{"id":475967,"date":"2023-08-09T07:24:43","date_gmt":"2023-08-09T07:24:43","guid":{"rendered":""},"modified":"2023-09-05T11:11:43","modified_gmt":"2023-09-05T11:11:43","slug":"bagging","status":"publish","type":"wiki","link":"https:\/\/oneproxy.pro\/jp\/wiki\/bagging\/","title":{"rendered":"\u888b\u8a70\u3081"},"content":{"rendered":"

\u30d0\u30ae\u30f3\u30b0 (Bootstrap Aggregating \u306e\u7565) \u306f\u3001\u6a5f\u68b0\u5b66\u7fd2\u3067\u4e88\u6e2c\u30e2\u30c7\u30eb\u306e\u7cbe\u5ea6\u3068\u5b89\u5b9a\u6027\u3092\u5411\u4e0a\u3055\u305b\u308b\u305f\u3081\u306b\u4f7f\u7528\u3055\u308c\u308b\u5f37\u529b\u306a\u30a2\u30f3\u30b5\u30f3\u30d6\u30eb\u5b66\u7fd2\u624b\u6cd5\u3067\u3059\u3002\u30d0\u30ae\u30f3\u30b0\u3067\u306f\u3001\u540c\u3058\u57fa\u672c\u5b66\u7fd2\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u306e\u8907\u6570\u306e\u30a4\u30f3\u30b9\u30bf\u30f3\u30b9\u3092\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0 \u30c7\u30fc\u30bf\u306e\u7570\u306a\u308b\u30b5\u30d6\u30bb\u30c3\u30c8\u3067\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u3057\u3001\u6295\u7968\u307e\u305f\u306f\u5e73\u5747\u5316\u306b\u3088\u3063\u3066\u4e88\u6e2c\u3092\u7d44\u307f\u5408\u308f\u305b\u307e\u3059\u3002\u30d0\u30ae\u30f3\u30b0\u306f\u3055\u307e\u3056\u307e\u306a\u5206\u91ce\u3067\u5e83\u304f\u4f7f\u7528\u3055\u308c\u3066\u304a\u308a\u3001\u904e\u5270\u9069\u5408\u3092\u6e1b\u3089\u3057\u3001\u30e2\u30c7\u30eb\u306e\u4e00\u822c\u5316\u3092\u5f37\u5316\u3059\u308b\u306e\u306b\u52b9\u679c\u7684\u3067\u3042\u308b\u3053\u3068\u304c\u8a3c\u660e\u3055\u308c\u3066\u3044\u307e\u3059\u3002<\/p>\n

\u30d0\u30ae\u30f3\u30b0\u306e\u8d77\u6e90\u306e\u6b74\u53f2\u3068\u305d\u306e\u6700\u521d\u306e\u8a00\u53ca<\/h2>\n

\u30d0\u30ae\u30f3\u30b0\u306e\u6982\u5ff5\u306f\u3001\u4e0d\u5b89\u5b9a\u306a\u63a8\u5b9a\u5024\u306e\u5206\u6563\u3092\u6e1b\u3089\u3059\u65b9\u6cd5\u3068\u3057\u3066\u30011994 \u5e74\u306b\u30ec\u30aa\u30fb\u30d6\u30ec\u30a4\u30de\u30f3\u306b\u3088\u3063\u3066\u521d\u3081\u3066\u5c0e\u5165\u3055\u308c\u307e\u3057\u305f\u3002\u30d6\u30ec\u30a4\u30de\u30f3\u306e\u72ec\u5275\u7684\u306a\u8ad6\u6587\u300c\u30d0\u30ae\u30f3\u30b0\u4e88\u6e2c\u5b50\u300d\u306f\u3001\u3053\u306e\u30a2\u30f3\u30b5\u30f3\u30d6\u30eb\u624b\u6cd5\u306e\u57fa\u790e\u3092\u7bc9\u304d\u307e\u3057\u305f\u3002\u5c0e\u5165\u4ee5\u6765\u3001\u30d0\u30ae\u30f3\u30b0\u306f\u4eba\u6c17\u3092\u535a\u3057\u3001\u6a5f\u68b0\u5b66\u7fd2\u306e\u5206\u91ce\u3067\u57fa\u672c\u7684\u306a\u624b\u6cd5\u3068\u306a\u3063\u3066\u3044\u307e\u3059\u3002<\/p>\n

