random.holdout {mldr.datasets} | R Documentation |
Hold-out partitioning of an mldr object
Description
Random partitioning
Usage
random.holdout(mld, p = 60, seed = 10, get.indices = FALSE)
Arguments
mld |
The |
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 |
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 <- random.holdout(emotions, p = 70)
summary(parts.emotions$train)
summary(parts.emotions$test)
## End(Not run)
[Package mldr.datasets version 0.4.2 Index]