{"id":1220,"date":"2023-07-27T05:56:29","date_gmt":"2023-07-27T05:56:29","guid":{"rendered":"https:\/\/statorials.org\/ar\/%d8%a7%d9%84%d8%aa%d8%b9%d8%a8%d9%8a%d9%94%d8%a9-%d9%81%d9%8a-%d8%b5\/"},"modified":"2023-07-27T05:56:29","modified_gmt":"2023-07-27T05:56:29","slug":"%d8%a7%d9%84%d8%aa%d8%b9%d8%a8%d9%8a%d9%94%d8%a9-%d9%81%d9%8a-%d8%b5","status":"publish","type":"post","link":"https:\/\/statorials.org\/ar\/%d8%a7%d9%84%d8%aa%d8%b9%d8%a8%d9%8a%d9%94%d8%a9-%d9%81%d9%8a-%d8%b5\/","title":{"rendered":"\u0643\u064a\u0641\u064a\u0629 \u0625\u062c\u0631\u0627\u0621 \u0627\u0644\u062a\u0639\u0628\u0626\u0629 \u0641\u064a r (\u062e\u0637\u0648\u0629 \u0628\u062e\u0637\u0648\u0629)"},"content":{"rendered":"

<\/p>\n

\n

\u0639\u0646\u062f\u0645\u0627 \u0646\u0642\u0648\u0645 \u0628\u0625\u0646\u0634\u0627\u0621 \u0634\u062c\u0631\u0629 \u0642\u0631\u0627\u0631<\/a> \u0644\u0645\u062c\u0645\u0648\u0639\u0629 \u0628\u064a\u0627\u0646\u0627\u062a \u0645\u0639\u064a\u0646\u0629\u060c \u0641\u0625\u0646\u0646\u0627 \u0646\u0633\u062a\u062e\u062f\u0645 \u0641\u0642\u0637 \u0645\u062c\u0645\u0648\u0639\u0629 \u0628\u064a\u0627\u0646\u0627\u062a \u062a\u062f\u0631\u064a\u0628 \u0648\u0627\u062d\u062f\u0629 \u0644\u0628\u0646\u0627\u0621 \u0627\u0644\u0646\u0645\u0648\u0630\u062c.<\/span><\/p>\n

\u0648\u0645\u0639 \u0630\u0644\u0643\u060c \u0641\u0625\u0646 \u0627\u0644\u062c\u0627\u0646\u0628 \u0627\u0644\u0633\u0644\u0628\u064a \u0644\u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0634\u062c\u0631\u0629 \u0642\u0631\u0627\u0631 \u0648\u0627\u062d\u062f\u0629 \u0647\u0648 \u0623\u0646\u0647\u0627 \u062a\u0645\u064a\u0644 \u0625\u0644\u0649 \u0627\u0644\u0645\u0639\u0627\u0646\u0627\u0629 \u0645\u0646 \u0627\u0644\u062a\u0628\u0627\u064a\u0646 \u0627\u0644\u0639\u0627\u0644\u064a<\/a> . \u0623\u064a \u0623\u0646\u0646\u0627 \u0625\u0630\u0627 \u0642\u0633\u0645\u0646\u0627 \u0645\u062c\u0645\u0648\u0639\u0629 \u0627\u0644\u0628\u064a\u0627\u0646\u0627\u062a \u0625\u0644\u0649 \u0646\u0635\u0641\u064a\u0646 \u0648\u0642\u0645\u0646\u0627 \u0628\u062a\u0637\u0628\u064a\u0642 \u0634\u062c\u0631\u0629 \u0627\u0644\u0642\u0631\u0627\u0631 \u0639\u0644\u0649 \u0643\u0644\u0627 \u0627\u0644\u0646\u0635\u0641\u064a\u0646\u060c \u0641\u0642\u062f \u062a\u0643\u0648\u0646 \u0627\u0644\u0646\u062a\u0627\u0626\u062c \u0645\u062e\u062a\u0644\u0641\u0629 \u062a\u0645\u0627\u0645\u064b\u0627.<\/span><\/p>\n