\u30d0\u30ae\u30f3\u30b0\u306b\u95a2\u3059\u308b\u8a73\u7d30\u60c5\u5831<\/h2>\n

\u30d0\u30ae\u30f3\u30b0\u3067\u306f\u3001\u30e9\u30f3\u30c0\u30e0 \u30b5\u30f3\u30d7\u30ea\u30f3\u30b0\u3068\u7f6e\u63db\u306b\u3088\u3063\u3066\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0 \u30c7\u30fc\u30bf\u306e\u8907\u6570\u306e\u30b5\u30d6\u30bb\u30c3\u30c8 (\u30d0\u30c3\u30b0) \u304c\u4f5c\u6210\u3055\u308c\u307e\u3059\u3002\u5404\u30b5\u30d6\u30bb\u30c3\u30c8\u306f\u3001\u57fa\u672c\u5b66\u7fd2\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u306e\u500b\u5225\u306e\u30a4\u30f3\u30b9\u30bf\u30f3\u30b9\u3092\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u3059\u308b\u305f\u3081\u306b\u4f7f\u7528\u3055\u308c\u307e\u3059\u3002\u57fa\u672c\u5b66\u7fd2\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u306f\u3001\u6c7a\u5b9a\u6728\u3001\u30cb\u30e5\u30fc\u30e9\u30eb \u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u3001\u30b5\u30dd\u30fc\u30c8 \u30d9\u30af\u30bf\u30fc \u30de\u30b7\u30f3\u306a\u3069\u3001\u8907\u6570\u306e\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0 \u30bb\u30c3\u30c8\u3092\u30b5\u30dd\u30fc\u30c8\u3059\u308b\u4efb\u610f\u306e\u30e2\u30c7\u30eb\u306b\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/p>\n

\u30a2\u30f3\u30b5\u30f3\u30d6\u30eb \u30e2\u30c7\u30eb\u306e\u6700\u7d42\u7684\u306a\u4e88\u6e2c\u306f\u3001\u30d9\u30fc\u30b9 \u30e2\u30c7\u30eb\u306e\u500b\u3005\u306e\u4e88\u6e2c\u3092\u96c6\u7d04\u3059\u308b\u3053\u3068\u306b\u3088\u3063\u3066\u884c\u308f\u308c\u307e\u3059\u3002\u5206\u985e\u30bf\u30b9\u30af\u306e\u5834\u5408\u3001\u4e00\u822c\u306b\u591a\u6570\u6c7a\u30b9\u30ad\u30fc\u30e0\u304c\u4f7f\u7528\u3055\u308c\u307e\u3059\u304c\u3001\u56de\u5e30\u30bf\u30b9\u30af\u306e\u5834\u5408\u3001\u4e88\u6e2c\u306f\u5e73\u5747\u5316\u3055\u308c\u307e\u3059\u3002<\/p>\n

Bagging \u306e\u5185\u90e8\u69cb\u9020: Bagging \u306e\u4ed5\u7d44\u307f<\/h2>\n

Bagging \u306e\u52d5\u4f5c\u539f\u7406\u306f\u3001\u6b21\u306e\u30b9\u30c6\u30c3\u30d7\u306b\u5206\u985e\u3067\u304d\u307e\u3059\u3002<\/p>\n

    \n
  1. \n

    \u30d6\u30fc\u30c8\u30b9\u30c8\u30e9\u30c3\u30d7\u30b5\u30f3\u30d7\u30ea\u30f3\u30b0<\/strong>: \u30c8\u30ec\u30fc\u30cb\u30f3\u30b0 \u30c7\u30fc\u30bf\u306e\u30e9\u30f3\u30c0\u30e0\u306a\u30b5\u30d6\u30bb\u30c3\u30c8\u306f\u3001\u7f6e\u63db\u3092\u4f34\u3046\u30b5\u30f3\u30d7\u30ea\u30f3\u30b0\u306b\u3088\u3063\u3066\u4f5c\u6210\u3055\u308c\u307e\u3059\u3002\u5404\u30b5\u30d6\u30bb\u30c3\u30c8\u306f\u3001\u5143\u306e\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0 \u30bb\u30c3\u30c8\u3068\u540c\u3058\u30b5\u30a4\u30ba\u3067\u3059\u3002<\/p>\n<\/li>\n

  2. \n

    \u30d9\u30fc\u30b9\u30e2\u30c7\u30eb\u306e\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0<\/strong>: \u500b\u5225\u306e\u57fa\u672c\u5b66\u7fd2\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u304c\u5404\u30d6\u30fc\u30c8\u30b9\u30c8\u30e9\u30c3\u30d7 \u30b5\u30f3\u30d7\u30eb\u3067\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u3055\u308c\u307e\u3059\u3002\u57fa\u672c\u30e2\u30c7\u30eb\u306f\u72ec\u7acb\u3057\u3066\u4e26\u884c\u3057\u3066\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u3055\u308c\u307e\u3059\u3002<\/p>\n<\/li>\n

