{"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\/vn\/wiki\/bagging\/","title":{"rendered":"\u0110\u00f3ng bao"},"content":{"rendered":"

\u0110\u00f3ng g\u00f3i, vi\u1ebft t\u1eaft c\u1ee7a Bootstrap Aggregating, l\u00e0 m\u1ed9t k\u1ef9 thu\u1eadt h\u1ecdc t\u1eadp t\u1ed5ng h\u1ee3p m\u1ea1nh m\u1ebd \u0111\u01b0\u1ee3c s\u1eed d\u1ee5ng trong h\u1ecdc m\u00e1y \u0111\u1ec3 c\u1ea3i thi\u1ec7n \u0111\u1ed9 ch\u00ednh x\u00e1c v\u00e0 t\u00ednh \u1ed5n \u0111\u1ecbnh c\u1ee7a c\u00e1c m\u00f4 h\u00ecnh d\u1ef1 \u0111o\u00e1n. N\u00f3 li\u00ean quan \u0111\u1ebfn vi\u1ec7c \u0111\u00e0o t\u1ea1o nhi\u1ec1u phi\u00ean b\u1ea3n c\u1ee7a c\u00f9ng m\u1ed9t thu\u1eadt to\u00e1n h\u1ecdc c\u01a1 s\u1edf tr\u00ean c\u00e1c t\u1eadp h\u1ee3p con kh\u00e1c nhau c\u1ee7a d\u1eef li\u1ec7u hu\u1ea5n luy\u1ec7n v\u00e0 k\u1ebft h\u1ee3p c\u00e1c d\u1ef1 \u0111o\u00e1n c\u1ee7a ch\u00fang th\u00f4ng qua b\u1ecf phi\u1ebfu ho\u1eb7c t\u00ednh trung b\u00ecnh. \u0110\u00f3ng bao \u0111\u01b0\u1ee3c s\u1eed d\u1ee5ng r\u1ed9ng r\u00e3i tr\u00ean nhi\u1ec1u l\u0129nh v\u1ef1c kh\u00e1c nhau v\u00e0 \u0111\u00e3 \u0111\u01b0\u1ee3c ch\u1ee9ng minh l\u00e0 c\u00f3 hi\u1ec7u qu\u1ea3 trong vi\u1ec7c gi\u1ea3m vi\u1ec7c trang b\u1ecb qu\u00e1 m\u1ee9c v\u00e0 t\u0103ng c\u01b0\u1eddng t\u00ednh t\u1ed5ng qu\u00e1t h\u00f3a c\u1ee7a c\u00e1c m\u00f4 h\u00ecnh.<\/p>\n

L\u1ecbch s\u1eed v\u1ec1 ngu\u1ed3n g\u1ed1c c\u1ee7a \u0110\u00f3ng bao v\u00e0 l\u1ea7n \u0111\u1ea7u ti\u00ean \u0111\u1ec1 c\u1eadp \u0111\u1ebfn n\u00f3<\/h2>\n

Kh\u00e1i ni\u1ec7m \u0110\u00f3ng bao \u0111\u01b0\u1ee3c Leo Breiman gi\u1edbi thi\u1ec7u l\u1ea7n \u0111\u1ea7u ti\u00ean v\u00e0o n\u0103m 1994 nh\u01b0 m\u1ed9t ph\u01b0\u01a1ng ph\u00e1p \u0111\u1ec3 gi\u1ea3m ph\u01b0\u01a1ng sai c\u1ee7a c\u00e1c c\u00f4ng c\u1ee5 \u01b0\u1edbc t\u00ednh kh\u00f4ng \u1ed5n \u0111\u1ecbnh. B\u00e0i b\u00e1o chuy\u00ean \u0111\u1ec1 \u201cD\u1ef1 \u0111o\u00e1n \u0111\u00f3ng bao\u201d c\u1ee7a Breiman \u0111\u00e3 \u0111\u1eb7t n\u1ec1n m\u00f3ng cho k\u1ef9 thu\u1eadt t\u1ed5ng h\u1ee3p n\u00e0y. K\u1ec3 t\u1eeb khi ra \u0111\u1eddi, Bagging \u0111\u00e3 tr\u1edf n\u00ean ph\u1ed5 bi\u1ebfn v\u00e0 tr\u1edf th\u00e0nh m\u1ed9t k\u1ef9 thu\u1eadt c\u01a1 b\u1ea3n trong l\u0129nh v\u1ef1c h\u1ecdc m\u00e1y.<\/p>\n

