{"id":4506,"date":"2023-07-10T13:36:46","date_gmt":"2023-07-10T13:36:46","guid":{"rendered":"https:\/\/statorials.org\/ar\/%d8%a7%d9%84%d8%aa%d8%ad%d9%83%d9%85-%d8%a8%d8%a7%d9%84%d9%82%d8%b7%d8%a7%d8%b1-%d8%b5\/"},"modified":"2023-07-10T13:36:46","modified_gmt":"2023-07-10T13:36:46","slug":"%d8%a7%d9%84%d8%aa%d8%ad%d9%83%d9%85-%d8%a8%d8%a7%d9%84%d9%82%d8%b7%d8%a7%d8%b1-%d8%b5","status":"publish","type":"post","link":"https:\/\/statorials.org\/ar\/%d8%a7%d9%84%d8%aa%d8%ad%d9%83%d9%85-%d8%a8%d8%a7%d9%84%d9%82%d8%b7%d8%a7%d8%b1-%d8%b5\/","title":{"rendered":"\u062c: \u0643\u064a\u0641\u064a\u0629 \u0627\u0633\u062a\u062e\u062f\u0627\u0645 traincontrol \u0644\u0644\u062a\u062d\u0643\u0645 \u0641\u064a \u0645\u0639\u0644\u0645\u0627\u062a \u0627\u0644\u062a\u062f\u0631\u064a\u0628"},"content":{"rendered":"

<\/p>\n

\n

\u0644\u062a\u0642\u064a\u064a\u0645 \u0645\u062f\u0649 \u0642\u062f\u0631\u0629 \u0627\u0644\u0646\u0645\u0648\u0630\u062c \u0639\u0644\u0649 \u0645\u0644\u0627\u0621\u0645\u0629 \u0645\u062c\u0645\u0648\u0639\u0629 \u0628\u064a\u0627\u0646\u0627\u062a\u060c \u0646\u062d\u062a\u0627\u062c \u0625\u0644\u0649 \u062a\u062d\u0644\u064a\u0644 \u0623\u062f\u0627\u0626\u0647 \u0628\u0646\u0627\u0621\u064b \u0639\u0644\u0649 \u0645\u0644\u0627\u062d\u0638\u0627\u062a \u0644\u0645 \u064a\u0633\u0628\u0642 \u0644\u0647 \u0631\u0624\u064a\u062a\u0647\u0627 \u0645\u0646 \u0642\u0628\u0644.<\/span><\/p>\n

\u0625\u062d\u062f\u0649 \u0627\u0644\u0637\u0631\u0642 \u0627\u0644\u0623\u0643\u062b\u0631 \u0634\u064a\u0648\u0639\u064b\u0627 \u0644\u062a\u062d\u0642\u064a\u0642 \u0630\u0644\u0643 \u0647\u064a \u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0627\u0644\u062a\u062d\u0642\u0642 \u0627\u0644\u0645\u062a\u0642\u0627\u0637\u0639 k-fold<\/a> \u060c \u0648\u0627\u0644\u0630\u064a \u064a\u0633\u062a\u062e\u062f\u0645 \u0627\u0644\u0646\u0647\u062c \u0627\u0644\u062a\u0627\u0644\u064a:<\/span><\/p>\n

1.<\/strong> \u0642\u0645 \u0628\u062a\u0642\u0633\u064a\u0645 \u0645\u062c\u0645\u0648\u0639\u0629 \u0627\u0644\u0628\u064a\u0627\u0646\u0627\u062a \u0639\u0634\u0648\u0627\u0626\u064a\u064b\u0627 \u0625\u0644\u0649 \u0645\u062c\u0645\u0648\u0639\u0627\u062a k<\/em> \u060c \u0623\u0648 “\u0637\u064a\u0627\u062a”\u060c \u0630\u0627\u062a \u062d\u062c\u0645 \u0645\u062a\u0633\u0627\u0648\u064d \u062a\u0642\u0631\u064a\u0628\u064b\u0627.<\/span><\/p>\n

2.<\/strong> \u0627\u062e\u062a\u0631 \u0625\u062d\u062f\u0649 \u0627\u0644\u0637\u064a\u0627\u062a \u0643\u0645\u062c\u0645\u0648\u0639\u0629 \u0636\u0628\u0637 \u0627\u0644\u0646\u0641\u0633. \u0627\u0636\u0628\u0637 \u0627\u0644\u0642\u0627\u0644\u0628 \u0639\u0644\u0649 \u0637\u064a\u0627\u062a k-1 \u0627\u0644\u0645\u062a\u0628\u0642\u064a\u0629. \u062d\u0633\u0627\u0628 \u0627\u062e\u062a\u0628\u0627\u0631 MSE \u0639\u0644\u0649 \u0627\u0644\u0645\u0644\u0627\u062d\u0638\u0627\u062a \u0641\u064a \u0627\u0644\u0637\u0628\u0642\u0629 \u0627\u0644\u062a\u064a \u062a\u0645 \u0634\u062f\u0647\u0627.<\/span><\/p>\n