  3. \n

    \u4e88\u6e2c\u306e\u96c6\u8a08<\/strong>: \u5206\u985e\u30bf\u30b9\u30af\u306e\u5834\u5408\u3001\u500b\u3005\u306e\u30e2\u30c7\u30eb\u4e88\u6e2c\u306e\u30e2\u30fc\u30c9 (\u6700\u3082\u983b\u7e41\u306a\u4e88\u6e2c) \u304c\u6700\u7d42\u7684\u306a\u30a2\u30f3\u30b5\u30f3\u30d6\u30eb\u4e88\u6e2c\u3068\u3057\u3066\u63a1\u7528\u3055\u308c\u307e\u3059\u3002\u56de\u5e30\u30bf\u30b9\u30af\u3067\u306f\u3001\u4e88\u6e2c\u304c\u5e73\u5747\u3055\u308c\u3066\u6700\u7d42\u7684\u306a\u4e88\u6e2c\u304c\u5f97\u3089\u308c\u307e\u3059\u3002<\/p>\n<\/li>\n<\/ol>\n

    Bagging \u306e\u4e3b\u306a\u6a5f\u80fd\u306e\u5206\u6790<\/h2>\n

    \u30d0\u30ae\u30f3\u30b0\u306b\u306f\u3001\u305d\u306e\u6709\u52b9\u6027\u306b\u5bc4\u4e0e\u3059\u308b\u3044\u304f\u3064\u304b\u306e\u91cd\u8981\u306a\u6a5f\u80fd\u304c\u3042\u308a\u307e\u3059\u3002<\/p>\n

      \n
    1. \n

      \u5dee\u7570\u306e\u524a\u6e1b<\/strong>: \u30d0\u30ae\u30f3\u30b0\u306f\u3001\u30c7\u30fc\u30bf\u306e\u7570\u306a\u308b\u30b5\u30d6\u30bb\u30c3\u30c8\u3067\u8907\u6570\u306e\u30e2\u30c7\u30eb\u3092\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u3059\u308b\u3053\u3068\u306b\u3088\u308a\u3001\u30a2\u30f3\u30b5\u30f3\u30d6\u30eb\u306e\u5206\u6563\u3092\u4f4e\u6e1b\u3057\u3001\u30a2\u30f3\u30b5\u30f3\u30d6\u30eb\u3092\u3088\u308a\u5805\u7262\u306b\u3057\u3001\u904e\u5b66\u7fd2\u3092\u8d77\u3053\u3057\u306b\u304f\u304f\u3057\u307e\u3059\u3002<\/p>\n<\/li>\n

    2. \n

      \u30e2\u30c7\u30eb\u306e\u591a\u69d8\u6027<\/strong>: \u30d0\u30ae\u30f3\u30b0\u306f\u3001\u5404\u30e2\u30c7\u30eb\u304c\u30c7\u30fc\u30bf\u306e\u7570\u306a\u308b\u30b5\u30d6\u30bb\u30c3\u30c8\u3067\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u3055\u308c\u308b\u305f\u3081\u3001\u57fa\u672c\u30e2\u30c7\u30eb\u9593\u306e\u591a\u69d8\u6027\u3092\u4fc3\u9032\u3057\u307e\u3059\u3002\u3053\u306e\u591a\u69d8\u6027\u306f\u3001\u30c7\u30fc\u30bf\u306b\u5b58\u5728\u3059\u308b\u3055\u307e\u3056\u307e\u306a\u30d1\u30bf\u30fc\u30f3\u3084\u30cb\u30e5\u30a2\u30f3\u30b9\u3092\u6349\u3048\u308b\u306e\u306b\u5f79\u7acb\u3061\u307e\u3059\u3002<\/p>\n<\/li>\n

    3. \n

      \u4e26\u5217\u5316<\/strong>: Bagging \u306e\u57fa\u672c\u30e2\u30c7\u30eb\u306f\u72ec\u7acb\u3057\u3066\u4e26\u884c\u3057\u3066\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u3055\u308c\u308b\u305f\u3081\u3001\u8a08\u7b97\u52b9\u7387\u304c\u9ad8\u304f\u3001\u5927\u898f\u6a21\u306a\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306b\u9069\u3057\u3066\u3044\u307e\u3059\u3002<\/p>\n<\/li>\n<\/ol>\n