Th\u00f4ng tin chi ti\u1ebft v\u1ec1 \u0110\u00f3ng bao<\/h2>\n

Trong \u0110\u00f3ng bao, nhi\u1ec1u t\u1eadp h\u1ee3p con (t\u00fai) c\u1ee7a d\u1eef li\u1ec7u hu\u1ea5n luy\u1ec7n \u0111\u01b0\u1ee3c t\u1ea1o th\u00f4ng qua l\u1ea5y m\u1eabu ng\u1eabu nhi\u00ean c\u00f3 thay th\u1ebf. M\u1ed7i t\u1eadp h\u1ee3p con \u0111\u01b0\u1ee3c s\u1eed d\u1ee5ng \u0111\u1ec3 hu\u1ea5n luy\u1ec7n m\u1ed9t phi\u00ean b\u1ea3n ri\u00eang bi\u1ec7t c\u1ee7a thu\u1eadt to\u00e1n h\u1ecdc c\u01a1 s\u1edf, c\u00f3 th\u1ec3 l\u00e0 b\u1ea5t k\u1ef3 m\u00f4 h\u00ecnh n\u00e0o h\u1ed7 tr\u1ee3 nhi\u1ec1u t\u1eadp hu\u1ea5n luy\u1ec7n, ch\u1eb3ng h\u1ea1n nh\u01b0 c\u00e2y quy\u1ebft \u0111\u1ecbnh, m\u1ea1ng th\u1ea7n kinh ho\u1eb7c m\u00e1y vect\u01a1 h\u1ed7 tr\u1ee3.<\/p>\n

D\u1ef1 \u0111o\u00e1n cu\u1ed1i c\u00f9ng c\u1ee7a m\u00f4 h\u00ecnh t\u1eadp h\u1ee3p \u0111\u01b0\u1ee3c th\u1ef1c hi\u1ec7n b\u1eb1ng c\u00e1ch t\u1ed5ng h\u1ee3p c\u00e1c d\u1ef1 \u0111o\u00e1n ri\u00eang l\u1ebb c\u1ee7a c\u00e1c m\u00f4 h\u00ecnh c\u01a1 s\u1edf. \u0110\u1ed1i v\u1edbi c\u00e1c nhi\u1ec7m v\u1ee5 ph\u00e2n lo\u1ea1i, s\u01a1 \u0111\u1ed3 bi\u1ec3u quy\u1ebft \u0111a s\u1ed1 th\u01b0\u1eddng \u0111\u01b0\u1ee3c s\u1eed d\u1ee5ng, trong khi \u0111\u1ed1i v\u1edbi c\u00e1c nhi\u1ec7m v\u1ee5 h\u1ed3i quy, c\u00e1c d\u1ef1 \u0111o\u00e1n \u0111\u01b0\u1ee3c t\u00ednh trung b\u00ecnh.<\/p>\n

C\u1ea5u tr\u00fac b\u00ean trong c\u1ee7a \u0110\u00f3ng bao: C\u00e1ch ho\u1ea1t \u0111\u1ed9ng c\u1ee7a \u0110\u00f3ng bao<\/h2>\n

Nguy\u00ean l\u00fd ho\u1ea1t \u0111\u1ed9ng c\u1ee7a \u0110\u00f3ng bao c\u00f3 th\u1ec3 \u0111\u01b0\u1ee3c chia th\u00e0nh c\u00e1c b\u01b0\u1edbc sau:<\/p>\n

