{"id":3175,"date":"2022-02-27T12:52:38","date_gmt":"2022-02-27T04:52:38","guid":{"rendered":"https:\/\/egonlin.com\/?p=3175"},"modified":"2022-02-27T12:52:38","modified_gmt":"2022-02-27T04:52:38","slug":"%e7%ac%ac%e4%b8%80%e7%af%87%ef%bc%9abagging%e7%ae%97%e6%b3%95%e5%92%8c%e9%9a%8f%e6%9c%ba%e6%a3%ae%e6%9e%97","status":"publish","type":"post","link":"https:\/\/egonlin.com\/?p=3175","title":{"rendered":"\u7b2c\u4e00\u7bc7\uff1aBagging\u7b97\u6cd5\u548c\u968f\u673a\u68ee\u6797"},"content":{"rendered":"

Bagging\u7b97\u6cd5\u548c\u968f\u673a\u68ee\u6797<\/h1>\n

  \u96c6\u6210\u5b66\u4e60\u4e3b\u8981\u5206\u6210\u4e24\u4e2a\u6d41\u6d3e\uff0c\u5176\u4e2d\u4e00\u4e2a\u662fBoosting\u7b97\u6cd5\uff0c\u53e6\u4e00\u4e2a\u5219\u662f\u672c\u6587\u8981\u8bb2\u7684Bagging\u7b97\u6cd5\uff0cBagging\u7b97\u6cd5
\n\u7b97\u6cd5\u7684\u5f31\u5b66\u4e60\u5668\u662f\u6ca1\u6709\u4f9d\u8d56\u5173\u7cfb\u7684\uff0c\u56e0\u6b64\u5f31\u5b66\u4e60\u4e4b\u95f4\u53ef\u4ee5\u5e76\u884c\u62df\u5408\u3002<\/p>\n

  Bagging\u7b97\u6cd5\u4e2d\u6700\u8457\u540d\u7684\u7b97\u6cd5\u662f\u968f\u673a\u68ee\u6797\uff0c\u7531\u4e8e\u968f\u673a\u68ee\u6797\u7684\u4e0d\u5bb9\u6613\u8fc7\u62df\u5408\u6027\u4ee5\u53ca\u7b80\u4fbf\u6027\uff0c\u53ef\u4ee5\u8bf4\u662f\u548c\u68af\u5ea6\u63d0\u5347\u6811\u540c\u540d\u7684\u7b97\u6cd5\u3002<\/p>\n

Bagging\u7b97\u6cd5\u548c\u968f\u673a\u68ee\u6797\u5b66\u4e60\u76ee\u6807<\/h1>\n
    \n
  1. Bagging\u7b97\u6cd5\u539f\u7406<\/li>\n
  2. Bagging\u7b97\u6cd5\u6d41\u7a0b<\/li>\n
  3. \u968f\u673a\u68ee\u6797\u548cBagging\u7b97\u6cd5\u533a\u522b<\/li>\n
  4. \u968f\u673a\u68ee\u6797\u6d41\u7a0b<\/li>\n<\/ol>\n

    Bagging\u7b97\u6cd5\u539f\u7406\u56de\u987e<\/h1>\n

      Bagging\u7b97\u6cd5\u7684\u5f31\u5b66\u4e60\u5668\u7684\u8bad\u7ec3\u96c6\u662f\u901a\u8fc7\u968f\u673a\u91c7\u6837\u5f97\u5230\u7684\u3002\u901a\u8fc7$T$\u6b21\u7684\u968f\u673a\u91c7\u6837\uff0c\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7\u81ea\u4e3b\u91c7\u6837\u6cd5(bootstrap sampling)\u5f97\u5230$T$\u4e2a\u91c7\u6837\u96c6\uff0c\u7136\u540e\u5bf9\u4e8e\u8fd9$T$\u4e2a\u91c7\u6837\u96c6\u72ec\u7acb\u7684\u8bad\u7ec3\u51fa$T$\u4e2a\u5f31\u5b66\u4e60\u5668\uff0c\u4e4b\u540e\u6211\u4eec\u901a\u8fc7\u67d0\u79cd\u7ed3\u5408\u7b56\u7565\u5c06\u8fd9$T$\u4e2a\u5f31\u5b66\u4e60\u5668\u6784\u9020\u6210\u4e00\u4e2a\u5f3a\u5b66\u4e60\u5668\u3002<\/p>\n