      \u888b\u8a70\u3081\u306e\u7a2e\u985e<\/h2>\n

      \u30d0\u30ae\u30f3\u30b0\u306b\u306f\u3001\u4f7f\u7528\u3059\u308b\u30b5\u30f3\u30d7\u30ea\u30f3\u30b0\u6226\u7565\u3068\u57fa\u672c\u30e2\u30c7\u30eb\u306b\u5fdc\u3058\u3066\u3001\u3055\u307e\u3056\u307e\u306a\u30d0\u30ea\u30a8\u30fc\u30b7\u30e7\u30f3\u304c\u3042\u308a\u307e\u3059\u3002\u4e00\u822c\u7684\u306a\u30d0\u30ae\u30f3\u30b0\u306e\u7a2e\u985e\u306b\u306f\u6b21\u306e\u3088\u3046\u306a\u3082\u306e\u304c\u3042\u308a\u307e\u3059\u3002<\/p>\n\n\n\n\n\n\n\n\n
      \u30bf\u30a4\u30d7<\/th>\n\u8aac\u660e<\/th>\n<\/tr>\n<\/thead>\n
      \u30d6\u30fc\u30c8\u30b9\u30c8\u30e9\u30c3\u30d7\u96c6\u7d04<\/td>\n\u30d6\u30fc\u30c8\u30b9\u30c8\u30e9\u30c3\u30d7 \u30b5\u30f3\u30d7\u30ea\u30f3\u30b0\u306b\u3088\u308b\u6a19\u6e96\u30d0\u30ae\u30f3\u30b0<\/td>\n<\/tr>\n
      \u30e9\u30f3\u30c0\u30e0\u90e8\u5206\u7a7a\u9593\u6cd5<\/td>\n\u6a5f\u80fd\u306f\u30d9\u30fc\u30b9\u30e2\u30c7\u30eb\u3054\u3068\u306b\u30e9\u30f3\u30c0\u30e0\u306b\u30b5\u30f3\u30d7\u30ea\u30f3\u30b0\u3055\u308c\u307e\u3059<\/td>\n<\/tr>\n
      \u30e9\u30f3\u30c0\u30e0\u30d1\u30c3\u30c1<\/td>\n\u30a4\u30f3\u30b9\u30bf\u30f3\u30b9\u3068\u6a5f\u80fd\u306e\u4e21\u65b9\u306e\u30e9\u30f3\u30c0\u30e0\u306a\u30b5\u30d6\u30bb\u30c3\u30c8<\/td>\n<\/tr>\n
      \u30e9\u30f3\u30c0\u30e0\u30d5\u30a9\u30ec\u30b9\u30c8<\/td>\n\u30c7\u30b7\u30b8\u30e7\u30f3 \u30c4\u30ea\u30fc\u3092\u30d9\u30fc\u30b9 \u30e2\u30c7\u30eb\u3068\u3057\u3066\u4f7f\u7528\u3057\u305f\u30d0\u30ae\u30f3\u30b0<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

      \u30d0\u30ae\u30f3\u30b0\u306e\u4f7f\u3044\u65b9\u3001\u4f7f\u7528\u4e0a\u306e\u554f\u984c\u70b9\u3068\u305d\u306e\u89e3\u6c7a\u7b56<\/h2>\n

      \u30d0\u30ae\u30f3\u30b0\u306e\u4f7f\u7528\u4f8b:<\/strong><\/p>\n

        \n
      1. \u5206\u985e<\/strong>\u30d0\u30ae\u30f3\u30b0\u306f\u3001\u5f37\u529b\u306a\u5206\u985e\u5668\u3092\u4f5c\u6210\u3059\u308b\u305f\u3081\u306b\u3001\u6c7a\u5b9a\u6728\u3068\u7d44\u307f\u5408\u308f\u305b\u3066\u4f7f\u7528\u3055\u308c\u308b\u3053\u3068\u304c\u3088\u304f\u3042\u308a\u307e\u3059\u3002<\/li>\n
      2. \u56de\u5e30<\/strong>: \u56de\u5e30\u554f\u984c\u306b\u9069\u7528\u3057\u3066\u4e88\u6e2c\u7cbe\u5ea6\u3092\u5411\u4e0a\u3055\u305b\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/li>\n
      3. \u7570\u5e38\u691c\u51fa<\/strong>: \u30d0\u30ae\u30f3\u30b0\u306f\u30c7\u30fc\u30bf\u5185\u306e\u5916\u308c\u5024\u691c\u51fa\u306b\u4f7f\u7528\u3067\u304d\u307e\u3059\u3002<\/li>\n<\/ol>\n