    \n
  1. \n

    L\u1ea5y m\u1eabu Bootstrap<\/strong>: C\u00e1c t\u1eadp h\u1ee3p con ng\u1eabu nhi\u00ean c\u1ee7a d\u1eef li\u1ec7u hu\u1ea5n luy\u1ec7n \u0111\u01b0\u1ee3c t\u1ea1o b\u1eb1ng c\u00e1ch l\u1ea5y m\u1eabu c\u00f3 thay th\u1ebf. M\u1ed7i t\u1eadp con c\u00f3 c\u00f9ng k\u00edch th\u01b0\u1edbc v\u1edbi t\u1eadp hu\u1ea5n luy\u1ec7n ban \u0111\u1ea7u.<\/p>\n<\/li>\n

  2. \n

    \u0110\u00e0o t\u1ea1o m\u00f4 h\u00ecnh c\u01a1 s\u1edf<\/strong>: M\u1ed9t thu\u1eadt to\u00e1n h\u1ecdc c\u01a1 s\u1edf ri\u00eang bi\u1ec7t \u0111\u01b0\u1ee3c hu\u1ea5n luy\u1ec7n tr\u00ean m\u1ed7i m\u1eabu bootstrap. C\u00e1c m\u00f4 h\u00ecnh c\u01a1 s\u1edf \u0111\u01b0\u1ee3c hu\u1ea5n luy\u1ec7n \u0111\u1ed9c l\u1eadp v\u00e0 song song.<\/p>\n<\/li>\n

  3. \n

    T\u1ed5ng h\u1ee3p d\u1ef1 \u0111o\u00e1n<\/strong>: \u0110\u1ed1i v\u1edbi c\u00e1c nhi\u1ec7m v\u1ee5 ph\u00e2n lo\u1ea1i, ch\u1ebf \u0111\u1ed9 (d\u1ef1 \u0111o\u00e1n th\u01b0\u1eddng xuy\u00ean nh\u1ea5t) c\u1ee7a c\u00e1c d\u1ef1 \u0111o\u00e1n m\u00f4 h\u00ecnh ri\u00eang l\u1ebb \u0111\u01b0\u1ee3c l\u1ea5y l\u00e0m d\u1ef1 \u0111o\u00e1n t\u1ed5ng h\u1ee3p cu\u1ed1i c\u00f9ng. Trong c\u00e1c nhi\u1ec7m v\u1ee5 h\u1ed3i quy, c\u00e1c d\u1ef1 \u0111o\u00e1n \u0111\u01b0\u1ee3c t\u00ednh trung b\u00ecnh \u0111\u1ec3 c\u00f3 \u0111\u01b0\u1ee3c d\u1ef1 \u0111o\u00e1n cu\u1ed1i c\u00f9ng.<\/p>\n<\/li>\n<\/ol>\n

    Ph\u00e2n t\u00edch c\u00e1c t\u00ednh n\u0103ng ch\u00ednh c\u1ee7a \u0110\u00f3ng bao<\/h2>\n

    \u0110\u00f3ng bao cung c\u1ea5p m\u1ed9t s\u1ed1 t\u00ednh n\u0103ng ch\u00ednh g\u00f3p ph\u1ea7n v\u00e0o hi\u1ec7u qu\u1ea3 c\u1ee7a n\u00f3:<\/p>\n

      \n
    1. \n

      Gi\u1ea3m ph\u01b0\u01a1ng sai<\/strong>: B\u1eb1ng c\u00e1ch \u0111\u00e0o t\u1ea1o nhi\u1ec1u m\u00f4 h\u00ecnh tr\u00ean c\u00e1c t\u1eadp h\u1ee3p con d\u1eef li\u1ec7u kh\u00e1c nhau, \u0110\u00f3ng bao l\u00e0m gi\u1ea3m ph\u01b0\u01a1ng sai c\u1ee7a t\u1eadp h\u1ee3p, khi\u1ebfn n\u00f3 tr\u1edf n\u00ean m\u1ea1nh m\u1ebd h\u01a1n v\u00e0 \u00edt c\u00f3 xu h\u01b0\u1edbng kh\u1edbp qu\u00e1 m\u1ee9c.<\/p>\n<\/li>\n

