# tune.control

##### Control Parameters for the Tune Function

Creates an object of class `tune.control`

to be used with
the `tune`

function, containing various control parameters.

- Keywords
- models

##### Usage

```
tune.control(random = FALSE, nrepeat = 1, repeat.aggregate = mean,
sampling = c("cross", "fix", "bootstrap"), sampling.aggregate = mean,
sampling.dispersion = sd,
cross = 10, fix = 2/3, nboot = 10, boot.size = 9/10, best.model = TRUE,
performances = TRUE, error.fun = NULL)
```

##### Arguments

- random
if an integer value is specified,

`random`

parameter vectors are drawn from the parameter space.- nrepeat
specifies how often training shall be repeated.

- repeat.aggregate
function for aggregating the repeated training results.

- sampling
sampling scheme. If

`sampling = "cross"`

, a`cross`

-times cross validation is performed. If`sampling = "boot"`

,`nboot`

training sets of size`boot.size`

(part) are sampled (with replacement) from the supplied data. If`sampling = "fix"`

, a single split into training/validation set is used, the training set containing a`fix`

part of the supplied data. Note that a separate validation set can be supplied via`validation.x`

and`validation.y`

. It is only used for`sampling = "boot"`

and`sampling = "fix"`

; in the latter case,`fix`

is set to 1.- sampling.aggregate,sampling.dispersion
functions for aggregating the training results on the generated training samples (default: mean and standard deviation).

- cross
number of partitions for cross-validation.

- fix
part of the data used for training in fixed sampling.

- nboot
number of bootstrap replications.

- boot.size
size of the bootstrap samples.

- best.model
if

`TRUE`

, the best model is trained and returned (the best parameter set is used for training on the complete training set).- performances
if

`TRUE`

, the performance results for all parameter combinations are returned.- error.fun
function returning the error measure to be minimized. It takes two arguments: a vector of true values and a vector of predicted values. If

`NULL`

, the misclassification error is used for categorical predictions and the mean squared error for numeric predictions.

##### Value

An object of class `"tune.control"`

containing all the above
parameters (either the defaults or the user specified values).

##### See Also

*Documentation reproduced from package e1071, version 1.7-3, License: GPL-2 | GPL-3*