Usage
trainControl(method = "boot",
number = ifelse(method %in% c("cv", "repeatedcv"), 10, 25),
repeats = ifelse(method %in% c("cv", "repeatedcv"), 1, number),
verboseIter = FALSE,
returnData = TRUE,
returnResamp = "final",
p = 0.75,
classProbs = FALSE,
summaryFunction = defaultSummary,
selectionFunction = "best",
preProcOptions = list(thresh = 0.95, ICAcomp = 3, k = 5),
index = NULL,
timingSamps = 0,
predictionBounds = rep(FALSE, 2))Arguments
method
The resampling method: boot, boot632, cv, repeatedcv,
LOOCV, LGOCV (for repeated training/test splits), or
oob (only for random forest, bagged trees, bagge
number
Either the number of folds or number of resampling iterations
repeats
For repeated k-fold cross-validation only: the number of complete sets of folds to compute
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
classProbs
a logical; should class probabilities be computed for classification models (along with predicted values) in each resample?
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 function itself. See best for details and other options. preProcOptions
A list of options to pass to preProcess. The type of pre-processing (e.g. center, scaling etc) is passed in via the preProc option in train. index
a list with elements for each resampling iteration. Each list element is the sample rows used for training at that iteration.
timingSamps
the number of training set samples that will be used to measure the time for predicting samples (zero indicates that the prediction time should not be estimated.
predictionBounds
a logical or numeric vector of length 2 (regression only). If logical, the predictions can be constrained to be within the limit of the training set outcomes. For example, a value of c(TRUE, FALSE) would only constrain the lower end of predic