trainControl {caret} | R Documentation |
Control of printing and resampling for train
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)
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, or conditional tree forest models) |
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 parallel. See the examples in link{train} . |
computeArgs |
Extra arguments to pass into the ... slore in computeFunction . See the examples in link{train} . |
An echo of the parameters specified
max Kuhn