Cookies help us display personalized product recommendations and ensure you have great shopping experience.

By using this site, you agree to the Privacy Policy and Terms of Use.
Accept
SmartData CollectiveSmartData Collective
  • Analytics
    AnalyticsShow More
    data analytics in ecommerce
    Analytics Technology Drives Conversions for Your eCommerce Site
    5 Min Read
    CRM Analytics
    CRM Analytics Helps Content Creators Develop an Edge in a Saturated Market
    5 Min Read
    data analytics and commerce media
    Leveraging Commerce Media & Data Analytics in Ecommerce
    8 Min Read
    big data in healthcare
    Leveraging Big Data and Analytics to Enhance Patient-Centered Care
    5 Min Read
    instagram visibility
    Data Analytics Plays a Key Role in Improving Instagram Visibility
    7 Min Read
  • Big Data
  • BI
  • Exclusive
  • IT
  • Marketing
  • Software
Search
© 2008-23 SmartData Collective. All Rights Reserved.
Reading: Validation, Correction, and Conversion: Presenting the PMML Converter!
Share
Notification Show More
Font ResizerAa
SmartData CollectiveSmartData Collective
Font ResizerAa
Search
  • About
  • Help
  • Privacy
Follow US
© 2008-23 SmartData Collective. All Rights Reserved.
SmartData Collective > Big Data > Data Mining > Validation, Correction, and Conversion: Presenting the PMML Converter!
Data Mining

Validation, Correction, and Conversion: Presenting the PMML Converter!

MichaelZeller
Last updated: November 14, 2009 1:38 pm
MichaelZeller
6 Min Read
SHARE


PMML, the Predictive Model Markup Language, is the de facto standard to represent predictive models. With PMML, models can be exported from one tool and easily imported by another, without all the hassle of dealing with proprietary code and incompatibilities.

Converting from one version to another

More often than not though, auto-generated PMML code is represented in different versions of PMML. A tool may export PMML 2.1 and another import PMML 3.2. This problem raises the issue of conversion. For true interoperability, PMML needs to be easily converted from one version to another.

Validating code against the schema

PMML is an XML-based language. The Data Mining Group (DMG) publishes a PMML Schema (.xsd file) that is specific on how PMML elements should be used. Unfortunately, some tools do not adhere 100% to the schema. For true interoperability, PMML needs to be successfully validated against the schema and if any problems are found, these need to be pointed out so that they can be fixed.

Correcting files so that they conform to the schema

Once schema incompatibilities are identified, life becomes a lot easier if problems are correctly automatically so that any PMML code that won’t .. …

More Read

For data-mining cops, tattoos are tags

Big Data: The Coming Sensor Data Driven Productivity Revolution
R and Cloud Computing
Improve R with Google’s Summer of Code
More on keeping decisions and processes separate




PMML, the Predictive Model Markup Language, is the de facto standard to represent predictive models. With PMML, models can be exported from one tool and easily imported by another, without all the hassle of dealing with proprietary code and incompatibilities.

Converting from one version to another

More often than not though, auto-generated PMML code is represented in different versions of PMML. A tool may export PMML 2.1 and another import PMML 3.2. This problem raises the issue of conversion. For true interoperability, PMML needs to be easily converted from one version to another.

Validating code against the schema

PMML is an XML-based language. The Data Mining Group (DMG) publishes a PMML Schema (.xsd file) that is specific on how PMML elements should be used. Unfortunately, some tools do not adhere 100% to the schema. For true interoperability, PMML needs to be successfully validated against the schema and if any problems are found, these need to be pointed out so that they can be fixed.

Correcting files so that they conform to the schema

Once schema incompatibilities are identified, life becomes a lot easier if problems are correctly automatically so that any PMML code that won’t validate against the schema at first is successfully validated after being corrected.

Obviously, one may wonder why not have perfect PMML code at all times, and in its latest version (version 4.0 was just release! June 2009). This is the ideal scenario, but in reality, PMML producers and consumers have different levels of support for the standard and have a tendency to lag behind when it comes to updating importers and exporters to accompany the latest release.

Given that we don’t leave on an ideal PMML world, the emergence of a PMML tool that can validate, correct, and convert PMML code is to be celebrated.

The PMML Converter

Zementis has released a version of such a tool. It is called the PMML Converter. It is available for use free of charge by the community at large via the DMG website and the PMML resources page in the Zementis website. The figure on the left encapsulates the key functionalities offered by the PMML Converter.

Besides schema validation, the PMML Converter automatically corrects known issues with PMML code from several sources/vendors. The aim is to successfully validate code in older versions of PMML (2.1, 3.0, 3.1) and convert them to PMML 3.2. Files in PMML 3.2 can also be passed through the converter so that they can be corrected and validated against the 3.2 schema.

If the PMML code cannot be converted, that usually means that it could not be automatically corrected. In that case, the PMML Converter embeds comments into the PMML code pinpointing the problem so that they can be fixed manually before being submitted again for conversion. This is done via a hyper-link in the converter which allows for the PMML file to be download after failed conversion.

For a list of modeling elements covered by the PMML Converter, click HERE.

For a guide on how to use the PMML Converter as well as a how-to video, click HERE.

Schema validation and convertion to PMML 4.0 is coming soon!

Comprehensive blog featuring topics related to predictive analytics with an emphasis on open standards, Predictive Model Markup Language (PMML), cloud computing, as well as the deployment and integration of predictive models in any business process.

Link to original post

TAGGED:zementis
Share This Article
Facebook Twitter Pinterest LinkedIn
Share

Follow us on Facebook

Latest News

trusted data management
The Future of Trusted Data Management: Striking a Balance between AI and Human Collaboration
Artificial Intelligence Big Data Data Management
data analytics in ecommerce
Analytics Technology Drives Conversions for Your eCommerce Site
Analytics Exclusive
data grids in big data apps
Best Practices for Integrating Data Grids into Data-Intensive Apps
Big Data Exclusive
AI helps create discord server bots
AI-Driven Discord Bots Can Track Server Stats
Artificial Intelligence Exclusive

Stay Connected

1.2kFollowersLike
33.7kFollowersFollow
222FollowersPin

You Might also Like

SAIC and Zementis to bring “smarts” to the Smart Grid

6 Min Read

PAW: High-Performance Scoring of Healthcare Data

6 Min Read

SmartData Collective is one of the largest & trusted community covering technical content about Big Data, BI, Cloud, Analytics, Artificial Intelligence, IoT & more.

ai chatbot
The Art of Conversation: Enhancing Chatbots with Advanced AI Prompts
Chatbots
ai is improving the safety of cars
From Bolts to Bots: How AI Is Fortifying the Automotive Industry
Artificial Intelligence

Quick Link

  • About
  • Contact
  • Privacy
Follow US
© 2008-24 SmartData Collective. All Rights Reserved.
Go to mobile version
Welcome Back!

Sign in to your account

Username or Email Address
Password

Lost your password?