R PMML Support: Data Transformations

[This article was first published on Predictive Analytics, Big Data, Hadoop, PMML, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

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.

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

R-bloggers.com offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)