{"id":1231,"date":"2023-07-27T04:52:33","date_gmt":"2023-07-27T04:52:33","guid":{"rendered":"https:\/\/statorials.org\/uk\/xgboost-%d0%b2-r\/"},"modified":"2023-07-27T04:52:33","modified_gmt":"2023-07-27T04:52:33","slug":"xgboost-%d0%b2-r","status":"publish","type":"post","link":"https:\/\/statorials.org\/uk\/xgboost-%d0%b2-r\/","title":{"rendered":"Xgboost \u0432 r: \u043f\u043e\u043a\u0440\u043e\u043a\u043e\u0432\u0438\u0439 \u043f\u0440\u0438\u043a\u043b\u0430\u0434"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\"><a href=\"https:\/\/statorials.org\/uk\/\u043f\u0456\u0434\u0432\u0438\u0449\u0438\u0442\u0438-\u043c\u0430\u0448\u0438\u043d\u043d\u0435-\u043d\u0430\u0432\u0447\u0430\u043d\u043d\u044f\/\" target=\"_blank\" rel=\"noopener noreferrer\">\u041f\u0456\u0434\u0432\u0438\u0449\u0435\u043d\u043d\u044f<\/a> \u2014 \u0446\u0435 \u0442\u0435\u0445\u043d\u0456\u043a\u0430 \u043c\u0430\u0448\u0438\u043d\u043d\u043e\u0433\u043e \u043d\u0430\u0432\u0447\u0430\u043d\u043d\u044f, \u044f\u043a\u0430, \u044f\u043a \u0431\u0443\u043b\u043e \u043f\u043e\u043a\u0430\u0437\u0430\u043d\u043e, \u0441\u0442\u0432\u043e\u0440\u044e\u0454 \u043c\u043e\u0434\u0435\u043b\u0456 \u0437 \u0432\u0438\u0441\u043e\u043a\u043e\u044e \u0442\u043e\u0447\u043d\u0456\u0441\u0442\u044e \u043f\u0440\u043e\u0433\u043d\u043e\u0437\u0443\u0432\u0430\u043d\u043d\u044f.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u041e\u0434\u043d\u0438\u043c \u0456\u0437 \u043d\u0430\u0439\u043f\u043e\u0448\u0438\u0440\u0435\u043d\u0456\u0448\u0438\u0445 \u0441\u043f\u043e\u0441\u043e\u0431\u0456\u0432 \u0440\u0435\u0430\u043b\u0456\u0437\u0430\u0446\u0456\u0457 \u043f\u043e\u0441\u0438\u043b\u0435\u043d\u043d\u044f \u043d\u0430 \u043f\u0440\u0430\u043a\u0442\u0438\u0446\u0456 \u0454 \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u0430\u043d\u043d\u044f <strong>XGBoost<\/strong> , \u0449\u043e \u0441\u043a\u043e\u0440\u043e\u0447\u0443\u0454\u0442\u044c\u0441\u044f \u0432\u0456\u0434 \u00abextreme gradient boosting\u00bb.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u0426\u0435\u0439 \u043f\u0456\u0434\u0440\u0443\u0447\u043d\u0438\u043a \u043d\u0430\u0434\u0430\u0454 \u043f\u043e\u043a\u0440\u043e\u043a\u043e\u0432\u0438\u0439 \u043f\u0440\u0438\u043a\u043b\u0430\u0434 \u0442\u043e\u0433\u043e, \u044f\u043a \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0432\u0430\u0442\u0438 XGBoost \u0434\u043b\u044f \u0430\u0434\u0430\u043f\u0442\u0430\u0446\u0456\u0457 \u0440\u043e\u0437\u0448\u0438\u0440\u0435\u043d\u043e\u0457 \u043c\u043e\u0434\u0435\u043b\u0456 \u0432 R.