      \u5728\u4e00\u4e2a\u6709$m$\u4e2a\u6837\u672c\u7684\u8bad\u7ec3\u6570\u636e\u4e2d\u968f\u673a\u91c7\u6837\uff0c\u4e00\u4e2a\u6837\u672c\u6bcf\u6b21\u88ab\u91c7\u6837\u7684\u6982\u7387\u4e3a${\\frac{1}{m}}$\uff0c\u4e0d\u88ab\u91c7\u96c6\u7684\u6982\u7387\u4e3a$1-{\\frac{1}{m}}$\uff0c\u5982\u679c$m$\u6b21\u91c7\u6837\u90fd\u6ca1\u6709\u91c7\u4e2d\u7684\u6982\u7387\u4e3a$(1-{\\frac{1}{m}})^m$\uff0c\u5f53$m\\rightarrow{\\infty}$\uff0c$(1-{\\frac{1}{m}})^m\\rightarrow{\\frac{1}{e}}\\approx0.368$\uff0c\u5373\u5728Bagging\u7b97\u6cd5\u7684\u968f\u673a\u91c7\u6837\u4e2d\uff0c\u5927\u7ea6\u6709$36.8%$\u7684\u6570\u636e\u6ca1\u6709\u88ab\u91c7\u4e2d\u3002<\/p>\n

      Bagging\u7b97\u6cd5\u7684\u7ed3\u5408\u7b56\u7565\uff0c\u5bf9\u4e8e\u5206\u7c7b\u95ee\u9898\uff0c\u4e00\u822c\u4f7f\u7528\u76f8\u5bf9\u591a\u6570\u6295\u7968\u6cd5\uff0c\u5373\u7968\u6570\u6700\u591a\u7684\u7c7b\u522b\u5373\u4e3a\u6837\u672c\u9884\u6d4b\u7c7b\u522b\uff1b\u5bf9\u4e8e\u56de\u5f52\u95ee\u9898\uff0c\u4e00\u822c\u4f7f\u7528\u7b80\u5355\u5e73\u5747\u6cd5\uff0c\u5373\u5bf9$T$\u4e2a\u5f31\u5b66\u4e60\u7684\u8f93\u51fa\u505a\u7b97\u672f\u5e73\u5747\u5f97\u5230\u6837\u672c\u9884\u6d4b\u503c\u3002<\/p>\n

    Bagging\u7b97\u6cd5\u6d41\u7a0b<\/h1>\n

    \u8f93\u5165<\/h2>\n

      $m$\u4e2a\u6837\u672c$n$\u4e2a\u7279\u5f81\u7684\u8bad\u7ec3\u6570\u636e\u96c6$D={(x_1,y_1),(x_2,y_2),\\cdots,(x_m,y_m)}$\uff1b\u5f31\u5b66\u4e60\u5668\u8fed\u4ee3\u6b21\u6570\u4e3a$T$\u3002<\/p>\n

    \u8f93\u51fa<\/h2>\n

      \u6700\u7ec8\u5f3a\u5b66\u4e60\u5668$f(x)$\u3002<\/p>\n

    \u6d41\u7a0b<\/h2>\n
      \n
    1. \u5bf9\u8bad\u7ec3\u96c6\u8fdb\u884c\u7b2c$t\\quad(t=1,2,\\cdots,T)$\u6b21\u968f\u673a\u91c7\u6837\uff0c\u5171\u91c7\u96c6$m$\u6b21\uff0c\u5f97\u5230\u6709$m$\u4e2a\u6837\u672c\u7684\u91c7\u6837\u96c6$D_t$<\/li>\n
    2. \u7528\u91c7\u6837\u96c6$D_t$\u8bad\u7ec3\u7b2c$t$\u4e2a\u5f31\u5b66\u4e60\u5668$G_t(x)$<\/li>\n
    3. \u5bf9\u4e8e\u5206\u7c7b\u95ee\u9898\uff0c\u4f7f\u7528\u76f8\u5bf9\u591a\u6570\u6295\u7968\u6cd5\u9884\u6d4b\u5206\u7c7b\u7ed3\u679c\uff1b\u5bf9\u4e8e\u56de\u5f52\u95ee\u9898\uff0c\u4f7f\u7528\u7b80\u5355\u5e73\u5747\u6cd5\u5f97\u5230\u9884\u6d4b\u503c<\/li>\n<\/ol>\n

