cv.platypus: k-fold cross validation for platypus

In graim/PLATYPUS: Multiple view and task learning

Description

k-fold cross validation for platypus

Usage

cv.platypus(view.list, fn.labs, classcol.labs = 1, cv.folds = 10,
  n.iters = 100, majority.threshold.percent = 100, nfolds = 10,
  expanded.output = FALSE, updating = FALSE,
  ignore.label = "intermediate", parallel = FALSE,
  output.folder = NA)

Arguments

view.list	List of view objects
fn.labs	File containing outcome labels
classcol.labs	Which column from the labels file to use for learning
cv.folds	number of folds for label learning validation (similar to cross validation folds), default=10
n.iters	Maximal number of iterations for each platypus run, default=100
majority.threshold.percent	Percent agreement required to learn a sample's class label, default=100
nfolds	Number of cross-validation folds
expanded.output	Expanded output: returned result list contains a list of trained views after each iteration, default=FALSE
updating	Updating the accuracies of the single views in each iteration, default=FALSE
ignore.label	Label class to ignore, if any. Defaults to 'intermediate'
parallel	Whether or not to run in parallel mode.
output.folder	Name of the folder where output is stored.

Value

A list containing fold.accuracy, labelling.matrix,labelling.matrices.views

graim/PLATYPUS documentation built on Oct. 4, 2019, 2:05 p.m.