{"id":1234,"date":"2023-07-27T04:52:33","date_gmt":"2023-07-27T04:52:33","guid":{"rendered":"https:\/\/statorials.org\/pt\/xgboost-em-r\/"},"modified":"2023-07-27T04:52:33","modified_gmt":"2023-07-27T04:52:33","slug":"xgboost-em-r","status":"publish","type":"post","link":"https:\/\/statorials.org\/pt\/xgboost-em-r\/","title":{"rendered":"Xgboost em r: um exemplo passo a passo"},"content":{"rendered":"

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Boosting<\/a> \u00e9 uma t\u00e9cnica de aprendizado de m\u00e1quina que demonstrou produzir modelos com alta precis\u00e3o preditiva.<\/span><\/p>\n

Uma das maneiras mais comuns de implementar o boosting na pr\u00e1tica \u00e9 usar XGBoost<\/strong> , abrevia\u00e7\u00e3o de “extreme gradiente boosting”.<\/span><\/p>\n

Este tutorial fornece um exemplo passo a passo de como usar o XGBoost para ajustar um modelo aprimorado em R.<\/span><\/p>\n

Passo 1: Carregue os pacotes necess\u00e1rios<\/span><\/strong><\/h3>\n

Primeiro, carregaremos as bibliotecas necess\u00e1rias.<\/span><\/p>\n

 library<\/span> (xgboost) #for fitting the xgboost model<\/span>\nlibrary<\/span> (caret) #for general data preparation and model fitting<\/span>\n<\/strong><\/pre>\n
 Etapa 2: carregar dados<\/strong><\/span><\/h3>\n
 Para este exemplo, ajustaremos um modelo de regress\u00e3o aprimorado ao conjunto de dados de Boston<\/strong> do pacote MASS<\/strong> .<\/span><\/p>\n
 Este conjunto de dados cont\u00e9m 13 vari\u00e1veis preditoras que usaremos para prever uma vari\u00e1vel de resposta<\/a> chamada mdev<\/strong> , que representa o valor mediano de resid\u00eancias em diferentes setores censit\u00e1rios ao redor de Boston.<\/span><\/p>\n
 #load the data\n<\/span>data = MASS::Boston\n\n#view the structure of the data\n<\/span>str(data) \n\n'data.frame': 506 obs. of 14 variables:\n $ crim: num 0.00632 0.02731 0.02729 0.03237 0.06905 ...\n $ zn : num 18 0 0 0 0 0 12.5 12.5 12.5 12.5 ...\n $ indus: num 2.31 7.07 7.07 2.18 2.18 2.18 7.87 7.87 7.87 7.87 ...\n $chas: int 0 0 0 0 0 0 0 0 0 0 ...\n $ nox: num 0.538 0.469 0.469 0.458 0.458 0.458 0.524 0.524 0.524 0.524 ...\n $rm: num 6.58 6.42 7.18 7 7.15 ...\n $ age: num 65.2 78.9 61.1 45.8 54.2 58.7 66.6 96.1 100 85.9 ...\n $ dis: num 4.09 4.97 4.97 6.06 6.06 ...\n $rad: int 1 2 2 3 3 3 5 5 5 5 ...\n $ tax: num 296 242 242 222 222 222 311 311 311 311 ...\n $ptratio: num 15.3 17.8 17.8 18.7 18.7 18.7 15.2 15.2 15.2 15.2 ...\n $ black: num 397 397 393 395 397 ...\n $ lstat: num 4.98 9.14 4.03 2.94 5.33 ...\n $ medv: num 24 21.6 34.7 33.4 36.2 28.7 22.9 27.1 16.5 18.9 ...\n<\/span><\/span><\/strong><\/pre>\n
 Etapa 3: preparar os dados<\/strong><\/span><\/h3>\n
 A seguir, usaremos a fun\u00e7\u00e3o createDataPartition()<\/strong> do pacote caret para dividir o conjunto de dados original em um conjunto de treinamento e teste.<\/span><\/p>\n
 Para este exemplo, escolheremos usar 80% do conjunto de dados original como parte do conjunto de treinamento.<\/span><\/p>\n
 Observe que o pacote xgboost tamb\u00e9m usa dados de matriz, ent\u00e3o usaremos a fun\u00e7\u00e3o data.matrix()<\/strong> para armazenar nossas vari\u00e1veis preditoras.<\/span><\/p>\n
