create_kfold_partition: Create the k-folds partition based on the specified algorithm

In utiml: Utilities for Multi-Label Learning

Description

This method create the kFoldPartition object, from it is possible create the dataset partitions to train, test and optionally to validation.

Usage

create_kfold_partition(
  mdata,
  k = 10,
  method = c("random", "iterative", "stratified")
)

Arguments

mdata	A mldr dataset.
k	The number of desirable folds. (Default: 10)
method	The method to split the data. The default methods are: random Split randomly the folds. iterative Split the folds considering the labels proportions individually. Some specific label can not occurs in all folds. stratified Split the folds considering the labelset proportions. You can also create your own partition method. See the note and example sections to more details. (Default: "random")

Value

An object of type kFoldPartition.

Note

To create your own split method, you need to build a function that receive a mldr object and a list with the proportions of examples in each fold and return an other list with the index of the elements for each fold.

References

Sechidis, K., Tsoumakas, G., & Vlahavas, I. (2011). On the stratification of multi-label data. In Proceedings of the Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD (pp. 145-158).

See Also

How to create the datasets from folds

Other sampling: create_holdout_partition(), create_random_subset(), create_subset()

Examples

k10 <- create_kfold_partition(toyml, 10)
k5 <- create_kfold_partition(toyml, 5, "stratified")

sequencial_split <- function (mdata, r) {
 S <- list()

 amount <- trunc(r * mdata$measures$num.instances)
 indexes <- c(0, cumsum(amount))
 indexes[length(r)+1] <- mdata$measures$num.instances

 S <- lapply(seq(length(r)), function (i) {
   seq(indexes[i]+1, indexes[i+1])
 })

 S
}
k3 <- create_kfold_partition(toyml, 3, "sequencial_split")

utiml documentation built on May 31, 2021, 9:09 a.m.