{"id":1234,"date":"2023-07-27T04:52:33","date_gmt":"2023-07-27T04:52:33","guid":{"rendered":"https:\/\/statorials.org\/my\/r-%e1%80%90%e1%80%bd%e1%80%84%e1%80%ba-xgboost\/"},"modified":"2023-07-27T04:52:33","modified_gmt":"2023-07-27T04:52:33","slug":"r-%e1%80%90%e1%80%bd%e1%80%84%e1%80%ba-xgboost","status":"publish","type":"post","link":"https:\/\/statorials.org\/my\/r-%e1%80%90%e1%80%bd%e1%80%84%e1%80%ba-xgboost\/","title":{"rendered":"R in xgboost- \u1021\u1006\u1004\u1037\u103a\u1006\u1004\u1037\u103a \u1025\u1015\u1019\u102c"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\"><a href=\"https:\/\/statorials.org\/my\/\u1005\u1000\u103a\u101e\u1004\u103a\u101a\u1030\u1019\u103e\u102f\u1000\u102d\u102f-\u1019\u103c\u103e\u1004\u1037\u103a\u1010\u1004\u103a\u1015\u102b\u104b\/\" target=\"_blank\" rel=\"noopener noreferrer\">Boosting<\/a> \u101e\u100a\u103a \u1000\u103c\u102d\u102f\u1010\u1004\u103a\u1001\u1014\u1037\u103a\u1019\u103e\u1014\u103a\u1038\u1010\u102d\u1000\u103b\u1019\u103e\u102f\u1019\u103c\u1004\u1037\u103a\u1019\u102c\u1038\u101e\u1031\u102c \u1019\u1031\u102c\u103a\u1012\u101a\u103a\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1011\u102f\u1010\u103a\u101c\u102f\u1015\u103a\u101b\u1014\u103a \u1015\u103c\u101e\u1011\u102c\u1038\u101e\u1031\u102c \u1005\u1000\u103a\u101e\u1004\u103a\u101a\u1030\u1019\u103e\u102f\u1014\u100a\u103a\u1038\u1015\u100a\u102c\u1010\u1005\u103a\u1001\u102f\u1016\u103c\u1005\u103a\u101e\u100a\u103a\u104b<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u101c\u1000\u103a\u1010\u103d\u1031\u1037\u1010\u103d\u1004\u103a \u1019\u103c\u103e\u1004\u1037\u103a\u1010\u1004\u103a\u1001\u103c\u1004\u103a\u1038\u1000\u102d\u102f \u1021\u1000\u1031\u102c\u1004\u103a\u1021\u1011\u100a\u103a\u1016\u1031\u102c\u103a\u101b\u1014\u103a \u1021\u101e\u102f\u1036\u1038\u1021\u1019\u103b\u102c\u1038\u1006\u102f\u1036\u1038\u1014\u100a\u103a\u1038\u101c\u1019\u103a\u1038\u1010\u1005\u103a\u1001\u102f\u1019\u103e\u102c &#8220; extreme gradient boosting&#8221;  \u104f\u1021\u1010\u102d\u102f\u1000\u1031\u102c\u1000\u103a\u1016\u103c\u1005\u103a\u101e\u1031\u102c <strong>XGBoost \u1000\u102d\u102f<\/strong> \u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u1001\u103c\u1004\u103a\u1038\u1016\u103c\u1005\u103a\u101e\u100a\u103a\u104b<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u1024\u101e\u1004\u103a\u1001\u1014\u103a\u1038\u1005\u102c\u101e\u100a\u103a R \u1010\u103d\u1004\u103a \u1021\u1006\u1004\u1037\u103a\u1019\u103c\u103e\u1004\u1037\u103a\u1011\u102c\u1038\u101e\u1031\u102c \u1019\u1031\u102c\u103a\u1012\u101a\u103a\u101c\u103a\u1010\u1005\u103a\u1001\u102f\u1014\u103e\u1004\u1037\u103a \u1021\u1036\u101d\u1004\u103a\u1001\u103d\u1004\u103a\u1000\u103b\u1016\u103c\u1005\u103a\u101b\u1014\u103a XGBoost \u1000\u102d\u102f \u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u1014\u100a\u103a\u1038 \u1021\u1006\u1004\u1037\u103a\u1006\u1004\u1037\u103a \u1025\u1015\u1019\u102c\u1000\u102d\u102f \u1015\u1031\u1038\u1015\u102b\u101e\u100a\u103a\u104b<\/span><\/p>\n<h3> <strong><span style=\"color: #000000;\">\u1021\u1006\u1004\u1037\u103a 1- \u101c\u102d\u102f\u1021\u1015\u103a\u101e\u1031\u102c \u1015\u1000\u103a\u1000\u1031\u1037\u1001\u103a\u103b\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1010\u1004\u103a\u1015\u102b\u104b<\/span><\/strong><\/h3>\n<p> <span style=\"color: #000000;\">\u1015\u1011\u1019\u1026\u1038\u1005\u103d\u102c \u101c\u102d\u102f\u1021\u1015\u103a\u101e\u1031\u102c \u1005\u102c\u1000\u103c\u100a\u1037\u103a\u1010\u102d\u102f\u1000\u103a\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1010\u1004\u103a\u1015\u102b\u1019\u100a\u103a\u104b<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #993300;\">library<\/span> (xgboost) <span style=\"color: #008080;\">#for fitting the