 #make this example reproducible\n<\/span>set.seed(0)\n\n#split into training (80%) and testing set (20%)\n<\/span>parts = createDataPartition(data$medv, p = .8<\/span> , list = F<\/span> )\ntrain = data[parts, ]\ntest = data[-parts, ]\n\n#define predictor and response variables in training set\n<\/span>train_x = data. matrix<\/span> (train[, -13])\ntrain_y = train[,13]\n\n#define predictor and response variables in testing set\n<\/span>test_x = data. matrix<\/span> (test[, -13])\ntest_y = test[, 13]\n\n#define final training and testing sets\n<\/span>xgb_train = xgb. DMatrix<\/span> (data = train_x, label = train_y)\nxgb_test = xgb. DMatrix<\/span> (data = test_x, label = test_y)\n<\/span><\/span><\/strong><\/pre>\n
 Etapa 4: ajuste o modelo<\/strong><\/span><\/h3>\n
 A seguir, ajustaremos o modelo XGBoost usando a fun\u00e7\u00e3o xgb.train()<\/strong> , que exibe o RMSE (erro quadr\u00e1tico m\u00e9dio) de treinamento e teste para cada ciclo de refor\u00e7o.<\/span><\/p>\n
 Observe que optamos por usar 70 rodadas neste exemplo, mas para conjuntos de dados muito maiores n\u00e3o \u00e9 incomum usar centenas ou mesmo milhares de rodadas. Basta ter em mente que quanto mais rodadas, maior ser\u00e1 o tempo de execu\u00e7\u00e3o.<\/span><\/p>\n
 Observe tamb\u00e9m que o argumento max.degree<\/strong> especifica a profundidade do desenvolvimento de \u00e1rvores de decis\u00e3o individuais. Geralmente escolhemos esse n\u00famero bem baixo, como 2 ou 3, para fazer crescer \u00e1rvores menores. Foi demonstrado que esta abordagem tende a produzir modelos mais precisos.<\/span><\/p>\n
 #define watchlist\n<\/span>watchlist = list(train=xgb_train, test=xgb_test)\n\n#fit XGBoost model and display training and testing data at each round\n<\/span>model = xgb.train(data = xgb_train, max.depth = 3<\/span> , watchlist=watchlist, nrounds = 70<\/span> )\n\n[1] train-rmse:10.167523 test-rmse:10.839775 \n[2] train-rmse:7.521903 test-rmse:8.329679 \n[3] train-rmse:5.702393 test-rmse:6.691415 \n[4] train-rmse:4.463687 test-rmse:5.631310 \n[5] train-rmse:3.666278 test-rmse:4.878750 \n[6] train-rmse:3.159799 test-rmse:4.485698 \n[7] train-rmse:2.855133 test-rmse:4.230533 \n[8] train-rmse:2.603367 test-rmse:4.099881 \n[9] train-rmse:2.445718 test-rmse:4.084360 \n[10] train-rmse:2.327318 test-rmse:3.993562 \n[11] train-rmse:2.267629 test-rmse:3.944454 \n[12] train-rmse:2.189527 test-rmse:3.930808 \n[13] train-rmse:2.119130 test-rmse:3.865036 \n[14] train-rmse:2.086450 test-rmse:3.875088 \n[15] train-rmse:2.038356 test-rmse:3.881442 \n[16] train-rmse:2.010995 test-rmse:3.883322 \n[17] train-rmse:1.949505 test-rmse:3.844382 \n[18] train-rmse:1.911711 test-rmse:3.809830 \n[19] train-rmse:1.888488 test-rmse:3.809830 \n[20] train-rmse:1.832443 test-rmse:3.758502 \n[21] train-rmse:1.816150 test-rmse:3.770216 \n[22] train-rmse:1.801369 test-rmse:3.770474 \n[23] train-rmse:1.788891 test-rmse:3.766608 \n[24] train-rmse:1.751795 test-rmse:3.749583 \n[25] train-rmse:1.713306 test-rmse:3.720173 \n[26] train-rmse:1.672227 test-rmse:3.675086 \n[27] train-rmse:1.648323 