{"id":1234,"date":"2023-07-27T04:52:33","date_gmt":"2023-07-27T04:52:33","guid":{"rendered":"https:\/\/statorials.org\/hi\/%e0%a4%86%e0%a4%b0-%e0%a4%ae%e0%a5%87%e0%a4%82-xgboost\/"},"modified":"2023-07-27T04:52:33","modified_gmt":"2023-07-27T04:52:33","slug":"%e0%a4%86%e0%a4%b0-%e0%a4%ae%e0%a5%87%e0%a4%82-xgboost","status":"publish","type":"post","link":"https:\/\/statorials.org\/hi\/%e0%a4%86%e0%a4%b0-%e0%a4%ae%e0%a5%87%e0%a4%82-xgboost\/","title":{"rendered":"R \u092e\u0947\u0902 xgboost: \u090f\u0915 \u091a\u0930\u0923-\u0926\u0930-\u091a\u0930\u0923 \u0909\u0926\u093e\u0939\u0930\u0923"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\"><a href=\"https:\/\/statorials.org\/hi\/\u092e\u0936\u0940\u0928-\u0932\u0930\u094d\u0928\u093f\u0902\u0917-\u0915\u094b-\u092c\u0922\u093c\u093e\u0935\u093e-\u0926\u0947\u0902\/\" target=\"_blank\" rel=\"noopener noreferrer\">\u092c\u0942\u0938\u094d\u091f\u093f\u0902\u0917<\/a> \u090f\u0915 \u092e\u0936\u0940\u0928 \u0932\u0930\u094d\u0928\u093f\u0902\u0917 \u0924\u0915\u0928\u0940\u0915 \u0939\u0948 \u091c\u093f\u0938\u0947 \u0909\u091a\u094d\u091a \u092a\u0942\u0930\u094d\u0935\u093e\u0928\u0941\u092e\u093e\u0928 \u0938\u091f\u0940\u0915\u0924\u093e \u0915\u0947 \u0938\u093e\u0925 \u092e\u0949\u0921\u0932 \u0924\u0948\u092f\u093e\u0930 \u0915\u0930\u0928\u0947 \u0915\u0947 \u0932\u093f\u090f \u0926\u093f\u0916\u093e\u092f\u093e \u0917\u092f\u093e \u0939\u0948\u0964<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u0935\u094d\u092f\u0935\u0939\u093e\u0930 \u092e\u0947\u0902 \u092c\u0942\u0938\u094d\u091f\u093f\u0902\u0917 \u0915\u094b \u0932\u093e\u0917\u0942 \u0915\u0930\u0928\u0947 \u0915\u0947 \u0938\u092c\u0938\u0947 \u0906\u092e \u0924\u0930\u0940\u0915\u094b\u0902 \u092e\u0947\u0902 \u0938\u0947 \u090f\u0915 \u0939\u0948 <strong>XGBoost \u0915\u093e<\/strong> \u0909\u092a\u092f\u094b\u0917 \u0915\u0930\u0928\u093e, \u091c\u093f\u0938\u0915\u093e \u0938\u0902\u0915\u094d\u0937\u093f\u092a\u094d\u0924 \u0930\u0942\u092a &#8220;\u090f\u0915\u094d\u0938\u091f\u094d\u0930\u0940\u092e \u0917\u094d\u0930\u0947\u0921\u093f\u090f\u0902\u091f \u092c\u0942\u0938\u094d\u091f\u093f\u0902\u0917&#8221; \u0939\u0948\u0964<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u092f\u0939 \u091f\u094d\u092f\u0942\u091f\u094b\u0930\u093f\u092f\u0932 R \u092e\u0947\u0902 \u090f\u0915 \u0909\u0928\u094d\u0928\u0924 \u092e\u0949\u0921\u0932 \u0915\u094b \u092b\u093f\u091f \u0915\u0930\u0928\u0947 \u0915\u0947 \u0932\u093f\u090f XGBoost \u0915\u093e \u0909\u092a\u092f\u094b\u0917 \u0915\u0930\u0928\u0947 \u0915\u093e \u091a\u0930\u0923-\u0926\u0930-\u091a\u0930\u0923 \u0909\u0926\u093e\u0939\u0930\u0923 \u092a\u094d\u0930\u0926\u093e\u0928 \u0915\u0930\u0924\u093e \u0939\u0948\u0964<\/span><\/p>\n<h3> <strong><span style=\"color: #000000;\">\u091a\u0930\u0923 1: \u0906\u0935\u0936\u094d\u092f\u0915 \u092a\u0948\u0915\u0947\u091c \u0932\u094b\u0921 \u0915\u0930\u0947\u0902<\/span><\/strong><\/h3>\n<p> <span style=\"color: #000000;\">\u0938\u092c\u0938\u0947 \u092a\u0939\u0932\u0947, \u0939\u092e \u0906\u0935\u0936\u094d\u092f\u0915 \u092a\u0941\u0938\u094d\u0924\u0915\u093e\u0932\u092f\u094b\u0902 \u0915\u094b \u0932\u094b\u0921 \u0915\u0930\u0947\u0902\u0917\u0947\u0964<\/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>\u091a\u0930\u0923 2: \u0921\u0947\u091f\u093e \u0932\u094b\u0921 \u0915\u0930\u0947\u0902<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\u0907\u0938 \u0909\u0926\u093e\u0939\u0930\u0923 \u0915\u0947 \u0932\u093f\u090f, \u0939\u092e <strong>MASS<\/strong> \u092a\u0948\u0915\u0947\u091c \u0938\u0947 <strong>\u092c\u094b\u0938\u094d\u091f\u0928<\/strong> \u0921\u0947\u091f\u093e\u0938\u0947\u091f \u092e\u0947\u0902 \u090f\u0915 \u092c\u0947\u0939\u0924\u0930 \u092a\u094d\u0930\u0924\u093f\u0917\u092e\u0928 \u092e\u0949\u0921\u0932 \u092b\u093f\u091f \u0915\u0930\u0947\u0902\u0917\u0947\u0964<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u0907\u0938 \u0921\u0947\u091f\u093e\u0938\u0947\u091f \u092e\u0947\u0902 13 \u092d\u0935\u093f\u0937\u094d\u092f\u0935\u0915\u094d\u0924\u093e \u091a\u0930 \u0936\u093e\u092e\u093f\u0932 \u0939\u0948\u0902 \u091c\u093f\u0928\u0915\u093e \u0909\u092a\u092f\u094b\u0917 \u0939\u092e <strong>mdev<\/strong> \u0928\u093e\u092e\u0915 \u090f\u0915 <a href=\"https:\/\/statorials.org\/hi\/\u091a\u0930-\u0935\u094d\u092f\u093e\u0916\u094d\u092f\u093e\u0924\u094d\u092e\u0915-\u092a\u094d\u0930\u0924\u093f\u0915\u094d\u0930\u093f\u092f\u093e\u090f\u0901\/\" target=\"_blank\" rel=\"noopener noreferrer\">\u092a\u094d\u0930\u0924\u093f\u0915\u094d\u0930\u093f\u092f\u093e \u091a\u0930 \u0915\u0940<\/a> \u092d\u0935\u093f\u0937\u094d\u092f\u0935\u093e\u0923\u0940 \u0915\u0930\u0928\u0947 \u0915\u0947 \u0932\u093f\u090f \u0915\u0930\u0947\u0902\u0917\u0947, \u091c\u094b \u092c\u094b\u0938\u094d\u091f\u0928 \u0915\u0947 \u0906\u0938\u092a\u093e\u0938 \u0935\u093f\u092d\u093f\u0928\u094d\u0928 \u091c\u0928\u0917\u0923\u0928\u093e \u0915\u094d\u0937\u0947\u0924\u094d\u0930\u094b\u0902 \u092e\u0947\u0902 \u0918\u0930\u094b\u0902 \u0915\u0947 \u0914\u0938\u0924 \u092e\u0942\u0932\u094d\u092f \u0915\u093e \u092a\u094d\u0930\u0924\u093f\u0928\u093f\u0927\u093f\u0924\u094d\u0935 \u0915\u0930\u0924\u093e \u0939\u0948\u0964<\/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;\">\u0939\u092e \u0926\u0947\u0916 \u0938\u0915\u0924\u0947 \u0939\u0948\u0902 \u0915\u093f \u0921\u0947\u091f\u093e\u0938\u0947\u091f \u092e\u0947\u0902 \u0915\u0941\u0932 \u092e\u093f\u0932\u093e\u0915\u0930 506 <a