xgboost model<\/span>\n<span style=\"color: #993300;\">library<\/span> (caret) <span style=\"color: #008080;\">#for general data preparation and model fitting<\/span>\n<\/strong><\/pre>\n<h3> <span style=\"color: #000000;\"><strong>\u1021\u1006\u1004\u1037\u103a 2: \u1012\u1031\u1010\u102c\u1000\u102d\u102f \u1010\u1004\u103a\u1015\u102b\u104b<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\u1024\u1025\u1015\u1019\u102c\u1021\u1010\u103d\u1000\u103a\u104a \u1000\u103b\u103d\u1014\u103a\u102f\u1015\u103a\u1010\u102d\u102f\u1037\u101e\u100a\u103a <strong>MASS<\/strong> \u1015\u1000\u103a\u1000\u1031\u1037\u1001\u103b\u103a\u1019\u103e <strong>Boston<\/strong> dataset \u1014\u103e\u1004\u1037\u103a \u1015\u102d\u102f\u1019\u102d\u102f\u1000\u1031\u102c\u1004\u103a\u1038\u1019\u103d\u1014\u103a\u101e\u1031\u102c \u1006\u102f\u1010\u103a\u101a\u102f\u1010\u103a\u1019\u103e\u102f\u1015\u102f\u1036\u1005\u1036\u1000\u102d\u102f \u1016\u103c\u100a\u1037\u103a\u1006\u100a\u103a\u1038\u1015\u1031\u1038\u1015\u102b\u1019\u100a\u103a\u104b<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u1024\u1012\u1031\u1010\u102c\u1021\u1010\u103d\u1032\u1010\u103d\u1004\u103a <strong>mdev<\/strong> \u101f\u102f\u1001\u1031\u102b\u103a\u101e\u1031\u102c <a href=\"https:\/\/statorials.org\/my\/\u1015\u103c\u1031\u102c\u1004\u103a\u1038\u101c\u1032\u1014\u102d\u102f\u1004\u103a\u101e\u1031\u102c-\u101b\u103e\u1004\u103a\u1038\u101c\u1004\u103a\u1038\u1001\u103b\u1000\u103a-\u1010\u102f\u1036\u1037\u1015\u103c\u1014\u103a\u1019\u103e\u102f\u1019\u103b\u102c\u1038\/\" target=\"_blank\" rel=\"noopener noreferrer\">\u1010\u102f\u1036\u1037\u1015\u103c\u1014\u103a\u1019\u103e\u102f\u1000\u102d\u1014\u103a\u1038\u101b\u103e\u1004\u103a\u1010\u1005\u103a\u1001\u102f\u1000\u102d\u102f<\/a> \u1001\u1014\u1037\u103a\u1019\u103e\u1014\u103a\u1038\u101b\u1014\u103a \u1000\u103b\u103d\u1014\u103a\u102f\u1015\u103a\u1010\u102d\u102f\u1037\u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u1019\u100a\u1037\u103a \u1000\u103c\u102d\u102f\u1010\u1004\u103a\u1001\u1014\u1037\u103a\u1019\u103e\u1014\u103a\u1038\u1000\u102d\u1014\u103a\u1038\u101b\u103e\u1004\u103a 13 \u1001\u102f\u1015\u102b\u101b\u103e\u102d\u101e\u100a\u103a\u104a \u1018\u1031\u102c\u1037\u1005\u1010\u103d\u1014\u103a\u1010\u1005\u103a\u101d\u102d\u102f\u1000\u103a\u101b\u103e\u102d \u1019\u1010\u1030\u100a\u102e\u101e\u1031\u102c\u101e\u1014\u103a\u1038\u1001\u1031\u102b\u1004\u103a\u1005\u102c\u101b\u1004\u103a\u1038\u101d\u1031\u1005\u102c\u1019\u103b\u102c\u1038\u1010\u103d\u1004\u103a \u1021\u102d\u1019\u103a\u1019\u103b\u102c\u1038\u104f \u1015\u103b\u1019\u103a\u1038\u1019\u103b\u103e\u1010\u1014\u103a\u1016\u102d\u102f\u1038\u1000\u102d\u102f \u1000\u102d\u102f\u101a\u103a\u1005\u102c\u1038\u1015\u103c\u102f\u101e\u100a\u103a\u104b<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #993300;\"><span style=\"color: #000000;\"><span style=\"color: #008080;\">#load the data\n<\/span>data = MASS::Boston\n\n<span style=\"color: #008080;\">#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<p> <span style=\"color: #000000;\">\u1012\u1031\u1010\u102c\u1021\u1010\u103d\u1032\u1010\u103d\u1004\u103a <a href=\"https:\/\/statorials.org\/my\/\u1005\u102c\u101b\u1004\u103a\u1038\u1007\u101a\u102c\u1038\u1019\u103b\u102c\u1038\u1010\u103d\u1004\u103a-\u1005\u1031\u102c\u1004\u1037\u103a\u1000\u103c\u100a\u1037\u103a\u101c\u1031\u1037\u101c\u102c\u1001\u103c\u1004\u103a\u1038\u104b\/\" target=\"_blank\" rel=\"noopener noreferrer\">\u101c\u1031\u1037\u101c\u102c\u1010\u103d\u1031\u1037\u101b\u103e\u102d\u1001\u103b\u1000\u103a\u1015\u1031\u102b\u1004\u103a\u1038<\/a> 506 \u1001\u102f\u1014\u103e\u1004\u1037\u103a \u1005\u102f\u1005\u102f\u1015\u1031\u102b\u1004\u103a\u1038 \u1000\u102d\u1014\u103a\u1038\u101b\u103e\u1004\u103a 