{"id":3043,"date":"2023-07-19T11:54:34","date_gmt":"2023-07-19T11:54:34","guid":{"rendered":"https:\/\/statorials.org\/ar\/%d9%82%d8%b7%d8%a7%d8%b1-%d8%a7%d8%ae%d8%aa%d8%a8%d8%a7%d8%b1-%d8%b3%d8%a8%d9%84%d9%8a%d8%aa-%d8%a7%d9%93%d8%b1\/"},"modified":"2023-07-19T11:54:34","modified_gmt":"2023-07-19T11:54:34","slug":"%d9%82%d8%b7%d8%a7%d8%b1-%d8%a7%d8%ae%d8%aa%d8%a8%d8%a7%d8%b1-%d8%b3%d8%a8%d9%84%d9%8a%d8%aa-%d8%a7%d9%93%d8%b1","status":"publish","type":"post","link":"https:\/\/statorials.org\/ar\/%d9%82%d8%b7%d8%a7%d8%b1-%d8%a7%d8%ae%d8%aa%d8%a8%d8%a7%d8%b1-%d8%b3%d8%a8%d9%84%d9%8a%d8%aa-%d8%a7%d9%93%d8%b1\/","title":{"rendered":"\u0643\u064a\u0641\u064a\u0629 \u062a\u0642\u0633\u064a\u0645 \u0627\u0644\u0628\u064a\u0627\u0646\u0627\u062a \u0641\u064a \u0627\u0644\u062a\u062f\u0631\u064a\u0628 & #038; \u0645\u062c\u0645\u0648\u0639\u0627\u062a \u0627\u0644\u0627\u062e\u062a\u0628\u0627\u0631 \u0641\u064a r (3 \u0637\u0631\u0642)"},"content":{"rendered":"

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\u0647\u0646\u0627\u0643 \u062b\u0644\u0627\u062b \u0637\u0631\u0642 \u0634\u0627\u0626\u0639\u0629 \u0644\u062a\u0642\u0633\u064a\u0645 \u0627\u0644\u0628\u064a\u0627\u0646\u0627\u062a \u0625\u0644\u0649 \u0645\u062c\u0645\u0648\u0639\u0627\u062a \u062a\u062f\u0631\u064a\u0628 \u0648\u0627\u062e\u062a\u0628\u0627\u0631 \u0641\u064a R:<\/span><\/p>\n

\u0627\u0644\u0637\u0631\u064a\u0642\u0629 \u0627\u0644\u0623\u0648\u0644\u0649: \u0627\u0633\u062a\u062e\u062f\u0645 Base R<\/strong><\/span><\/p>\n

