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2837360753931bf6077672f51f032e58a1bed39e Mon Sep 17 00:00:00 2001 From: sandernogueira <70956764+sandernogueira@users.noreply.github.com> Date: Thu, 22 Oct 2020 11:23:41 -0300 Subject: [PATCH 2/3] Add files via upload --- .../Rafael Nogueira ML (Bagging).md | 27 ++++++++++++++++++ 5-tratamentodedados/bagging.webp | Bin 0 -> 10516 bytes 2 files changed, 27 insertions(+) create mode 100644 5-tratamentodedados/Rafael Nogueira ML (Bagging).md create mode 100644 5-tratamentodedados/bagging.webp diff --git a/5-tratamentodedados/Rafael Nogueira ML (Bagging).md b/5-tratamentodedados/Rafael Nogueira ML (Bagging).md new file mode 100644 index 0000000..fd3ef45 --- /dev/null +++ b/5-tratamentodedados/Rafael Nogueira ML (Bagging).md @@ -0,0 +1,27 @@ +# Algoritmo Bootstrap Aggregatíng (Bagging) + +- Tipo de aprendizado: N/A +- Subcategoria: Meta - Algoritmo utilizado para aumento da precisão de preditores. +- Leo Breiman: Bagging Predictors, 1996 + +## Descrição + +### Onde é usado (tecnicamente) + +O Bagging é um meta-algoritmo que busca o aumento da precisão dos preditores por meio da geração de sub populações (bootstrap) de treinamento e consolidação dos resultados obtidos em um preditor final. A vantagem dessa técnica é que as sub populações são geradas a partir de amostras da população original, não havendo a necessidade de aumento ou obtenção de novos dados e, mesmo assim, a técnica demonstrou ter capacidade de aumentar a precisão dos algoritmos. Adicionalmente, a capacidade de paralelização por meio da execução concorrente dos treinamentos nas sub populações, se mostra como mais uma vantagem dessa abordagem. + +![Funcionamento Bagging](bagging.webp) + +### Como é utilizado + +Esse método demonstrou sua efetividade em preditores instáveis (Ex. Redes neurais, árvores de classificação e regressão e seleção de variáveis em regressão linear). De acordo com Alexandre Rubesam em sua dissertação, 2004, um preditor é instável se pequenas perturbações no conjunto de treinamento causam grande variações nos preditores treinados com ele. A técnica de bagging não é indicada para ser aplicada em algoritmos Nearest Neighbors. + +### Exemplos de caso de uso + +Em seu artigo de apresentação, Leo Breiman aplicou o bagging em aliado ao algoritmo de árvores de classificação e regressão. No seu experimento ele utilizou datasets publicados pela UCI e obteve reduções significativas no erro dos modelos quando a técnica de bagging foi aplicada. + +## Referências +[Leo Breiman](https://www.stat.berkeley.edu/~breiman/bagging.pdf) +[Dissertação Rusam, Alexandre](http://repositorio.unicamp.br/jspui/bitstream/REPOSIP/306510/1/Rubesam_Alexandre_M.pdf) +[Corporate Finance Institute.](https://corporatefinanceinstitute.com/resources/knowledge/other/bagging-bootstrap-aggregation/) + diff --git a/5-tratamentodedados/bagging.webp b/5-tratamentodedados/bagging.webp new file mode 100644 index 0000000000000000000000000000000000000000..c854a7ebcb76fbfe198ee9e4985efeb88b7d9b4c GIT binary patch literal 10516 zcmV+vDeKl!Nk&EtDF6UhMM6+kP&gm}DF6U)vH+a{DpCUX06uLpl}IEaq@g2Iyl{{W ziDz#6^gtyf&?j5}!Eawg9f)tDp$9k3ZY} zHT*Z@Kc{|4e|z4Y{wvUL<6l{RyZw*$!~BQuciunE|DOI_e{TJU_Q&2A=>L^J0Kbrb z9R8{OoA)pLC+`=wPwtr=>}=jH==L&%b&1?>_zK-@NS8f&>h{LL#l^YHD<-(@l&B!A3jr8`zF*-XWB5XF!9J>a z$Y{SXc`b#$0w@J03Z**)>=q{b&^D~qox(x!du1OWJ`$&o2#LQ8-9VPxQ?u_xjnrB* zV;}&#goBZ<%=d(bYPx+F9(T=Q#_&%G2Nf2y=4=4y>YJF#OL)?>29nxgr5;^Hv!UJE zo=kKV!TambP+lmy?+uko`1tS!lj!l=sica)Mk*jLl4{%-W5!Cc0eq8Tbq9<~zpHi? zFlQxiB-m7vQ~&q|KyaIMmDQYmhGC4^U{GHeCMQ_svnIeO~b8Ck@rxTHori32y*!Y4rLmG|RwQv57 zI%~47E9c}TB{bGbOQnpmR@mZxQ>mW$Ic|J9);qHbD?mbFG&@oY(3w9+?CkYq(jBJ! 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