iterative.stratification.partitions: Generic partitioning of an mldr object
In mldr.datasets: R Ultimate Multilabel Dataset Repository

Description Usage Arguments Value Examples

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.

1 2	iterative.stratification.partitions(mld, is.cv = FALSE, r, seed = 10, get.indices = FALSE)

`mld`	The `mldr` object to be partitioned
`is.cv`	Option to enable treatment of partitions as cross-validation test folds
`r`	A vector of percentages of instances to be selected for each partition
`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

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

## Not run: 
library(mldr.datasets)
library(mldr)
parts.emotions <- iterative.stratification.partitions(emotions, r = c(35, 25, 40))
summary(parts.emotions[[2]])

## End(Not run)

Attaching package: 'mldr.datasets'

The following object is masked from 'package:stats':

    density

  num.attributes num.instances num.inputs num.labels num.labelsets
1             78           153         72          6            22
  num.single.labelsets max.frequency cardinality   density  meanIR     scumble
1                    5            23    1.810458 0.3017429 1.47763 0.009950483
  scumble.cv      tcs
1   1.349885 9.159468