        \u8ab2\u984c\u3068\u89e3\u6c7a\u7b56:<\/strong><\/p>\n

          \n
        1. \n

          \u4e0d\u5747\u8861\u306a\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8<\/strong>: \u30af\u30e9\u30b9\u306e\u4e0d\u5747\u8861\u306e\u5834\u5408\u3001\u30d0\u30ae\u30f3\u30b0\u306f\u591a\u6570\u6d3e\u306e\u30af\u30e9\u30b9\u3092\u512a\u5148\u3059\u308b\u53ef\u80fd\u6027\u304c\u3042\u308a\u307e\u3059\u3002\u3053\u308c\u306b\u5bfe\u51e6\u3059\u308b\u306b\u306f\u3001\u30d0\u30e9\u30f3\u30b9\u306e\u3068\u308c\u305f\u30af\u30e9\u30b9\u306e\u91cd\u307f\u3092\u4f7f\u7528\u3059\u308b\u304b\u3001\u30b5\u30f3\u30d7\u30ea\u30f3\u30b0\u6226\u7565\u3092\u5909\u66f4\u3057\u307e\u3059\u3002<\/p>\n<\/li>\n

        2. \n

          \u30e2\u30c7\u30eb\u306e\u9078\u629e<\/strong>: \u9069\u5207\u306a\u30d9\u30fc\u30b9\u30e2\u30c7\u30eb\u3092\u9078\u629e\u3059\u308b\u3053\u3068\u304c\u91cd\u8981\u3067\u3059\u3002\u591a\u69d8\u306a\u30e2\u30c7\u30eb\u306e\u30bb\u30c3\u30c8\u306b\u3088\u308a\u3001\u30d1\u30d5\u30a9\u30fc\u30de\u30f3\u30b9\u304c\u5411\u4e0a\u3057\u307e\u3059\u3002<\/p>\n<\/li>\n

        3. \n

          \u8a08\u7b97\u30aa\u30fc\u30d0\u30fc\u30d8\u30c3\u30c9<\/strong>: \u8907\u6570\u306e\u30e2\u30c7\u30eb\u306e\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u306b\u306f\u6642\u9593\u304c\u304b\u304b\u308b\u5834\u5408\u304c\u3042\u308a\u307e\u3059\u3002\u4e26\u5217\u5316\u3084\u5206\u6563\u30b3\u30f3\u30d4\u30e5\u30fc\u30c6\u30a3\u30f3\u30b0\u306a\u3069\u306e\u6280\u8853\u306b\u3088\u308a\u3001\u3053\u306e\u554f\u984c\u3092\u8efd\u6e1b\u3067\u304d\u307e\u3059\u3002<\/p>\n<\/li>\n<\/ol>\n

          \u4e3b\u306a\u7279\u5fb4\u3068\u985e\u4f3c\u7528\u8a9e\u3068\u306e\u6bd4\u8f03<\/h2>\n\n\n\n\n\n\n\n\n\n
          \u5074\u9762<\/th>\n\u888b\u8a70\u3081<\/th>\n\u30d6\u30fc\u30b9\u30c6\u30a3\u30f3\u30b0<\/th>\n\u30b9\u30bf\u30c3\u30ad\u30f3\u30b0<\/th>\n<\/tr>\n<\/thead>\n
          \u5ba2\u89b3\u7684<\/td>\n\u5dee\u7570\u3092\u6e1b\u3089\u3059<\/td>\n\u30e2\u30c7\u30eb\u306e\u7cbe\u5ea6\u3092\u9ad8\u3081\u308b<\/td>\n\u30e2\u30c7\u30eb\u306e\u4e88\u6e2c\u3092\u7d44\u307f\u5408\u308f\u305b\u308b<\/td>\n<\/tr>\n
          \u30e2\u30c7\u30eb\u306e\u72ec\u7acb\u6027<\/td>\n\u72ec\u7acb\u3057\u305f\u30d9\u30fc\u30b9\u30e2\u30c7\u30eb<\/td>\n\u9806\u6b21\u4f9d\u5b58<\/td>\n\u72ec\u7acb\u3057\u305f\u30d9\u30fc\u30b9\u30e2\u30c7\u30eb<\/td>\n<\/tr>\n
          \u30d9\u30fc\u30b9\u30e2\u30c7\u30eb\u306e\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u9806\u5e8f<\/td>\n\u5e73\u884c<\/td>\n\u4e00\u9023<\/td>\n\u5e73\u884c<\/td>\n<\/tr>\n
          \u57fa\u672c\u30e2\u30c7\u30eb\u306e\u6295\u7968\u306e\u91cd\u307f\u4ed8\u3051<\/td>\n\u30e6\u30cb\u30d5\u30a9\u30fc\u30e0<\/td>\n\u30d1\u30d5\u30a9\u30fc\u30de\u30f3\u30b9\u6b21\u7b2c<\/td>\n\u30e1\u30bf\u30e2\u30c7\u30eb\u306b\u4f9d\u5b58\u3059\u308b<\/td>\n<\/tr>\n
          \u904e\u5b66\u7fd2\u306e\u5f71\u97ff\u3092\u53d7\u3051\u3084\u3059\u3044<\/td>\n\u4f4e\u3044<\/td>\n\u9ad8\u3044<\/td>\n\u9069\u5ea6<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