    2. \n

      \u0110a d\u1ea1ng m\u1eabu m\u00e3<\/strong>: Vi\u1ec7c \u0111\u00f3ng bao khuy\u1ebfn kh\u00edch s\u1ef1 \u0111a d\u1ea1ng gi\u1eefa c\u00e1c m\u00f4 h\u00ecnh c\u01a1 s\u1edf, v\u00ec m\u1ed7i m\u00f4 h\u00ecnh \u0111\u01b0\u1ee3c hu\u1ea5n luy\u1ec7n tr\u00ean m\u1ed9t t\u1eadp h\u1ee3p con d\u1eef li\u1ec7u kh\u00e1c nhau. S\u1ef1 \u0111a d\u1ea1ng n\u00e0y gi\u00fap n\u1eafm b\u1eaft c\u00e1c m\u1eabu v\u00e0 s\u1eafc th\u00e1i kh\u00e1c nhau c\u00f3 trong d\u1eef li\u1ec7u.<\/p>\n<\/li>\n

    3. \n

      Song song h\u00f3a<\/strong>: C\u00e1c m\u00f4 h\u00ecnh c\u01a1 s\u1edf trong Bagging \u0111\u01b0\u1ee3c hu\u1ea5n luy\u1ec7n \u0111\u1ed9c l\u1eadp v\u00e0 song song, gi\u00fap t\u00ednh to\u00e1n hi\u1ec7u qu\u1ea3 v\u00e0 ph\u00f9 h\u1ee3p v\u1edbi c\u00e1c t\u1eadp d\u1eef li\u1ec7u l\u1edbn.<\/p>\n<\/li>\n<\/ol>\n

      C\u00e1c lo\u1ea1i \u0111\u00f3ng bao<\/h2>\n

      C\u00f3 nhi\u1ec1u bi\u1ebfn th\u1ec3 kh\u00e1c nhau c\u1ee7a \u0110\u00f3ng bao, t\u00f9y thu\u1ed9c v\u00e0o chi\u1ebfn l\u01b0\u1ee3c l\u1ea5y m\u1eabu v\u00e0 m\u00f4 h\u00ecnh c\u01a1 s\u1edf \u0111\u01b0\u1ee3c s\u1eed d\u1ee5ng. M\u1ed9t s\u1ed1 ki\u1ec3u \u0111\u00f3ng bao ph\u1ed5 bi\u1ebfn bao g\u1ed3m:<\/p>\n\n\n\n\n\n\n\n\n
      Ki\u1ec3u<\/th>\nS\u1ef1 mi\u00eau t\u1ea3<\/th>\n<\/tr>\n<\/thead>\n
      T\u1ed5ng h\u1ee3p Bootstrap<\/td>\n\u0110\u00f3ng bao ti\u00eau chu\u1ea9n v\u1edbi l\u1ea5y m\u1eabu bootstrap<\/td>\n<\/tr>\n
      Ph\u01b0\u01a1ng ph\u00e1p kh\u00f4ng gian con ng\u1eabu nhi\u00ean<\/td>\nC\u00e1c t\u00ednh n\u0103ng \u0111\u01b0\u1ee3c l\u1ea5y m\u1eabu ng\u1eabu nhi\u00ean cho t\u1eebng m\u1eabu c\u01a1 s\u1edf<\/td>\n<\/tr>\n
      B\u1ea3n v\u00e1 ng\u1eabu nhi\u00ean<\/td>\nT\u1eadp h\u1ee3p con ng\u1eabu nhi\u00ean c\u1ee7a c\u1ea3 phi\u00ean b\u1ea3n v\u00e0 t\u00ednh n\u0103ng<\/td>\n<\/tr>\n
      R\u1eebng ng\u1eabu nhi\u00ean<\/td>\n\u0110\u00f3ng g\u00f3i v\u1edbi c\u00e2y quy\u1ebft \u0111\u1ecbnh l\u00e0m m\u00f4 h\u00ecnh c\u01a1 s\u1edf<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