      \u968f\u673a\u68ee\u6797\u8be6\u89e3<\/h1>\n

      \u968f\u673a\u68ee\u6797\u548cBagging\u7b97\u6cd5\u533a\u522b<\/h2>\n

        \u968f\u673a\u68ee\u6797(random forest\uff0cRF)\u57fa\u4e8eBagging\u7b97\u6cd5\u7684\u601d\u60f3\uff0c\u505a\u4e86\u4e00\u5b9a\u7684\u6539\u8fdb\uff0c\u5373\u5bf9\u7279\u5f81\u8fdb\u884c\u4e86\u9009\u62e9\u3002<\/p>\n

        RF\u4f7f\u7528CART\u51b3\u7b56\u6811\u4f5c\u4e3a\u5f31\u5b66\u4e60\u5668\uff0c\u4f46\u662fRF\u5bf9\u666e\u901a\u7684CART\u51b3\u7b56\u6811\u505a\u4e86\u6539\u8fdb\uff0c\u666e\u901a\u7684CART\u6811\u9009\u62e9\u6700\u4f18\u7279\u5f81\u4f5c\u4e3a\u51b3\u7b56\u6811\u5212\u5206\u7684\u6761\u4ef6\uff1bRF\u7684CART\u51b3\u7b56\u6811\u5219\u662f\u5728\u8bad\u7ec3\u6570\u636e\u7684$n$\u4e2a\u7279\u5f81\u4e2d\u968f\u673a\u9009\u62e9$n{sub}$\u7279\u5f81\uff0c\u4e00\u822c\u60c5\u51b5\u4e0b$n<\/em>{sub}<n$\uff0c\u7136\u540e\u5728\u8fd9$n_{sub}$\u4e2a\u7279\u5f81\u4e2d\u9009\u62e9\u6700\u4f18\u7279\u5f81\u4f5c\u4e3a\u51b3\u7b56\u6811\u5212\u5206\u7684\u6761\u4ef6\u3002<\/p>\n

        \u5982\u679c$n{sub}=n$\uff0c\u5219RF\u7684\u51b3\u7b56\u6811\u4e3a\u666e\u901a\u7684\u51b3\u7b56\u6811\uff1b$n<\/em>{sub}$\u8d8a\u5c0f\uff0c\u5219\u6a21\u578b\u9c81\u68d2\u6027\u8d8a\u597d\uff0c\u6a21\u578b\u65b9\u5dee\u4e5f\u4f1a\u51cf\u5c0f\uff0c\u4f46\u6a21\u578b\u5bf9\u8bad\u7ec3\u96c6\u7684\u62df\u5408\u7a0b\u5ea6\u4f1a\u53d8\u5dee\uff0c\u53cd\u4e4b\uff0c\u6a21\u578b\u7684\u504f\u5dee\u4f1a\u53d8\u5c0f\uff0c\u6a21\u578b\u5bf9\u8bad\u7ec3\u96c6\u7684\u6cdb\u5316\u80fd\u529b\u4f1a\u53d8\u5dee\u3002<\/p>\n

      \u968f\u673a\u68ee\u6797\u62d3\u5c55<\/h1>\n

        RF\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\u4e0d\u4ec5\u80fd\u89e3\u51b3\u5206\u7c7b\u548c\u56de\u5f52\u95ee\u9898\uff0c\u8fd8\u53ef\u4ee5\u7528\u4e8e\u7279\u5f81\u8f6c\u6362\u3001\u5f02\u5e38\u70b9\u68c0\u6d4b\u7b49\u3002<\/p>\n

