trainControl
From caret v5.04-007
by Max Kuhn
Control parameters for train
Control the computational nuances of the train
function
- Keywords
- utilities
Usage
trainControl(method = "boot",
number = ifelse(method %in% c("cv", "repeatedcv"), 10, 25),
repeats = ifelse(method %in% c("cv", "repeatedcv"), 1, number),
verboseIter = FALSE,
returnData = TRUE,
returnResamp = "final",
p = 0.75,
classProbs = FALSE,
summaryFunction = defaultSummary,
selectionFunction = "best",
preProcOptions = list(thresh = 0.95, ICAcomp = 3, k = 5),
index = NULL,
timingSamps = 0,
predictionBounds = rep(FALSE, 2))
Arguments
- method
- The resampling method:
boot
,boot632
,cv
,repeatedcv
,LOOCV
,LGOCV
(for repeated training/test splits), oroob
(only for random forest, bagged trees, bagge - number
- Either the number of folds or number of resampling iterations
- repeats
- For repeated k-fold cross-validation only: the number of complete sets of folds to compute
- 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
- classProbs
- a logical; should class probabilities be computed for classification models (along with predicted values) in each resample?
- 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 function itself. See
best
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 intrain
. - index
- a list with elements for each resampling iteration. Each list element is the sample rows used for training at that iteration.
- timingSamps
- the number of training set samples that will be used to measure the time for predicting samples (zero indicates that the prediction time should not be estimated.
- predictionBounds
- a logical or numeric vector of length 2 (regression only). If logical, the predictions can be constrained to be within the limit of the training set outcomes. For example, a value of
c(TRUE, FALSE)
would only constrain the lower end of predic
Value
- An echo of the parameters specified
Community examples
RAVINDARMADISHETTY@GMAIL.COM
at
Jul 23, 2018
caret
v6.0-80
I am getting below error while submitting a text x = trainControl(method = "repeatedcv", number = numbers, repeats = repeats, classProbs = TRUE, summaryFunction = twoClassSummary) Error: Please suggesrt Error in trainControl(method = "repeatedcv", number = numbers, repeats = repeats, : could not find function "trainControl"