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.
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
Cox Regression Models
Linear and Logistic Regression Models
Support Vector Machines
Generalized Linear Models
Random Survival Forest Models
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.