      C\u00e1ch s\u1eed d\u1ee5ng Bagging, c\u00e1c v\u1ea5n \u0111\u1ec1 v\u00e0 gi\u1ea3i ph\u00e1p li\u00ean quan \u0111\u1ebfn vi\u1ec7c s\u1eed d\u1ee5ng<\/h2>\n

      C\u00e1c tr\u01b0\u1eddng h\u1ee3p s\u1eed d\u1ee5ng \u0111\u00f3ng bao:<\/strong><\/p>\n

        \n
      1. Ph\u00e2n lo\u1ea1i<\/strong>: \u0110\u00f3ng g\u00f3i th\u01b0\u1eddng \u0111\u01b0\u1ee3c s\u1eed d\u1ee5ng v\u1edbi c\u00e2y quy\u1ebft \u0111\u1ecbnh \u0111\u1ec3 t\u1ea1o ra c\u00e1c b\u1ed9 ph\u00e2n lo\u1ea1i m\u1ea1nh m\u1ebd.<\/li>\n
      2. h\u1ed3i quy<\/strong>: N\u00f3 c\u00f3 th\u1ec3 \u0111\u01b0\u1ee3c \u00e1p d\u1ee5ng cho c\u00e1c b\u00e0i to\u00e1n h\u1ed3i quy \u0111\u1ec3 c\u1ea3i thi\u1ec7n \u0111\u1ed9 ch\u00ednh x\u00e1c c\u1ee7a d\u1ef1 \u0111o\u00e1n.<\/li>\n
      3. Ph\u00e1t hi\u1ec7n b\u1ea5t th\u01b0\u1eddng<\/strong>: \u0110\u00f3ng g\u00f3i c\u00f3 th\u1ec3 \u0111\u01b0\u1ee3c s\u1eed d\u1ee5ng \u0111\u1ec3 ph\u00e1t hi\u1ec7n ngo\u1ea1i l\u1ec7 trong d\u1eef li\u1ec7u.<\/li>\n<\/ol>\n

        Nh\u1eefng th\u00e1ch th\u1ee9c v\u00e0 gi\u1ea3i ph\u00e1p:<\/strong><\/p>\n

          \n
        1. \n

          B\u1ed9 d\u1eef li\u1ec7u kh\u00f4ng c\u00e2n b\u1eb1ng<\/strong>: Trong tr\u01b0\u1eddng h\u1ee3p l\u1edbp kh\u00f4ng c\u00e2n b\u1eb1ng, \u0110\u00f3ng bao c\u00f3 th\u1ec3 thi\u00ean v\u1ec1 l\u1edbp \u0111a s\u1ed1. Gi\u1ea3i quy\u1ebft v\u1ea5n \u0111\u1ec1 n\u00e0y b\u1eb1ng c\u00e1ch s\u1eed d\u1ee5ng tr\u1ecdng s\u1ed1 l\u1edbp c\u00e2n b\u1eb1ng ho\u1eb7c s\u1eeda \u0111\u1ed5i chi\u1ebfn l\u01b0\u1ee3c l\u1ea5y m\u1eabu.<\/p>\n<\/li>\n

        2. \n

          L\u1ef1a ch\u1ecdn m\u00f4 h\u00ecnh<\/strong>: Vi\u1ec7c l\u1ef1a ch\u1ecdn m\u00f4 h\u00ecnh c\u01a1 s\u1edf th\u00edch h\u1ee3p l\u00e0 r\u1ea5t quan tr\u1ecdng. M\u1ed9t t\u1eadp h\u1ee3p c\u00e1c m\u00f4 h\u00ecnh \u0111a d\u1ea1ng c\u00f3 th\u1ec3 mang l\u1ea1i hi\u1ec7u su\u1ea5t t\u1ed1t h\u01a1n.<\/p>\n<\/li>\n

        3. \n

          Chi ph\u00ed t\u00ednh to\u00e1n<\/strong>: Vi\u1ec7c \u0111\u00e0o t\u1ea1o nhi\u1ec1u m\u00f4 h\u00ecnh c\u00f3 th\u1ec3 t\u1ed1n th\u1eddi gian. C\u00e1c k\u1ef9 thu\u1eadt nh\u01b0 song song h\u00f3a v\u00e0 t\u00ednh to\u00e1n ph\u00e2n t\u00e1n c\u00f3 th\u1ec3 gi\u1ea3m thi\u1ec3u v\u1ea5n \u0111\u1ec1 n\u00e0y.<\/p>\n<\/li>\n<\/ol>\n