href=\"https:\/\/statorials.org\/hi\/\u0938\u093e\u0902\u0916\u094d\u092f\u093f\u0915\u0940-\u092e\u0947\u0902-\u0905\u0935\u0932\u094b\u0915\u0928\/\" target=\"_blank\" rel=\"noopener noreferrer\">\u0905\u0935\u0932\u094b\u0915\u0928<\/a> \u0914\u0930 14 \u091a\u0930 \u0939\u0948\u0902\u0964<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>\u091a\u0930\u0923 3: \u0921\u0947\u091f\u093e \u0924\u0948\u092f\u093e\u0930 \u0915\u0930\u0947\u0902<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\u0907\u0938\u0915\u0947 \u092c\u093e\u0926, \u0939\u092e \u092e\u0942\u0932 \u0921\u0947\u091f\u093e\u0938\u0947\u091f \u0915\u094b \u092a\u094d\u0930\u0936\u093f\u0915\u094d\u0937\u0923 \u0914\u0930 \u092a\u0930\u0940\u0915\u094d\u0937\u0923 \u0938\u0947\u091f \u092e\u0947\u0902 \u0935\u093f\u092d\u093e\u091c\u093f\u0924 \u0915\u0930\u0928\u0947 \u0915\u0947 \u0932\u093f\u090f \u0915\u0948\u0930\u0947\u091f \u092a\u0948\u0915\u0947\u091c \u0938\u0947 <strong>createDataPartition()<\/strong> \u092b\u093c\u0902\u0915\u094d\u0936\u0928 \u0915\u093e \u0909\u092a\u092f\u094b\u0917 \u0915\u0930\u0947\u0902\u0917\u0947\u0964<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u0907\u0938 \u0909\u0926\u093e\u0939\u0930\u0923 \u0915\u0947 \u0932\u093f\u090f, \u0939\u092e \u092a\u094d\u0930\u0936\u093f\u0915\u094d\u0937\u0923 \u0938\u0947\u091f \u0915\u0947 \u0939\u093f\u0938\u094d\u0938\u0947 \u0915\u0947 \u0930\u0942\u092a \u092e\u0947\u0902 80% \u092e\u0942\u0932 \u0921\u0947\u091f\u093e\u0938\u0947\u091f \u0915\u093e \u0909\u092a\u092f\u094b\u0917 \u0915\u0930\u0928\u093e \u091a\u0941\u0928\u0947\u0902\u0917\u0947\u0964<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u0927\u094d\u092f\u093e\u0928 \u0926\u0947\u0902 \u0915\u093f xgboost \u092a\u0948\u0915\u0947\u091c \u092e\u0948\u091f\u094d\u0930\u093f\u0915\u094d\u0938 \u0921\u0947\u091f\u093e \u0915\u093e \u092d\u0940 \u0909\u092a\u092f\u094b\u0917 \u0915\u0930\u0924\u093e \u0939\u0948, \u0907\u0938\u0932\u093f\u090f \u0939\u092e \u0905\u092a\u0928\u0947 \u092a\u094d\u0930\u0947\u0921\u093f\u0915\u094d\u091f\u0930 \u0935\u0947\u0930\u093f\u090f\u092c\u0932\u094d\u0938 \u0915\u094b \u0939\u094b\u0932\u094d\u0921 \u0915\u0930\u0928\u0947 \u0915\u0947 \u0932\u093f\u090f <strong>data.matrix()<\/strong> \u092b\u093c\u0902\u0915\u094d\u0936\u0928 \u0915\u093e \u0909\u092a\u092f\u094b\u0917 \u0915\u0930\u0947\u0902\u0917\u0947\u0964<\/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>\u091a\u0930\u0923 4: \u092e\u0949\u0921\u0932 \u0915\u094b \u0938\u092e\u093e\u092f\u094b\u091c\u093f\u0924 \u0915\u0930\u0947\u0902<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\u0907\u0938\u0915\u0947 \u092c\u093e\u0926, \u0939\u092e <strong>xgb.train()<\/strong> \u092b\u093c\u0902\u0915\u094d\u0936\u0928 \u0915\u093e \u0909\u092a\u092f\u094b\u0917 \u0915\u0930\u0915\u0947 XGBoost \u092e\u0949\u0921\u0932 \u0915\u094b \u091f\u094d\u092f\u0942\u0928 \u0915\u0930\u0947\u0902\u0917\u0947, \u091c\u094b \u092a\u094d\u0930\u0924\u094d\u092f\u0947\u0915 \u092c\u0942\u0938\u094d\u091f\u093f\u0902\u0917 \u091a\u0915\u094d\u0930 \u0915\u0947 \u0932\u093f\u090f \u092a\u094d\u0930\u0936\u093f\u0915\u094d\u0937\u0923 \u0914\u0930 \u092a\u0930\u0940\u0915\u094d\u0937\u0923 RMSE (\u092e\u093e\u0927\u094d\u092f \u0935\u0930\u094d\u0917 \u0924\u094d\u0930\u0941\u091f\u093f) \u092a\u094d\u0930\u0926\u0930\u094d\u0936\u093f\u0924 \u0915\u0930\u0924\u093e \u0939\u0948\u0964<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u0927\u094d\u092f\u093e\u0928 \u0926\u0947\u0902 \u0915\u093f \u0939\u092e\u0928\u0947 \u0907\u0938 \u0909\u0926\u093e\u0939\u0930\u0923 \u0915\u0947 \u0932\u093f\u090f 70 \u0930\u093e\u0909\u0902\u0921 \u0915\u093e \u0909\u092a\u092f\u094b\u0917 \u0915\u0930\u0928\u093e \u091a\u0941\u0928\u093e \u0939\u0948, \u0932\u0947\u0915\u093f\u0928 \u092c\u0939\u0941\u0924 \u092c\u0921\u093c\u0947 \u0921\u0947\u091f\u093e\u0938\u0947\u091f \u0915\u0947 \u0932\u093f\u090f \u0938\u0948\u0915\u0921\u093c\u094b\u0902 \u092f\u093e \u0939\u091c\u093e\u0930\u094b\u0902 \u0930\u093e\u0909\u0902\u0921 \u0915\u093e \u0909\u092a\u092f\u094b\u0917 \u0915\u0930\u0928\u093e \u0905\u0938\u093e\u092e\u093e\u0928\u094d\u092f \u0928\u0939\u0940\u0902 \u0939\u0948\u0964 \u092c\u0938 \u092f\u0939 \u0927\u094d\u092f\u093e\u0928 \u0930\u0916\u0947\u0902 \u0915\u093f \u091c\u093f\u0924\u0928\u0947 \u0905\u0927\u093f\u0915 \u0930\u093e\u0909\u0902\u0921 \u0939\u094b\u0902\u0917\u0947, \u0930\u0928\u091f\u093e\u0907\u092e \u0909\u0924\u0928\u093e \u0939\u0940 \u0932\u0902\u092c\u093e \u0939\u094b\u0917\u093e\u0964<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u092f\u0939 \u092d\u0940 \u0927\u094d\u092f\u093e\u0928 \u0926\u0947\u0902 \u0915\u093f <strong>\u0905\u0927\u093f\u0915\u0924\u092e \u0921\u093f\u0917\u094d\u0930\u0940<\/strong> \u0924\u0930\u094d\u0915 \u0935\u094d\u092f\u0915\u094d\u0924\u093f\u0917\u0924 \u0928\u093f\u0930\u094d\u0923\u092f \u092a\u0947\u0921\u093c\u094b\u0902 \u0915\u0947 \u0935\u093f\u0915\u093e\u0938 \u0915\u0940 \u0917\u0939\u0930\u093e\u0908 \u0915\u094b \u0928\u093f\u0930\u094d\u0926\u093f\u0937\u094d\u091f \u0915\u0930\u0924\u093e \u0939\u0948\u0964 \u091b\u094b\u091f\u0947 \u092a\u0947\u0921\u093c \u0909\u0917\u093e\u0928\u0947 \u0915\u0947 \u0932\u093f\u090f \u0939\u092e \u0906\u092e\u0924\u094c\u0930 \u092a\u0930 \u092f\u0939 \u0938\u0902\u0916\u094d\u092f\u093e \u0915\u093e\u092b\u0940 \u0915\u092e \u091a\u0941\u0928\u0924\u0947 \u0939\u0948\u0902, \u091c\u0948\u0938\u0947 2 \u092f\u093e 3\u0964 \u092f\u0939 \u0926\u093f\u0916\u093e\u092f\u093e \u0917\u092f\u093e \u0939\u0948 \u0915\u093f \u092f\u0939 \u0926\u0943\u0937\u094d\u091f\u093f\u0915\u094b\u0923 \u0905\u0927\u093f\u0915 \u0938\u091f\u0940\u0915 \u092e\u0949\u0921\u0932 \u0924\u0948\u092f\u093e\u0930 \u0915\u0930\u0924\u093e \u0939\u0948\u0964<\/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;\">\u092a\u0930\u093f\u0923\u093e\u092e \u0938\u0947, \u0939\u092e \u0926\u0947\u0916 \u0938\u0915\u0924\u0947 \u0939\u0948\u0902 \u0915\u093f \u0928\u094d\u092f\u0942\u0928\u0924\u092e \u092a\u0930\u0940\u0915\u094d\u0937\u0923 \u0906\u0930\u090f\u092e\u090f\u0938\u0908 <strong>56<\/strong> \u0930\u093e\u0909\u0902\u0921 \u092e\u0947\u0902 \u0939\u093e\u0938\u093f\u0932 \u0915\u093f\u092f\u093e \u0917\u092f\u093e \u0939\u0948\u0964 \u0907\u0938 \u092c\u093f\u0902\u0926\u0941 \u0938\u0947 \u092a\u0930\u0947, \u092a\u0930\u0940\u0915\u094d\u0937\u0923 \u0906\u0930\u090f\u092e\u090f\u0938\u0908 \u092c\u0922\u093c\u0928\u093e \u0936\u0941\u0930\u0942 \u0939\u094b \u091c\u093e\u0924\u093e \u0939\u0948, \u092f\u0939 \u0926\u0930\u094d\u0936\u093e\u0924\u093e \u0939\u0948 \u0915\u093f \u0939\u092e <a href=\"https:\/\/statorials.org\/hi\/\u092e\u0936\u0940\u0928-\u0932\u0930\u094d\u0928\u093f\u0902\u0917-\u0913\u0935\u0930\u092b\u093f\u091f\u093f\u0902\u0917\/\" target=\"_blank\" rel=\"noopener noreferrer\">\u092a\u094d\u0930\u0936\u093f\u0915\u094d\u0937\u0923 \u0921\u0947\u091f\u093e \u0915\u094b \u0913\u0935\u0930\u092b\u093f\u091f \u0915\u0930 \u0930\u0939\u0947 \u0939\u0948\u0902<\/a> \u0964<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u0907\u0938\u0932\u093f\u090f, \u0939\u092e \u0905\u092a\u0928\u0947 \u0905\u0902\u0924\u093f\u092e XGBoost \u092e\u0949\u0921\u0932 \u0915\u094b 56 \u0930\u093e\u0909\u0902\u0921 \u0915\u093e \u0909\u092a\u092f\u094b\u0917 \u0915\u0930\u0928\u0947 \u0915\u0947 \u0932\u093f\u090f \u0938\u0947\u091f \u0915\u0930\u0947\u0902\u0917\u0947:<\/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;\">\u0928\u094b\u091f: <strong>\u0935\u0930\u094d\u092c\u094b\u091c\u093c = 0<\/strong> \u0924\u0930\u094d\u0915 \u0906\u0930 \u0915\u094b \u092a\u094d\u0930\u0924\u094d\u092f\u0947\u0915 \u0926\u094c\u0930 \u0915\u0947 \u0932\u093f\u090f \u092a\u094d\u0930\u0936\u093f\u0915\u094d\u0937\u0923 \u0914\u0930 \u092a\u0930\u0940\u0915\u094d\u0937\u0923 \u0924\u094d\u0930\u0941\u091f\u093f \u092a\u094d\u0930\u0926\u0930\u094d\u0936\u093f\u0924 \u0928\u0939\u0940\u0902 \u0915\u0930\u0928\u0947 \u0915\u0947 \u0932\u093f\u090f \u0915\u0939\u0924\u093e \u0939\u0948\u0964<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>\u091a\u0930\u0923 5: \u092a\u0942\u0930\u094d\u0935\u093e\u0928\u0941\u092e\u093e\u0928 \u0932\u0917\u093e\u0928\u0947 \u0915\u0947 \u0932\u093f\u090f \u092e\u0949\u0921\u0932 \u0915\u093e \u0909\u092a\u092f\u094b\u0917 \u0915\u0930\u0947\u0902<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\u0905\u0902\u0924 \u092e\u0947\u0902, \u0939\u092e \u092a\u0930\u0940\u0915\u094d\u0937\u0923 \u0938\u0947\u091f \u092e\u0947\u0902 \u092c\u094b\u0938\u094d\u091f\u0928 \u0918\u0930\u094b\u0902 \u0915\u0947 \u0914\u0938\u0924 \u092e\u0942\u0932\u094d\u092f \u0915\u0947 \u092c\u093e\u0930\u0947 \u092e\u0947\u0902 \u092d\u0935\u093f\u0937\u094d\u092f\u0935\u093e\u0923\u0940 \u0915\u0930\u0928\u0947 \u0915\u0947 \u0932\u093f\u090f \u0905\u0902\u0924\u093f\u092e \u0909\u0928\u094d\u0928\u0924 \u092e\u0949\u0921\u0932 \u0915\u093e \u0909\u092a\u092f\u094b\u0917 \u0915\u0930 \u0938\u0915\u0924\u0947 \u0939\u0948\u0902\u0964<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u092b\u093f\u0930 \u0939\u092e \u092e\u0949\u0921\u0932 \u0915\u0947 \u0932\u093f\u090f \u0928\u093f\u092e\u094d\u0928\u0932\u093f\u0916\u093f\u0924 \u0938\u091f\u0940\u0915\u0924\u093e \u092e\u0947\u091f\u094d\u0930\u093f\u0915\u094d\u0938 \u0915\u0940 \u0917\u0923\u0928\u093e \u0915\u0930\u0947\u0902\u0917\u0947:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>\u090f\u092e\u090f\u0938\u0908:<\/strong> \u092e\u093e\u0927\u094d\u092f \u0935\u0930\u094d\u0917 \u0924\u094d\u0930\u0941\u091f\u093f<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>\u090f\u092e\u090f\u0908:<\/strong> \u092e\u0924\u0932\u092c \u092a\u0942\u0930\u094d\u0923 \u0924\u094d\u0930\u0941\u091f\u093f<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>\u0906\u0930\u090f\u092e\u090f\u0938\u0908:<\/strong> \u092e\u0942\u0932 \u092e\u093e\u0927\u094d\u092f \u0935\u0930\u094d\u0917 \u0924\u094d\u0930\u0941\u091f\u093f<\/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;\">\u092e\u093e\u0927\u094d\u092f \u0935\u0930\u094d\u0917 \u0924\u094d\u0930\u0941\u091f\u093f <strong>3.674457<\/strong> \u0928\u093f\u0915\u0932\u0940\u0964 \u092f\u0939 \u0914\u0938\u0924 \u0918\u0930 \u092e\u0942\u0932\u094d\u092f\u094b\u0902 \u0915\u0947 \u0932\u093f\u090f \u0915\u0940 \u0917\u0908 \u092d\u0935\u093f\u0937\u094d\u092f\u0935\u093e\u0923\u0940 \u0914\u0930 \u092a\u0930\u0940\u0915\u094d\u0937\u0923 \u0938\u0947\u091f \u092e\u0947\u0902 \u0926\u0947\u0916\u0947 \u0917\u090f \u0935\u093e\u0938\u094d\u0924\u0935\u093f\u0915 \u0918\u0930 \u092e\u0942\u0932\u094d\u092f\u094b\u0902 \u0915\u0947 \u092c\u0940\u091a \u0914\u0938\u0924 \u0905\u0902\u0924\u0930 \u0915\u093e \u092a\u094d\u0930\u0924\u093f\u0928\u093f\u0927\u093f\u0924\u094d\u0935 \u0915\u0930\u0924\u093e \u0939\u0948\u0964<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u092f\u0926\u093f \u0939\u092e \u091a\u093e\u0939\u0947\u0902, \u0924\u094b \u0939\u092e \u0907\u0938 \u0906\u0930\u090f\u092e\u090f\u0938\u0908 \u0915\u0940 \u0924\u0941\u0932\u0928\u093e \u0905\u0928\u094d\u092f \u092e\u0949\u0921\u0932\u094b\u0902 \u091c\u0948\u0938\u0947 <a