{"id":1235,"date":"2023-07-27T04:52:33","date_gmt":"2023-07-27T04:52:33","guid":{"rendered":"https:\/\/statorials.org\/tr\/rde-xgboost\/"},"modified":"2023-07-27T04:52:33","modified_gmt":"2023-07-27T04:52:33","slug":"rde-xgboost","status":"publish","type":"post","link":"https:\/\/statorials.org\/tr\/rde-xgboost\/","title":{"rendered":"R'de xgboost: ad\u0131m ad\u0131m \u00f6rnek"},"content":{"rendered":"

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Boosting<\/a> , y\u00fcksek tahmin do\u011frulu\u011funa sahip modeller \u00fcretti\u011fi kan\u0131tlanm\u0131\u015f bir makine \u00f6\u011frenme tekni\u011fidir.<\/span><\/p>\n

G\u00fc\u00e7lendirmeyi pratikte uygulaman\u0131n en yayg\u0131n yollar\u0131ndan biri, “ekstrem degrade g\u00fc\u00e7lendirme”nin k\u0131saltmas\u0131 olan XGBoost’u<\/strong> kullanmakt\u0131r.<\/span><\/p>\n

Bu e\u011fitimde, R’de geli\u015ftirilmi\u015f bir modele uyum sa\u011flamak i\u00e7in XGBoost’un nas\u0131l kullan\u0131laca\u011f\u0131na ili\u015fkin ad\u0131m ad\u0131m bir \u00f6rnek sunulmaktad\u0131r.<\/span><\/p>\n

Ad\u0131m 1: Gerekli paketleri y\u00fckleyin<\/span><\/strong><\/h3>\n

\u00d6ncelikle gerekli k\u00fct\u00fcphaneleri y\u00fckleyece\u011fiz.<\/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
 2. Ad\u0131m: Verileri y\u00fckleyin<\/strong><\/span><\/h3>\n
 Bu \u00f6rnek i\u00e7in, MASS<\/strong> paketinden Boston<\/strong> veri setine geli\u015ftirilmi\u015f bir regresyon modeli yerle\u015ftirece\u011fiz.<\/span><\/p>\n
 Bu veri seti, Boston \u00e7evresindeki farkl\u0131 n\u00fcfus say\u0131m b\u00f6lgelerindeki evlerin medyan de\u011ferini temsil eden mdev<\/strong> ad\u0131 verilen bir yan\u0131t de\u011fi\u015fkenini<\/a> tahmin etmek i\u00e7in kullanaca\u011f\u0131m\u0131z 13 \u00f6ng\u00f6r\u00fcc\u00fc de\u011fi\u015fkeni i\u00e7erir.<\/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
 3. Ad\u0131m: Verileri haz\u0131rlay\u0131n<\/strong><\/span><\/h3>\n
 Daha sonra, orijinal veri k\u00fcmesini bir e\u011fitim ve test k\u00fcmesine b\u00f6lmek i\u00e7in caret paketindeki createDataPartition()<\/strong> i\u015flevini kullanaca\u011f\u0131z.<\/span><\/p>\n
 Bu \u00f6rnekte, e\u011fitim setinin bir par\u00e7as\u0131 olarak orijinal veri setinin %80’ini kullanmay\u0131 se\u00e7ece\u011fiz.<\/span><\/p>\n
 Xgboost paketinin ayn\u0131 zamanda matris verilerini kulland\u0131\u011f\u0131n\u0131 unutmay\u0131n; dolay\u0131s\u0131yla tahmin de\u011fi\u015fkenlerimizi tutmak i\u00e7in data.matrix()<\/strong> i\u015flevini kullanaca\u011f\u0131z.<\/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
 Ad\u0131m 4: Modeli Ayarlay\u0131n<\/strong><\/span><\/h3>\n
 Daha sonra, her g\u00fc\u00e7lendirme d\u00f6ng\u00fcs\u00fc i\u00e7in e\u011fitim ve test RMSE’sini (ortalama kare hatas\u0131) g\u00f6r\u00fcnt\u00fcleyen xgb.train()<\/strong> i\u015flevini kullanarak XGBoost modelini ayarlayaca\u011f\u0131z.<\/span><\/p>\n
 Bu \u00f6rnek i\u00e7in 70 tur kullanmay\u0131 se\u00e7ti\u011fimizi unutmay\u0131n, ancak \u00e7ok daha b\u00fcy\u00fck veri k\u00fcmeleri i\u00e7in y\u00fczlerce, hatta binlerce tur kullan\u0131lmas\u0131 al\u0131\u015f\u0131lmad\u0131k bir durum de\u011fildir. Unutmay\u0131n ki tur say\u0131s\u0131 artt\u0131k\u00e7a \u00e7al\u0131\u015fma s\u00fcresi de uzar.<\/span><\/p>\n
 Ayr\u0131ca maksimum derece<\/strong> arg\u00fcman\u0131n\u0131n bireysel karar a\u011fa\u00e7lar\u0131n\u0131n geli\u015fim derinli\u011fini belirtti\u011fine dikkat edin. Daha k\u00fc\u00e7\u00fck a\u011fa\u00e7lar yeti\u015ftirmek i\u00e7in genellikle bu say\u0131y\u0131 2 veya 3 gibi olduk\u00e7a d\u00fc\u015f\u00fck se\u00e7iyoruz. Bu yakla\u015f\u0131m\u0131n daha do\u011fru modeller \u00fcretme e\u011filiminde oldu\u011fu g\u00f6sterilmi\u015ftir.<\/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
 B\u00f6ylece son XGBoost modelimizi 56 tur kullanacak \u015fekilde ayarlayaca\u011f\u0131z:<\/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
 Not: Verbose=0<\/strong> arg\u00fcman\u0131 R’ye her turda e\u011fitim ve test hatas\u0131n\u0131 g\u00f6r\u00fcnt\u00fclememesini s\u00f6yler.<\/span><\/p>\n
 Ad\u0131m 5: Tahminlerde bulunmak i\u00e7in modeli kullan\u0131n<\/strong><\/span><\/h3>\n
 Son olarak, test setindeki Boston evlerinin ortalama de\u011feri hakk\u0131nda tahminlerde bulunmak i\u00e7in geli\u015ftirilmi\u015f son modeli kullanabiliriz.<\/span><\/p>\n
 Daha sonra model i\u00e7in a\u015fa\u011f\u0131daki do\u011fruluk \u00f6l\u00e7\u00fcmlerini hesaplayaca\u011f\u0131z:<\/span><\/p>\n
 Sonu\u00e7tan minimum RMSE testinin 56<\/strong> turda elde edildi\u011fini g\u00f6rebiliriz. Bu noktan\u0131n \u00f6tesinde, RMSE testi artmaya ba\u015flar ve bu da e\u011fitim verilerine gere\u011finden fazla uyum sa\u011flad\u0131\u011f\u0131m\u0131z\u0131<\/a> g\u00f6sterir.<\/span><\/p>\n
 Veri setinin toplamda 506 g\u00f6zlem<\/a> ve 14 de\u011fi\u015fken i\u00e7erdi\u011fini g\u00f6r\u00fcyoruz.<\/span><\/p>\n