          \u888b\u8a70\u3081\u306b\u95a2\u3059\u308b\u672a\u6765\u306e\u8996\u70b9\u3068\u6280\u8853<\/h2>\n

          \u30d0\u30ae\u30f3\u30b0\u306f\u30a2\u30f3\u30b5\u30f3\u30d6\u30eb\u5b66\u7fd2\u306e\u57fa\u672c\u7684\u306a\u624b\u6cd5\u3067\u3042\u308a\u3001\u4eca\u5f8c\u3082\u91cd\u8981\u306a\u610f\u5473\u3092\u6301\u3061\u7d9a\u3051\u308b\u3068\u601d\u308f\u308c\u307e\u3059\u3002\u3057\u304b\u3057\u3001\u6a5f\u68b0\u5b66\u7fd2\u306e\u9032\u6b69\u3068\u30c7\u30a3\u30fc\u30d7\u30e9\u30fc\u30cb\u30f3\u30b0\u306e\u53f0\u982d\u306b\u3088\u308a\u3001\u30d0\u30ae\u30f3\u30b0\u3068\u4ed6\u306e\u624b\u6cd5\u3092\u7d44\u307f\u5408\u308f\u305b\u305f\u3001\u3088\u308a\u8907\u96d1\u306a\u30a2\u30f3\u30b5\u30f3\u30d6\u30eb\u624b\u6cd5\u3084\u30cf\u30a4\u30d6\u30ea\u30c3\u30c9\u30a2\u30d7\u30ed\u30fc\u30c1\u304c\u767b\u5834\u3059\u308b\u53ef\u80fd\u6027\u304c\u3042\u308a\u307e\u3059\u3002<\/p>\n

          \u5c06\u6765\u306e\u958b\u767a\u306f\u3001\u30a2\u30f3\u30b5\u30f3\u30d6\u30eb\u69cb\u9020\u306e\u6700\u9069\u5316\u3001\u3088\u308a\u52b9\u7387\u7684\u306a\u57fa\u672c\u30e2\u30c7\u30eb\u306e\u8a2d\u8a08\u3001\u304a\u3088\u3073\u5909\u5316\u3059\u308b\u30c7\u30fc\u30bf\u5206\u5e03\u306b\u52d5\u7684\u306b\u8abf\u6574\u3059\u308b\u30a2\u30f3\u30b5\u30f3\u30d6\u30eb\u3092\u4f5c\u6210\u3059\u308b\u305f\u3081\u306e\u9069\u5fdc\u30a2\u30d7\u30ed\u30fc\u30c1\u306e\u63a2\u7d22\u306b\u7126\u70b9\u3092\u5f53\u3066\u308b\u53ef\u80fd\u6027\u304c\u3042\u308a\u307e\u3059\u3002<\/p>\n

          \u30d7\u30ed\u30ad\u30b7\u30b5\u30fc\u30d0\u30fc\u306e\u4f7f\u7528\u65b9\u6cd5\u3084\u30d0\u30ae\u30f3\u30b0\u3068\u306e\u95a2\u9023\u4ed8\u3051\u65b9\u6cd5<\/h2>\n

          \u30d7\u30ed\u30ad\u30b7 \u30b5\u30fc\u30d0\u30fc\u306f\u3001Web \u30b9\u30af\u30ec\u30a4\u30d4\u30f3\u30b0\u3001\u30c7\u30fc\u30bf \u30de\u30a4\u30cb\u30f3\u30b0\u3001\u30c7\u30fc\u30bf\u533f\u540d\u6027\u306a\u3069\u3001\u3055\u307e\u3056\u307e\u306a Web \u95a2\u9023\u30a2\u30d7\u30ea\u30b1\u30fc\u30b7\u30e7\u30f3\u3067\u91cd\u8981\u306a\u5f79\u5272\u3092\u679c\u305f\u3057\u307e\u3059\u3002\u30d0\u30ae\u30f3\u30b0\u306b\u95a2\u3057\u3066\u306f\u3001\u30d7\u30ed\u30ad\u30b7 \u30b5\u30fc\u30d0\u30fc\u3092\u4f7f\u7528\u3057\u3066\u3001\u6b21\u306e\u3088\u3046\u306a\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0 \u30d7\u30ed\u30bb\u30b9\u3092\u5f37\u5316\u3067\u304d\u307e\u3059\u3002<\/p>\n

