OptimClassifier (version 0.1.5)

Optim.SVM: Discover the best SVM for your data

Description

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

Usage

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

Arguments

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

Value

An object of class Optim. See Optim.object

Examples

# NOT RUN {
if(interactive()){

## Load a Dataset
data(AustralianCredit)

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

}

# }