caret (version 4.18)

trainControl: Control parameters for train

Description

Control of printing and resampling for train

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}.

Value

• An echo of the parameters specified