test-rmse:3.675977 \n[28] train-rmse:1.609927 test-rmse:3.745338 \n[29] train-rmse:1.594891 test-rmse:3.756049 \n[30] train-rmse:1.578573 test-rmse:3.760104 \n[31] train-rmse:1.559810 test-rmse:3.727940 \n[32] train-rmse:1.547852 test-rmse:3.731702 \n[33] train-rmse:1.534589 test-rmse:3.729761 \n[34] train-rmse:1.520566 test-rmse:3.742681 \n[35] train-rmse:1.495155 test-rmse:3.732993 \n[36] train-rmse:1.467939 test-rmse:3.738329 \n[37] train-rmse:1.446343 test-rmse:3.713748 \n[38] train-rmse:1.435368 test-rmse:3.709469 \n[39] train-rmse:1.401356 test-rmse:3.710637 \n[40] train-rmse:1.390318 test-rmse:3.709461 \n[41] train-rmse:1.372635 test-rmse:3.708049 \n[42] train-rmse:1.367977 test-rmse:3.707429 \n[43] train-rmse:1.359531 test-rmse:3.711663 \n[44] train-rmse:1.335347 test-rmse:3.709101 \n[45] train-rmse:1.331750 test-rmse:3.712490 \n[46] train-rmse:1.313087 test-rmse:3.722981 \n[47] train-rmse:1.284392 test-rmse:3.712840 \n[48] train-rmse:1.257714 test-rmse:3.697482 \n[49] train-rmse:1.248218 test-rmse:3.700167 \n[50] train-rmse:1.243377 test-rmse:3.697914 \n[51] train-rmse:1.231956 test-rmse:3.695797 \n[52] train-rmse:1.219341 test-rmse:3.696277 \n[53] train-rmse:1.207413 test-rmse:3.691465 \n[54] train-rmse:1.197197 test-rmse:3.692108 \n[55] train-rmse:1.171748 test-rmse:3.683577 \n[56] train-rmse:1.156332 test-rmse:3.674458 \n[57] train-rmse:1.147686 test-rmse:3.686367 \n[58] train-rmse:1.143572 test-rmse:3.686375 \n[59] train-rmse:1.129780 test-rmse:3.679791 \n[60] train-rmse:1.111257 test-rmse:3.679022 \n[61] train-rmse:1.093541 test-rmse:3.699670 \n[62] train-rmse:1.083934 test-rmse:3.708187 \n[63] train-rmse:1.067109 test-rmse:3.712538 \n[64] train-rmse:1.053887 test-rmse:3.722480 \n[65] train-rmse:1.042127 test-rmse:3.720720 \n[66] train-rmse:1.031617 test-rmse:3.721224 \n[67] train-rmse:1.016274 test-rmse:3.699549 \n[68] train-rmse:1.008184 test-rmse:3.709522 \n[69] train-rmse:0.999220 test-rmse:3.708000 \n[70] train-rmse:0.985907 test-rmse:3.705192 \n<\/span><\/span><\/strong><\/pre>\n
 Ent\u00e3o, definiremos nosso modelo XGBoost final para usar 56 rodadas:<\/span><\/p>\n
 #define final model\n<\/span>final = xgboost(data = xgb_train, max.depth = 3<\/span> , nrounds = 56<\/span> , verbose = 0<\/span> )<\/span><\/span><\/strong><\/pre>\n
 Nota: O argumento verbose=0<\/strong> diz ao R para n\u00e3o exibir o erro de treinamento e teste para cada rodada.<\/span><\/p>\n
 Etapa 5: use o modelo para fazer previs\u00f5es<\/strong><\/span><\/h3>\n
 Finalmente, podemos usar o modelo final melhorado para fazer previs\u00f5es sobre o valor m\u00e9dio das casas de Boston no conjunto de teste.<\/span><\/p>\n
 Em seguida, calcularemos as seguintes m\u00e9tricas de precis\u00e3o para o modelo:<\/span><\/p>\n
 A partir do resultado, podemos ver que o RMSE m\u00ednimo do teste \u00e9 alcan\u00e7ado em 56<\/strong> rodadas. Al\u00e9m deste ponto, o RMSE do teste come\u00e7a a aumentar, indicando que estamos superajustando os dados de treinamento<\/a> .<\/span><\/p>\n
 Podemos ver que o conjunto de dados cont\u00e9m 506 observa\u00e7\u00f5es<\/a> e 14 vari\u00e1veis no total.<\/span><\/p>\n