trainNWSControl {caretNWS} | R Documentation |
Control of printing and resampling for trainNWS
trainNWSControl( method = "boot", number = ifelse(method == "cv", 10, 25), verboseIter = TRUE, returnData = TRUE, returnResamp = "final", p = .75, summaryFunction = caret:::defaultSummary, selectionFunction = "best", index = NULL, start = startNWS)
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. |
start |
a function that starts a slighPro
object. Note: if the sleighObj is not NULL in the link{trainNWS} call,
this function is ignored. See the link{startNWS} function in this package as an
example |
An echo of the parameters specified
max Kuhn