          C\u00e1c \u0111\u1eb7c \u0111i\u1ec3m ch\u00ednh v\u00e0 so s\u00e1nh kh\u00e1c v\u1edbi c\u00e1c thu\u1eadt ng\u1eef t\u01b0\u01a1ng t\u1ef1<\/h2>\n\n\n\n\n\n\n\n\n\n
          Di\u1ec7n m\u1ea1o<\/th>\n\u0110\u00f3ng bao<\/th>\nT\u0103ng c\u01b0\u1eddng<\/th>\nX\u1ebfp ch\u1ed3ng<\/th>\n<\/tr>\n<\/thead>\n
          Kh\u00e1ch quan<\/td>\nGi\u1ea3m ph\u01b0\u01a1ng sai<\/td>\nT\u0103ng \u0111\u1ed9 ch\u00ednh x\u00e1c c\u1ee7a m\u00f4 h\u00ecnh<\/td>\nK\u1ebft h\u1ee3p d\u1ef1 \u0111o\u00e1n c\u1ee7a c\u00e1c m\u00f4 h\u00ecnh<\/td>\n<\/tr>\n
          \u0110\u1ed9c l\u1eadp ki\u1ec3u m\u1eabu<\/td>\nM\u00f4 h\u00ecnh c\u01a1 s\u1edf \u0111\u1ed9c l\u1eadp<\/td>\nPh\u1ee5 thu\u1ed9c tu\u1ea7n t\u1ef1<\/td>\nM\u00f4 h\u00ecnh c\u01a1 s\u1edf \u0111\u1ed9c l\u1eadp<\/td>\n<\/tr>\n
          Th\u1ee9 t\u1ef1 hu\u1ea5n luy\u1ec7n c\u00e1c m\u00f4 h\u00ecnh c\u01a1 s\u1edf<\/td>\nSong song<\/td>\ntu\u1ea7n t\u1ef1<\/td>\nSong song<\/td>\n<\/tr>\n
          Tr\u1ecdng s\u1ed1 phi\u1ebfu b\u1ea7u c\u1ee7a c\u00e1c m\u00f4 h\u00ecnh c\u01a1 s\u1edf<\/td>\n\u0110\u1ed3ng ph\u1ee5c<\/td>\nPh\u1ee5 thu\u1ed9c v\u00e0o hi\u1ec7u su\u1ea5t<\/td>\nPh\u1ee5 thu\u1ed9c v\u00e0o si\u00eau m\u00f4 h\u00ecnh<\/td>\n<\/tr>\n
          D\u1ec5 b\u1ecb trang b\u1ecb qu\u00e1 m\u1ee9c<\/td>\nTh\u1ea5p<\/td>\nCao<\/td>\nV\u1eeba ph\u1ea3i<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

          Quan \u0111i\u1ec3m v\u00e0 c\u00f4ng ngh\u1ec7 c\u1ee7a t\u01b0\u01a1ng lai li\u00ean quan \u0111\u1ebfn \u0110\u00f3ng bao<\/h2>\n

