\u7528\u6237\u63a8\u8350\u4ea7\u54c1\u3001\u7535\u5f71\u6216\u97f3\u4e50\u3002<\/p>\n<\/li>\n<\/ol>\n

          \u5c3d\u7ba1 LightGBM \u5177\u6709\u8bf8\u591a\u4f18\u70b9\uff0c\u4f46\u7528\u6237\u5728\u4f7f\u7528\u65f6\u4ecd\u53ef\u80fd\u4f1a\u9047\u5230\u4e00\u4e9b\u6311\u6218\uff1a<\/p>\n

            \n
          1. \n

            \u4e0d\u5e73\u8861\u7684\u6570\u636e\u96c6<\/strong>\uff1aLightGBM \u53ef\u80fd\u4f1a\u96be\u4ee5\u5904\u7406\u4e0d\u5e73\u8861\u7684\u6570\u636e\u96c6\uff0c\u4ece\u800c\u5bfc\u81f4\u9884\u6d4b\u51fa\u73b0\u504f\u5dee\u3002\u4e00\u79cd\u89e3\u51b3\u65b9\u6848\u662f\u5728\u8bad\u7ec3\u671f\u95f4\u4f7f\u7528\u7c7b\u6743\u91cd\u6216\u91c7\u6837\u6280\u672f\u6765\u5e73\u8861\u6570\u636e\u3002<\/p>\n<\/li>\n

          2. \n

            \u8fc7\u62df\u5408<\/strong>\uff1a\u867d\u7136 LightGBM \u91c7\u7528\u6b63\u5219\u5316\u6280\u672f\u6765\u9632\u6b62\u8fc7\u5ea6\u62df\u5408\uff0c\u4f46\u5982\u679c\u6570\u636e\u4e0d\u8db3\u6216\u6a21\u578b\u8fc7\u4e8e\u590d\u6742\uff0c\u4ecd\u7136\u53ef\u80fd\u4f1a\u51fa\u73b0\u8fc7\u5ea6\u62df\u5408\u3002\u4ea4\u53c9\u9a8c\u8bc1\u548c\u8d85\u53c2\u6570\u8c03\u6574\u53ef\u4ee5\u5e2e\u52a9\u7f13\u89e3\u6b64\u95ee\u9898\u3002<\/p>\n<\/li>\n

          3. \n

            \u8d85\u53c2\u6570\u8c03\u4f18<\/strong>\uff1aLightGBM \u7684\u6027\u80fd\u5f88\u5927\u7a0b\u5ea6\u4e0a\u53d6\u51b3\u4e8e\u8d85\u53c2\u6570\u7684\u8c03\u6574\u3002\u53ef\u4ee5\u4f7f\u7528\u7f51\u683c\u641c\u7d22\u6216\u8d1d\u53f6\u65af\u4f18\u5316\u6765\u627e\u5230\u6700\u4f73\u7684\u8d85\u53c2\u6570\u7ec4\u5408\u3002<\/p>\n<\/li>\n

          4. \n

            \u6570\u636e\u9884\u5904\u7406<\/strong>\uff1a\u5206\u7c7b\u7279\u5f81\u9700\u8981\u9002\u5f53\u7684\u7f16\u7801\uff0c\u5e76\u4e14\u5728\u5c06\u7f3a\u5931\u6570\u636e\u8f93\u5165\u5230LightGBM\u4e4b\u524d\u5e94\u8be5\u5bf9\u5176\u8fdb\u884c\u9002\u5f53\u7684\u5904\u7406\u3002<\/p>\n<\/li>\n<\/ol>\n

            \u4e3b\u8981\u7279\u70b9\u53ca\u4e0e\u540c\u7c7b\u672f\u8bed\u7684\u5176\u4ed6\u6bd4\u8f83<\/h2>\n

            \u8ba9\u6211\u4eec\u5c06 LightGBM \u4e0e\u5176\u4ed6\u4e00\u4e9b\u6d41\u884c\u7684\u68af\u5ea6\u589e\u5f3a\u5e93\u8fdb\u884c\u6bd4\u8f83\uff1a<\/p>\n\n\n\n\n\n\n\n\n\n
            \u7279\u5f81<\/th>\n\u5149GBM<\/th>\nXGBoost<\/th>\nCatBoost<\/th>\n<\/tr>\n<\/thead>\n
            \u6811\u6728\u751f\u957f\u7b56\u7565<\/td>\n\u53f6\u5b50\u7ea7<\/td>\n\u9010\u7ea7<\/td>\n\u5bf9\u79f0<\/td>\n<\/tr>\n
            \u5185\u5b58\u4f7f\u7528\u60c5\u51b5<\/td>\n\u9ad8\u6548\u7684<\/td>\n\u7f13\u548c<\/td>\n\u7f13\u548c<\/td>\n<\/tr>\n
            \u5206\u7c7b\u652f\u6301<\/td>\n\u662f\u7684<\/td>\n\u6709\u9650\u7684<\/td>\n\u662f\u7684<\/td>\n<\/tr>\n
            GPU \u52a0\u901f<\/td>\n\u662f\u7684<\/td>\n\u662f\u7684<\/td>\n\u6709\u9650\u7684<\/td>\n<\/tr>\n
            \u8868\u73b0<\/td>\n\u5feb\u70b9<\/td>\n\u6bd4 LGBM \u6162<\/td>\n\u53ef\u6bd4<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

