regressoR (version 1.2.1)

nn_prediction: nn_prediction

Description

generates the code to create the prediction of the neural network model.

Usage

nn_prediction(
  data = "datos.prueba",
  variable.pred = NULL,
  model.var = "modelo.nn",
  pred.var = "prediccion.nn",
  mean.var = "mean.nn",
  sd.var = "sd.nn"
)

Arguments

data

the name of the test data.

variable.pred

the name of the variable to be predicted.

model.var

the name of the variable that stores the resulting model.

pred.var

the name of the variable that stores the resulting prediction.

mean.var

the name of the variable that stores the mean of the columns.

sd.var

the name of the variable that stores the standard deviation of the columns.

See Also

compute

Examples

# NOT RUN {
library(neuralnet)
library(dummies)
library(dplyr)

x <- nn_model('iris', 'Petal.Length','modelo.nn', 'mean.nn', 'sd.nn', 0.05, 2000, 3, 30, 50, 30)
exe(x)

x <- nn_prediction('iris', 'Petal.Length')
exe(x)
print(prediccion.nn)
# }