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Splits a dataset into training and test sets based on k-fold cross-validation. The function takes a list of data partitions (folds) and a specified fold index k. It returns the data corresponding to the k-th fold as the test set, and combines all other folds to form the training set.
train_test_from_folds(folds, k)returns a list with two elements:
train: A data frame containing the combined data from all folds except the k-th fold, used as the training set.
test: A data frame corresponding to the k-th fold, used as the test set.
data partitioned into folds
k-fold for test set, all reminder for training set
# Create k-fold partitions of a dataset (e.g., iris)
folds <- k_fold(sample_random(), iris, k = 5)
# Use the first fold as the test set and combine the remaining folds for the training set
train_test_split <- train_test_from_folds(folds, k = 1)
# Display the training set
head(train_test_split$train)
# Display the test set
head(train_test_split$test)
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