如何使用LOOCV在R中找到比全集分类更好的子集

library(caret)
library(mlbench)

data(Sonar)  # 以Sonar数据集为例

feature_set <- c("feature1", "feature2", "feature3")  # 替换为实际的特征名称

folds <- createFolds(1:nrow(Sonar), k = length(feature_set))

ctrl <- trainControl(method = "LOOCV")

results <- lapply(folds, function(fold) {
  train_data <- Sonar[-fold, feature_set]
  train_labels <- Sonar[-fold, "Class"]
  
  test_data <- Sonar[fold, feature_set]
  test_labels <- Sonar[fold, "Class"]
  
  model <- train(train_data, train_labels, method = "classification", trControl = ctrl)
  
  # 在测试集上进行预测
  predictions <- predict(model, test_data)
  
  # 计算预测准确率
  accuracy <- sum(predictions == test_labels) / length(test_labels)
  
  return(accuracy)
})

best_subset <- feature_set[which.max(results)]