pmml.randomForest: Generate the PMML representation for a randomForest object...

In pmml: Generate PMML for Various Models

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

Description

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

Usage

## S3 method for class '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.

Details

This function outputs a Random Forest in PMML format.

Value

PMML representation of the randomForest object.

Author(s)

Tridivesh Jena

References

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

Examples

## Not run: 
# 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)

## End(Not run)

pmml documentation built on March 18, 2022, 5:49 p.m.