R PMML Support: BetteR than EveR!

April 9, 2012
By

(This article was first published on Predictive Analytics, Big Data, Hadoop, PMML, and kindly contributed to R-bloggers)

PMML, the Predictive Model Markup Language, has become the de-facto standard to represent not only predictive models, but also data pre- and post-processing. In so doing, it allows for the interchange of models among different tools and environments, avoiding proprietary issues and incompatibilities.

R PMML Package

The PMML Package exports a variety of predictive models form R to PMML. The PMML package itself was conceived at first as part of Togaware’s data mining toolkit Rattle. Although it can easily be accessed through Rattle’s GUI, it can also be accessed directly in R.

R Package
To download the PMML Package from CRAN, the R Archive, click HERE.

Extended PMML Support

Traditionally, the PMML Package offered support for the following data mining algorithms:

  • ksvm(kernlab): Support Vector Machines
  • nnet: Neural Networks
  • rpart: C&RT Decision Trees
  • lm & glm (stats): Linear and Binary Logistic Regression Models
  • arules: Association Rules
  • kmeans and hclust: Clustering Model

Recently, it has been expanded to support:

  • multinom (nnet): Multinomial Logistic Regression Models;
  • glm (stats): Generalized Linear Models for classification and regression with a wide variety of link functions
  • randomForest: Random Forest Models for classification and regression
  • coxph (survival): Cox Regression Models to calculate survival and stratified cumulative hazards
  • ada: Stochastic Boosting
  • naiveBayes (e1071): Naive Bayes Classifiers
  • svm (e1071): Support Vector Machines

Once exported in PMML, your R model can be readily deployed in the Zementis ADAPA Scoring Engine, where it can be put to work immediately.

To leave a comment for the author, please follow the link and comment on their blog: Predictive Analytics, Big Data, Hadoop, PMML.

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