          \u0110\u00f3ng bao \u0111\u00e3 l\u00e0 m\u1ed9t k\u1ef9 thu\u1eadt c\u01a1 b\u1ea3n trong h\u1ecdc t\u1eadp t\u1ed5ng h\u1ee3p v\u00e0 c\u00f3 th\u1ec3 s\u1ebd v\u1eabn c\u00f3 \u00fd ngh\u0129a quan tr\u1ecdng trong t\u01b0\u01a1ng lai. Tuy nhi\u00ean, v\u1edbi nh\u1eefng ti\u1ebfn b\u1ed9 trong h\u1ecdc m\u00e1y v\u00e0 s\u1ef1 ph\u00e1t tri\u1ec3n c\u1ee7a h\u1ecdc s\u00e2u, c\u00e1c ph\u01b0\u01a1ng ph\u00e1p t\u1ed5ng h\u1ee3p ph\u1ee9c t\u1ea1p h\u01a1n v\u00e0 c\u00e1c ph\u01b0\u01a1ng ph\u00e1p ti\u1ebfp c\u1eadn k\u1ebft h\u1ee3p c\u00f3 th\u1ec3 xu\u1ea5t hi\u1ec7n, k\u1ebft h\u1ee3p \u0110\u00f3ng bao v\u1edbi c\u00e1c k\u1ef9 thu\u1eadt kh\u00e1c.<\/p>\n

          S\u1ef1 ph\u00e1t tri\u1ec3n trong t\u01b0\u01a1ng lai c\u00f3 th\u1ec3 t\u1eadp trung v\u00e0o vi\u1ec7c t\u1ed1i \u01b0u h\u00f3a c\u00e1c c\u1ea5u tr\u00fac t\u1eadp h\u1ee3p, thi\u1ebft k\u1ebf c\u00e1c m\u00f4 h\u00ecnh c\u01a1 s\u1edf hi\u1ec7u qu\u1ea3 h\u01a1n v\u00e0 kh\u00e1m ph\u00e1 c\u00e1c ph\u01b0\u01a1ng ph\u00e1p ti\u1ebfp c\u1eadn th\u00edch \u1ee9ng \u0111\u1ec3 t\u1ea1o ra c\u00e1c t\u1eadp h\u1ee3p c\u00f3 kh\u1ea3 n\u0103ng \u0111i\u1ec1u ch\u1ec9nh linh ho\u1ea1t \u0111\u1ec3 thay \u0111\u1ed5i ph\u00e2n ph\u1ed1i d\u1eef li\u1ec7u.<\/p>\n

          C\u00e1ch s\u1eed d\u1ee5ng ho\u1eb7c li\u00ean k\u1ebft m\u00e1y ch\u1ee7 proxy v\u1edbi Bagging<\/h2>\n

          M\u00e1y ch\u1ee7 proxy \u0111\u00f3ng m\u1ed9t vai tr\u00f2 quan tr\u1ecdng trong c\u00e1c \u1ee9ng d\u1ee5ng li\u00ean quan \u0111\u1ebfn web kh\u00e1c nhau, bao g\u1ed3m qu\u00e9t web, khai th\u00e1c d\u1eef li\u1ec7u v\u00e0 \u1ea9n danh d\u1eef li\u1ec7u. Khi n\u00f3i \u0111\u1ebfn \u0110\u00f3ng g\u00f3i, m\u00e1y ch\u1ee7 proxy c\u00f3 th\u1ec3 \u0111\u01b0\u1ee3c s\u1eed d\u1ee5ng \u0111\u1ec3 n\u00e2ng cao qu\u00e1 tr\u00ecnh \u0111\u00e0o t\u1ea1o b\u1eb1ng c\u00e1ch:<\/p>\n

            \n
          1. \n

            Thu th\u1eadp d\u1eef li\u1ec7u<\/strong>: \u0110\u00f3ng bao th\u01b0\u1eddng y\u00eau c\u1ea7u m\u1ed9t l\u01b0\u1ee3ng l\u1edbn d\u1eef li\u1ec7u hu\u1ea5n luy\u1ec7n. M\u00e1y ch\u1ee7 proxy c\u00f3 th\u1ec3 gi\u00fap thu th\u1eadp d\u1eef li\u1ec7u t\u1eeb nhi\u1ec1u ngu\u1ed3n kh\u00e1c nhau \u0111\u1ed3ng th\u1eddi gi\u1ea3m nguy c\u01a1 b\u1ecb ch\u1eb7n ho\u1eb7c g\u1eafn c\u1edd.<\/p>\n<\/li>\n

