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

# S3 method for randomForest
pmml(
  model,
  model_name = "randomForest_Model",
  app_name = "SoftwareAG PMML Generator",
  description = "Random Forest Tree Model",
  copyright = NULL,
  model_version = NULL,
  transforms = NULL,
  missing_value_replacement = NULL,
  parent_invalid_value_treatment = "returnInvalid",
  child_invalid_value_treatment = "asIs",
  ...
)

Arguments

model: A randomForest 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.
parent_invalid_value_treatment: Invalid value treatment at the top MiningField level.
child_invalid_value_treatment: Invalid value treatment at the model segment MiningField level.
...: Further arguments passed to or from other methods.

Value

PMML representation of the randomForest object.

Details

This function outputs a Random Forest in PMML format.

References

randomForest: Breiman and Cutler's random forests for classification and regression

Author

Tridivesh Jena

Examples

if (FALSE) {
# Build a randomForest model
library(randomForest)
iris_rf <- randomForest(Species ~ ., data = iris, ntree = 20)

# Convert to pmml
iris_rf_pmml <- pmml(iris_rf)

rm(iris_rf)
}