How to plot k-fold cross validation in R

Question

I have a model similar to the following, and I am wondering, is there a beautifull and effective way to to plot the folds to show the stability and performance of my model?

data(iris)
df=iris[,1:4]
con = trainControl(method="cv", number=5,savePredictions = TRUE)
for_train = createDataPartition(df$Sepal.Length, p=.70, list=FALSE) 
train=df[for_train,] 
test=df[-for_train,] 

trf_iris = train(Sepal.Length~ .,
                 data=train,ntree=5000,method="rf",metric="Rsquared",trControl=con,importance = TRUE)

Answer 1

If you run str(trf_iris), you will find that trf_iris$control$index contains the lists of row indices for each fold. You can extract them to recreate the five fold subsets and then plot them.

library(dplyr)
library(ggplot2)

# get fold subsets
fold_data <- lapply(trf_iris$control$index, function(index) iris[index,]) %>% 
    bind_rows(.id = "Fold")

# example plots
ggplot(fold_data, aes(Sepal.Length, col = Fold)) + geom_density()

ggplot(fold_data, aes(Sepal.Width, Sepal.Length, col = Fold)) + 
    geom_point(col = "black") + 
    geom_smooth(method = lm, se = FALSE)

You can add + facet_wrap(.~Fold) if you find separating the fold plots look better.