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