      Extra Trees<\/h2>\n

        Extra Trees\u548cRF\u7684\u533a\u522b\u6709\u4ee5\u4e0b\u4e24\u70b9\uff1a<\/p>\n

        \n
      1. \u5bf9\u4e8e\u6bcf\u4e2a\u51b3\u7b56\u6811\u7684\u8bad\u7ec3\u96c6\uff0cRF\u91c7\u7528\u7684\u968f\u673a\u91c7\u6837\u96c6\uff1b\u5bf9\u4e8eextra trees\uff0c\u6bcf\u4e2a\u51b3\u7b56\u6811\u91c7\u7528\u539f\u59cb\u6570\u636e\u96c6<\/li>\n
      2. RF\u4f1a\u57fa\u4e8e\u57fa\u5c3c\u7cfb\u6570\u3001\u4fe1\u606f\u71b5\u7684\u65b9\u5f0f\uff0c\u9009\u62e9\u4e00\u4e2a\u6700\u4f18\u7684\u7279\u5f81\u503c\u5212\u5206\uff1bextra teees\u5219\u4f1a\u968f\u673a\u9009\u62e9\u4e00\u4e2a\u7279\u5f81\u503c\u5212\u5206\u7279\u5f81\u6570<\/li>\n<\/ol>\n

          \u4ee5\u4e0a\u4e24\u70b9\u5bfc\u81f4extra trees\u751f\u6210\u7684\u968f\u673a\u68ee\u6797\u7684\u51b3\u7b56\u6811\u89c4\u6a21\u4e00\u822c\u4f1a\u5927\u4e8eRF\u3002\u5373\u53ef\u4ee5\u4e00\u5b9a\u7a0b\u5ea6\u7684\u51cf\u5c0f\u6a21\u578b\u7684\u65b9\u5dee\uff0c\u589e\u5f3a\u6a21\u578b\u7684\u6cdb\u5316\u80fd\u529b\u3002<\/p>\n