 #make this example reproducible\n<\/span>set. seeds<\/span> (1)\n\n#use 70% of dataset as training set and 30% as test set\n<\/span>sample <- sample(c( TRUE<\/span> , FALSE<\/span> ), nrow(df), replace= TRUE<\/span> , prob=c( 0.7<\/span> , 0.3<\/span> ))\ntrain <- df[sample, ]\ntest <- df[!sample, ]<\/strong><\/pre>\n
 \u0627\u0644\u0637\u0631\u064a\u0642\u0629 \u0627\u0644\u062b\u0627\u0646\u064a\u0629: \u0627\u0633\u062a\u062e\u062f\u0645 \u062d\u0632\u0645\u0629 caTools<\/strong><\/span><\/p>\n
 library<\/span> (caTools)<\/span>\n\n#make this example reproducible\n<\/span>set. seeds<\/span> (1)\n\n#use 70% of dataset as training set and 30% as test set\n<\/span>sample <- sample. split<\/span> (df$any_column_name, SplitRatio = 0.7<\/span> )\ntrain <- subset(df, sample == TRUE<\/span> )\ntest <- subset(df, sample == FALSE<\/span> )<\/strong><\/pre>\n
 \u0627\u0644\u0637\u0631\u064a\u0642\u0629 \u0627\u0644\u062b\u0627\u0644\u062b\u0629: \u0627\u0633\u062a\u062e\u062f\u0645 \u062d\u0632\u0645\u0629 dplyr<\/strong><\/span><\/p>\n
 library<\/span> (dplyr)<\/span>\n\n#make this example reproducible\n<\/span>set. seeds<\/span> (1)\n\n#create ID column\n<\/span>df$id <- 1:nrow(df)\n\n#use 70% of dataset as training set and 30% as test set<\/span>\ntrain <- df %>% dplyr::sample_frac( 0.70<\/span> )\ntest <- dplyr::anti_join(df, train, by = ' id<\/span> ')<\/strong><\/pre>\n
 \u062a\u0648\u0636\u062d \u0627\u0644\u0623\u0645\u062b\u0644\u0629 \u0627\u0644\u062a\u0627\u0644\u064a\u0629 \u0643\u064a\u0641\u064a\u0629 \u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0643\u0644 \u0637\u0631\u064a\u0642\u0629 \u0639\u0645\u0644\u064a\u064b\u0627 \u0645\u0639 \u0645\u062c\u0645\u0648\u0639\u0629 \u0628\u064a\u0627\u0646\u0627\u062a \u0627\u0644\u0642\u0632\u062d\u064a\u0629<\/a> \u0627\u0644\u0645\u0636\u0645\u0646\u0629 \u0641\u064a R.<\/span><\/p>\n
 \u0645\u062b\u0627\u0644 1: \u062a\u0642\u0633\u064a\u0645 \u0627\u0644\u0628\u064a\u0627\u0646\u0627\u062a \u0625\u0644\u0649 \u0645\u062c\u0645\u0648\u0639\u0627\u062a \u062a\u062f\u0631\u064a\u0628 \u0648\u0627\u062e\u062a\u0628\u0627\u0631 \u0628\u0627\u0633\u062a\u062e\u062f\u0627\u0645 Base R<\/strong><\/span><\/h3>\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 \u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0642\u0627\u0639\u062f\u0629 R \u0644\u062a\u0642\u0633\u064a\u0645 \u0645\u062c\u0645\u0648\u0639\u0629 \u0628\u064a\u0627\u0646\u0627\u062a \u0627\u0644\u0642\u0632\u062d\u064a\u0629 \u0625\u0644\u0649 \u0645\u062c\u0645\u0648\u0639\u0629 \u062a\u062f\u0631\u064a\u0628 \u0648\u0627\u062e\u062a\u0628\u0627\u0631\u060c \u0628\u0627\u0633\u062a\u062e\u062f\u0627\u0645 70% \u0645\u0646 \u0627\u0644\u0635\u0641\u0648\u0641 \u0643\u0645\u062c\u0645\u0648\u0639\u0629 \u062a\u062f\u0631\u064a\u0628 \u064830% \u0627\u0644\u0645\u062a\u0628\u0642\u064a\u0629 \u0643\u0645\u062c\u0645\u0648\u0639\u0629 \u0627\u062e\u062a\u0628\u0627\u0631:<\/span><\/span><\/p>\n
 #load iris dataset\n<\/span>data(iris)\n\n#make this example reproducible\n<\/span>set. seeds<\/span> (1)\n\n#Use 70% of dataset as training set and remaining 30% as testing set\n<\/span>sample <- sample(c( TRUE<\/span> , FALSE<\/span> ), nrow(iris), replace= TRUE<\/span> , prob=c( 0.7<\/span> , 0.3<\/span> ))\ntrain <- iris[sample, ]\ntest <- iris[!sample, ]\n\n#view dimensions of training set\n<\/span>sun(train)\n\n[1] 106 5\n\n#view dimensions of test set\n<\/span>dim(test)\n\n[1] 44 5<\/strong><\/pre>\n
 \u0648\u0645\u0646 \u0627\u0644\u0646\u062a\u064a\u062c\u0629 \u064a\u0645\u0643\u0646\u0646\u0627 \u0623\u0646 \u0646\u0631\u0649:<\/span><\/p>\n