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), or oob (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 the preProc option in train.
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

Documentation reproduced from package caret, version 5.04-007, License: GPL-2

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"