SuperLearner.CV.control: Control parameters for the cross validation steps in...

In SuperLearner: Super Learner Prediction

Control parameters for the cross validation steps in SuperLearner

Description

Control parameters for the cross validation steps in SuperLearner

Usage

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

Arguments

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.

Value

A list containing the control parameters

SuperLearner documentation built on July 26, 2023, 6:05 p.m.