            \n
          1. \n

            \u30c7\u30fc\u30bf\u53ce\u96c6<\/strong>: \u30d0\u30ae\u30f3\u30b0\u306b\u306f\u5927\u91cf\u306e\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0 \u30c7\u30fc\u30bf\u304c\u5fc5\u8981\u306b\u306a\u308b\u3053\u3068\u304c\u3088\u304f\u3042\u308a\u307e\u3059\u3002\u30d7\u30ed\u30ad\u30b7 \u30b5\u30fc\u30d0\u30fc\u306f\u3001\u30d6\u30ed\u30c3\u30af\u3055\u308c\u305f\u308a\u30d5\u30e9\u30b0\u304c\u7acb\u3066\u3089\u308c\u305f\u308a\u3059\u308b\u30ea\u30b9\u30af\u3092\u8efd\u6e1b\u3057\u306a\u304c\u3089\u3001\u3055\u307e\u3056\u307e\u306a\u30bd\u30fc\u30b9\u304b\u3089\u30c7\u30fc\u30bf\u3092\u53ce\u96c6\u3059\u308b\u306e\u306b\u5f79\u7acb\u3061\u307e\u3059\u3002<\/p>\n<\/li>\n

          2. \n

            \u533f\u540d\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0<\/strong>: \u30d7\u30ed\u30ad\u30b7 \u30b5\u30fc\u30d0\u30fc\u306f\u3001\u30e2\u30c7\u30eb\u306e\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u4e2d\u306b\u30aa\u30f3\u30e9\u30a4\u30f3 \u30ea\u30bd\u30fc\u30b9\u306b\u30a2\u30af\u30bb\u30b9\u3059\u308b\u969b\u306b\u30e6\u30fc\u30b6\u30fc\u306e ID \u3092\u96a0\u3059\u3053\u3068\u304c\u3067\u304d\u308b\u305f\u3081\u3001\u30d7\u30ed\u30bb\u30b9\u306e\u5b89\u5168\u6027\u304c\u9ad8\u307e\u308a\u3001IP \u30d9\u30fc\u30b9\u306e\u5236\u9650\u304c\u56de\u907f\u3055\u308c\u307e\u3059\u3002<\/p>\n<\/li>\n

          3. \n

            \u30ed\u30fc\u30c9\u30d0\u30e9\u30f3\u30b7\u30f3\u30b0<\/strong>: \u30ea\u30af\u30a8\u30b9\u30c8\u3092\u7570\u306a\u308b\u30d7\u30ed\u30ad\u30b7 \u30b5\u30fc\u30d0\u30fc\u306b\u5206\u6563\u3059\u308b\u3053\u3068\u3067\u3001\u5404\u30b5\u30fc\u30d0\u30fc\u306e\u8ca0\u8377\u304c\u5206\u6563\u3055\u308c\u3001\u30c7\u30fc\u30bf\u53ce\u96c6\u30d7\u30ed\u30bb\u30b9\u306e\u52b9\u7387\u304c\u5411\u4e0a\u3057\u307e\u3059\u3002<\/p>\n<\/li>\n<\/ol>\n

            \u95a2\u9023\u30ea\u30f3\u30af<\/h2>\n

            \u30d0\u30ae\u30f3\u30b0\u3068\u30a2\u30f3\u30b5\u30f3\u30d6\u30eb\u5b66\u7fd2\u30c6\u30af\u30cb\u30c3\u30af\u306e\u8a73\u7d30\u306b\u3064\u3044\u3066\u306f\u3001\u6b21\u306e\u30ea\u30bd\u30fc\u30b9\u3092\u53c2\u7167\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/p>\n

              \n
            1. Scikit-learn \u30d0\u30ae\u30f3\u30b0\u306e\u30c9\u30ad\u30e5\u30e1\u30f3\u30c8<\/a><\/li>\n
            2. \u888b\u8a70\u3081\u306b\u95a2\u3059\u308b\u30ec\u30aa\u30fb\u30d6\u30e9\u30a4\u30de\u30f3\u306e\u30aa\u30ea\u30b8\u30ca\u30eb\u8ad6\u6587<\/a><\/li>\n
            3. \u30a2\u30f3\u30b5\u30f3\u30d6\u30eb\u5b66\u7fd2\u3068\u30d0\u30ae\u30f3\u30b0\u306e\u6982\u8981<\/a><\/li>\n<\/ol>\n

              \u30d0\u30ae\u30f3\u30b0\u306f\u5f15\u304d\u7d9a\u304d\u6a5f\u68b0\u5b66\u7fd2\u306e\u5f37\u529b\u306a\u30c4\u30fc\u30eb\u3067\u3042\u308a\u3001\u305d\u306e\u8907\u96d1\u3055\u3092\u7406\u89e3\u3059\u308b\u3053\u3068\u306f\u3001\u4e88\u6e2c\u30e2\u30c7\u30ea\u30f3\u30b0\u3068\u30c7\u30fc\u30bf\u5206\u6790\u306b\u5927\u304d\u306a\u5229\u76ca\u3092\u3082\u305f\u3089\u3057\u307e\u3059\u3002<\/p>","protected":false},"featured_media":467687,"menu_order":0,"template":"","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"class_list":["post-475967","wiki","type-wiki","status-publish","has-post-thumbnail","hentry"],"acf":{"faq_title":"Frequently Asked Questions about Bagging: An Ensemble Learning Technique<\/mark>","faq_items":[{"question":"What is Bagging and how does it improve machine learning models?","answer":"

