trainNWSControl {caretNWS}R Documentation

Control parameters for train

Description

Control of printing and resampling for trainNWS

Usage

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)

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, 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

Value

An echo of the parameters specified

Author(s)

max Kuhn


[Package caretNWS version 0.23 Index]