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

Description Usage Arguments Value Examples

Optim.NN function allows to find the best NN.

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Optim.NN(formula, data, p, criteria = c("success_rate", "ti_error",
  "tii_error"), includedata = FALSE, seed = NULL,
  maxhiddenlayers = 10, maxit = 500, MaxNWts = 2000, ...)

`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.
`maxhiddenlayers`	the high number of hidden layers for the neural network considers.
`maxit`	the maximum allowable number of weights. There is no intrinsic limit in the code, but increasing `MaxNWts` will probably allow fits that are very slow and time-consuming.
`MaxNWts`	maximum number of iterations. Default 500.
`...`	arguments passed to `nnet`

An object of class Optim. See Optim.object

if(interactive()){
## Load a Dataset
data(AustralianCredit)
## Generate a model
modelFit <- Optim.NN(Y~., AustralianCredit, p = 0.7, seed=2018)
modelFit
}