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