cv: Multi-label cross-validation

In utiml: Utilities for Multi-Label Learning

Description

Perform the cross validation procedure for multi-label learning.

Usage

cv(
  mdata,
  method,
  ...,
  cv.folds = 10,
  cv.sampling = c("random", "iterative", "stratified"),
  cv.results = FALSE,
  cv.predictions = FALSE,
  cv.measures = "all",
  cv.cores = getOption("utiml.cores", 1),
  cv.seed = getOption("utiml.seed", NA)
)

Arguments

mdata	A mldr dataset.
method	The multi-label classification method. It also accepts the name of the method as a string.
...	Additional parameters required by the method.
cv.folds	Number of folds. (Default: 10)
cv.sampling	The method to split the data. The default methods are: random Split randomly the folds. iterative Split the folds considering the labels proportions individually. Some specific label can not occurs in all folds. stratified Split the folds considering the labelset proportions. (Default: "random")
cv.results	Logical value indicating if the folds results should be reported (Default: FALSE).
cv.predictions	Logical value indicating if the predictions should be reported (Default: FALSE).
cv.measures	The measures names to be computed. Call multilabel_measures() to see the expected measures. You can also use "bipartition", "ranking", "label-based", "example-based", "macro-based", "micro-based" and "label-problem" to include a set of measures. (Default: "all").
cv.cores	The number of cores to parallelize the cross validation procedure. (Default: options("utiml.cores", 1))
cv.seed	An optional integer used to set the seed. (Default: options("utiml.seed", NA))

Value

If cv.results and cv.prediction are FALSE, the return is a vector with the expected multi-label measures, otherwise, a list contained the multi-label and the other expected results (the label measures and/or the prediction object) for each fold.

See Also

Other evaluation: multilabel_confusion_matrix(), multilabel_evaluate(), multilabel_measures()

Examples

#Run 10 folds for BR method
res1 <- cv(toyml, br, base.algorithm="RANDOM", cv.folds=10)

#Run 3 folds for RAkEL method and get the fold results and the prediction
res2 <- cv(mdata=toyml, method="rakel", base.algorithm="RANDOM", k=2, m=10,
 cv.folds=3, cv.results=TRUE, cv.predictions=TRUE)

Example output

Loading required package: mldr
Loading required package: parallel
Loading required package: ROCR
Loading required package: gplots

Attaching package: 'gplots'

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

    lowess

utiml documentation built on May 31, 2021, 9:09 a.m.