iterative.stratification.holdout: Hold-out partitioning of an mldr object

In mldr.datasets: R Ultimate Multilabel Dataset Repository

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.holdout(mld, p = 60, seed = 10,
  get.indices = FALSE)

Arguments

mld	The mldr object to be partitioned
p	The percentage of instances to be selected for the training 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

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)
parts.emotions <- iterative.stratification.holdout(emotions, p = 70)
summary(parts.emotions$train)
summary(parts.emotions$test)

## End(Not run)

Example output

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           414         72          6            26
  num.single.labelsets max.frequency cardinality   density   meanIR    scumble
1                    4            54    1.874396 0.3123994 1.478652 0.01117508
  scumble.cv      tcs
1   1.255687 9.326522
  num.attributes num.instances num.inputs num.labels num.labelsets
1             78           179         72          6            24
  num.single.labelsets max.frequency cardinality   density   meanIR    scumble
1                    9            31    1.854749 0.3091248 1.476775 0.01034334
  scumble.cv      tcs
1   1.286273 9.246479

mldr.datasets documentation built on May 2, 2019, 3:43 p.m.