Control parameters for train
Control the computational nuances of the
trainControl(method = "boot", number = ifelse(method %in% c("cv", "repeatedcv"), 10, 25), repeats = ifelse(method %in% c("cv", "repeatedcv"), 1, number), p = 0.75, initialWindow = NULL, horizon = 1, fixedWindow = TRUE, verboseIter = FALSE, returnData = TRUE, returnResamp = "final", savePredictions = FALSE, classProbs = FALSE, summaryFunction = defaultSummary, selectionFunction = "best", custom = NULL, preProcOptions = list(thresh = 0.95, ICAcomp = 3, k = 5), index = NULL, indexOut = NULL, timingSamps = 0, predictionBounds = rep(FALSE, 2), allowParallel = TRUE)
- The resampling method:
LGOCV(for repeated training/test splits), or
oob(only for random forest, bagged trees, bagge
- Either the number of folds or number of resampling iterations
- For repeated k-fold cross-validation only: the number of complete sets of folds to compute
- A logical for printing a training log.
- A logical for saving the data
- A character string indicating how much of the resampled summary metrics should be saved. Values can be ``final'', ``all'' or ``none''
- a logical to save the hold-out predictions for each resample
- For leave-group out cross-validation: the training percentage
- initialWindow, horizon, fixedWindow
- possible arguments to
- a logical; should class probabilities be computed for classification models (along with predicted values) in each resample?
- a function to compute performance metrics across resamples. The arguments to the function should be the same as those in
- an optional list of functions that can be used to fit custom models. See the details below and worked examples at
http://caret.r-forge.r-project.org/. . This is an "experimental" version for testing. Please send emails to the maintainer for su
- the function used to select the optimal tuning parameter. This can be a name of the function or the function itself. See
bestfor details and other options.
- A list of options to pass to
preProcess. The type of pre-processing (e.g. center, scaling etc) is passed in via the
- a list with elements for each resampling iteration. Each list element is the sample rows used for training at that iteration.
- a list (the same length as
index) that dictates which sample are held-out for each resample. If
NULL, then the unique set of samples not contained in
- 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.
- 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
- if a parallel backend is loaded and available, should the function use it?
For custom modeling functions, several functions can be specified using the
- An echo of the parameters specified
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"