mergeMeltBestScoreCV

mergeMeltBestScoreCV returns a list of data frames that contain the best performance of the different learners without any focus on sparsity.

Usage

mergeMeltBestScoreCV(
  list.results.digest,
  k_catalogue = NULL,
  score = "auc_",
  penalty = 0,
  min.kfold.nb = FALSE
)

Arguments

list.results.digest:

a list of digest objects one for each learner used. For example, list(res.terda.digest, res.terga.digest, res.terbeam.digest)

k_catalogue:

the k_catalogue that will serve to build the result matrix (default:NULL)

score:

the name of the score that needs to be used for the whole dataset visualization.

min.kfold.nb:

wether we should restrict all experiments in the smallest number of k-folds of a comparative analyses (default = FALSE)

Value

a list of two data.frames

Details

Merge a list of cross validation scores form digest results