trainControl function - RDocumentation (2024)

method

The resampling method: "boot", "boot632","optimism_boot", "boot_all","cv", "repeatedcv", "LOOCV", "LGOCV" (forrepeated training/test splits), "none" (only fits one model to theentire training set), "oob" (only for random forest, bagged trees,bagged earth, bagged flexible discriminant analysis, or conditional treeforest models), timeslice, "adaptive_cv", "adaptive_boot" or"adaptive_LGOCV"

number

Either the number of folds or number of resampling iterations

repeats

For repeated k-fold cross-validation only: the number ofcomplete sets of folds to compute

p

For leave-group out cross-validation: the training percentage

search

Either "grid" or "random", describing how thetuning parameter grid is determined. See details below.

initialWindow, horizon, fixedWindow, skip

verboseIter

A logical for printing a training log.

returnData

A logical for saving the data

returnResamp

A character string indicating how much of the resampledsummary metrics should be saved. Values can be "final", "all"or "none"

savePredictions

an indicator of how much of the hold-out predictionsfor each resample should be saved. Values can be either "all","final", or "none". A logical value can also be used thatconvert to "all" (for true) or "none" (for false)."final" saves the predictions for the optimal tuning parameters.

classProbs

a logical; should class probabilities be computed forclassification models (along with predicted values) in each resample?

summaryFunction

a function to compute performance metrics acrossresamples. The arguments to the function should be the same as those indefaultSummary. Note that if method = "oob" is used,this option is ignored and a warning is issued.

selectionFunction

the function used to select the optimal tuningparameter. This can be a name of the function or the function itself. Seebest 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 thepreProc option in train.

sampling

a single character value describing the type of additionalsampling that is conducted after resampling (usually to resolve classimbalances). Values are "none", "down", "up","smote", or "rose". The latter two values require thethemis and ROSE packages, respectively. This argument can also bea list to facilitate custom sampling and these details can be found on thecaret package website for sampling (link below).

index

a list with elements for each resampling iteration. Each listelement is a vector of integers corresponding to the rows used for trainingat that iteration.

indexOut

a list (the same length as index) that dictates whichdata are held-out for each resample (as integers). If NULL, then theunique set of samples not contained in index is used.

indexFinal

an optional vector of integers indicating which samplesare used to fit the final model after resampling. If NULL, thenentire data set is used.

timingSamps

the number of training set samples that will be used tomeasure the time for predicting samples (zero indicates that the predictiontime should not be estimated.

predictionBounds

a logical or numeric vector of length 2 (regressiononly). If logical, the predictions can be constrained to be within the limitof the training set outcomes. For example, a value of c(TRUE, FALSE)would only constrain the lower end of predictions. If numeric, specificbounds can be used. For example, if c(10, NA), values below 10 wouldbe predicted as 10 (with no constraint in the upper side).

seeds

an optional set of integers that will be used to set the seedat each resampling iteration. This is useful when the models are run inparallel. A value of NA will stop the seed from being set within theworker processes while a value of NULL will set the seeds using arandom set of integers. Alternatively, a list can be used. The list shouldhave B+1 elements where B is the number of resamples, unlessmethod is "boot632" in which case B is the number ofresamples plus 1. The first B elements of the list should be vectorsof integers of length M where M is the number of models beingevaluated. The last element of the list only needs to be a single integer(for the final model). See the Examples section below and the Detailssection.

adaptive

a list used when method is "adaptive_cv","adaptive_boot" or "adaptive_LGOCV". See Details below.

trim

a logical. If TRUE the final model inobject\$finalModel may have some components of the object removed soreduce the size of the saved object. The predict method will stillwork, but some other features of the model may not work. triming willoccur only for models where this feature has been implemented.

allowParallel

if a parallel backend is loaded and available, shouldthe function use it?

Examples

Run this code

# NOT RUN {# }# NOT RUN {## Do 5 repeats of 10-Fold CV for the iris data. We will fit## a KNN model that evaluates 12 values of k and set the seed## at each iteration.set.seed(123)seeds <- vector(mode = "list", length = 51)for(i in 1:50) seeds[[i]] <- sample.int(1000, 22)## For the last model:seeds[[51]] <- sample.int(1000, 1)ctrl <- trainControl(method = "repeatedcv", repeats = 5, seeds = seeds)set.seed(1)mod <- train(Species ~ ., data = iris, method = "knn", tuneLength = 12, trControl = ctrl)ctrl2 <- trainControl(method = "adaptive_cv", repeats = 5, verboseIter = TRUE, seeds = seeds)set.seed(1)mod2 <- train(Species ~ ., data = iris, method = "knn", tuneLength = 12, trControl = ctrl2)# }# NOT RUN {# }

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