14 \u1001\u102f\u1015\u102b\u1040\u1004\u103a\u1000\u103c\u1031\u102c\u1004\u103a\u1038 \u1000\u103b\u103d\u1014\u103a\u102f\u1015\u103a\u1010\u102d\u102f\u1037\u1010\u103d\u1031\u1037\u1019\u103c\u1004\u103a\u1014\u102d\u102f\u1004\u103a\u1015\u102b\u101e\u100a\u103a\u104b<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>\u1021\u1006\u1004\u1037\u103a 3: \u1012\u1031\u1010\u102c\u1000\u102d\u102f\u1015\u103c\u1004\u103a\u1006\u1004\u103a\u1015\u102b\u104b<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\u1011\u102d\u102f\u1037\u1014\u1031\u102c\u1000\u103a\u104a \u1000\u103b\u103d\u1014\u103a\u102f\u1015\u103a\u1010\u102d\u102f\u1037\u101e\u100a\u103a \u1019\u1030\u101b\u1004\u103a\u1038\u1012\u1031\u1010\u102c\u1021\u1010\u103d\u1032\u1000\u102d\u102f \u101c\u1031\u1037\u1000\u103b\u1004\u1037\u103a\u101b\u1031\u1038\u1014\u103e\u1004\u1037\u103a \u1005\u1019\u103a\u1038\u101e\u1015\u103a\u1019\u103e\u102f\u1021\u1005\u102f\u1021\u1016\u103c\u1005\u103a \u1001\u103d\u1032\u101b\u1014\u103a caret package \u1019\u103e <strong>createDataPartition()<\/strong> \u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u1001\u103b\u1000\u103a\u1000\u102d\u102f \u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u1015\u102b\u1019\u100a\u103a\u104b<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u1024\u1025\u1015\u1019\u102c\u1021\u1010\u103d\u1000\u103a\u104a \u1000\u103b\u103d\u1014\u103a\u102f\u1015\u103a\u1010\u102d\u102f\u1037\u101e\u100a\u103a \u101c\u1031\u1037\u1000\u103b\u1004\u1037\u103a\u101b\u1031\u1038\u1021\u1005\u102f\u104f \u1010\u1005\u103a\u1005\u102d\u1010\u103a\u1010\u1005\u103a\u1015\u102d\u102f\u1004\u103a\u1038\u1021\u1016\u103c\u1005\u103a \u1019\u1030\u101b\u1004\u103a\u1038\u1012\u1031\u1010\u102c\u1021\u1010\u103d\u1032\u104f 80% \u1000\u102d\u102f \u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u101b\u1014\u103a \u101b\u103d\u1031\u1038\u1001\u103b\u101a\u103a\u1015\u102b\u1019\u100a\u103a\u104b<\/span><\/p>\n<p> <span style=\"color: #000000;\">xgboost \u1015\u1000\u103a\u1000\u1031\u1037\u1001\u103b\u103a\u101e\u100a\u103a \u1019\u1000\u103a\u1011\u101b\u1005\u103a\u1012\u1031\u1010\u102c\u1000\u102d\u102f\u101c\u100a\u103a\u1038 \u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u1000\u103c\u1031\u102c\u1004\u103a\u1038 \u101e\u1010\u102d\u1015\u103c\u102f\u1015\u102b\u104a \u1011\u102d\u102f\u1037\u1000\u103c\u1031\u102c\u1004\u1037\u103a \u1000\u103b\u103d\u1014\u103a\u102f\u1015\u103a\u1010\u102d\u102f\u1037\u104f \u1000\u103c\u102d\u102f\u1010\u1004\u103a\u1001\u1014\u1037\u103a\u1019\u103e\u1014\u103a\u1038\u1000\u102d\u1014\u103a\u1038\u101b\u103e\u1004\u103a\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1011\u102d\u1014\u103a\u1038\u101e\u102d\u1019\u103a\u1038\u101b\u1014\u103a\u1021\u1010\u103d\u1000\u103a <strong>data.matrix()<\/strong> \u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u1001\u103b\u1000\u103a\u1000\u102d\u102f \u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u1015\u102b\u1019\u100a\u103a\u104b<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #993300;\"><span style=\"color: #000000;\"><span style=\"color: #008080;\">#make this example reproducible\n<\/span>set.seed(0)\n\n<span style=\"color: #008080;\">#split into training (80%) and testing set (20%)\n<\/span>parts = createDataPartition(data$medv, p = <span style=\"color: #008000;\">.8<\/span> , list = <span style=\"color: #008000;\">F<\/span> )\ntrain = data[parts, ]\ntest = data[-parts, ]\n\n<span style=\"color: #008080;\">#define predictor and response variables in training set\n<\/span>train_x = data. <span style=\"color: #3366ff;\">matrix<\/span> (train[, -13])\ntrain_y = train[,13]\n\n<span style=\"color: #008080;\">#define predictor and response variables in testing set\n<\/span>test_x = data. <span