method = "boot",
number = ifelse(method == "cv", 10, 25),
verboseIter = TRUE,
returnData = TRUE,
returnResamp = "final",
p = 0.75,
summaryFunction = defaultSummary,
selectionFunction = "best",
index = NULL,
workers = 1,
computeFunction = lapply,
computeArgs = NULL)
The resampling method:
LGOCV (for repeated training/test splits), or
oob (only for random forest, bagged trees, bagged earth, bagged flexible discriminant analysis,
Either the number of folds or number of resampling iterations
A logical for printing a training log.
A logical for saving the data
A character string indicating how much of the resampled summary metrics should be saved. Values can be ``final'', ``all'' or ``none''
For leave-group out cross-validation: the training percentage
a function to compute performance metrics across resamples. The arguments to the function should be the same as those in
the function used to select the optimal tuning parameter. This can be a name of the function or the funciton itself. See
best for details and other options.
a list with elements for each resampling iteration. Each list element is the sample rows used for training at that iteration.
an integer that specifies how many machines/processors will be used
a function that is
lapply or emulates
lapply. It must have arguments
computeFunction can be used to build models in parall
Extra arguments to pass into the
... slore in
computeFunction. See the examples in