R PMML Support: Data Transformations

R and PMML Export     R is becoming the tool of choice for many data scientists. It is no wonder that many commercial and open-source statistical tools are also embracing R. Predictive Models A set of robust predictive analytic techniques is but one set of tools available to data scientists in R. Another important set is the ability to export PMML for a host of predictive models.  By using the pmml package (version 1.2.33 or higher), users can export PMML from R for: Random Forest Models Neural Networks Clustering Models Cox Regression Models Linear and Logistic Regression Models Support Vector Machines Association Rules Generalized Linear Models Random Survival Forest Models Data Transformations And now, another R package extends this functionality by providing PMML export for data transformations. The new pmmlTransformations package has just made its way to CRAN (the Comprehensive R Archive Network).  Want to apply a Z-scoring normalization procedure to your continuous input variables before presenting them to a neural network? No problem. Use the pmmlTransformations package in conjunction with the pmml package (version 1.2.33 or higher) to export the entire process (pre-processing + model) into a PMML file.  To look at the package's documentation in CRAN, click HERE.

Agile Predictive Analytics Deployment Once represented as a PMML file, a predictive solution (data transformations + model) can be readily moved into the operational environment where it can be put to work immediately. That's the promise of PMML. Zementis offers a host of products for the agile deployment and execution of your PMML-based solutions. Our ADAPA and UPPI scoring engines are available for: Hadoop: Datameer and Hadoop/Hive In-database: EMC Greenplum, IBM Netezza, SAP Sybase IQ, Teradata, and Teradata Aster Cloud: Amazon EC2 and IBM SmartCloud Enterprise On-site: On your own servers Real-time or Big Data requirements? Zementis has you covered. Contact us today for more information or to schedule a presentation/demo.

R PMML Support: BetteR than EveR!

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.
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.