<\/span><\/p>\n<h3> <strong><span style=\"color: #000000;\">\u041a\u0440\u043e\u043a 1: \u0417\u0430\u0432\u0430\u043d\u0442\u0430\u0436\u0442\u0435 \u043d\u0435\u043e\u0431\u0445\u0456\u0434\u043d\u0456 \u043f\u0430\u043a\u0435\u0442\u0438<\/span><\/strong><\/h3>\n<p> <span style=\"color: #000000;\">\u0421\u043f\u043e\u0447\u0430\u0442\u043a\u0443 \u043c\u0438 \u0437\u0430\u0432\u0430\u043d\u0442\u0430\u0436\u0438\u043c\u043e \u043d\u0435\u043e\u0431\u0445\u0456\u0434\u043d\u0456 \u0431\u0456\u0431\u043b\u0456\u043e\u0442\u0435\u043a\u0438.<\/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>\u041a\u0440\u043e\u043a 2. \u0417\u0430\u0432\u0430\u043d\u0442\u0430\u0436\u0442\u0435 \u0434\u0430\u043d\u0456<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\u0414\u043b\u044f \u0446\u044c\u043e\u0433\u043e \u043f\u0440\u0438\u043a\u043b\u0430\u0434\u0443 \u043c\u0438 \u043f\u0456\u0434\u0431\u0435\u0440\u0435\u043c\u043e \u0432\u0434\u043e\u0441\u043a\u043e\u043d\u0430\u043b\u0435\u043d\u0443 \u043c\u043e\u0434\u0435\u043b\u044c \u0440\u0435\u0433\u0440\u0435\u0441\u0456\u0457 \u0434\u043e <strong>\u0411\u043e\u0441\u0442\u043e\u043d\u0441\u044c\u043a\u043e\u0433\u043e<\/strong> \u043d\u0430\u0431\u043e\u0440\u0443 \u0434\u0430\u043d\u0438\u0445 \u0456\u0437 \u043f\u0430\u043a\u0435\u0442\u0443 <strong>MASS<\/strong> .<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u0426\u0435\u0439 \u043d\u0430\u0431\u0456\u0440 \u0434\u0430\u043d\u0438\u0445 \u043c\u0456\u0441\u0442\u0438\u0442\u044c 13 \u0437\u043c\u0456\u043d\u043d\u0438\u0445 \u043f\u0440\u0435\u0434\u0438\u043a\u0442\u043e\u0440\u0456\u0432, \u044f\u043a\u0456 \u043c\u0438 \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0432\u0430\u0442\u0438\u043c\u0435\u043c\u043e \u0434\u043b\u044f \u043f\u0440\u043e\u0433\u043d\u043e\u0437\u0443\u0432\u0430\u043d\u043d\u044f <a href=\"https:\/\/statorials.org\/uk\/\u0437\u043c\u0456\u043d\u043d\u0456-\u043f\u043e\u044f\u0441\u043d\u044e\u0432\u0430\u043b\u044c\u043d\u0456-\u0432\u0456\u0434\u043f\u043e\u0432\u0456\u0434\u0456\/\" target=\"_blank\" rel=\"noopener noreferrer\">\u0437\u043c\u0456\u043d\u043d\u043e\u0457 \u0432\u0456\u0434\u043f\u043e\u0432\u0456\u0434\u0456<\/a> \u043f\u0456\u0434 \u043d\u0430\u0437\u0432\u043e\u044e <strong>mdev<\/strong> , \u044f\u043a\u0430 \u043f\u0440\u0435\u0434\u0441\u0442\u0430\u0432\u043b\u044f\u0454 \u0441\u0435\u0440\u0435\u0434\u043d\u0454 \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f \u0431\u0443\u0434\u0438\u043d\u043a\u0456\u0432 \u0443 \u0440\u0456\u0437\u043d\u0438\u0445 \u0440\u0430\u0439\u043e\u043d\u0430\u0445 \u043f\u0435\u0440\u0435\u043f\u0438\u0441\u0443 \u043d\u0430\u0432\u043a\u043e\u043b\u043e \u0411\u043e\u0441\u0442\u043e\u043d\u0430.