              Bagging, short for Bootstrap Aggregating, is an ensemble learning technique that aims to enhance the accuracy and stability of machine learning models. It works by training multiple instances of the same base learning algorithm on different subsets of the training data. The final prediction is obtained by aggregating the individual predictions of these models through voting or averaging. Bagging reduces overfitting, increases model robustness, and improves generalization capabilities.<\/p>"},{"question":"Who introduced the concept of Bagging and when was it first mentioned?","answer":"

              The concept of Bagging was introduced by Leo Breiman in 1994 in his paper \"Bagging Predictors.\" It was the first mention of this powerful ensemble learning technique that has since become widely adopted in the machine learning community.<\/p>"},{"question":"How does Bagging work internally?","answer":"

              Bagging works in several steps:<\/p>

              1. Bootstrap Sampling<\/strong>: Random subsets of the training data are created through sampling with replacement.<\/li>
              2. Base Model Training<\/strong>: Each subset is used to train separate instances of the base learning algorithm.<\/li>
              3. Prediction Aggregation<\/strong>: The individual model predictions are combined through voting or averaging to obtain the final ensemble prediction.<\/li><\/ol>"},{"question":"What are the key features of Bagging?","answer":"

                Bagging offers the following key features:<\/p>

                1. Variance Reduction<\/strong>: It reduces the variance of the ensemble, making it more robust and less prone to overfitting.<\/li>
                2. Model Diversity<\/strong>: Bagging encourages diversity among base models, capturing different patterns in the data.<\/li>
                3. Parallelization<\/strong>: The base models are trained independently and in parallel, making it computationally efficient.<\/li><\/ol>"},{"question":"What types of Bagging exist?","answer":"

                  There are several types of Bagging, each with its characteristics:<\/p>

                  • Bootstrap Aggregating: Standard Bagging with bootstrap sampling.<\/li>
                  • Random Subspace Method: Randomly sampling features for each base model.<\/li>
                  • Random Patches: Random subsets of both instances and features.<\/li>
                  • Random Forest: Bagging with decision trees as base models.<\/li><\/ul>"},{"question":"How can Bagging be used, and what are the common challenges?","answer":"

                    Bagging finds applications in classification, regression, and anomaly detection. Common challenges include dealing with imbalanced datasets, selecting appropriate base models, and addressing computational overhead. Solutions involve using balanced class weights, creating diverse models, and employing parallelization or distributed computing.<\/p>"},{"question":"How does Bagging compare with other ensemble techniques like Boosting and Stacking?","answer":"

                    Bagging aims to reduce variance, while Boosting focuses on increasing model accuracy. Stacking combines predictions of models. Bagging uses independent base models in parallel, while Boosting uses models sequentially dependent on each other.<\/p>"},{"question":"What does the future hold for Bagging in machine learning?","answer":"

                    Bagging will continue to be a fundamental technique in ensemble learning. Future developments may involve optimizing ensemble structures, designing efficient base models, and exploring adaptive approaches for dynamic data distributions.<\/p>"},{"question":"How are proxy servers associated with Bagging and how do they enhance the process?","answer":"

                    Proxy servers play a vital role in improving Bagging efficiency. They help with data collection by preventing blocks or flags, provide anonymity during model training, and offer load balancing to distribute requests across different servers.<\/p>

                    For more information and in-depth insights into Bagging and ensemble learning, check out the related links provided in the article.<\/p>"}]},"_links":{"self":[{"href":"https:\/\/oneproxy.pro\/jp\/wp-json\/wp\/v2\/wiki\/475967","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/oneproxy.pro\/jp\/wp-json\/wp\/v2\/wiki"}],"about":[{"href":"https:\/\/oneproxy.pro\/jp\/wp-json\/wp\/v2\/types\/wiki"}],"version-history":[{"count":0,"href":"https:\/\/oneproxy.pro\/jp\/wp-json\/wp\/v2\/wiki\/475967\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/oneproxy.pro\/jp\/wp-json\/wp\/v2\/media\/467687"}],"wp:attachment":[{"href":"https:\/\/oneproxy.pro\/jp\/wp-json\/wp\/v2\/media?parent=475967"}],"curies":[{"name":"\u3046\u30fc\u3093","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}