iterative.stratification.kfolds {mldr.datasets}

Partition an mldr object into k folds

Description

Iterative stratification

Implemented from the algorithm explained in: Konstantinos Sechidis, Grigorios Tsoumakas, and Ioannis Vlahavas. 2011. On the stratification of multi-label data. In Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part III (ECML PKDD'11), Dimitrios Gunopulos, Thomas Hofmann, Donato Malerba, and Michalis Vazirgiannis (Eds.), Vol. Part III. Springer-Verlag, Berlin, Heidelberg, 145-158.

Usage

iterative.stratification.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 <- iterative.stratification.kfolds(emotions)
summary(folds.emotions[[1]]$train)
summary(folds.emotions[[1]]$test)

## End(Not run)