如何使用插入符号绘制每次交叉验证的ROC曲线

我有以下代码：

库（mlbench）
图书馆（插入符号）
图书馆（GG2）
种子集（998）
#准备数据------------------------------------------------------------
数据（声纳）
我的数据谢谢你。如何获得每个折叠的AUC分数？您好@scamander，模型不会输出每个折叠的AUC分数。但是您可以使用模型中的
pred
和
obs
计算这些分数。请看我的最新答案。
library(mlbench)
library(caret)
library(ggplot2)
set.seed(998)

# Prepare data ------------------------------------------------------------

data(Sonar)
my_data <- Sonar

# Cross Validation Definition ---------------------------------------------------

fitControl <-
  trainControl(
    method = "cv",
    number = 10,
    classProbs = T,
    savePredictions = T,
    summaryFunction = twoClassSummary
  )


# Training with Random Forest ----------------------------------------------------------------


model <- train(
  Class ~ .,
  data = my_data,
  method = "rf",
  trControl = fitControl,
  metric = "ROC"
)


for_lift <- data.frame(Class = model$pred$obs, rf = model$pred$R, resample = model$pred$Resample)
lift_df <-  data.frame()
for (fold in unique(for_lift$resample)) {
  fold_df <- dplyr::filter(for_lift, resample == fold)
  lift_obj_data <- lift(Class ~ rf, data = fold_df, class = "R")$data
  lift_obj_data$fold = fold
  lift_df = rbind(lift_df, lift_obj_data)
}
lift_obj <- lift(Class ~ rf, data = for_lift, class = "R")


# Plot ROC ----------------------------------------------------------------

ggplot(lift_df) +
  geom_line(aes(1 - Sp, Sn, color = fold)) +
  scale_color_discrete(guide = guide_legend(title = "Fold"))

model <- train(
  Class ~ .,
  data = my_data,
  method = "rf",
  trControl = fitControl,
  metric = "ROC"
)

library(plyr)
library(MLmetrics)
ddply(model$pred, "Resample", summarise,
      accuracy = Accuracy(pred, obs))

   Resample  accuracy
1    Fold01 0.8253968
2    Fold02 0.8095238
3    Fold03 0.8000000
4    Fold04 0.8253968
5    Fold05 0.8095238
6    Fold06 0.8253968
7    Fold07 0.8333333
8    Fold08 0.8253968
9    Fold09 0.9841270
10   Fold10 0.7936508