Optim.SVM: Discover the best SVM for your data
In OptimClassifier: Create the Best Train for Classification Models

Description Usage Arguments Value Examples

This function allows to find the best kernel for tune your support vector machine (SVM).

1 2	Optim.SVM(formula, data, p, criteria = c("rmse", "success", "ti_error", "tii_error"), includedata = FALSE, seed = NULL, ...)

`formula`	A formula of the form `y ~ x1 + x2 + ...`
`data`	Data frame from which variables specified in `formula` are preferentially to be taken.
`p`	A percentage of training elements
`criteria`	This variable selects the criteria to select the best threshold. The default value is `success_rate`.
`includedata`	logicals. If TRUE the training and testing datasets are returned.
`seed`	a single value, interpreted as an integer, or `NULL`. The default value is `NULL`, but for future checks of the model or models generated it is advisable to set a random seed to be able to reproduce it.
`...`	arguments passed to `svm`

An object of class Optim. See Optim.object

if(interactive()){

## Load a Dataset
data(AustralianCredit)

## Generate a model
modelFit <- Optim.SVM(Y~., AustralianCredit, p = 0.7, seed=2018)
modelFit

}