3.<\/strong> \u0643\u0631\u0631 \u0647\u0630\u0647 \u0627\u0644\u0639\u0645\u0644\u064a\u0629 \u0639\u062f\u0629<\/em> \u0645\u0631\u0627\u062a\u060c \u0641\u064a \u0643\u0644 \u0645\u0631\u0629 \u0628\u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0645\u062c\u0645\u0648\u0639\u0629 \u0645\u062e\u062a\u0644\u0641\u0629 \u0643\u0645\u062c\u0645\u0648\u0639\u0629 \u0627\u0644\u0627\u0633\u062a\u0628\u0639\u0627\u062f.<\/span><\/p>\n

4.<\/strong> \u0627\u062d\u0633\u0628 \u0627\u062e\u062a\u0628\u0627\u0631 MSE \u0627\u0644\u0625\u062c\u0645\u0627\u0644\u064a \u0643\u0645\u062a\u0648\u0633\u0637 \u0644\u0627\u062e\u062a\u0628\u0627\u0631 k<\/em> MSEs.<\/span><\/p>\n

\u0623\u0633\u0647\u0644 \u0637\u0631\u064a\u0642\u0629 \u0644\u0625\u062c\u0631\u0627\u0621 \u0627\u0644\u062a\u062d\u0642\u0642 \u0645\u0646 \u0635\u062d\u0629 k-fold \u0641\u064a R \u0647\u064a \u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0627\u0644\u062f\u0627\u0644\u062a\u064a\u0646 TrainControl()<\/strong> \u0648 Train()<\/strong> \u0645\u0646 \u0645\u0643\u062a\u0628\u0629 \u0639\u0644\u0627\u0645\u0629 \u0627\u0644\u0625\u0642\u062d\u0627\u0645<\/strong> \u0641\u064a R.<\/span><\/p>\n

\u064a\u062a\u0645 \u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0648\u0638\u064a\u0641\u0629 TrainControl ()<\/strong> \u0644\u062a\u062d\u062f\u064a\u062f \u0645\u0639\u0644\u0645\u0627\u062a \u0627\u0644\u062a\u062f\u0631\u064a\u0628 (\u0639\u0644\u0649 \u0633\u0628\u064a\u0644 \u0627\u0644\u0645\u062b\u0627\u0644\u060c \u0646\u0648\u0639 \u0627\u0644\u062a\u062d\u0642\u0642 \u0627\u0644\u0645\u062a\u0628\u0627\u062f\u0644 \u0627\u0644\u0645\u0637\u0644\u0648\u0628 \u0627\u0633\u062a\u062e\u062f\u0627\u0645\u0647\u060c \u0648\u0639\u062f\u062f \u0627\u0644\u0637\u064a\u0627\u062a \u0627\u0644\u0645\u0631\u0627\u062f \u0627\u0633\u062a\u062e\u062f\u0627\u0645\u0647\u0627\u060c \u0648\u0645\u0627 \u0625\u0644\u0649 \u0630\u0644\u0643) \u0648\u064a\u062a\u0645 \u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0648\u0638\u064a\u0641\u0629 Train ()<\/strong> \u0644\u0645\u0644\u0627\u0621\u0645\u0629 \u0627\u0644\u0646\u0645\u0648\u0630\u062c \u0645\u0639 \u0627\u0644\u0628\u064a\u0627\u0646\u0627\u062a \u0641\u0639\u0644\u064a\u064b\u0627. .<\/span><\/p>\n

\u064a\u0648\u0636\u062d \u0627\u0644\u0645\u062b\u0627\u0644 \u0627\u0644\u062a\u0627\u0644\u064a \u0643\u064a\u0641\u064a\u0629 \u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0627\u0644\u062f\u0627\u0644\u062a\u064a\u0646 TrainControl()<\/strong> \u0648 train()<\/strong> \u0639\u0645\u0644\u064a\u064b\u0627.<\/span><\/p>\n

\u0645\u062b\u0627\u0644: \u0643\u064a\u0641\u064a\u0629 \u0627\u0633\u062a\u062e\u062f\u0627\u0645 TrainControl() \u0641\u064a R<\/strong><\/span><\/h2>\n