href=\"https:\/\/statorials.org\/hi\/\u090f\u0915\u093e\u0927\u093f\u0915-\u0930\u0948\u0916\u093f\u0915-\u092a\u094d\u0930\u0924\u093f\u0917\u092e\u0928-\u0906\u0930\/\" target=\"_blank\" rel=\"noopener noreferrer\">\u092e\u0932\u094d\u091f\u0940\u092a\u0932 \u0932\u0940\u0928\u093f\u092f\u0930 \u0930\u093f\u0917\u094d\u0930\u0947\u0936\u0928<\/a> , <a href=\"https:\/\/statorials.org\/hi\/\u0906\u0930-\u092e\u0947\u0902-\u0936\u093f\u0916\u093e-\u092a\u094d\u0930\u0924\u093f\u0917\u092e\u0928\/\" target=\"_blank\" rel=\"noopener noreferrer\">\u0930\u093f\u091c \u0930\u093f\u0917\u094d\u0930\u0947\u0936\u0928<\/a> , <a href=\"https:\/\/statorials.org\/hi\/\u0906\u0930-\u092e\u0947\u0902-\u092a\u094d\u0930\u092e\u0941\u0916-\u0918\u091f\u0915-\u092a\u094d\u0930\u0924\u093f\u0917\u092e\u0928\/\" target=\"_blank\" rel=\"noopener noreferrer\">\u092a\u094d\u0930\u093f\u0902\u0938\u093f\u092a\u0932 \u0915\u0902\u092a\u094b\u0928\u0947\u0902\u091f \u0930\u093f\u0917\u094d\u0930\u0947\u0936\u0928<\/a> \u0906\u0926\u093f \u0938\u0947 \u0915\u0930 \u0938\u0915\u0924\u0947 \u0939\u0948\u0902\u0964 \u092f\u0939 \u0926\u0947\u0916\u0928\u0947 \u0915\u0947 \u0932\u093f\u090f \u0915\u093f \u0915\u094c\u0928 \u0938\u093e \u092e\u0949\u0921\u0932 \u0938\u092c\u0938\u0947 \u0938\u091f\u0940\u0915 \u092d\u0935\u093f\u0937\u094d\u092f\u0935\u093e\u0923\u093f\u092f\u093e\u0901 \u0915\u0930\u0924\u093e \u0939\u0948\u0964<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u0906\u092a \u0907\u0938 \u0909\u0926\u093e\u0939\u0930\u0923 \u092e\u0947\u0902 \u092a\u094d\u0930\u092f\u0941\u0915\u094d\u0924 \u092a\u0942\u0930\u093e \u0906\u0930 \u0915\u094b\u0921 <a href=\"https:\/\/github.com\/Statorials\/R-Guides\/blob\/main\/xgboost.R\" target=\"_blank\" rel=\"noopener noreferrer\">\u092f\u0939\u093e\u0902<\/a> \u092a\u093e \u0938\u0915\u0924\u0947 \u0939\u0948\u0902\u0964<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u092c\u0942\u0938\u094d\u091f\u093f\u0902\u0917 \u090f\u0915 \u092e\u0936\u0940\u0928 \u0932\u0930\u094d\u0928\u093f\u0902\u0917 \u0924\u0915\u0928\u0940\u0915 \u0939\u0948 \u091c\u093f\u0938\u0947 \u0909\u091a\u094d\u091a \u092a\u0942\u0930\u094d\u0935\u093e\u0928\u0941\u092e\u093e\u0928 \u0938\u091f\u0940\u0915\u0924\u093e \u0915\u0947 \u0938\u093e\u0925 \u092e\u0949\u0921\u0932 \u0924\u0948\u092f\u093e\u0930 \u0915\u0930\u0928\u0947 \u0915\u0947 \u0932\u093f\u090f \u0926\u093f\u0916\u093e\u092f\u093e \u0917\u092f\u093e \u0939\u0948\u0964 \u0935\u094d\u092f\u0935\u0939\u093e\u0930 \u092e\u0947\u0902 \u092c\u0942\u0938\u094d\u091f\u093f\u0902\u0917 \u0915\u094b \u0932\u093e\u0917\u0942 \u0915\u0930\u0928\u0947 \u0915\u0947 \u0938\u092c\u0938\u0947 \u0906\u092e \u0924\u0930\u0940\u0915\u094b\u0902 \u092e\u0947\u0902 \u0938\u0947 \u090f\u0915 \u0939\u0948 XGBoost \u0915\u093e \u0909\u092a\u092f\u094b\u0917 \u0915\u0930\u0928\u093e, \u091c\u093f\u0938\u0915\u093e \u0938\u0902\u0915\u094d\u0937\u093f\u092a\u094d\u0924 \u0930\u0942\u092a &#8220;\u090f\u0915\u094d\u0938\u091f\u094d\u0930\u0940\u092e \u0917\u094d\u0930\u0947\u0921\u093f\u090f\u0902\u091f \u092c\u0942\u0938\u094d\u091f\u093f\u0902\u0917&#8221; \u0939\u0948\u0964 \u092f\u0939 \u091f\u094d\u092f\u0942\u091f\u094b\u0930\u093f\u092f\u0932 R \u092e\u0947\u0902 \u090f\u0915 \u0909\u0928\u094d\u0928\u0924 \u092e\u0949\u0921\u0932 \u0915\u094b \u092b\u093f\u091f \u0915\u0930\u0928\u0947 [&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":[],"class_list":["post-1234","post","type-post","status-publish","format-standard","hentry","category-3"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>R \u092e\u0947\u0902 XGBoost: \u090f\u0915 \u091a\u0930\u0923-\u0926\u0930-\u091a\u0930\u0923 \u0909\u0926\u093e\u0939\u0930\u0923<\/title>\n<meta name=\"description\" content=\"\u092f\u0939 \u091f\u094d\u092f\u0942\u091f\u094b\u0930\u093f\u092f\u0932 \u090f\u0915 \u0932\u094b\u0915\u092a\u094d\u0930\u093f\u092f \u092e\u0936\u0940\u0928 \u0932\u0930\u094d\u0928\u093f\u0902\u0917 \u0924\u0915\u0928\u0940\u0915 XGBoost \u0915\u094b R \u092e\u0947\u0902 \u091a\u0932\u093e\u0928\u0947 \u0915\u093e \u091a\u0930\u0923-\u0926\u0930-\u091a\u0930\u0923 \u0909\u0926\u093e\u0939\u0930\u0923 \u092a\u094d\u0930\u0926\u093e\u0928 \u0915\u0930\u0924\u093e \u0939\u0948\u0964\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/statorials.org\/hi\/\u0906\u0930-\u092e\u0947\u0902-xgboost\/\" \/>\n<meta property=\"og:locale\" content=\"hi_IN\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"R \u092e\u0947\u0902 XGBoost: \u090f\u0915 \u091a\u0930\u0923-\u0926\u0930-\u091a\u0930\u0923 \u0909\u0926\u093e\u0939\u0930\u0923\" \/>\n<meta property=\"og:description\" content=\"\u092f\u0939 \u091f\u094d\u092f\u0942\u091f\u094b\u0930\u093f\u092f\u0932 \u090f\u0915 \u0932\u094b\u0915\u092a\u094d\u0930\u093f\u092f \u092e\u0936\u0940\u0928 \u0932\u0930\u094d\u0928\u093f\u0902\u0917 \u0924\u0915\u0928\u0940\u0915 XGBoost \u0915\u094b R \u092e\u0947\u0902 \u091a\u0932\u093e\u0928\u0947 \u0915\u093e \u091a\u0930\u0923-\u0926\u0930-\u091a\u0930\u0923 \u0909\u0926\u093e\u0939\u0930\u0923 \u092a\u094d\u0930\u0926\u093e\u0928 \u0915\u0930\u0924\u093e \u0939\u0948\u0964\" \/>\n<meta property=\"og:url\" content=\"https:\/\/statorials.org\/hi\/\u0906\u0930-\u092e\u0947\u0902-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=\"\u0921\u0949. \u092c\u0947\u0902\u091c\u093e\u092e\u093f\u0928 \u090f\u0902\u0921\u0930\u0938\u0928\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"\u0926\u094d\u0935\u093e\u0930\u093e \u0932\u093f\u0916\u093f\u0924\" \/>\n\t<meta name=\"twitter:data1\" content=\"\u0921\u0949. \u092c\u0947\u0902\u091c\u093e\u092e\u093f\u0928 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