<\/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;\">\u041c\u0438 \u0431\u0430\u0447\u0438\u043c\u043e, \u0449\u043e \u043d\u0430\u0431\u0456\u0440 \u0434\u0430\u043d\u0438\u0445 \u043c\u0456\u0441\u0442\u0438\u0442\u044c \u0437\u0430\u0433\u0430\u043b\u043e\u043c 506 <a href=\"https:\/\/statorials.org\/uk\/\u0441\u043f\u043e\u0441\u0442\u0435\u0440\u0435\u0436\u0435\u043d\u043d\u044f-\u0432-\u0441\u0442\u0430\u0442\u0438\u0441\u0442\u0438\u0446\u0456\/\" target=\"_blank\" rel=\"noopener noreferrer\">\u0441\u043f\u043e\u0441\u0442\u0435\u0440\u0435\u0436\u0435\u043d\u044c<\/a> \u0456 14 \u0437\u043c\u0456\u043d\u043d\u0438\u0445.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>\u041a\u0440\u043e\u043a 3: \u041f\u0456\u0434\u0433\u043e\u0442\u0443\u0439\u0442\u0435 \u0434\u0430\u043d\u0456<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\u0414\u0430\u043b\u0456 \u043c\u0438 \u0441\u043a\u043e\u0440\u0438\u0441\u0442\u0430\u0454\u043c\u043e\u0441\u044f \u0444\u0443\u043d\u043a\u0446\u0456\u0454\u044e <strong>createDataPartition()<\/strong> \u0456\u0437 \u043f\u0430\u043a\u0435\u0442\u0430 \u043a\u0430\u0440\u0435\u0442\u043a\u0438, \u0449\u043e\u0431 \u0440\u043e\u0437\u0434\u0456\u043b\u0438\u0442\u0438 \u0432\u0438\u0445\u0456\u0434\u043d\u0438\u0439 \u043d\u0430\u0431\u0456\u0440 \u0434\u0430\u043d\u0438\u0445 \u043d\u0430 \u043d\u0430\u0431\u0456\u0440 \u0434\u043b\u044f \u043d\u0430\u0432\u0447\u0430\u043d\u043d\u044f \u0442\u0430 \u0442\u0435\u0441\u0442\u0443\u0432\u0430\u043d\u043d\u044f.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u0414\u043b\u044f \u0446\u044c\u043e\u0433\u043e \u043f\u0440\u0438\u043a\u043b\u0430\u0434\u0443 \u043c\u0438 \u0432\u0438\u0440\u0456\u0448\u0438\u043c\u043e \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0432\u0430\u0442\u0438 80% \u0432\u0438\u0445\u0456\u0434\u043d\u043e\u0433\u043e \u043d\u0430\u0431\u043e\u0440\u0443 \u0434\u0430\u043d\u0438\u0445 \u044f\u043a \u0447\u0430\u0441\u0442\u0438\u043d\u0443 \u043d\u0430\u0432\u0447\u0430\u043b\u044c\u043d\u043e\u0433\u043e \u043d\u0430\u0431\u043e\u0440\u0443.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u0417\u0432\u0435\u0440\u043d\u0456\u0442\u044c \u0443\u0432\u0430\u0433\u0443, \u0449\u043e \u043f\u0430\u043a\u0435\u0442 xgboost \u0442\u0430\u043a\u043e\u0436 \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0454 \u043c\u0430\u0442\u0440\u0438\u0447\u043d\u0456 \u0434\u0430\u043d\u0456, \u0442\u043e\u043c\u0443 \u043c\u0438 \u0431\u0443\u0434\u0435\u043c\u043e \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0432\u0430\u0442\u0438 \u0444\u0443\u043d\u043a\u0446\u0456\u044e <strong>data.matrix()<\/strong> \u0434\u043b\u044f \u0437\u0431\u0435\u0440\u0456\u0433\u0430\u043d\u043d\u044f \u043d\u0430\u0448\u0438\u0445 \u0437\u043c\u0456\u043d\u043d\u0438\u0445 \u043f\u0440\u0435\u0434\u0438\u043a\u0442\u043e\u0440\u0430.