          2. \n

            \u0110\u00e0o t\u1ea1o \u1ea9n danh<\/strong>: M\u00e1y ch\u1ee7 proxy c\u00f3 th\u1ec3 \u1ea9n danh t\u00ednh c\u1ee7a ng\u01b0\u1eddi d\u00f9ng trong khi truy c\u1eadp t\u00e0i nguy\u00ean tr\u1ef1c tuy\u1ebfn trong qu\u00e1 tr\u00ecnh \u0111\u00e0o t\u1ea1o m\u00f4 h\u00ecnh, gi\u00fap quy tr\u00ecnh tr\u1edf n\u00ean an to\u00e0n h\u01a1n v\u00e0 ng\u0103n ch\u1eb7n c\u00e1c h\u1ea1n ch\u1ebf d\u1ef1a tr\u00ean IP.<\/p>\n<\/li>\n

          3. \n

            C\u00e2n b\u1eb1ng t\u1ea3i<\/strong>: B\u1eb1ng c\u00e1ch ph\u00e2n ph\u1ed1i y\u00eau c\u1ea7u th\u00f4ng qua c\u00e1c m\u00e1y ch\u1ee7 proxy kh\u00e1c nhau, t\u1ea3i tr\u00ean m\u1ed7i m\u00e1y ch\u1ee7 c\u00f3 th\u1ec3 \u0111\u01b0\u1ee3c c\u00e2n b\u1eb1ng, n\u00e2ng cao hi\u1ec7u qu\u1ea3 c\u1ee7a qu\u00e1 tr\u00ecnh thu th\u1eadp d\u1eef li\u1ec7u.<\/p>\n<\/li>\n<\/ol>\n

            Li\u00ean k\u1ebft li\u00ean quan<\/h2>\n

            \u0110\u1ec3 bi\u1ebft th\u00eam th\u00f4ng tin v\u1ec1 k\u1ef9 thu\u1eadt \u0111\u00f3ng g\u00f3i v\u00e0 h\u1ecdc t\u1eadp t\u1ed5ng h\u1ee3p, h\u00e3y tham kh\u1ea3o c\u00e1c t\u00e0i nguy\u00ean sau:<\/p>\n

              \n
            1. T\u00e0i li\u1ec7u \u0111\u00f3ng g\u00f3i Scikit-learn<\/a><\/li>\n
            2. B\u00e0i vi\u1ebft g\u1ed1c v\u1ec1 \u0111\u00f3ng bao c\u1ee7a Leo Breiman<\/a><\/li>\n
            3. Gi\u1edbi thi\u1ec7u v\u1ec1 Ensemble Learning v\u00e0 Bagging<\/a><\/li>\n<\/ol>\n

              \u0110\u00f3ng bao ti\u1ebfp t\u1ee5c l\u00e0 m\u1ed9t c\u00f4ng c\u1ee5 m\u1ea1nh m\u1ebd trong kho v\u0169 kh\u00ed h\u1ecdc m\u00e1y v\u00e0 vi\u1ec7c hi\u1ec3u \u0111\u01b0\u1ee3c s\u1ef1 ph\u1ee9c t\u1ea1p c\u1ee7a n\u00f3 c\u00f3 th\u1ec3 mang l\u1ea1i l\u1ee3i \u00edch \u0111\u00e1ng k\u1ec3 cho vi\u1ec7c l\u1eadp m\u00f4 h\u00ecnh d\u1ef1 \u0111o\u00e1n v\u00e0 ph\u00e2n t\u00edch d\u1eef li\u1ec7u.<\/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\/vn\/wp-json\/wp\/v2\/wiki\/475967","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/oneproxy.pro\/vn\/wp-json\/wp\/v2\/wiki"}],"about":[{"href":"https:\/\/oneproxy.pro\/vn\/wp-json\/wp\/v2\/types\/wiki"}],"version-history":[{"count":0,"href":"https:\/\/oneproxy.pro\/vn\/wp-json\/wp\/v2\/wiki\/475967\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/oneproxy.pro\/vn\/wp-json\/wp\/v2\/media\/467687"}],"wp:attachment":[{"href":"https:\/\/oneproxy.pro\/vn\/wp-json\/wp\/v2\/media?parent=475967"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}