# trainControl

From caret v4.87
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 = TRUE,
returnData = TRUE,
returnResamp = "final",
p = 0.75,
classProbs = FALSE,
summaryFunction = defaultSummary,
selectionFunction = "best",
PCAthresh = 0.95,
ICAcomp = 3,
k = 5,
index = NULL,
workers = 1,
predictionBounds = rep(FALSE, 2),
computeFunction = lapply,
computeArgs = NULL)
```

##### 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. - PCAthresh
- When invoking
`train`

with the`preProcess = "pca"`

option, this parameter allows the user to determine how many PCA components should be kept on the basis of the cumulative amount of variance explai - ICAcomp
- When invoking
`train`

with the`preProcess = "ica"`

option, this parameter allows the user to determine how many ICA components should be kept. See`preProcess`

- k
- When invoking
`train`

with the`preProcess = "knnImpute"`

option, this parameter allows the user to determine how many neighbors should be used for imputation. See - 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
- 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 - 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
`...`

slot in`computeFunction`

. See the examples in`train`

.

##### Value

- An echo of the parameters specified

*Documentation reproduced from package caret, version 4.87, 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"