<\/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>\u041a\u0440\u043e\u043a 4: \u041d\u0430\u043b\u0430\u0448\u0442\u0443\u0439\u0442\u0435 \u043c\u043e\u0434\u0435\u043b\u044c<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\u0414\u0430\u043b\u0456 \u043c\u0438 \u043d\u0430\u043b\u0430\u0448\u0442\u0443\u0454\u043c\u043e \u043c\u043e\u0434\u0435\u043b\u044c XGBoost \u0437\u0430 \u0434\u043e\u043f\u043e\u043c\u043e\u0433\u043e\u044e \u0444\u0443\u043d\u043a\u0446\u0456\u0457 <strong>xgb.train()<\/strong> , \u044f\u043a\u0430 \u0432\u0456\u0434\u043e\u0431\u0440\u0430\u0436\u0430\u0454 RMSE \u043d\u0430\u0432\u0447\u0430\u043d\u043d\u044f \u0442\u0430 \u0442\u0435\u0441\u0442\u0443\u0432\u0430\u043d\u043d\u044f (\u0441\u0435\u0440\u0435\u0434\u043d\u044f \u043a\u0432\u0430\u0434\u0440\u0430\u0442\u0438\u0447\u043d\u0430 \u043f\u043e\u043c\u0438\u043b\u043a\u0430) \u0434\u043b\u044f \u043a\u043e\u0436\u043d\u043e\u0433\u043e \u0446\u0438\u043a\u043b\u0443 \u043f\u0456\u0434\u0432\u0438\u0449\u0435\u043d\u043d\u044f.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u0417\u0430\u0443\u0432\u0430\u0436\u0442\u0435, \u0449\u043e \u0434\u043b\u044f \u0446\u044c\u043e\u0433\u043e \u043f\u0440\u0438\u043a\u043b\u0430\u0434\u0443 \u043c\u0438 \u0432\u0438\u0440\u0456\u0448\u0438\u043b\u0438 \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0432\u0430\u0442\u0438 70 \u0440\u0430\u0443\u043d\u0434\u0456\u0432, \u0430\u043b\u0435 \u0434\u043b\u044f \u043d\u0430\u0431\u0430\u0433\u0430\u0442\u043e \u0431\u0456\u043b\u044c\u0448\u0438\u0445 \u043d\u0430\u0431\u043e\u0440\u0456\u0432 \u0434\u0430\u043d\u0438\u0445 \u043d\u0435\u0440\u0456\u0434\u043a\u043e \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u044e\u0442\u044c \u0441\u043e\u0442\u043d\u0456 \u0447\u0438 \u043d\u0430\u0432\u0456\u0442\u044c \u0442\u0438\u0441\u044f\u0447\u0456 \u0440\u0430\u0443\u043d\u0434\u0456\u0432. \u0422\u0456\u043b\u044c\u043a\u0438 \u043c\u0430\u0439\u0442\u0435 \u043d\u0430 \u0443\u0432\u0430\u0437\u0456, \u0449\u043e \u0447\u0438\u043c \u0431\u0456\u043b\u044c\u0448\u0435 \u0440\u0430\u0443\u043d\u0434\u0456\u0432, \u0442\u0438\u043c \u0434\u043e\u0432\u0448\u0438\u0439 \u0447\u0430\u0441 \u0440\u043e\u0431\u043e\u0442\u0438.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u0422\u0430\u043a\u043e\u0436 \u0437\u0430\u0443\u0432\u0430\u0436\u0442\u0435, \u0449\u043e \u0430\u0440\u0433\u0443\u043c\u0435\u043d\u0442 <strong>max.degree<\/strong> \u0432\u0438\u0437\u043d\u0430\u0447\u0430\u0454 \u0433\u043b\u0438\u0431\u0438\u043d\u0443 \u0440\u043e\u0437\u0440\u043e\u0431\u043a\u0438 \u043e\u043a\u0440\u0435\u043c\u0438\u0445 \u0434\u0435\u0440\u0435\u0432 \u0440\u0456\u0448\u0435\u043d\u044c. \u0417\u0430\u0437\u0432\u0438\u0447\u0430\u0439 \u043c\u0438 \u043e\u0431\u0438\u0440\u0430\u0454\u043c\u043e \u0446\u0435 \u0447\u0438\u0441\u043b\u043e \u0434\u043e\u0441\u0438\u0442\u044c \u043d\u0438\u0437\u044c\u043a\u0435, \u043d\u0430\u043f\u0440\u0438\u043a\u043b\u0430\u0434 2 \u0430\u0431\u043e 3, \u0449\u043e\u0431 \u0432\u0438\u0440\u043e\u0441\u0442\u0438\u0442\u0438 \u043c\u0435\u043d\u0448\u0456 \u0434\u0435\u0440\u0435\u0432\u0430. \u041f\u043e\u043a\u0430\u0437\u0430\u043d\u043e, \u0449\u043e \u0446\u0435\u0439 \u043f\u0456\u0434\u0445\u0456\u0434 \u0434\u0430\u0454 \u0431\u0456\u043b\u044c\u0448 \u0442\u043e\u0447\u043d\u0456 \u043c\u043e\u0434\u0435\u043b\u0456.<\/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;\">\u0417 \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442\u0443 \u043c\u0438 \u0431\u0430\u0447\u0438\u043c\u043e, \u0449\u043e \u043c\u0456\u043d\u0456\u043c\u0430\u043b\u044c\u043d\u0438\u0439 \u0442\u0435\u0441\u0442\u043e\u0432\u0438\u0439 RMSE \u0434\u043e\u0441\u044f\u0433\u0430\u0454\u0442\u044c\u0441\u044f \u043f\u0440\u0438 <strong>56<\/strong> \u0440\u0430\u0443\u043d\u0434\u0430\u0445. \u0417\u0430 \u043c\u0435\u0436\u0430\u043c\u0438 \u0446\u0456\u0454\u0457 \u0442\u043e\u0447\u043a\u0438 \u0442\u0435\u0441\u0442\u043e\u0432\u0438\u0439 RMSE \u043f\u043e\u0447\u0438\u043d\u0430\u0454 \u0437\u0431\u0456\u043b\u044c\u0448\u0443\u0432\u0430\u0442\u0438\u0441\u044f, \u0449\u043e \u0432\u043a\u0430\u0437\u0443\u0454 \u043d\u0430 \u0442\u0435, \u0449\u043e \u043c\u0438 <a href=\"https:\/\/statorials.org\/uk\/\u043f\u0435\u0440\u0435\u043e\u0431\u043b\u0430\u0434\u043d\u0430\u043d\u043d\u044f-\u043c\u0430\u0448\u0438\u043d\u043d\u043e\u0433\u043e-\u043d\u0430\u0432\u0447\u0430\u043d\u043d\u044f\/\" target=\"_blank\" rel=\"noopener noreferrer\">\u043f\u0435\u0440\u0435\u043d\u0430\u043b\u0430\u0448\u0442\u043e\u0432\u0443\u0454\u043c\u043e \u043d\u0430\u0432\u0447\u0430\u043b\u044c\u043d\u0456 \u0434\u0430\u043d\u0456<\/a> .<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u041e\u0442\u0436\u0435, \u043c\u0438 \u043d\u0430\u043b\u0430\u0448\u0442\u0443\u0454\u043c\u043e \u043d\u0430\u0448\u0443 \u043e\u0441\u0442\u0430\u0442\u043e\u0447\u043d\u0443 \u043c\u043e\u0434\u0435\u043b\u044c XGBoost \u043d\u0430 \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u0430\u043d\u043d\u044f 56 \u0440\u0430\u0443\u043d\u0434\u0456\u0432:<\/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;\">\u041f\u0440\u0438\u043c\u0456\u0442\u043a\u0430. \u0410\u0440\u0433\u0443\u043c\u0435\u043d\u0442 <strong>verbose=0<\/strong> \u043f\u043e\u0432\u0456\u0434\u043e\u043c\u043b\u044f\u0454 R \u043d\u0435 \u0432\u0456\u0434\u043e\u0431\u0440\u0430\u0436\u0430\u0442\u0438 \u043f\u043e\u043c\u0438\u043b\u043a\u0443 \u043d\u0430\u0432\u0447\u0430\u043d\u043d\u044f \u0442\u0430 \u0442\u0435\u0441\u0442\u0443\u0432\u0430\u043d\u043d\u044f \u0434\u043b\u044f \u043a\u043e\u0436\u043d\u043e\u0433\u043e \u0440\u0430\u0443\u043d\u0434\u0443.