mergeMeltBestScoreCV: mergeMeltBestScoreCV

In predomics/predomicspkg: Interpretable Prediction in Omics Data

mergeMeltBestScoreCV

Description

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)

Details

Merge a list of cross validation scores form digest results

Value

a list of two data.frames

predomics/predomicspkg documentation built on Dec. 11, 2024, 11:06 a.m.