            LightGBM \u5728\u901f\u5ea6\u4e0a\u4f18\u4e8e XGBoost\uff0c\u800c CatBoost \u4e0e LightGBM \u7684\u6027\u80fd\u6bd4\u8f83\u63a5\u8fd1\u3002LightGBM \u5728\u5904\u7406\u5927\u6570\u636e\u96c6\u548c\u9ad8\u6548\u5229\u7528\u5185\u5b58\u65b9\u9762\u8868\u73b0\u4f18\u5f02\uff0c\u662f\u5927\u6570\u636e\u573a\u666f\u7684\u9996\u9009\u3002<\/p>\n

            \u4e0e LightGBM \u76f8\u5173\u7684\u672a\u6765\u89c2\u70b9\u548c\u6280\u672f<\/h2>\n

            \u968f\u7740\u673a\u5668\u5b66\u4e60\u9886\u57df\u7684\u53d1\u5c55\uff0cLightGBM \u53ef\u80fd\u4f1a\u5f97\u5230\u8fdb\u4e00\u6b65\u7684\u6539\u8fdb\u548c\u53d1\u5c55\u3002\u4e00\u4e9b\u6f5c\u5728\u7684\u672a\u6765\u53d1\u5c55\u5305\u62ec\uff1a<\/p>\n

              \n
            1. \n

              \u589e\u5f3a\u6b63\u5219\u5316\u6280\u672f<\/strong>\uff1a\u7814\u7a76\u4eba\u5458\u53ef\u80fd\u4f1a\u63a2\u7d22\u66f4\u590d\u6742\u7684\u6b63\u5219\u5316\u65b9\u6cd5\u6765\u589e\u5f3a\u6a21\u578b\u6982\u62ec\u548c\u5904\u7406\u590d\u6742\u6570\u636e\u96c6\u7684\u80fd\u529b\u3002<\/p>\n<\/li>\n

            2. \n

              \u795e\u7ecf\u7f51\u7edc\u7684\u96c6\u6210<\/strong>\uff1a\u53ef\u80fd\u4f1a\u5c1d\u8bd5\u5c06\u795e\u7ecf\u7f51\u7edc\u548c\u6df1\u5ea6\u5b66\u4e60\u67b6\u6784\u4e0e LightGBM \u7b49\u68af\u5ea6\u589e\u5f3a\u6846\u67b6\u76f8\u7ed3\u5408\uff0c\u4ee5\u63d0\u9ad8\u6027\u80fd\u548c\u7075\u6d3b\u6027\u3002<\/p>\n<\/li>\n

            3. \n

              \u81ea\u52a8\u673a\u5668\u5b66\u4e60\u96c6\u6210<\/strong>\uff1aLightGBM \u53ef\u4ee5\u96c6\u6210\u5230\u81ea\u52a8\u5316\u673a\u5668\u5b66\u4e60 (AutoML) \u5e73\u53f0\u4e2d\uff0c\u4f7f\u975e\u4e13\u5bb6\u80fd\u591f\u5229\u7528\u5176\u529f\u80fd\u5b8c\u6210\u5404\u79cd\u4efb\u52a1\u3002<\/p>\n<\/li>\n

            4. \n

              \u652f\u6301\u5206\u5e03\u5f0f\u8ba1\u7b97<\/strong>\uff1a\u4f7fLightGBM\u80fd\u591f\u5728Apache Spark\u7b49\u5206\u5e03\u5f0f\u8ba1\u7b97\u6846\u67b6\u4e0a\u8fd0\u884c\u7684\u52aa\u529b\u53ef\u4ee5\u8fdb\u4e00\u6b65\u63d0\u9ad8\u5927\u6570\u636e\u573a\u666f\u7684\u53ef\u6269\u5c55\u6027\u3002<\/p>\n<\/li>\n<\/ol>\n

              \u5982\u4f55\u4f7f\u7528\u4ee3\u7406\u670d\u52a1\u5668\u6216\u5c06\u5176\u4e0e LightGBM \u5173\u8054<\/h2>\n