\u062a\u064f\u0639\u0631\u0641 \u0625\u062d\u062f\u0649 \u0627\u0644\u0637\u0631\u0642 \u0627\u0644\u062a\u064a \u064a\u0645\u0643\u0646\u0646\u0627 \u0627\u0633\u062a\u062e\u062f\u0627\u0645\u0647\u0627 \u0644\u062a\u0642\u0644\u064a\u0644 \u0627\u0644\u062a\u0628\u0627\u064a\u0646 \u0641\u064a \u0634\u062c\u0631\u0629 \u0642\u0631\u0627\u0631 \u0648\u0627\u062d\u062f\u0629 \u0628\u0627\u0633\u0645 \u0627\u0644\u062a\u0639\u0628\u0626\u0629<\/a> \u060c \u0648\u0627\u0644\u062a\u064a \u062a\u0633\u0645\u0649 \u0623\u062d\u064a\u0627\u0646\u064b\u0627 \u062a\u062c\u0645\u064a\u0639 \u0627\u0644\u062a\u0645\u0647\u064a\u062f<\/em> .<\/span><\/p>\n

\u062a\u0639\u0645\u0644 \u0639\u0645\u0644\u064a\u0629 \u0627\u0644\u062a\u0639\u0628\u0626\u0629 \u0639\u0644\u0649 \u0627\u0644\u0646\u062d\u0648 \u0627\u0644\u062a\u0627\u0644\u064a:<\/span><\/p>\n

1.<\/strong> \u062e\u0630 \u0639\u064a\u0646\u0627\u062a \u062a\u0645\u0647\u064a\u062f\u064a\u0629<\/em> \u0645\u0646 \u0645\u062c\u0645\u0648\u0639\u0629 \u0627\u0644\u0628\u064a\u0627\u0646\u0627\u062a \u0627\u0644\u0623\u0635\u0644\u064a\u0629.<\/span><\/p>\n

2.<\/strong> \u0642\u0645 \u0628\u0625\u0646\u0634\u0627\u0621 \u0634\u062c\u0631\u0629 \u0642\u0631\u0627\u0631\u0627\u062a \u0644\u0643\u0644 \u0639\u064a\u0646\u0629 \u062a\u0645\u0647\u064a\u062f\u064a\u0629.<\/span><\/p>\n

3.<\/strong> \u0645\u062a\u0648\u0633\u0637 \u0627\u0644\u062a\u0648\u0642\u0639\u0627\u062a \u0645\u0646 \u0643\u0644 \u0634\u062c\u0631\u0629 \u0644\u0644\u062d\u0635\u0648\u0644 \u0639\u0644\u0649 \u0627\u0644\u0646\u0645\u0648\u0630\u062c \u0627\u0644\u0646\u0647\u0627\u0626\u064a.<\/span><\/p>\n

\u0645\u0646 \u062e\u0644\u0627\u0644 \u0628\u0646\u0627\u0621 \u0627\u0644\u0645\u0626\u0627\u062a \u0623\u0648 \u062d\u062a\u0649 \u0627\u0644\u0622\u0644\u0627\u0641 \u0645\u0646 \u0623\u0634\u062c\u0627\u0631 \u0627\u0644\u0642\u0631\u0627\u0631 \u0627\u0644\u0641\u0631\u062f\u064a\u0629 \u0648\u0623\u062e\u0630 \u0645\u062a\u0648\u0633\u0637 \u0627\u0644\u062a\u0646\u0628\u0624\u0627\u062a \u0644\u062c\u0645\u064a\u0639 \u0627\u0644\u0623\u0634\u062c\u0627\u0631\u060c \u063a\u0627\u0644\u0628\u064b\u0627 \u0645\u0627 \u0646\u0646\u062a\u0647\u064a \u0628\u0646\u0645\u0648\u0630\u062c \u062d\u0642\u064a\u0628\u0629 \u0645\u064f\u062c\u0647\u0632 \u064a\u0646\u062a\u062c \u0645\u0639\u062f\u0644 \u062e\u0637\u0623 \u0641\u064a \u0627\u0644\u0627\u062e\u062a\u0628\u0627\u0631 \u0623\u0642\u0644 \u0628\u0643\u062b\u064a\u0631 \u0645\u0642\u0627\u0631\u0646\u0629 \u0628\u0634\u062c\u0631\u0629 \u0642\u0631\u0627\u0631 \u0648\u0627\u062d\u062f\u0629.<\/span><\/p>\n