style=\"color: #3366ff;\">matrix<\/span> (test[, -13])\ntest_y = test[, 13]\n\n<span style=\"color: #008080;\">#define final training and testing sets\n<\/span>xgb_train = xgb. <span style=\"color: #3366ff;\">DMatrix<\/span> (data = train_x, label = train_y)\nxgb_test = xgb. <span style=\"color: #3366ff;\">DMatrix<\/span> (data = test_x, label = test_y)\n<\/span><\/span><\/strong><\/pre>\n<h3> <span style=\"color: #000000;\"><strong>\u1021\u1006\u1004\u1037\u103a 4: \u1019\u1031\u102c\u103a\u1012\u101a\u103a\u1000\u102d\u102f \u1001\u103b\u102d\u1014\u103a\u100a\u103e\u102d\u1015\u102b\u104b<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\u1011\u102d\u102f\u1037\u1014\u1031\u102c\u1000\u103a\u104a \u1000\u103b\u103d\u1014\u103a\u102f\u1015\u103a\u1010\u102d\u102f\u1037\u101e\u100a\u103a \u1019\u103c\u103e\u1004\u1037\u103a\u1010\u1004\u103a\u1001\u103c\u1004\u103a\u1038\u1005\u1000\u103a\u101d\u1014\u103a\u1038\u1010\u1005\u103a\u1001\u102f\u1005\u102e\u1021\u1010\u103d\u1000\u103a \u101c\u1031\u1037\u1000\u103b\u1004\u1037\u103a\u1001\u103c\u1004\u103a\u1038\u1014\u103e\u1004\u1037\u103a \u1005\u1019\u103a\u1038\u101e\u1015\u103a\u1001\u103c\u1004\u103a\u1038 RMSE (\u1015\u103b\u1019\u103a\u1038\u1019\u103b\u103e\u1005\u1010\u102f\u101b\u1014\u103a\u1038\u1021\u1019\u103e\u102c\u1038) \u1000\u102d\u102f\u1015\u103c\u101e\u101e\u100a\u1037\u103a <strong>xgb.train()<\/strong> \u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u1001\u103b\u1000\u103a\u1000\u102d\u102f \u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u104d XGBoost \u1019\u1031\u102c\u103a\u1012\u101a\u103a\u1000\u102d\u102f \u1001\u103b\u102d\u1014\u103a\u100a\u103e\u102d\u1015\u102b\u1019\u100a\u103a\u104b<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u1024\u1025\u1015\u1019\u102c\u1021\u1010\u103d\u1000\u103a \u1021\u1000\u103c\u102d\u1019\u103a 70 \u1000\u102d\u102f\u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u101b\u1014\u103a \u1000\u103b\u103d\u1014\u103a\u102f\u1015\u103a\u1010\u102d\u102f\u1037\u101b\u103d\u1031\u1038\u1001\u103b\u101a\u103a\u1001\u1032\u1037\u101e\u100a\u103a\u1000\u102d\u102f \u101e\u1010\u102d\u1015\u103c\u102f\u1015\u102b\u104a \u101e\u102d\u102f\u1037\u101e\u1031\u102c\u103a \u1015\u102d\u102f\u1019\u102d\u102f\u1000\u103c\u102e\u1038\u1019\u102c\u1038\u101e\u1031\u102c\u1012\u1031\u1010\u102c\u1021\u1010\u103d\u1032\u1019\u103b\u102c\u1038\u1021\u1010\u103d\u1000\u103a \u101b\u102c\u1014\u103e\u1004\u1037\u103a\u1001\u103b\u102e \u101e\u102d\u102f\u1037\u1019\u101f\u102f\u1010\u103a \u1011\u1031\u102c\u1004\u103a\u1001\u103b\u102e\u101e\u1031\u102c\u1021\u1000\u103c\u102d\u1019\u103a\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u1001\u103c\u1004\u103a\u1038\u101e\u100a\u103a \u1021\u1006\u1014\u103a\u1038\u1019\u101f\u102f\u1010\u103a\u1015\u1031\u104b \u1021\u101c\u103e\u100a\u1037\u103a\u1010\u103d\u1031\u1019\u103b\u102c\u1038\u101c\u1031\u104a runtime \u1015\u102d\u102f\u1000\u103c\u102c\u101c\u1031\u1006\u102d\u102f\u1010\u102c \u101e\u1010\u102d\u1011\u102c\u1038\u1015\u102b\u104b<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>max.degree<\/strong> \u1021\u1004\u103c\u1004\u103a\u1038\u1021\u1001\u102f\u1036\u101e\u100a\u103a \u1010\u1005\u103a\u1026\u1038\u1001\u103b\u1004\u103a\u1038 \u1006\u102f\u1036\u1038\u1016\u103c\u1010\u103a\u1001\u103b\u1000\u103a\u101e\u1005\u103a\u1015\u1004\u103a\u1019\u103b\u102c\u1038\u104f \u1016\u103d\u1036\u1037\u1016\u103c\u102d\u102f\u1038\u1010\u102d\u102f\u1038\u1010\u1000\u103a\u1019\u103e\u102f\u1021\u1010\u102d\u1019\u103a\u1021\u1014\u1000\u103a\u1000\u102d\u102f \u1016\u1031\u102c\u103a\u1015\u103c\u101e\u100a\u103a\u1000\u102d\u102f \u101e\u1010\u102d\u1015\u103c\u102f\u1015\u102b\u104b \u101e\u1031\u1038\u1004\u101a\u103a\u101e\u1031\u102c\u101e\u1005\u103a\u1015\u1004\u103a\u1019\u103b\u102c\u1038\u1000\u103c\u102e\u1038\u1011\u103d\u102c\u1038\u101b\u1014\u103a\u1021\u1010\u103d\u1000\u103a \u1000\u103b\u103d\u1014\u103a\u102f\u1015\u103a\u1010\u102d\u102f\u1037\u101e\u100a\u103a \u1024\u1021\u101b\u1031\u1021\u1010\u103d\u1000\u103a\u1000\u102d\u102f 2 \u101e\u102d\u102f\u1037\u1019\u101f\u102f\u1010\u103a 3 \u1000\u1032\u1037\u101e\u102d\u102f\u1037\u1021\u1014\u100a\u103a\u1038\u1021\u1019\u103b\u102c\u1038\u101b\u103d\u1031\u1038\u1001\u103b\u101a\u103a\u101c\u1031\u1037\u101b\u103e\u102d\u101e\u100a\u103a\u104b \u1024\u1001\u103b\u1009\u103a\u1038\u1000\u1015\u103a\u1019\u103e\u102f\u101e\u100a\u103a \u1015\u102d\u102f\u1019\u102d\u102f\u1010\u102d\u1000\u103b\u101e\u1031\u102c \u1019\u1031\u102c\u103a\u1012\u101a\u103a\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1011\u102f\u1010\u103a\u101c\u102f\u1015\u103a\u101c\u1031\u1037\u101b\u103e\u102d\u1000\u103c\u1031\u102c\u1004\u103a\u1038 \u1015\u103c\u101e\u1011\u102c\u1038\u101e\u100a\u103a\u104b<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #993300;\"><span style=\"color: #000000;\"><span style=\"color: #008080;\">#define watchlist\n<\/span>watchlist = list(train=xgb_train, test=xgb_test)\n\n<span style=\"color: #008080;\">#fit XGBoost model and display training and testing data at each round\n<\/span>model = xgb.train(data = xgb_train, max.depth = <span style=\"color: #008000;\">3<\/span> , watchlist=watchlist, nrounds = <span style=\"color: #008000;\">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<p> <span style=\"color: #000000;\">\u101b\u101c\u1012\u103a\u1019\u103e\u104a \u1021\u1014\u102d\u1019\u1037\u103a\u1006\u102f\u1036\u1038\u1005\u1019\u103a\u1038\u101e\u1015\u103a\u1019\u103e\u102f RMSE \u1000\u102d\u102f <strong>56<\/strong> \u1015\u1010\u103a\u1010\u103d\u1004\u103a\u1021\u1031\u102c\u1004\u103a\u1019\u103c\u1004\u103a\u1000\u103c\u1031\u102c\u1004\u103a\u1038\u1000\u103b\u103d\u1014\u103a\u102f\u1015\u103a\u1010\u102d\u102f\u1037\u101e\u102d\u1014\u102d\u102f\u1004\u103a\u101e\u100a\u103a\u104b \u1024\u1021\u1001\u103b\u1000\u103a\u1000\u102d\u102f\u1000\u103b\u1031\u102c\u103a\u101c\u103d\u1014\u103a\u104d \u1005\u102c\u1019\u1031\u1038\u1015\u103d\u1032 RMSE \u101e\u100a\u103a \u1010\u102d\u102f\u1038\u101c\u102c\u1000\u102c \u1000\u103b\u103d\u1014\u103a\u102f\u1015\u103a\u1010\u102d\u102f\u1037\u101e\u100a\u103a <a href=\"https:\/\/statorials.org\/my\/\u1005\u1000\u103a\u101e\u1004\u103a\u101a\u1030\u1019\u103e\u102f-\u1021\u101c\u103d\u1014\u103a\u1021\u1000\u103b\u103d\u1036\/\" target=\"_blank\" rel=\"noopener noreferrer\">\u101c\u1031\u1037\u1000\u103b\u1004\u1037\u103a\u101b\u1031\u1038\u1012\u1031\u1010\u102c\u1014\u103e\u1004\u1037\u103a \u1000\u102d\u102f\u1000\u103a\u100a\u102e\u1014\u1031\u1015\u103c\u102e\u1016\u103c\u1005\u103a\u1000\u103c\u1031\u102c\u1004\u103a\u1038<\/a> \u100a\u103d\u103e\u1014\u103a\u1015\u103c\u1015\u102b\u101e\u100a\u103a\u104b<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u1011\u102d\u102f\u1037\u1000\u103c\u1031\u102c\u1004\u1037\u103a\u104a \u1000\u103b\u103d\u1014\u103a\u102f\u1015\u103a\u1010\u102d\u102f\u1037\u101e\u100a\u103a \u1000\u103b\u103d\u1014\u103a\u102f\u1015\u103a\u1010\u102d\u102f\u1037\u104f\u1014\u1031\u102c\u1000\u103a\u1006\u102f\u1036\u1038 XGBoost \u1019\u1031\u102c\u103a\u1012\u101a\u103a\u1000\u102d\u102f 56 \u1015\u1010\u103a\u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u101b\u1014\u103a \u101e\u1010\u103a\u1019\u103e\u1010\u103a\u1015\u102b\u1019\u100a\u103a\u104b<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #993300;\"><span style=\"color: #000000;\"><span style=\"color: #008080;\">#define final model\n<\/span>final = xgboost(data = xgb_train, max.depth = <span style=\"color: #008000;\">3<\/span> , nrounds = <span style=\"color: #008000;\">56<\/span> , verbose = <span style=\"color: #008000;\">0<\/span> )<\/span><\/span><\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\u1019\u103e\u1010\u103a\u1001\u103b\u1000\u103a- <strong>verbose=0<\/strong> \u1021\u1004\u103c\u1004\u103a\u1038\u1021\u1001\u102f\u1036\u1000 R \u101e\u100a\u103a \u1021\u1000\u103c\u102d\u1019\u103a\u1010\u102d\u102f\u1004\u103a\u1038\u1021\u1010\u103d\u1000\u103a \u101c\u1031\u1037\u1000\u103b\u1004\u1037\u103a\u1019\u103e\u102f\u1014\u103e\u1004\u1037\u103a \u1005\u1019\u103a\u1038\u101e\u1015\u103a\u1019\u103e\u102f\u1021\u1019\u103e\u102c\u1038\u1000\u102d\u102f \u1019\u1015\u103c\u101e\u101b\u1014\u103a \u1015\u103c\u1031\u102c\u1011\u102c\u1038\u101e\u100a\u103a\u104b<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>\u1021\u1006\u1004\u1037\u103a 5- \u1001\u1014\u1037\u103a\u1019\u103e\u1014\u103a\u1038\u1001\u103b\u1000\u103a\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1015\u103c\u102f\u101c\u102f\u1015\u103a\u101b\u1014\u103a \u1019\u1031\u102c\u103a\u1012\u101a\u103a\u1000\u102d\u102f \u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u1015\u102b\u104b<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\u1014\u1031\u102c\u1000\u103a\u1006\u102f\u1036\u1038\u1010\u103d\u1004\u103a\u104a \u1005\u1019\u103a\u1038\u101e\u1015\u103a\u1019\u103e\u102f\u1021\u1005\u102f\u1036\u1010\u103d\u1004\u103a \u1018\u1031\u102c\u103a\u1005\u1010\u103d\u1014\u103a\u1021\u102d\u1019\u103a\u1019\u103b\u102c\u1038\u104f \u1015\u103b\u1019\u103a\u1038\u1019\u103b\u103e\u1010\u1014\u103a\u1016\u102d\u102f\u1038\u1021\u1000\u103c\u1031\u102c\u1004\u103a\u1038 \u1001\u1014\u1037\u103a\u1019\u103e\u1014\u103a\u1038\u1001\u103b\u1000\u103a\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1015\u103c\u102f\u101c\u102f\u1015\u103a\u101b\u1014\u103a \u1014\u1031\u102c\u1000\u103a\u1006\u102f\u1036\u1038\u1021\u1006\u1004\u1037\u103a\u1019\u103c\u103e\u1004\u1037\u103a\u1010\u1004\u103a\u1011\u102c\u1038\u101e\u1031\u102c \u1019\u1031\u102c\u103a\u1012\u101a\u103a\u1000\u102d\u102f \u1000\u103b\u103d\u1014\u103a\u102f\u1015\u103a\u1010\u102d\u102f\u1037\u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u1014\u102d\u102f\u1004\u103a\u1015\u102b\u101e\u100a\u103a\u104b<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u1011\u102d\u102f\u1037\u1014\u1031\u102c\u1000\u103a \u1019\u1031\u102c\u103a\u1012\u101a\u103a\u1021\u1010\u103d\u1000\u103a \u1021\u1031\u102c\u1000\u103a\u1016\u1031\u102c\u103a\u1015\u103c\u1015\u102b \u1010\u102d\u1000\u103b\u1019\u103e\u102f \u1019\u1000\u103a\u1011\u101b\u1005\u103a\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1010\u103d\u1000\u103a\u1001\u103b\u1000\u103a\u1015\u102b\u1019\u100a\u103a\u104b<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>MSE &#8211;<\/strong> \u1006\u102d\u102f\u101c\u102d\u102f\u101b\u1004\u103a\u1038\u1019\u103e\u102c \u1005\u1010\u102f\u101b\u1014\u103a\u1038\u1021\u1019\u103e\u102c\u1038<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>MAE:<\/strong> \u1006\u102d\u102f\u101c\u102d\u102f\u1010\u102c\u1000 \u101c\u102f\u1036\u1038\u101d\u1021\u1019\u103e\u102c\u1038<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>RMSE-<\/strong> root mean square \u1021\u1019\u103e\u102c\u1038<\/span><\/li>\n<\/ul>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #993300;\"><span style=\"color: #000000;\"><span style=\"color: #008080;\"><span style=\"color: #000000;\">mean((test_y - pred_y)^2)<\/span> #mse\n<span style=\"color: #000000;\">caret::MAE(test_y, pred_y)<\/span> #mae\n<span style=\"color: #000000;\">caret::RMSE(test_y, pred_y)<\/span> #rmse\n\n<\/span>[1] 13.50164\n[1] 2.409426\n[1] 3.674457<\/span><\/span><\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\u1015\u103b\u1019\u103a\u1038\u1019\u103b\u103e\u1005\u1010\u102f\u101b\u1014\u103a\u1038\u1021\u1019\u103e\u102c\u1038\u101e\u100a\u103a <strong>3.674457<\/strong> \u1016\u103c\u1005\u103a\u101c\u102c\u101e\u100a\u103a\u104b \u104e\u1004\u103a\u1038\u101e\u100a\u103a \u1015\u103b\u1019\u103a\u1038\u1019\u103b\u103e\u1021\u102d\u1019\u103a\u1010\u1014\u103a\u1016\u102d\u102f\u1038\u1019\u103b\u102c\u1038\u1021\u1010\u103d\u1000\u103a \u1015\u103c\u102f\u101c\u102f\u1015\u103a\u1011\u102c\u1038\u101e\u1031\u102c \u1001\u1014\u1037\u103a\u1019\u103e\u1014\u103a\u1038\u1001\u103b\u1000\u103a\u1014\u103e\u1004\u1037\u103a \u1005\u1019\u103a\u1038\u101e\u1015\u103a\u1019\u103e\u102f\u1021\u1005\u102f\u1010\u103d\u1004\u103a \u1010\u103d\u1031\u1037\u101b\u103e\u102d\u1011\u102c\u1038\u101e\u100a\u1037\u103a \u1021\u1019\u103e\u1014\u103a\u1010\u1000\u101a\u103a \u1021\u102d\u1019\u103a\u1010\u1014\u103a\u1016\u102d\u102f\u1038\u1019\u103b\u102c\u1038\u1000\u103c\u102c\u1038\u1010\u103d\u1004\u103a \u1015\u103b\u1019\u103a\u1038\u1019\u103b\u103e\u1000\u103d\u102c\u1001\u103c\u102c\u1038\u1001\u103b\u1000\u103a\u1000\u102d\u102f \u1000\u102d\u102f\u101a\u103a\u1005\u102c\u1038\u1015\u103c\u102f\u101e\u100a\u103a\u104b<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u1021\u1000\u101a\u103a\u104d \u1000\u103b\u103d\u1014\u103a\u102f\u1015\u103a\u1010\u102d\u102f\u1037\u101c\u102d\u102f\u1001\u103b\u1004\u103a\u1015\u102b\u1000\u104a \u1024 RMSE \u1000\u102d\u102f <a href=\"https:\/\/statorials.org\/my\/multiple-linear-regression-r\/\" target=\"_blank\" rel=\"noopener noreferrer\">multiple linear regression<\/a> \u104a <a href=\"https:\/\/statorials.org\/my\/r-\u1010\u103d\u1004\u103a-crest-regression\/\" target=\"_blank\" rel=\"noopener noreferrer\">ridge regression<\/a> \u104a <a href=\"https:\/\/statorials.org\/my\/r-\u1010\u103d\u1004\u103a-\u1021\u1013\u102d\u1000-\u1021\u1005\u102d\u1010\u103a\u1021\u1015\u102d\u102f\u1004\u103a\u1038\u1019\u103b\u102c\u1038-\u1006\u102f\u1010\u103a\u101a\u102f\u1010\u103a\u1019\u103e\u102f\/\" target=\"_blank\" rel=\"noopener noreferrer\">principal component regression<\/a> \u1005\u101e\u100a\u103a\u1016\u103c\u1004\u1037\u103a \u1014\u103e\u102d\u102f\u1004\u103a\u1038\u101a\u103e\u1009\u103a\u1014\u102d\u102f\u1004\u103a\u101e\u100a\u103a\u104b \u1018\u101a\u103a\u1019\u1031\u102c\u103a\u1012\u101a\u103a\u1000 \u1021\u1010\u102d\u1000\u103b\u1006\u102f\u1036\u1038 \u1001\u1014\u1037\u103a\u1019\u103e\u1014\u103a\u1038\u1001\u103b\u1000\u103a\u1010\u103d\u1031\u1000\u102d\u102f \u1011\u102f\u1010\u103a\u1015\u1031\u1038\u101c\u1032\u1006\u102d\u102f\u1010\u102c \u1000\u103c\u100a\u1037\u103a\u1016\u102d\u102f\u1037\u1015\u102b\u104b<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u1024\u1025\u1015\u1019\u102c\u1010\u103d\u1004\u103a\u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u1011\u102c\u1038\u101e\u1031\u102c R \u1000\u102f\u1012\u103a\u1021\u1015\u103c\u100a\u1037\u103a\u1021\u1005\u102f\u1036\u1000\u102d\u102f <a href=\"https:\/\/github.com\/Statorials\/R-Guides\/blob\/main\/xgboost.R\" target=\"_blank\" rel=\"noopener noreferrer\">\u1024\u1014\u1031\u101b\u102c\u1010\u103d\u1004\u103a<\/a> \u101b\u103e\u102c\u1010\u103d\u1031\u1037\u1014\u102d\u102f\u1004\u103a\u1015\u102b\u101e\u100a\u103a\u104b<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Boosting \u101e\u100a\u103a \u1000\u103c\u102d\u102f\u1010\u1004\u103a\u1001\u1014\u1037\u103a\u1019\u103e\u1014\u103a\u1038\u1010\u102d\u1000\u103b\u1019\u103e\u102f\u1019\u103c\u1004\u1037\u103a\u1019\u102c\u1038\u101e\u1031\u102c \u1019\u1031\u102c\u103a\u1012\u101a\u103a\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1011\u102f\u1010\u103a\u101c\u102f\u1015\u103a\u101b\u1014\u103a \u1015\u103c\u101e\u1011\u102c\u1038\u101e\u1031\u102c \u1005\u1000\u103a\u101e\u1004\u103a\u101a\u1030\u1019\u103e\u102f\u1014\u100a\u103a\u1038\u1015\u100a\u102c\u1010\u1005\u103a\u1001\u102f\u1016\u103c\u1005\u103a\u101e\u100a\u103a\u104b \u101c\u1000\u103a\u1010\u103d\u1031\u1037\u1010\u103d\u1004\u103a \u1019\u103c\u103e\u1004\u1037\u103a\u1010\u1004\u103a\u1001\u103c\u1004\u103a\u1038\u1000\u102d\u102f \u1021\u1000\u1031\u102c\u1004\u103a\u1021\u1011\u100a\u103a\u1016\u1031\u102c\u103a\u101b\u1014\u103a \u1021\u101e\u102f\u1036\u1038\u1021\u1019\u103b\u102c\u1038\u1006\u102f\u1036\u1038\u1014\u100a\u103a\u1038\u101c\u1019\u103a\u1038\u1010\u1005\u103a\u1001\u102f\u1019\u103e\u102c &#8220; extreme gradient boosting&#8221; \u104f\u1021\u1010\u102d\u102f\u1000\u1031\u102c\u1000\u103a\u1016\u103c\u1005\u103a\u101e\u1031\u102c XGBoost \u1000\u102d\u102f \u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u1001\u103c\u1004\u103a\u1038\u1016\u103c\u1005\u103a\u101e\u100a\u103a\u104b \u1024\u101e\u1004\u103a\u1001\u1014\u103a\u1038\u1005\u102c\u101e\u100a\u103a R \u1010\u103d\u1004\u103a \u1021\u1006\u1004\u1037\u103a\u1019\u103c\u103e\u1004\u1037\u103a\u1011\u102c\u1038\u101e\u1031\u102c \u1019\u1031\u102c\u103a\u1012\u101a\u103a\u101c\u103a\u1010\u1005\u103a\u1001\u102f\u1014\u103e\u1004\u1037\u103a \u1021\u1036\u101d\u1004\u103a\u1001\u103d\u1004\u103a\u1000\u103b\u1016\u103c\u1005\u103a\u101b\u1014\u103a XGBoost \u1000\u102d\u102f \u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u1014\u100a\u103a\u1038 \u1021\u1006\u1004\u1037\u103a\u1006\u1004\u1037\u103a \u1025\u1015\u1019\u102c\u1000\u102d\u102f \u1015\u1031\u1038\u1015\u102b\u101e\u100a\u103a\u104b \u1021\u1006\u1004\u1037\u103a 1- \u101c\u102d\u102f\u1021\u1015\u103a\u101e\u1031\u102c \u1015\u1000\u103a\u1000\u1031\u1037\u1001\u103a\u103b\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1010\u1004\u103a\u1015\u102b\u104b \u1015\u1011\u1019\u1026\u1038\u1005\u103d\u102c \u101c\u102d\u102f\u1021\u1015\u103a\u101e\u1031\u102c \u1005\u102c\u1000\u103c\u100a\u1037\u103a\u1010\u102d\u102f\u1000\u103a\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1010\u1004\u103a\u1015\u102b\u1019\u100a\u103a\u104b library (xgboost) #for fitting the xgboost model library (caret) #for general data preparation and model [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3],"tags":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>R in XGBoost- \u1021\u1006\u1004\u1037\u103a\u1006\u1004\u1037\u103a \u1025\u1015\u1019\u102c<\/title>\n<meta name=\"description\" content=\"\u1024\u101e\u1004\u103a\u1001\u1014\u103a\u1038\u1005\u102c\u101e\u100a\u103a \u101b\u1031\u1015\u1014\u103a\u1038\u1005\u102c\u1038\u101e\u1031\u102c \u1005\u1000\u103a\u101e\u1004\u103a\u101a\u1030\u1019\u103e\u102f\u1014\u100a\u103a\u1038\u1015\u100a\u102c\u1016\u103c\u1005\u103a\u101e\u100a\u1037\u103a R \u1010\u103d\u1004\u103a XGBoost \u1000\u102d\u102f \u1019\u100a\u103a\u101e\u102d\u102f\u1037\u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u101b\u1019\u100a\u103a\u1000\u102d\u102f 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\u1000\u102d\u102f \u1019\u100a\u103a\u101e\u102d\u102f\u1037\u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u101b\u1019\u100a\u103a\u1000\u102d\u102f \u1021\u1006\u1004\u1037\u103a\u1006\u1004\u1037\u103a \u1025\u1015\u1019\u102c\u1015\u1031\u1038\u1011\u102c\u1038\u101e\u100a\u103a\u104b\" \/>\n<meta property=\"og:url\" content=\"https:\/\/statorials.org\/my\/r-\u1010\u103d\u1004\u103a-xgboost\/\" \/>\n<meta property=\"og:site_name\" content=\"Statorials\" \/>\n<meta property=\"article:published_time\" content=\"2023-07-27T04:52:33+00:00\" \/>\n<meta name=\"author\" content=\"Benjamin Anderson\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Benjamin Anderson\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"2 minutes\" \/>\n<script type=\"application\/ld+json\" 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