\u0644\u0646\u0641\u062a\u0631\u0636 \u0623\u0646 \u0644\u062f\u064a\u0646\u0627 \u0645\u062c\u0645\u0648\u0639\u0629 \u0627\u0644\u0628\u064a\u0627\u0646\u0627\u062a \u0627\u0644\u062a\u0627\u0644\u064a\u0629 \u0641\u064a R:<\/span><\/p>\n

 #create data frame<\/span>\ndf <- data.frame(y=c(6, 8, 12, 14, 14, 15, 17, 22, 24, 23),\n                 x1=c(2, 5, 4, 3, 4, 6, 7, 5, 8, 9),\n                 x2=c(14, 12, 12, 13, 7, 8, 7, 4, 6, 5))\n\n#view data frame\n<\/span>df\n\ny x1 x2\n6 2 14\n8 5 12\n12 4 12\n14 3 13\n14 4 7\n15 6 8\n17 7 7\n22 5 4\n24 8 6\n23 9 5\n<\/strong><\/pre>\n
 \u0644\u0646\u0641\u062a\u0631\u0636 \u0627\u0644\u0622\u0646 \u0623\u0646\u0646\u0627 \u0646\u0633\u062a\u062e\u062f\u0645 \u0627\u0644\u062f\u0627\u0644\u0629 lm()<\/a> \u0644\u0645\u0644\u0627\u0621\u0645\u0629 \u0646\u0645\u0648\u0630\u062c \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0627\u0644\u062e\u0637\u064a \u0627\u0644\u0645\u062a\u0639\u062f\u062f<\/a> \u0644\u0645\u062c\u0645\u0648\u0639\u0629 \u0627\u0644\u0628\u064a\u0627\u0646\u0627\u062a \u0647\u0630\u0647\u060c \u0628\u0627\u0633\u062a\u062e\u062f\u0627\u0645 x1<\/strong> \u0648 x2<\/strong> \u0643\u0645\u062a\u063a\u064a\u0631\u064a\u0646 \u0645\u062a\u0648\u0642\u0639\u064a\u0646 \u0648 y<\/strong> \u0643\u0645\u062a\u063a\u064a\u0631 \u0627\u0644\u0627\u0633\u062a\u062c\u0627\u0628\u0629:<\/span><\/p>\n
 #fit multiple linear regression model to data<\/span>\nfit <- lm(y ~ x1 + x2, data=df)\n\n#view model summary\n<\/span>summary(fit)\n\nCall:\nlm(formula = y ~ x1 + x2, data = df)\n\nResiduals:\n    Min 1Q Median 3Q Max \n-3.6650 -1.9228 -0.3684 1.2783 5.0208 \n\nCoefficients:\n            Estimate Std. Error t value Pr(>|t|)  \n(Intercept) 21.2672 6.9927 3.041 0.0188 *\nx1 0.7803 0.6942 1.124 0.2981  \nx2 -1.1253 0.4251 -2.647 0.0331 *\n---\nSignificant. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\nResidual standard error: 3.093 on 7 degrees of freedom\nMultiple R-squared: 0.801, Adjusted R-squared: 0.7441 \nF-statistic: 14.09 on 2 and 7 DF, p-value: 0.003516\n<\/strong><\/pre>\n
 \u0648\u0628\u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0627\u0644\u0645\u0639\u0627\u0645\u0644\u0627\u062a \u0627\u0644\u0645\u0648\u062c\u0648\u062f\u0629 \u0641\u064a \u0645\u062e\u0631\u062c\u0627\u062a \u0627\u0644\u0646\u0645\u0648\u0630\u062c \u064a\u0645\u0643\u0646\u0646\u0627 \u0643\u062a\u0627\u0628\u0629 \u0646\u0645\u0648\u0630\u062c \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0627\u0644\u0645\u0644\u0627\u0626\u0645:<\/span><\/p>\n
 \u0635 = 21.2672 + 0.7803*(\u00d7 1<\/sub> ) \u2013 1.1253(\u00d7 2<\/sub> )<\/strong><\/span><\/p>\n
