Developed by the Data Mining Group, an independent, vendor led committee, PMML provides an open standard for representing data mining models. In this way, models can easily be shared between different applications avoiding proprietary issues and incompatibilities. Currently, all major commercial and open source data mining tools already support PMML

PMML is an XML-based language which follows a very intuitive structure to describe data pre- and post-processing as well as predictive algorithms. Not only does PMML represent a wide range of statistical techniques, but it can also be used to represent input data as well as the data transformations necessary to transform raw data into meaningful features.

As part of the Data Mining Group, Zementis is committed to the continual development of PMML. It is our vision for the community that users will be free to share models among many solutions, benefiting from an environment in which interoperability is truly attainable. For this reason, we have made available to the data mining community two very important PMML tools:

1.  The PMML Converter: allows users to convert older PMML models into its latest version.
2.  The Transformations Generator: allows users to interactively designed data transformations in PMML.

To access both tools, make sure to check our PMML Tools page.

PMML Examples

To experiment with the PMML example files, please follow these steps:

1.  Save model (.XML) and data file (.CSV) to your local computer.
2.  Upload a model file in ADAPA.
3.  Validate the model by executing the respective data file.

The examples we provide are based on publicly available datasets. The DMG publishes a list of PMML sample models which inspired our collection of PMML 3.2 examples.

For more information on the Iris and El Nino datasets, please refer to: Asuncion, A. & Newman, D.J. (2007). UCI Machine Learning Repository. Irvine, CA: University of California, School of Information and Computer Science.

The audit dataset is available through the R rattle package. For more information on rattle, please refer to http://rattle.togaware.com.

The shuttle O-ring data is based on a number of O-ring failures for each shuttle flight preceding the Challenger disaster. The cause of the explosion was determined to be an O-ring failure in the right solid rocket booster. The Challenger disaster has become a case study in the possible consequences of poor data analysis.

PMML Community Forum

For an on-going discussion and to read about the latest PMML news, we would like to invite you to join the PMML group in LinkedIn or the discussion forum in the PMML group on Analytic Bridge, a social network community for analytics professionals.

PMML Book

PMML In Action PMML in Action

PMML (Predictive Model Markup Language) is the de facto standard used to represent and share predictive analytic solutions between applications. This enables data mining scientists and users alike to easily build, visualize, and deploy their solutions using different platforms and systems.

This book presents PMML from a practical perspective. It contains a variety of code snippets so that concepts are made clear through the use of examples. "PMML in Action" is a great way to learn how to represent your predictive models through a mature open standard. The book is divided into six parts, taking you into a PMML journey in which language elements and attributes are used to represent not only modeling techniques but also data transformations.

With PMML, users benefit from a single and concise standard to represent data and models, thus avoiding the need for custom code and proprietary solutions.

You too can join the PMML movement! Unleash the power of predictive analytics and data mining today!

Available for purchase on Amazon.com

Reviews:

"The very first book that covers the industry standard for transferring and integrating predictive models across systems, this is a milestone for predictive analytics. If you want the long and short on engineering for versatility in how predictive models can be deployed and put to work, get started by curling up with this book."

Eric Siegel, Ph.D., President, Prediction Impact, Inc., Conference Chair, Predictive Analytics World (Predictive Analytics World)

"Open standards facilitate innovation and progress (web is a great example). PMML (the Predictive Model Markup Language) is an open standard for predictive analytics and data mining, developed over more than 12 years and supported by most industry leaders. This easy to read book covers data transformations, many modeling methods (Associations, Clustering, Decision Trees, Neural Nets, Regression, SVM, and more), model ensembles, and verification. This book is your essential guide to PMML!"

Gregory Piatetsky, Ph.D., Editor KDnuggets, Founder KDD/SIGKDD (KDNuggets.com)

"Next generation enterprise are going to be driven by analytics, especially predictive analytics. Sharing and rapidly deploying predictive analytic models is essential and PMML is the open standard that delivers the interoperability and agility that these predictive enterprises need."

James Taylor, CEO, Decision Management Solutions, Co-author of "Smart (Enough) Systems: How to Deliver Competitive Advantage by Automating Hidden Decisions" (JTonEDM.com)

"PMML in Action" may be destined to become an analog to the famous Kernighan and Richie book, "The C Programming Language", published in 1978. This book (affectionately known as K&R) became the standard guide for ANSII C programming practice. I expect that "PMML in Action" will function likewise in the burgeoning development of PMML in analytical tools now, and in the future. It is the "cookbook" for PMML programming. Julia Child made French cuisine kiss-simple for housewives to create. Now, programmers can follow the descriptions and practices in this book to implement analytical solutions in PMML as easily and efficiently as Julia enabled a housewife to make a French soufflé."

Robert A. Nisbet, Ph.D., (Co-author of "Handbook of Statistical Analysis & Data Mining Applications")

PMML Links

We have compiled a list of useful PMML links below. Please, make sure to check them if you would like to become a PMML pro.