SAS GF paper on RPM

Sas enterprise miner to examine these details and try

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Unformatted text preview: ERING AND SCORING THE MODEL After the model has been computed, you can also register the model to the SAS Metadata Repository from within the SAS Rapid Predictive Modeler task. A registered model can be used by your colleagues in their scoring processes in SAS Enterprise Guide or SAS Add-In for Microsoft Office, or imported into their SAS Enterprise Miner sessions for comparison, review, and further development. The model can also be imported into SAS Model Manager, both for management with all of your other modeling assets and also for monitoring model degradation. SAS Data Integration Studio users can import models to create managed and scheduled scoring processes. Figure 11 shows applying a registered SAS Rapid Predictive Modeler model to score new data with the Model Scoring task. SAS Rapid Predictive Modeler models can also be published using the SAS Scoring Accelerator for Teradata, DB2, or Netezza. Figure 11. Model Registration from SAS Enterprise Guide 11 SAS Global Forum 2010 Customer Intelligence CONCLUSION Organizations today are increasing their use of predictive analytics to more accurately predict their business outcomes, to improve business performance, and to increase profitability. SAS Rapid Predictive Modeler provides business analysts with self-sufficient access to an automated easy-to-use predictive modeling task to support a variety of applications such as cross-selling, up-selling, retention, and acquisition. SAS Rapid Predictive Modeler also delivers proven best-practices modeling methodologies for routine modeling building tasks, freeing up time for the experienced statistician to focus on more complex data mining problems. Although the SAS Rapid Predictive Modeler is very automated and easy to use, it is not a black box. The analysis can be saved to a SAS Enterprise Miner project for support further customizing. The SAS Rapid Predictive Modeler also generates key reporting elements necessary for reviewing the model results with decision makers. SAS Rapid Predictive Modeler models can also be registered to SAS metadata, enabling full integration with the SAS Business Analytics Framework. Models can be scored using the scoring transformation of SAS Data Integration Studio or the model scoring task of SAS Enteprise Guide and SAS Add-In for Microsoft Office. The SAS Rapid Predictive Modeler score code is also fully compatible with the SAS Scoring Accelerators to support in-database scoring. Models can be registered to SAS Model Manager for ongoing maintenance and monitoring of the model. The authors anticipate that many new SAS users will leverage the power of SAS data mining through this easy-to-use modeling tool. REFERENCES • SAS Institute Inc. 2009. “SAS Add-in for Microsoft Office.” • SAS Institute Inc. 2009. “SAS Enterprise Guide 4.2 Fact Sheet.” 2009. • SAS Institute Inc. 2009. SAS Enterprise Guide 4.2 Reference Help. Cary, NC: SAS Institute Inc. • SAS Institute Inc. 2009. “SAS Enterprise Miner 6.1 Fact Sheet.” • SAS Institute Inc. 2009. SAS Enterprise Miner 6.1 Reference Help. Cary, NC: SAS Institute Inc. • Wielenga, Doug. 2007. “Identifying and Overcoming Common Data Mining Mistakes.” Proceedings of the SAS Global Forum 2007 Conference. Cary, NC: SAS Institute Inc. ACKNOWLEDGMENTS The authors thank Billie Anderson, Michael Burke, Susan Haller, Kevin Hodge, Brian Johnson, Jagruti Kanjia, Dominique Latour, Bob Lucas, Renee Sember, Carol Thompson and Doug Wielenga for helping design, develop and test SAS RPM. CONTACT INFORMATION Your comments and questions are valued and encouraged. Contact the authors at: David Duling S6102 SAS Institute Inc. SAS Campus Drive Cary, North Carolina, 277513 [email protected] Wayne Thompson S6100 SAS Institute Inc. SAS Campus Drive Cary, North Carolina, 277513 [email protected] SAS and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc. in the USA and other countries. ® indicates USA registration. Other brand and product names are trademarks of their respective companies. 12...
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This note was uploaded on 09/30/2013 for the course FINANCE 4013 taught by Professor Jamescameron during the Summer '10 term at Ohio State.

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