mldr.datasets (version 0.4.2)

stratified.kfolds: Partition an mldr object into k folds

Description

This method partitions the given dataset into k folds using a stratified strategy, providing training and test partitions for each fold.

Implementation of the algorithm defined in: Charte, F., Rivera, A., del Jesus, M. J., & Herrera, F. (2016, April). On the impact of dataset complexity and sampling strategy in multilabel classifiers performance. In International Conference on Hybrid Artificial Intelligence Systems (pp. 500-511). Springer, Cham.

Usage

stratified.kfolds(mld, k = 5, seed = 10, get.indices = FALSE)

Arguments

mld

The mldr object to be partitioned

k

The number of folds to be generated. By default is 5

seed

The seed to initialize the random number generator. By default is 10. Change it if you want to obtain partitions containing different samples, for instance to use a 2x5 fcv strategy

get.indices

A logical value indicating whether to return lists of indices or lists of "mldr" objects

Value

An mldr.folds object. This is a list containing k elements, one for each fold. Each element is made up of two mldr objects, called train and test

Examples

# NOT RUN {
library(mldr.datasets)
library(mldr)
folds.emotions <- stratified.kfolds(emotions)
summary(folds.emotions[[1]]$train)
summary(folds.emotions[[1]]$test)
# }