SuperLearner.CV.control

Control parameters for the cross validation steps in <code>SuperLearner</code>

utilities

Implements the super learner prediction method and contains a
library of prediction algorithms to be used in the super learner.

Eric Polley

SuperLearner

Super Learner Prediction

Erin LeDell

Chris Kennedy

Sam Lendle

Mark van der Laan

SuperLearner.CV.control function

<dl> <dt>V</dt>
<dd>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.</dd> <dt>stratifyCV</dt>
<dd>Logical. Should the data splits be stratified by a binary response? Attempts to maintain the same ratio in each training and validation sample.</dd> <dt>shuffle</dt>
<dd>Logical. Should the rows of <code>X</code> be shuffled before creating the splits.</dd> <dt>validRows</dt>
<dd>A List. Use this to pass pre-specified rows for the sample splits. The length of the list should be <code>V</code> and each entry in the list should contain a vector with the row numbers of the corresponding validation sample.</dd></dl>

Arguments

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

<dl>

 <dt>V</dt>
<dd>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.</dd>

 <dt>stratifyCV</dt>
<dd>Logical. Should the data splits be stratified by a binary response? Attempts to maintain the same ratio in each training and validation sample.</dd>

 <dt>shuffle</dt>
<dd>Logical. Should the rows of <code>X</code> be shuffled before creating the splits.</dd>

 <dt>validRows</dt>
<dd>A List. Use this to pass pre-specified rows for the sample splits. The length of the list should be <code>V</code> and each entry in the list should contain a vector with the row numbers of the corresponding validation sample.</dd>

</dl>

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

Description

Usage

Value

Arguments