        # \u4f7f\u7528ExtraTree\u5224\u65ad\u7279\u5f81\u91cd\u8981\u6027\u7a0b\u5ea6\nfrom sklearn.datasets import make_friedman1\nfrom sklearn.ensemble import ExtraTreesRegressor\n\nX, y = make_friedman1(n_samples=100, n_features=10, random_state=0)\n\n# \u901a\u8fc7ExtraTreesRegressor\u6a21\u578b\u83b7\u53d6\u6bcf\u4e2a\u7279\u5f81\u7684\u91cd\u8981\u6027\net = ExtraTreesRegressor(n_estimators=10)\net = et.fit(X, y)\nprint('10\u4e2a\u7279\u5f81\u5404\u81ea\u7684\u91cd\u8981\u6027:{}'.format(et.feature_importances_))<\/code><\/pre>\n
        10\u4e2a\u7279\u5f81\u5404\u81ea\u7684\u91cd\u8981\u6027:[0.11488041 0.12557425 0.08477273 0.45483849 0.09753123 0.01384401\n 0.0364967  0.0256125  0.01965904 0.02679065]<\/code><\/pre>\n
        Totally Random Trees Embedding<\/h2>\n
           Totally Random Trees Embedding(TRTE)\u662f\u4e00\u79cd\u65e0\u76d1\u7763\u5b66\u4e60\u7684\u6570\u636e\u8f6c\u6362\u65b9\u5f0f\uff0c\u5b83\u53ef\u4ee5\u5c06\u4f4e\u7ef4\u7684\u6570\u636e\u96c6\u6620\u5c04\u5230\u9ad8\u7ef4\uff0c\u5728\u652f\u6301\u5411\u91cf\u673a\u4e2d\u4f7f\u7528\u4e86\u6838\u6280\u5de7\u5c06\u4f4e\u7ef4\u7684\u6570\u636e\u6620\u5c04\u5230\u9ad8\u7ef4\uff0cTRTE\u63d0\u4f9b\u4e86\u4e0d\u540c\u4e8e\u6838\u6280\u5de7\u7684\u65b9\u6cd5\u3002<\/p>\n
          TRTE\u9996\u5148\u4f1a\u6784\u6210\u4e00\u4e2a\u7c7b\u4f3cRF\u7684\u968f\u673a\u68ee\u6797\u6a21\u578b\uff0c\u6a21\u578b\u5b9a\u4e0b\u6765\u540e\u6a21\u578b\u4e2d$T$\u4e2a\u51b3\u7b56\u6811\u7684\u53f6\u5b50\u8282\u70b9\u4f4d\u7f6e\u4e5f\u4f1a\u88ab\u786e\u5b9a\u3002<\/p>\n
          \u73b0\u5728\u5047\u8bbe\u6211\u4eec\u67093\u68f5\u51b3\u7b56\u6811\uff0c\u6bcf\u4e2a\u51b3\u7b56\u6811\u67094\u4e2a\u53f6\u5b50\u8282\u70b9\uff0c\u67d0\u4e2a\u6570\u636e\u7279\u5f81$x$\u5212\u5206\u5230\u7b2c1\u4e2a\u51b3\u7b56\u6811\u7684\u7b2c1\u4e2a\u53f6\u5b50\u8282\u70b9\uff0c\u7b2c2\u68f5\u51b3\u7b56\u6811\u7684\u7b2c2\u4e2a\u8282\u70b9\uff0c\u7b2c3\u68f5\u51b3\u7b56\u6811\u7684\u7b2c4\u4e2a\u8282\u70b9\uff0c\u5219$x$\u6620\u5c04\u540e\u7684\u7279\u5f81\u7f16\u7801\u4e3a$(1,0,0,0\\quad0,1,0,0\\quad0,0,0,1)$\uff0c\u7531\u6b64\u65e2\u53ef\u4ee5\u5f97\u523012\u7ef4\u7684\u9ad8\u7ef4\u7279\u5f81\u3002<\/p>\n
        Isolation Forest<\/h2>\n
          Isolation Forest(IForest)\u53ef\u4ee5\u68c0\u6d4b\u5f02\u5e38\u70b9\u3002<\/p>\n
          IForest\u7c7b\u4f3c\u4e8eRF\uff0c\u4f46\u5728\u968f\u673a\u91c7\u6837\u7684\u65f6\u5019\uff0cIForest\u968f\u673a\u91c7\u6837\u7684\u6570\u91cf\u5e76\u4e0d\u662f$m$\u4e2a\uff0c\u800c\u662f\u8fdc\u8fdc\u5c0f\u4e8e\u8bad\u7ec3\u96c6\u4e2a\u6570\uff0c\u56e0\u4e3aIForest\u7684\u4f5c\u7528\u662f\u68c0\u6d4b\u5f02\u5e38\u70b9\uff0c\u5982\u679c\u91c7\u6837\u8fc7\u591a\u6b63\u786e\u6837\u672c\u4f1a\u63a9\u76d6\u6389\u5f02\u5e38\u70b9\u3002<\/p>\n
          \u5728\u5212\u5206\u7279\u5f81\u7684\u65f6\u5019\uff0cIForest\u5bf9\u5212\u5206\u7279\u5f81\u968f\u673a\u9009\u62e9\u4e00\u4e2a\u5212\u5206\u9608\u503c\uff0c\u5e76\u968f\u673a\u9009\u62e9\u4e00\u4e2a\u7279\u5f81\u5212\u5206\u51b3\u7b56\u6811\u3002<\/p>\n
