defaultControl: Construct a default train control for use with caretList

In caretEnsemble: Ensembles of Caret Models

Construct a default train control for use with caretList

Description

Unlike caret::trainControl, this function defaults to 5 fold CV. CV is good for stacking, as every observation is in the test set exactly once. We use 5 instead of 10 to save compute time, as caretList is for fitting many models. We also construct explicit fold indexes and return the stacked predictions, which are needed for stacking. For classification models we return class probabilities.

Usage

defaultControl(
  target,
  method = "cv",
  number = 5L,
  savePredictions = "final",
  index = caret::createFolds(target, k = number, list = TRUE, returnTrain = TRUE),
  is_class = is.factor(target) || is.character(target),
  is_binary = length(unique(target)) == 2L,
  ...
)

Arguments

target	the target variable.
method	the method to use for trainControl.
number	the number of folds to use.
savePredictions	the type of predictions to save.
index	the fold indexes to use.
is_class	logical, is this a classification or regression problem.
is_binary	logical, is this binary classification.
...	other arguments to pass to trainControl

caretEnsemble documentation built on Sept. 13, 2024, 1:11 a.m.