{"id":1235,"date":"2023-07-27T04:52:33","date_gmt":"2023-07-27T04:52:33","guid":{"rendered":"https:\/\/statorials.org\/id\/xgboost-di-r\/"},"modified":"2023-07-27T04:52:33","modified_gmt":"2023-07-27T04:52:33","slug":"xgboost-di-r","status":"publish","type":"post","link":"https:\/\/statorials.org\/id\/xgboost-di-r\/","title":{"rendered":"Xgboost di r: contoh langkah demi langkah"},"content":{"rendered":"

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Boosting<\/a> adalah teknik pembelajaran mesin yang terbukti menghasilkan model dengan akurasi prediksi tinggi.<\/span><\/p>\n

Salah satu cara paling umum untuk menerapkan peningkatan dalam praktik adalah dengan menggunakan XGBoost<\/strong> , kependekan dari “peningkatan gradien ekstrem”.<\/span><\/p>\n

Tutorial ini memberikan contoh langkah demi langkah tentang cara menggunakan XGBoost agar sesuai dengan model yang disempurnakan di R.<\/span><\/p>\n

Langkah 1: Muat paket yang diperlukan<\/span><\/strong><\/h3>\n

Pertama, kita akan memuat perpustakaan yang diperlukan.<\/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
 Langkah 2: Muat data<\/strong><\/span><\/h3>\n
 Untuk contoh ini, kami akan menyesuaikan model regresi yang ditingkatkan ke kumpulan data Boston<\/strong> dari paket MASS<\/strong> .<\/span><\/p>\n
 Kumpulan data ini berisi 13 variabel prediktor yang akan kita gunakan untuk memprediksi variabel respons<\/a> yang disebut mdev<\/strong> , yang mewakili nilai median rumah di berbagai wilayah sensus di sekitar 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
 Langkah 3: Siapkan datanya<\/strong><\/span><\/h3>\n
 Selanjutnya, kita akan menggunakan fungsi createDataPartition()<\/strong> dari paket caret untuk membagi kumpulan data asli menjadi kumpulan pelatihan dan pengujian.<\/span><\/p>\n
 Untuk contoh ini, kami akan memilih untuk menggunakan 80% kumpulan data asli sebagai bagian dari kumpulan pelatihan.<\/span><\/p>\n
 Perhatikan bahwa paket xgboost juga menggunakan data matriks, jadi kita akan menggunakan fungsi data.matrix()<\/strong> untuk menampung variabel prediktor kita.<\/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
 Langkah 4: Sesuaikan Model<\/strong><\/span><\/h3>\n
 Selanjutnya, kita akan menyetel model XGBoost menggunakan fungsi xgb.train()<\/strong> , yang menampilkan pelatihan dan pengujian RMSE (mean square error) untuk setiap siklus peningkatan.<\/span><\/p>\n
 Perhatikan bahwa kami memilih untuk menggunakan 70 putaran untuk contoh ini, namun untuk kumpulan data yang jauh lebih besar, tidak jarang menggunakan ratusan atau bahkan ribuan putaran. Perlu diingat bahwa semakin banyak putaran, semakin lama waktu prosesnya.<\/span><\/p>\n
 Perhatikan juga bahwa argumen max.degree<\/strong> menentukan kedalaman pengembangan pohon keputusan individu. Kami biasanya memilih angka yang cukup rendah, seperti 2 atau 3, untuk menumbuhkan pohon yang lebih kecil. Pendekatan ini terbukti cenderung menghasilkan model yang lebih akurat.<\/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
 Jadi, kami akan menetapkan model XGBoost terakhir kami untuk menggunakan 56 putaran:<\/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
 Catatan: Argumen verbose=0<\/strong> memberitahu R untuk tidak menampilkan kesalahan pelatihan dan pengujian untuk setiap putaran.<\/span><\/p>\n
 Langkah 5: Gunakan model untuk membuat prediksi<\/strong><\/span><\/h3>\n
 Terakhir, kita dapat menggunakan model akhir yang ditingkatkan untuk membuat prediksi tentang nilai median rumah di Boston pada set pengujian.<\/span><\/p>\n
 Kami kemudian akan menghitung metrik akurasi berikut untuk model tersebut:<\/span><\/p>\n
 Dari hasil tersebut terlihat bahwa tes RMSE minimum dicapai pada 56<\/strong> putaran. Di luar titik ini, RMSE pengujian mulai meningkat, yang menunjukkan bahwa kami melakukan overfitting pada data pelatihan<\/a> .<\/span><\/p>\n
 Kita dapat melihat bahwa dataset tersebut berisi 506 observasi<\/a> dan total 14 variabel.<\/span><\/p>\n