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>\u041a\u0440\u043e\u043a 5. \u0412\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0439\u0442\u0435 \u043c\u043e\u0434\u0435\u043b\u044c \u0434\u043b\u044f \u043f\u0440\u043e\u0433\u043d\u043e\u0437\u0443\u0432\u0430\u043d\u043d\u044f<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\u041d\u0430\u0440\u0435\u0448\u0442\u0456, \u043c\u0438 \u043c\u043e\u0436\u0435\u043c\u043e \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u0430\u0442\u0438 \u043e\u0441\u0442\u0430\u0442\u043e\u0447\u043d\u0443 \u043f\u043e\u043a\u0440\u0430\u0449\u0435\u043d\u0443 \u043c\u043e\u0434\u0435\u043b\u044c, \u0449\u043e\u0431 \u0437\u0440\u043e\u0431\u0438\u0442\u0438 \u043f\u0440\u043e\u0433\u043d\u043e\u0437\u0438 \u0449\u043e\u0434\u043e \u0441\u0435\u0440\u0435\u0434\u043d\u044c\u043e\u0457 \u0432\u0430\u0440\u0442\u043e\u0441\u0442\u0456 \u0431\u0443\u0434\u0438\u043d\u043a\u0456\u0432 \u0443 \u0411\u043e\u0441\u0442\u043e\u043d\u0456 \u0432 \u0442\u0435\u0441\u0442\u043e\u0432\u043e\u043c\u0443 \u043d\u0430\u0431\u043e\u0440\u0456.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u041f\u043e\u0442\u0456\u043c \u043c\u0438 \u0440\u043e\u0437\u0440\u0430\u0445\u0443\u0454\u043c\u043e \u0442\u0430\u043a\u0456 \u043f\u043e\u043a\u0430\u0437\u043d\u0438\u043a\u0438 \u0442\u043e\u0447\u043d\u043e\u0441\u0442\u0456 \u0434\u043b\u044f \u043c\u043e\u0434\u0435\u043b\u0456:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>MSE:<\/strong> \u0441\u0435\u0440\u0435\u0434\u043d\u044f \u043a\u0432\u0430\u0434\u0440\u0430\u0442\u0438\u0447\u043d\u0430 \u043f\u043e\u043c\u0438\u043b\u043a\u0430<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>MAE:<\/strong> \u0441\u0435\u0440\u0435\u0434\u043d\u044f \u0430\u0431\u0441\u043e\u043b\u044e\u0442\u043d\u0430 \u043f\u043e\u0445\u0438\u0431\u043a\u0430<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>RMSE:<\/strong> \u0441\u0435\u0440\u0435\u0434\u043d\u044c\u043e\u043a\u0432\u0430\u0434\u0440\u0430\u0442\u0438\u0447\u043d\u0430 \u043f\u043e\u043c\u0438\u043b\u043a\u0430<\/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;\">\u0421\u0435\u0440\u0435\u0434\u043d\u044f \u043a\u0432\u0430\u0434\u0440\u0430\u0442\u0438\u0447\u043d\u0430 \u043f\u043e\u043c\u0438\u043b\u043a\u0430 \u0432\u0438\u044f\u0432\u043b\u044f\u0454\u0442\u044c\u0441\u044f <strong>3,674457<\/strong> . \u0426\u0435 \u044f\u0432\u043b\u044f\u0454 \u0441\u043e\u0431\u043e\u044e \u0441\u0435\u0440\u0435\u0434\u043d\u044e \u0440\u0456\u0437\u043d\u0438\u0446\u044e \u043c\u0456\u0436 \u043f\u0440\u043e\u0433\u043d\u043e\u0437\u043e\u043c, \u0437\u0440\u043e\u0431\u043b\u0435\u043d\u0438\u043c \u0434\u043b\u044f \u0441\u0435\u0440\u0435\u0434\u043d\u0456\u0445 \u0437\u043d\u0430\u0447\u0435\u043d\u044c \u0431\u0443\u0434\u0438\u043d\u043a\u0443, \u0456 \u0444\u0430\u043a\u0442\u0438\u0447\u043d\u0438\u043c\u0438 \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f\u043c\u0438 \u0431\u0443\u0434\u0438\u043d\u043a\u0443, \u0449\u043e \u0441\u043f\u043e\u0441\u0442\u0435\u0440\u0456\u0433\u0430\u044e\u0442\u044c\u0441\u044f \u0432 \u0442\u0435\u0441\u0442\u043e\u0432\u043e\u043c\u0443 \u043d\u0430\u0431\u043e\u0440\u0456.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u042f\u043a\u0449\u043e \u043c\u0438 \u0445\u043e\u0447\u0435\u043c\u043e, \u043c\u0438 \u043c\u043e\u0436\u0435\u043c\u043e \u043f\u043e\u0440\u0456\u0432\u043d\u044f\u0442\u0438 \u0446\u044e RMSE \u0437 \u0456\u043d\u0448\u0438\u043c\u0438 \u043c\u043e\u0434\u0435\u043b\u044f\u043c\u0438, \u0442\u0430\u043a\u0438\u043c\u0438 \u044f\u043a <a href=\"https:\/\/statorials.org\/uk\/\u043c\u043d\u043e\u0436\u0438\u043d\u043d\u0430-\u043b\u0456\u043d\u0456\u0438\u043d\u0430-\u0440\u0435\u0433\u0440\u0435\u0441\u0456\u044f-r\/\" target=\"_blank\" rel=\"noopener noreferrer\">\u043c\u043d\u043e\u0436\u0438\u043d\u043d\u0430 \u043b\u0456\u043d\u0456\u0439\u043d\u0430 \u0440\u0435\u0433\u0440\u0435\u0441\u0456\u044f<\/a> , <a href=\"https:\/\/statorials.org\/uk\/\u0440\u0435\u0433\u0440\u0435\u0441\u0456\u044f-\u0433\u0440\u0435\u0431\u0435\u043d\u044f-\u0432-r\/\" target=\"_blank\" rel=\"noopener noreferrer\">\u0433\u0440\u0435\u0431\u0435\u043d\u0435\u0432\u0430 \u0440\u0435\u0433\u0440\u0435\u0441\u0456\u044f<\/a> , <a href=\"https:\/\/statorials.org\/uk\/\u0440\u0435\u0433\u0440\u0435\u0441\u0456\u044f-\u0433\u043e\u043b\u043e\u0432\u043d\u0438\u0445-\u043a\u043e\u043c\u043f\u043e\u043d\u0435\u043d\u0442-\u0432-r\/\" target=\"_blank\" rel=\"noopener noreferrer\">\u0440\u0435\u0433\u0440\u0435\u0441\u0456\u044f \u0433\u043e\u043b\u043e\u0432\u043d\u043e\u0457 \u043a\u043e\u043c\u043f\u043e\u043d\u0435\u043d\u0442\u0438<\/a> \u0442\u043e\u0449\u043e. \u0449\u043e\u0431 \u043f\u043e\u0431\u0430\u0447\u0438\u0442\u0438, \u044f\u043a\u0430 \u043c\u043e\u0434\u0435\u043b\u044c \u0434\u0430\u0454 \u043d\u0430\u0439\u0442\u043e\u0447\u043d\u0456\u0448\u0456 \u043f\u0440\u043e\u0433\u043d\u043e\u0437\u0438.