SuperLearner.CV.control: Control parameters for the cross validation steps in...
In ecpolley/SuperLearner: Super Learner Prediction

SuperLearner.CV.control

R Documentation

Control parameters for the cross validation steps in `SuperLearner`

Control parameters for the cross validation steps in SuperLearner

SuperLearner.CV.control(V = 10L, stratifyCV = FALSE, shuffle = TRUE, 
  validRows = NULL)

`V`	Integer. Number of splits for the V-fold cross-validation step. The default is 10. In most cases, between 10 and 20 splits works well.
`stratifyCV`	Logical. Should the data splits be stratified by a binary response? Attempts to maintain the same ratio in each training and validation sample.
`shuffle`	Logical. Should the rows of `X` be shuffled before creating the splits.
`validRows`	A List. Use this to pass pre-specified rows for the sample splits. The length of the list should be `V` and each entry in the list should contain a vector with the row numbers of the corresponding validation sample.