OptimClassifier (version 0.1.5)

Optim.NN: Discover the best Neural Network for your data

Description

Optim.NN function allows to find the best NN.

Usage

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

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.

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

Value

An object of class Optim. See Optim.object

Examples

# NOT RUN {
if(interactive()){
## Load a Dataset
data(AustralianCredit)
## Generate a model
modelFit <- Optim.NN(Y~., AustralianCredit, p = 0.7, seed=2018)
modelFit
}

# }