stratified.holdout: Hold-out partitioning of an mldr object

In fcharte/mldr.datasets: R Ultimate Multilabel Dataset Repository

Description

Stratified partitioning

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

## End(Not run)

fcharte/mldr.datasets documentation built on May 16, 2019, 12:06 p.m.