              \u5728\u5404\u79cd\u573a\u666f\u4e2d\u4f7f\u7528 LightGBM \u65f6\uff0c\u4ee3\u7406\u670d\u52a1\u5668\u53ef\u4ee5\u53d1\u6325\u81f3\u5173\u91cd\u8981\u7684\u4f5c\u7528\uff1a<\/p>\n

                \n
              1. \n

                \u6570\u636e\u6293\u53d6<\/strong>\uff1a\u5728\u4e3a\u673a\u5668\u5b66\u4e60\u4efb\u52a1\u6536\u96c6\u6570\u636e\u65f6\uff0c\u53ef\u4ee5\u4f7f\u7528\u4ee3\u7406\u670d\u52a1\u5668\u4ece\u7f51\u7ad9\u6293\u53d6\u4fe1\u606f\uff0c\u540c\u65f6\u9632\u6b62 IP \u963b\u6b62\u6216\u901f\u7387\u9650\u5236\u95ee\u9898\u3002<\/p>\n<\/li>\n

              2. \n

                \u6570\u636e\u9690\u79c1<\/strong>\uff1a\u4ee3\u7406\u670d\u52a1\u5668\u53ef\u4ee5\u5728\u6a21\u578b\u8bad\u7ec3\u671f\u95f4\u533f\u540d\u5316\u7528\u6237\u7684 IP \u5730\u5740\u6765\u589e\u5f3a\u6570\u636e\u9690\u79c1\uff0c\u5c24\u5176\u662f\u5728\u6570\u636e\u4fdd\u62a4\u81f3\u5173\u91cd\u8981\u7684\u5e94\u7528\u4e2d\u3002<\/p>\n<\/li>\n

              3. \n

                \u5206\u5e03\u5f0f\u8bad\u7ec3<\/strong>\uff1a\u5bf9\u4e8e\u5206\u5e03\u5f0f\u673a\u5668\u5b66\u4e60\u8bbe\u7f6e\uff0c\u53ef\u4ee5\u5229\u7528\u4ee3\u7406\u670d\u52a1\u5668\u6765\u7ba1\u7406\u8282\u70b9\u4e4b\u95f4\u7684\u901a\u4fe1\uff0c\u4fc3\u8fdb\u4e0d\u540c\u4f4d\u7f6e\u4e4b\u95f4\u7684\u534f\u4f5c\u8bad\u7ec3\u3002<\/p>\n<\/li>\n

              4. \n

                \u8d1f\u8f7d\u5747\u8861<\/strong>\uff1a\u4ee3\u7406\u670d\u52a1\u5668\u53ef\u4ee5\u5c06\u4f20\u5165\u7684\u8bf7\u6c42\u5206\u53d1\u5230\u591a\u4e2a LightGBM \u5b9e\u4f8b\uff0c\u4f18\u5316\u8ba1\u7b97\u8d44\u6e90\u7684\u4f7f\u7528\u5e76\u63d0\u9ad8\u6574\u4f53\u6027\u80fd\u3002<\/p>\n<\/li>\n<\/ol>\n

                \u76f8\u5173\u94fe\u63a5<\/h2>\n

                \u6709\u5173 LightGBM \u7684\u66f4\u591a\u4fe1\u606f\uff0c\u8bf7\u8003\u8651\u63a2\u7d22\u4ee5\u4e0b\u8d44\u6e90\uff1a<\/p>\n

                  \n
                1. \n

                  LightGBM \u5b98\u65b9 GitHub \u4ed3\u5e93<\/a>\uff1a\u8bbf\u95ee LightGBM \u7684\u6e90\u4ee3\u7801\u3001\u6587\u6863\u548c\u95ee\u9898\u8ddf\u8e2a\u5668\u3002<\/p>\n<\/li>\n

                2. \n

                  \u5fae\u8f6f\u5173\u4e8e LightGBM \u7684\u7814\u7a76\u8bba\u6587<\/a>\uff1a\u9605\u8bfb\u4ecb\u7ecdLightGBM\u7684\u539f\u59cb\u7814\u7a76\u8bba\u6587\u3002<\/p>\n<\/li>\n

                3. \n

                  LightGBM \u6587\u6863<\/a>\uff1a\u8bf7\u53c2\u9605\u5b98\u65b9\u6587\u6863\uff0c\u4e86\u89e3\u8be6\u7ec6\u7684\u4f7f\u7528\u8bf4\u660e\u3001API \u53c2\u8003\u548c\u6559\u7a0b\u3002<\/p>\n<\/li>\n

                4. \n

                  Kaggle \u7ade\u8d5b<\/a>\uff1a\u63a2\u7d22\u5e7f\u6cdb\u4f7f\u7528 LightGBM \u7684 Kaggle \u7ade\u8d5b\uff0c\u5e76\u4ece\u793a\u4f8b\u7b14\u8bb0\u672c\u548c\u5185\u6838\u4e2d\u5b66\u4e60\u3002<\/p>\n<\/li>\n<\/ol>\n

