"repeatedcv", number = 10, repeats = 3) methods <- c('gbm', 'rpart', 'svmRadial') trained <- methods %>% purrr::map(~ train(diabetes ~ ., data = PimaIndiansDiabetes, method = ., trControl = control)) resampled <- resamples(trained) summary(resampled) ## Call: ## summary.resamples(object = resampled) ## ## Models: Model1, Model2, Model3 ## Number of resamples: 30 ## ## Accuracy ## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's ## Model1 0.6883 0.7435 0.7662 0.7682 0.7915 0.8442 0 ## Model2 0.6711 0.7273 0.7451 0.7499 0.7785 0.8312 0 ## Model3 0.6883 0.7403 0.7662 0.7678 0.7922 0.8312 0 ## ## Kappa ## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's ## Model1 0.3037 0.4297 0.4763 0.4711 0.5268 0.6578 0 ## Model2 0.2339 0.3657 0.4078 0.4245 0.4733 0.6128 0 ## Model3 0.2518 0.4043 0.4610 0.4607 0.5138 0.6165 0 ൺֱ͍ͨ͠ϞσϧΛϦετʹ͠ɺ ֤ϞσϧΛUSBJO