\u064a\u0642\u062f\u0645 \u0647\u0630\u0627 \u0627\u0644\u0628\u0631\u0646\u0627\u0645\u062c \u0627\u0644\u062a\u0639\u0644\u064a\u0645\u064a \u0645\u062b\u0627\u0644\u0627\u064b \u062e\u0637\u0648\u0629 \u0628\u062e\u0637\u0648\u0629 \u062d\u0648\u0644 \u0643\u064a\u0641\u064a\u0629 \u0625\u0646\u0634\u0627\u0621 \u0646\u0645\u0648\u0630\u062c \u0645\u064f\u0639\u0628\u0623 \u0641\u064a R.<\/span><\/p>\n

\u0627\u0644\u062e\u0637\u0648\u0629 1: \u062a\u062d\u0645\u064a\u0644 \u0627\u0644\u062d\u0632\u0645 \u0627\u0644\u0644\u0627\u0632\u0645\u0629<\/strong><\/span><\/h3>\n

\u0623\u0648\u0644\u0627\u064b\u060c \u0633\u0646\u0642\u0648\u0645 \u0628\u062a\u062d\u0645\u064a\u0644 \u0627\u0644\u062d\u0632\u0645 \u0627\u0644\u0644\u0627\u0632\u0645\u0629 \u0644\u0647\u0630\u0627 \u0627\u0644\u0645\u062b\u0627\u0644:<\/span><\/p>\n

