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r - R中的k-fold嵌套重复交叉验证

问题描述

我需要进行四重嵌套重复交叉验证来训练模型。我编写了以下代码，它具有内部交叉验证，但现在我正在努力创建外部。

fitControl <- trainControl(## 10-fold CV
                           method = "repeatedcv",
                           number = 10,
                           ## repeated five times
                           repeats = 5,
                           savePredictions = TRUE,
                           classProbs = TRUE,
                           summaryFunction = twoClassSummary)

model_SVM_P <- train(Group ~ ., data = training_set, 
                 method = "svmPoly", 
                 trControl = fitControl,
                 verbose = FALSE,
                 tuneLength = 5)

我试图解决这个问题：

ntrain=length(training_set)    
train.ext=createFolds(training_set,k=4,returnTrain=TRUE)
test.ext=lapply(train.ext,function(x) (1:ntrain)[-x])

for (i in 1:4){
    model_SVM_P <- train(Group ~ ., data = training_set[train.ext[[i]]], 
                 method = "svmRadial", 
                 trControl = fitControl,
                 verbose = FALSE,
                 tuneLength = 5) 

    }

但它没有奏效。我该怎么做这个外循环？

标签： rmachine-learningr-caret

解决方案

该包已在函数rsample中实现了外循环，请参阅文档。nested_cv()

要评估由 nested_cv 训练的模型，请查看此小插图，它显示了“繁重”的完成位置：

# `object` is an `rsplit` object in `results$inner_resamples` 
summarize_tune_results <- function(object) {
  # Return row-bound tibble that has the 25 bootstrap results
  map_df(object$splits, tune_over_cost) %>%
    # For each value of the tuning parameter, compute the 
    # average RMSE which is the inner bootstrap estimate. 
    group_by(cost) %>%
    summarize(mean_RMSE = mean(RMSE, na.rm = TRUE),
              n = length(RMSE),
              .groups = "drop")
}

tuning_results <- map(results$inner_resamples, summarize_tune_results)

此代码将tune_over_cost函数应用于训练数据的每个超参数和拆分（或折叠），这里称为“评估数据”。

请查看小插图以获取更多有用的代码，包括并行化。

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