                  \u901a\u8fc7\u5229\u7528 LightGBM \u7684\u5f3a\u5927\u529f\u80fd\u5e76\u4e86\u89e3\u5176\u7ec6\u5fae\u5dee\u522b\uff0c\u6570\u636e\u79d1\u5b66\u5bb6\u548c\u7814\u7a76\u4eba\u5458\u53ef\u4ee5\u589e\u5f3a\u4ed6\u4eec\u7684\u673a\u5668\u5b66\u4e60\u6a21\u578b\uff0c\u5e76\u5728\u5e94\u5bf9\u590d\u6742\u7684\u73b0\u5b9e\u6311\u6218\u4e2d\u83b7\u5f97\u7ade\u4e89\u4f18\u52bf\u3002\u65e0\u8bba\u662f\u7528\u4e8e\u5927\u89c4\u6a21\u6570\u636e\u5206\u6790\u3001\u51c6\u786e\u9884\u6d4b\u8fd8\u662f\u4e2a\u6027\u5316\u63a8\u8350\uff0cLightGBM \u90fd\u7ee7\u7eed\u4ee5\u5176\u5353\u8d8a\u7684\u901f\u5ea6\u548c\u6548\u7387\u4e3a AI \u793e\u533a\u63d0\u4f9b\u652f\u6301\u3002<\/p>","protected":false},"featured_media":468775,"menu_order":0,"template":"","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"class_list":["post-477828","wiki","type-wiki","status-publish","has-post-thumbnail","hentry"],"acf":{"faq_title":"Frequently Asked Questions about LightGBM: Boosting Performance with Speed and Efficiency<\/mark>","faq_items":[{"question":"What is LightGBM?","answer":"

                  LightGBM is a powerful and efficient open-source machine learning library designed for gradient boosting. It is developed by Microsoft and is widely used for handling large-scale datasets with high accuracy.<\/p>"},{"question":"How did LightGBM originate?","answer":"

                  LightGBM was introduced in 2017 by Microsoft researchers in a paper titled \"LightGBM: A Highly Efficient Gradient Boosting Decision Tree.\" The paper presented LightGBM as a novel method for boosting efficiency in gradient boosting algorithms.<\/p>"},{"question":"How does LightGBM work?","answer":"

                  LightGBM operates on the gradient boosting technique with a leaf-wise tree growth strategy. It selects the leaf node with the maximum loss reduction during each tree expansion, resulting in a more accurate model with fewer leaves. The library optimizes memory usage through techniques like Gradient-based One-Side Sampling (GOSS) and Exclusive Feature Bundling (EFB).<\/p>"},{"question":"What are the key features of LightGBM?","answer":"

                  LightGBM boasts high speed, memory efficiency, scalability, and flexibility. Its leaf-wise tree growth strategy enhances predictive accuracy, and it supports various machine learning tasks, such as regression, classification, ranking, and recommendation systems.<\/p>"},{"question":"What types of LightGBM are there?","answer":"

                  LightGBM offers two main types: Gradient Boosting Machine (GBM) and Dart. GBM uses leaf-wise tree growth, while Dart includes dropout-based regularization to prevent overfitting.<\/p>"},{"question":"How can LightGBM be used?","answer":"

                  LightGBM is versatile and can be used for classification, regression, ranking, and recommendation systems. It is effective in handling large datasets and provides accurate predictions.<\/p>"},{"question":"What are the challenges in using LightGBM?","answer":"

                  Users may face challenges with imbalanced datasets, overfitting, hyperparameter tuning, and data preprocessing. However, solutions like class weights, cross-validation, and proper data handling can help mitigate these issues.<\/p>"},{"question":"How does LightGBM compare to other gradient boosting libraries?","answer":"

                  In comparison to XGBoost and CatBoost, LightGBM stands out with its faster speed and efficient memory usage. It excels in handling large datasets and offers similar performance to CatBoost.<\/p>"},{"question":"What does the future hold for LightGBM?","answer":"

                  The future of LightGBM may involve enhanced regularization techniques, integration with neural networks, AutoML support, and distributed computing capabilities to further improve its performance.<\/p>"},{"question":"How can proxy servers be associated with LightGBM?","answer":"

                  Proxy servers can be beneficial in data scraping, data privacy, distributed training, and load balancing when using LightGBM for machine learning tasks.<\/p>

                  For more detailed information, please refer to the article above.<\/p>"}]},"_links":{"self":[{"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/wiki\/477828","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/wiki"}],"about":[{"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/types\/wiki"}],"version-history":[{"count":0,"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/wiki\/477828\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/media\/468775"}],"wp:attachment":[{"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/media?parent=477828"}],"curies":[{"name":"\u53ef\u6e7f\u6027\u7c89\u5242","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}