pmml.rpart: Generate PMML for an rpart object

Description

Generate the PMML (Predictive Model Markup Language) representation of an rpart object (classification tree). The rpart object (currently expected to be a classification tree) is converted into a PMML representation.

Usage

## S3 method for class 'rpart':
pmml(model, model.name="RPart_Model", app.name="Rattle/PMML",
     description="RPart Decision Tree Model", copyright=NULL,
     transforms=NULL, dataset=NULL, \dots)

Arguments

model

an rpart object.

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.

the copyright notice for the model.

transforms

a coded list of transforms performed.

dataset

the orginal training dataset, if available.

...

further arguments passed to or from other methods.

Details

PMML 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.

The generated PMML can be imported into any PMML consuming application, such as the Zementis ADAPA and UPPI scoring engines which allow for predictive models built in R to be deployed and executed on site, in the cloud (Amazon, IBM, and FICO), in-database (IBM Netezza, Pivotal, Sybase IQ, Teradata and Teradata Aster) or Hadoop (Datameer and Hive).

References

Rattle home page: http://rattle.togaware.com

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

A. Guazzelli, W. Lin, T. Jena (2012), PMML in Action: Unleashing the Power of Open Standards for Data Mining and Predictive Analytics. CreativeSpace (Second Edition) - Available on Amazon.com - http://www.amazon.com/dp/1470003244.

A. Guazzelli, M. Zeller, W. Lin, G. Williams (2009), PMML: An Open Standard for Sharing Models. The R journal, Volume 1/1, 60-65

Examples

Run this code

library(rpart)
(iris.rpart <- rpart(Species ~ ., data=iris))
pmml(iris.rpart)