R: Generate PMML for a randomForest object

pmml.randomForest {pmml}

R Documentation

Generate PMML for a randomForest object

Description

Generate the Predictive Model Markup Language (PMML) representation of a randomForest forest object. In particular, this function gives the user the ability to save the geometry of a forest as a PMML XML document.

Usage

## S3 method for class 'randomForest'
pmml(model, model.name="randomForest_Model", app.name="Rattle/PMML",
     description="randomForest model", copyright, ...)

Arguments

`model`	the `forest` object contained in an object of class randomForest, as that contained in the object returned by the function `randomForest`.
`model.name`	a name to give to the model in the PMML.
`app.name`	the name of the application that generated the PMML.
`description`	a descriptive text for the header of the PMML.
`copyright`	the copyright notice for the model.
`...`	further arguments passed to or from other methods.

Details

The Predictive Model Markup Language is an XML based language which provides a way for applications to define statistical and data mining models and to share models between PMML compliant applications. More information about PMML and the Data Mining Group can be found at http://www.dmg.org.

Use of PMML and pmml.randomForest requires the XML package. Be aware that XML is a very verbose data format. Reasonably sized trees and data sets can lead to extremely large text files. XML, while achieving interoperability, is not an efficient data storage mechanism in this case.

This function is used to export the structure of the forest to other PMML compliant applications, including graphics packages that are capable of printing binary trees.

Value

An object of class XMLNode as that defined by the XML package. This represents the top level, or root node, of the XML document and is of type PMML. It can be written to file with saveXML.

Author(s)

Hemant Ishwaran hemant.ishwaran@gmail.com and Udaya B. Kogalur ubk2101@columbia.edu with incorporation into the pmml package by Graham.Williams@togaware.com

References

H. Ishwaran and Udaya B. Kogalur (2006). Random Survival Forests. Cleveland Clinic Technical Report.

PMML home page: http://www.dmg.org