          \u7531\u4e8eIForest\u7684\u6837\u672c\u6570\u8fc7\u5c11\uff0cIForest\u4e5f\u4f1a\u9009\u62e9\u4e00\u4e2a\u8f83\u5c0f\u7684\u6700\u5927\u51b3\u7b56\u6811\u6df1\u5ea6\u63a7\u5236\u51b3\u7b56\u6811\u7684\u6df1\u5ea6\u3002<\/p>\n
          \u5c06\u6d4b\u8bd5\u6837\u672c$x$\u62df\u5408\u5230$T$\u68f5\u51b3\u7b56\u6811\uff0c\u8ba1\u7b97\u6bcf\u9897\u51b3\u7b56\u6811\u4e0a\u8be5\u6837\u672c\u7684\u53f6\u5b50\u8282\u70b9\u7684\u6df1\u5ea6$h_t(x)$\uff0c\u4ece\u800c\u8ba1\u7b97\u51fa\u5e73\u5747\u9ad8\u5ea6\uff0c\u5219\u6837\u672c\u70b9$x$\u7684\u5f02\u5e38\u6982\u7387\u4e3a
        \n$$
        \ns(x,m) = 2^{-{\\frac{h(x)}{c(m)}}}
        \n$$
        \n\u5176\u4e2d$m$\u4e3a\u6837\u672c\u4e2a\u6570\uff0c$c(m)$\u7684\u8868\u8fbe\u5f0f\u4e3a
        \n$$
        \nc(m) = 2\\ln(m-1)+\\xi-2{\\frac{m-1}{m}}
        \n$$
        \n\u5176\u4e2d$\\xi$\u4e3a\u6b27\u62c9\u5e38\u6570\uff0c$s(x,m)$\u7684\u53d6\u503c\u8303\u56f4\u662f$[0,1]$\uff0c\u53d6\u503c\u8d8a\u63a5\u8fd11\uff0c\u6d4b\u8bd5\u6837\u672c\u70b9\u662f\u5f02\u5e38\u70b9\u7684\u6982\u7387\u8d8a\u5927\u3002<\/p>\n
        \u968f\u673a\u68ee\u6797\u6d41\u7a0b<\/h1>\n
        \u8f93\u5165<\/h2>\n
          $m$\u4e2a\u6837\u672c$n$\u4e2a\u7279\u5f81\u7684\u8bad\u7ec3\u6570\u636e\u96c6$D={(x_1,y_1),(x_2,y_2),\\cdots,(x_m,y_m)}$\uff1b\u5f31\u5b66\u4e60\u5668\u8fed\u4ee3\u6b21\u6570\u4e3a$T$\u3002<\/p>\n
        \u8f93\u51fa<\/h2>\n
          \u6700\u7ec8\u5f3a\u5b66\u4e60\u5668$f(x)$\u3002<\/p>\n
        \u6d41\u7a0b<\/h2>\n
          \n
        1. \u5bf9\u8bad\u7ec3\u96c6\u8fdb\u884c\u7b2c$t\\quad(t=1,2,\\cdots,T)$\u6b21\u968f\u673a\u91c7\u6837\uff0c\u5171\u91c7\u96c6$m$\u6b21\uff0c\u5f97\u5230\u6709$m$\u4e2a\u6837\u672c\u7684\u91c7\u6837\u96c6$D_t$<\/li>\n
        2. \u7528\u91c7\u6837\u96c6$D_t$\u8bad\u7ec3\u7b2c$t$\u4e2a\u51b3\u7b56\u6811\u6a21\u578b$Gt(x)$\uff0c\u5728\u8bad\u7ec3\u51b3\u7b56\u6811\u6a21\u578b\u7684\u65f6\u5019\uff0c\u4ece$n$\u4e2a\u7279\u5f81\u4e2d\u968f\u673a\u9009\u62e9$n<\/em>{sub}$\u4e2a\u7279\u5f81\uff0c\u7136\u540e\u4ece$n_{sub}$\u4e2a\u7279\u5f81\u4e2d\u9009\u62e9\u6700\u4f18\u7684\u7279\u5f81\u4f5c\u4e3a\u5212\u5206\u51b3\u7b56\u6811\u7684\u6761\u4ef6\u3002<\/li>\n
        3. \u5bf9\u4e8e\u5206\u7c7b\u95ee\u9898\uff0c\u4f7f\u7528\u76f8\u5bf9\u591a\u6570\u6295\u7968\u6cd5\u9884\u6d4b\u5206\u7c7b\u7ed3\u679c\uff1b\u5bf9\u4e8e\u56de\u5f52\u95ee\u9898\uff0c\u4f7f\u7528\u7b80\u5355\u5e73\u5747\u6cd5\u5f97\u5230\u9884\u6d4b\u503c<\/li>\n<\/ol>\n
          \u968f\u673a\u68ee\u6797\u4f18\u7f3a\u70b9<\/h1>\n
          \u4f18\u70b9<\/h2>\n
            \n
          1. \u7531\u4e8e\u5f31\u5b66\u4e60\u5668\u4e4b\u95f4\u4e0d\u5b58\u5728\u4f9d\u8d56\u5173\u7cfb\uff0c\u6240\u4ee5\u53ef\u4ee5\u5e76\u884c\u8bad\u7ec3\u6a21\u578b\uff0c\u8fd9\u5bf9\u4e8e\u5927\u6570\u636e\u975e\u5e38\u6709\u4f18\u52bf<\/li>\n
          2. \u65e2\u53ef\u4ee5\u89e3\u51b3\u56de\u5f52\u95ee\u9898\u53c8\u53ef\u4ee5\u89e3\u51b3\u5206\u7c7b\u95ee\u9898\uff0c\u7075\u6d3b<\/li>\n
