The PMML Revolution: Predictive analytics at the speed of business

September 19, 2012
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(This article was first published on Revolutions, and kindly contributed to R-bloggers)

This guest post is by Alex Guazzelli, VP of Analytics at Zementis Inc. -- ed.

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PMML, the Predictive Model Markup Language, is the de facto standard to represent predictive analytics and data mining models. With PMML, it is extremely easy to move a predictive solution from one system to another, since it avoids proprietary issues and incompatibilities.

Companies around the globe are benefiting from PMML to make instant use of their predictive solutions. With PMML, there is no need for custom coding: you can easily move your solution from the scientist’s desktop, where it was built, to the production environment, where it is operationally deployed. Companies also use PMML as the common language between service providers and external vendors. In this way, it defines a single and clear process for the exchange of predictive solutions. It becomes the bridge not only between data analysis, model building, and deployment systems, but also between all the people and teams involved in the analytical process. This is extremely important, since PMML is used to disseminate knowledge and best practices, and to ensure transparency.

All the top analytical tools, commercial and open-source, support PMML. And, the language itself has reached a great level of maturity and refinement. PMML 4.1, its latest version, makes it extremely easy for predictive solutions to be represented in an open and standard way. With PMML, you can represent a myriad of pre- and post-processing steps, besides the predictive modeling techniques per se. PMML 4.1 allows for multiple models (model composition, chaining, segmentation, and ensemble, which includes random forest models), to be represented by a single and concise language element. It also allows for model outputs to be transformed into business decisions. Therefore, a PMML file is able to represent the entire solution, from raw data to business decision, with one or multiple predictive models.

The availability of a standard such as PMML combined with scoring solutions in the cloud, for Hadoop, and in-database make it possible for predictive analytics to fulfill its promise and crack the big data code. Zementis, Inc. has been in the forefront of PMML-based scoring, first through its ADAPA Scoring Engine, which is available for on-site deployment or as a service on cloud (Amazon and IBM), and lately through its Universal PMML Plug-in which is offered for a range of databases and for Hadoop. Zementis has partnered with Revolution Analytics, so that predictive solutions built in R can benefit from the vast scoring infrastructure already in place. I am proud to be associated with Zementis and excited to be part of an ever-growing PMML community.

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A PMML package for R that exports all kinds of predictive models is available directly from CRAN.

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 Models 

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 (click HERE for examples);
  • rsf (randomSurvivalForest): Random Survival Forest Models;

And, this expansion is still on-going as the R community implements support for other packages and techniques. For more on the PMML package, please take a look at the paper we published with Graham Williams from Togaware in “The R Journal”. For that just follow the link below:

PMML: An Open Standard for Sharing Models

There may be quite a few reasons for you to move your predictive solution from R to an independent deployment platform. Among them, you may want parallel execution on big data or real-time scoring for applications such as fraud detection or recommender systems. With PMML you can easily move your model to the cloud or inside the database for scoring. Or, even have it executed on Hadoop. It is really up to you! On top of that, PMML allows for side-by-side deployment of predictive assets from R as well as other commercial data mining tools, supporting a multi-vendor environment as well as platform independent deployment.

More and more companies and individuals are using the PMML standard for the obvious benefits it provides, putting their predictive solutions on the fast track. With PMML, the speed of predictive solutions can be on par with the speed of business.

Dr. Alex Guazzelli is the VP of Analytics at Zementis Inc. where he is responsible for developing core technology and predictive solutions under ADAPA, a PMML-based decisioning platform. With more than 20 years of experience in predictive analytics, Dr. Guazzelli holds a PhD in Computer Science from the University of Southern California and has co-authored the book PMML in Action: Unleashing the Power of Open Standards for Data Mining and Predictive Analytics, now in its second edition (paperback and kindle). You can follow him at @DrAlexGuazzelli.

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