 \u0644\u0644\u062d\u0635\u0648\u0644 \u0639\u0644\u0649 \u0641\u0643\u0631\u0629 \u0639\u0646 \u0645\u062f\u0649 \u062c\u0648\u062f\u0629 \u0623\u062f\u0627\u0621 \u0647\u0630\u0627 \u0627\u0644\u0646\u0645\u0648\u0630\u062c \u0641\u064a \u0627\u0644\u0645\u0644\u0627\u062d\u0638\u0627\u062a<\/a> \u063a\u064a\u0631 \u0627\u0644\u0645\u0631\u0626\u064a\u0629\u060c \u064a\u0645\u0643\u0646\u0646\u0627 \u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0627\u0644\u062a\u062d\u0642\u0642 \u0627\u0644\u0645\u062a\u0642\u0627\u0637\u0639 k-fold.<\/span><\/p>\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 \u0648\u0638\u064a\u0641\u0629 TrainControl()<\/strong> \u0627\u0644\u062e\u0627\u0635\u0629 \u0628\u062d\u0632\u0645\u0629 \u0639\u0644\u0627\u0645\u0629 \u0627\u0644\u0625\u0642\u062d\u0627\u0645<\/strong> \u0644\u062a\u062d\u062f\u064a\u062f \u0627\u0644\u062a\u062d\u0642\u0642 \u0627\u0644\u0645\u062a\u0628\u0627\u062f\u0644 k-fold ( \u0627\u0644\u0623\u0633\u0644\u0648\u0628 = “cv”<\/strong> ) \u0627\u0644\u0630\u064a \u064a\u0633\u062a\u062e\u062f\u0645 5 \u0637\u064a\u0627\u062a ( number=5<\/strong> ).<\/span><\/p>\n
 \u0646\u0642\u0648\u0645 \u0628\u0639\u062f \u0630\u0644\u0643 \u0628\u062a\u0645\u0631\u064a\u0631 \u0648\u0638\u064a\u0641\u0629 TrainControl ()<\/strong> \u0647\u0630\u0647 \u0625\u0644\u0649 \u0648\u0638\u064a\u0641\u0629 Train ()<\/strong> \u0644\u0625\u062c\u0631\u0627\u0621 \u0627\u0644\u062a\u062d\u0642\u0642 \u0645\u0646 \u0627\u0644\u0635\u062d\u0629 \u0639\u0628\u0631 k-fold:<\/span><\/p>\n
 library<\/span> (caret)<\/span>\n\n#specify the cross-validation method<\/span>\nctrl <- trainControl(method = \" cv<\/span> \", number = 5<\/span> )\n\n#fit a regression model and use k-fold CV to evaluate performance\n<\/span>model <- train(y ~ x1 + x2, data = df, method = \" lm<\/span> \", trControl = ctrl)\n\n#view summary of k-fold CV               \n<\/span>print<\/span> (model)\n\nLinear Regression \n\n10 samples\n 2 predictors\n\nNo pre-processing\nResampling: Cross-Validated (5 fold) \nSummary of sample sizes: 8, 8, 8, 8, 8 \nResampling results:\n\n  RMSE Rsquared MAE     \n  3.612302 1 3.232153\n\nTuning parameter 'intercept' was held constant at a value of TRUE\n<\/strong><\/pre>\n
 \u0645\u0646 \u0627\u0644\u0646\u062a\u064a\u062c\u0629 \u064a\u0645\u0643\u0646\u0646\u0627 \u0623\u0646 \u0646\u0631\u0649 \u0623\u0646\u0647 \u062a\u0645 \u062a\u0631\u0643\u064a\u0628 \u0627\u0644\u0646\u0645\u0648\u0630\u062c 5<\/strong> \u0645\u0631\u0627\u062a \u0628\u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u062d\u062c\u0645 \u0639\u064a\u0646\u0629 \u0645\u0643\u0648\u0646 \u0645\u0646 8<\/strong> \u0645\u0644\u0627\u062d\u0638\u0627\u062a \u0641\u064a \u0643\u0644 \u0645\u0631\u0629.<\/span><\/p>\n
 \u0648\u0641\u064a \u0643\u0644 \u0645\u0631\u0629\u060c \u062a\u0645 \u0628\u0639\u062f \u0630\u0644\u0643 \u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0627\u0644\u0646\u0645\u0648\u0630\u062c \u0644\u0644\u062a\u0646\u0628\u0624 \u0628\u0642\u064a\u0645 \u0627\u0644\u0645\u0634\u0627\u0647\u062f\u062a\u064a\u0646 \u0627\u0644\u0645\u062d\u062a\u0641\u0638 \u0628\u0647\u0645\u0627<\/strong> \u0648\u062a\u0645 \u062d\u0633\u0627\u0628 \u0627\u0644\u0645\u0642\u0627\u064a\u064a\u0633 \u0627\u0644\u062a\u0627\u0644\u064a\u0629 \u0641\u064a \u0643\u0644 \u0645\u0631\u0629:<\/span><\/p>\n