          3. \u7531\u4e8e\u5728\u751f\u6210\u6a21\u578b\u7684\u65f6\u5019\uff0c\u53ef\u4ee5\u81ea\u7531\u9009\u62e9\u7279\u5f81\u7684\u5212\u5206\uff0c\u53ef\u4ee5\u4e00\u5b9a\u7a0b\u5ea6\u89e3\u51b3\u7279\u5f81\u7ef4\u5ea6\u8f83\u9ad8\u7684\u95ee\u9898<\/li>\n
          4. RF\u76f8\u5f53\u4e8eAdaBoost\u548cGBDT\uff0c\u7b80\u5355\uff0c\u770b\u6570\u5b66\u516c\u5f0f\u63a8\u5bfc\u91cf\u5c31\u77e5\u9053\u4e86<\/li>\n
          5. \u7531\u591a\u4e2a\u4e0d\u5b58\u5728\u4f9d\u8d56\u5173\u7cfb\u7684\u5f31\u5b66\u4e60\u5668\u7ed3\u5408\u800c\u6210\uff0c\u6240\u4ee5\u5bf9\u90e8\u5206\u7279\u5f81\u7f3a\u5931\u4e0d\u654f\u611f<\/li>\n<\/ol>\n
            \u7f3a\u70b9<\/h2>\n
              \n
            1. \u7531\u4e8e\u51b3\u7b56\u6811\u6a21\u578b\u5bf9\u7279\u5f81\u8fdb\u884c\u4e86\u9009\u62e9\uff0c\u56e0\u6b64\u53d6\u503c\u5212\u5206\u8f83\u591a\u7684\u7279\u5f81\uff0c\u4f1a\u5f71\u54cdRF\u6a21\u578b\u62df\u5408\u7684\u6548\u679c<\/li>\n<\/ol>\n
              \u5c0f\u7ed3<\/h1>\n
                \u96c6\u6210\u5b66\u4e60\u5230\u8fd9\u4e5f\u7b97\u662f\u544a\u4e00\u6bb5\u843d\u4e86\uff0c\u76f8\u4fe1\u5927\u5bb6\u5bf9Boosting\u548cBagging\u7b97\u6cd5\u6709\u4e86\u5f88\u6e05\u6670\u7684\u4e86\u89e3\uff0c\u5bf9\u4e8eBoosting\u4e2d\u7684AdaBoost\u548cGBDT\uff0c\u7531\u4e8e\u903b\u8f91\u8f83\u4e3a\u590d\u6742\uff0c\u53ef\u4ee5\u591a\u770b\u4e00\u770b\uff1b\u5bf9\u4e8eBagging\u4e2d\u7684\u968f\u673a\u68ee\u6797\uff0c\u968f\u673a\u68ee\u6797\u672c\u8eab\u5e76\u4e0d\u662f\u5f88\u96be\u7406\u89e3\uff0c\u53ea\u8981\u80fd\u591f\u5f88\u81ea\u5982\u7684\u8fd0\u7528\u968f\u673a\u68ee\u6797\u7684\u4e00\u4e9b\u62d3\u5c55\u7b97\u6cd5\u5373\u53ef\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"
              Bagging\u7b97\u6cd5\u548c\u968f\u673a\u68ee\u6797   \u96c6\u6210\u5b66\u4e60\u4e3b\u8981\u5206\u6210\u4e24\u4e2a\u6d41\u6d3e\uff0c\u5176\u4e2d\u4e00\u4e2a\u662fBoosting\u7b97\u6cd5 […]<\/p>\n","protected":false},"author":3,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":[],"categories":[276,298,293],"tags":[],"_links":{"self":[{"href":"https:\/\/egonlin.com\/index.php?rest_route=\/wp\/v2\/posts\/3175"}],"collection":[{"href":"https:\/\/egonlin.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/egonlin.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/egonlin.com\/index.php?rest_route=\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/egonlin.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=3175"}],"version-history":[{"count":0,"href":"https:\/\/egonlin.com\/index.php?rest_route=\/wp\/v2\/posts\/3175\/revisions"}],"wp:attachment":[{"href":"https:\/\/egonlin.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=3175"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/egonlin.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=3175"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/egonlin.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=3175"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}