Generate the PMML representation for a nnet object from package nnet.

# S3 method for nnet
pmml(
  model,
  model_name = "NeuralNet_model",
  app_name = "SoftwareAG PMML Generator",
  description = "Neural Network Model",
  copyright = NULL,
  model_version = NULL,
  transforms = NULL,
  missing_value_replacement = NULL,
  ...
)

Arguments

model: A nnet object.
model_name: A name to be given to the PMML model.
app_name: The name of the application that generated the PMML.
description: A descriptive text for the Header element of the PMML.
copyright: The copyright notice for the model.
model_version: A string specifying the model version.
transforms: Data transformations.
missing_value_replacement: Value to be used as the 'missingValueReplacement' attribute for all MiningFields.
...: Further arguments passed to or from other methods.

Value

PMML representation of the nnet object.

Details

This function supports both regression and classification neural network models. The model is represented in the PMML NeuralNetwork format.

References

nnet: Feed-forward Neural Networks and Multinomial Log-Linear Models (on CRAN)

Author

Tridivesh Jena

Examples

if (FALSE) {
library(nnet)
fit <- nnet(Species ~ ., data = iris, size = 4)
fit_pmml <- pmml(fit)

rm(fit)
}