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u0412\u0438 \u043c\u043e\u0436\u0435\u0442\u0435 \u0437\u043d\u0430\u0439\u0442\u0438 \u043f\u043e\u0432\u043d\u0438\u0439 \u043a\u043e\u0434 R, \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u0430\u043d\u0438\u0439 \u0443 \u0446\u044c\u043e\u043c\u0443 \u043f\u0440\u0438\u043a\u043b\u0430\u0434\u0456 <a href=\"https:\/\/github.com\/Statology\/R-Guides\/blob\/main\/xgboost.R\" target=\"_blank\" rel=\"noopener noreferrer\">, \u0442\u0443\u0442<\/a> .<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u041f\u0456\u0434\u0432\u0438\u0449\u0435\u043d\u043d\u044f \u2014 \u0446\u0435 \u0442\u0435\u0445\u043d\u0456\u043a\u0430 \u043c\u0430\u0448\u0438\u043d\u043d\u043e\u0433\u043e \u043d\u0430\u0432\u0447\u0430\u043d\u043d\u044f, \u044f\u043a\u0430, \u044f\u043a \u0431\u0443\u043b\u043e \u043f\u043e\u043a\u0430\u0437\u0430\u043d\u043e, \u0441\u0442\u0432\u043e\u0440\u044e\u0454 \u043c\u043e\u0434\u0435\u043b\u0456 \u0437 \u0432\u0438\u0441\u043e\u043a\u043e\u044e \u0442\u043e\u0447\u043d\u0456\u0441\u0442\u044e \u043f\u0440\u043e\u0433\u043d\u043e\u0437\u0443\u0432\u0430\u043d\u043d\u044f. \u041e\u0434\u043d\u0438\u043c \u0456\u0437 \u043d\u0430\u0439\u043f\u043e\u0448\u0438\u0440\u0435\u043d\u0456\u0448\u0438\u0445 \u0441\u043f\u043e\u0441\u043e\u0431\u0456\u0432 \u0440\u0435\u0430\u043b\u0456\u0437\u0430\u0446\u0456\u0457 \u043f\u043e\u0441\u0438\u043b\u0435\u043d\u043d\u044f \u043d\u0430 \u043f\u0440\u0430\u043a\u0442\u0438\u0446\u0456 \u0454 \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u0430\u043d\u043d\u044f XGBoost , \u0449\u043e \u0441\u043a\u043e\u0440\u043e\u0447\u0443\u0454\u0442\u044c\u0441\u044f \u0432\u0456\u0434 \u00abextreme gradient boosting\u00bb. \u0426\u0435\u0439 \u043f\u0456\u0434\u0440\u0443\u0447\u043d\u0438\u043a \u043d\u0430\u0434\u0430\u0454 \u043f\u043e\u043a\u0440\u043e\u043a\u043e\u0432\u0438\u0439 \u043f\u0440\u0438\u043a\u043b\u0430\u0434 \u0442\u043e\u0433\u043e, \u044f\u043a \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0432\u0430\u0442\u0438 XGBoost \u0434\u043b\u044f \u0430\u0434\u0430\u043f\u0442\u0430\u0446\u0456\u0457 \u0440\u043e\u0437\u0448\u0438\u0440\u0435\u043d\u043e\u0457 \u043c\u043e\u0434\u0435\u043b\u0456 \u0432 R. \u041a\u0440\u043e\u043a 1: \u0417\u0430\u0432\u0430\u043d\u0442\u0430\u0436\u0442\u0435 \u043d\u0435\u043e\u0431\u0445\u0456\u0434\u043d\u0456 \u043f\u0430\u043a\u0435\u0442\u0438 \u0421\u043f\u043e\u0447\u0430\u0442\u043a\u0443 [&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>XGBoost \u0432 R: 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\u0441\u0442\u0430\u0442\u0438\u0441\u0442\u0438\u0447\u043d\u043e\u0457 \u043a\u043e\u043c\u043f\u0435\u0442\u0435\u043d\u0442\u043d\u043e\u0441\u0442\u0456!\",\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/statorials.org\/uk\/?s={search_term_string}\"},\"query-input\":\"required 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