 library<\/span> (dplyr) #for data wrangling<\/span>\nlibrary<\/span> (e1071) #for calculating variable importance<\/span>\nlibrary<\/span> (caret) #for general model fitting<\/span>\nlibrary<\/span> (rpart) #for fitting decision trees<\/span>\nlibrary<\/span> (ipred) #for fitting bagged decision trees\n<\/span><\/strong><\/pre>\n
 \u0627\u0644\u062e\u0637\u0648\u0629 2: \u0642\u0645 \u0628\u062a\u0631\u0643\u064a\u0628 \u0627\u0644\u0646\u0645\u0648\u0630\u062c \u0627\u0644\u0645\u0639\u0628\u0623 \u0641\u064a \u0627\u0644\u062d\u0642\u064a\u0628\u0629<\/strong><\/span><\/h3>\n
 \u0641\u064a \u0647\u0630\u0627 \u0627\u0644\u0645\u062b\u0627\u0644\u060c \u0633\u0648\u0641 \u0646\u0633\u062a\u062e\u062f\u0645 \u0645\u062c\u0645\u0648\u0639\u0629 \u0628\u064a\u0627\u0646\u0627\u062a R \u0645\u062f\u0645\u062c\u0629 \u062a\u0633\u0645\u0649 \u062c\u0648\u062f\u0629 \u0627\u0644\u0647\u0648\u0627\u0621<\/strong> \u0648\u0627\u0644\u062a\u064a \u062a\u062d\u062a\u0648\u064a \u0639\u0644\u0649 \u0642\u064a\u0627\u0633\u0627\u062a \u0644\u062c\u0648\u062f\u0629 \u0627\u0644\u0647\u0648\u0627\u0621 \u0641\u064a \u0645\u062f\u064a\u0646\u0629 \u0646\u064a\u0648\u064a\u0648\u0631\u0643 \u0639\u0644\u0649 \u0645\u062f\u0627\u0631 153 \u064a\u0648\u0645\u064b\u0627 \u0641\u0631\u062f\u064a\u064b\u0627.<\/span><\/p>\n
 #view structure of air quality dataset<\/span>\nstr(airquality)\n\n'data.frame': 153 obs. of 6 variables:\n $ Ozone: int 41 36 12 18 NA 28 23 19 8 NA ...\n $Solar.R: int 190 118 149 313 NA NA 299 99 19 194 ...\n $ Wind: num 7.4 8 12.6 11.5 14.3 14.9 8.6 13.8 20.1 8.6 ...\n $ Temp: int 67 72 74 62 56 66 65 59 61 69 ...\n $Month: int 5 5 5 5 5 5 5 5 5 5 ...\n $Day: int 1 2 3 4 5 6 7 8 9 10 ...\n<\/strong><\/pre>\n
 \u064a\u0648\u0636\u062d \u0627\u0644\u062a\u0639\u0644\u064a\u0645\u0629 \u0627\u0644\u0628\u0631\u0645\u062c\u064a\u0629 \u0627\u0644\u062a\u0627\u0644\u064a\u0629 \u0643\u064a\u0641\u064a\u0629 \u0645\u0644\u0627\u0621\u0645\u0629 \u0646\u0645\u0648\u0630\u062c \u0645\u0639\u0628\u0623 \u0641\u064a R \u0628\u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0648\u0638\u064a\u0641\u0629 \u0627\u0644\u062a\u0639\u0628\u0626\u0629 ()<\/strong> \u0645\u0646 \u0645\u0643\u062a\u0628\u0629 ipred<\/strong> .<\/span><\/p>\n
 #make this example reproducible\n<\/span>set.seed(1)\n\n#fit the bagged model\n<\/span>bag <- bagging(\n  formula = Ozone ~ .,\n  data = airquality,\n  nbagg = 150<\/span> ,   \n  coob = TRUE<\/span> ,\n  control = rpart. control<\/span> (minsplit = 2<\/span> , cp = 0<\/span> )\n)\n\n#display fitted bagged model\n<\/span>bag\n\nBagging regression trees with 150 bootstrap replications \n\nCall: bagging.data.frame(formula = Ozone ~ ., data = airquality, nbagg = 150, \n    coob = TRUE, control = rpart.control(minsplit = 2, cp = 0))\n\nOut-of-bag estimate of root mean squared error: 17.4973<\/strong><\/pre>\n
 \u0644\u0627\u062d\u0638 \u0623\u0646\u0646\u0627 \u0627\u062e\u062a\u0631\u0646\u0627 \u0627\u0633\u062a\u062e\u062f\u0627\u0645 150<\/strong> \u0639\u064a\u0646\u0629 \u062a\u0645 \u062a\u0645\u0647\u064a\u062f\u0647\u0627 \u0644\u0628\u0646\u0627\u0621 \u0627\u0644\u0646\u0645\u0648\u0630\u062c \u0627\u0644\u0630\u064a \u062a\u0645 \u062a\u0639\u0628\u0626\u062a\u0647 \u0641\u064a \u0627\u0644\u0643\u064a\u0633 \u0648\u062d\u062f\u062f\u0646\u0627 \u0623\u0646 coob<\/strong> \u0647\u0648 TRUE<\/strong> \u0644\u0644\u062d\u0635\u0648\u0644 \u0639\u0644\u0649 \u0627\u0644\u062e\u0637\u0623 \u0627\u0644\u0645\u0642\u062f\u0631 \u062e\u0627\u0631\u062c \u0627\u0644\u062d\u0642\u064a\u0628\u0629.<\/span><\/p>\n
 \u0627\u0633\u062a\u062e\u062f\u0645\u0646\u0627 \u0623\u064a\u0636\u064b\u0627 \u0627\u0644\u0645\u0648\u0627\u0635\u0641\u0627\u062a \u0627\u0644\u062a\u0627\u0644\u064a\u0629 \u0641\u064a \u0627\u0644\u062f\u0627\u0644\u0629 rpart.control()<\/strong> :<\/span><\/p>\n