random.holdout: Hold-out partitioning of an mldr object
Description
Random partitioning
Usage
random.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
# NOT RUN {library(mldr.datasets)
library(mldr)
parts.emotions <- random.holdout(emotions, p = 70)
summary(parts.emotions$train)
summary(parts.emotions$test)
# }