English

Advanced Predictive Modeling Workshop Offered at Predictive Analytics World Boston

Report this content

BOSTON – Salford Systems is sponsoring an advanced, hands-on workshop for predictive modeling, covering model ensembles, at Predictive Analytics World in Boston, MA, on October 3. Course instructor Dean Abbott, president of Abbott Analytics, will lead a class of active participants to a higher understanding of advanced model ensembles using the Salford Predictive Modeler® software suite.

The course will cover the SPM™ software suite’s core components including, but not limited to, RandomForests® and TreeNet® stochastic gradient boosting to illustrate the importance of model ensembles when implementing advanced methods of modeling. The course will also teach best practices and attention will be paid to how these powerful algorithms work qualitatively and how the resulting models can be interpreted. Tricks and tips will also bee covered including how to automate the building of ensembles by changing key parameters.

"Ensemble learning has revolutionized the theory and the practice of contemporary data mining and every practitioner should have a solid understanding of the approach," elaborates Dr. Dan Steinberg, CEO and Founder of Salford Systems. "Further, Stanford University Professor Jerome H. Friedman's innovations have become the de facto standard for 'out of the box' ensembles and there is no better way to learn about all this than to attend Dean Abbott's new course on the topic." 

“The most effective approach to win predictive analytics ‘data’ competitions and producing highly accurate predictive models is the use of model ensembles, a technique that combines predictions from multiple models into a single score,” explains Dean Abbott, President of Abbott Analytics and workshop instructor. “This workshop describes approaches to building ensembles, explains why ensembles work so well, and allows participants to gain experience building model ensembles using the commercial software package that has won more data mining data competitions than any other: SPM from Salford Systems.”

Registration is available online at http://www.predictiveanalyticsworld.com/boston/register.php. If you wish to learn more about model ensembles but are unable to attend this workshop, please submit your request for additional information online at http://2.salford-systems.com/paw-boston-workshop-2012.


About Salford Systems

Founded in 1893, Salford Systems specializes in providing new-generation data mining and predictive modeling software and consulting services for industries for industries such as banking, insurance, healthcare, pharmaceutical, telecommunications, transportation, manufacturing, retail and catalog sales, and education. The company’s CART®, MARS®, TreeNet® and RandomForests® data mining software are currently installed in over 3,5000 sites worldwide, including 300 major universities. Salford Systems is headquartered in San Diego, CA. For more information, visit http://www.salford-systems.com or telephone 619-543-8880.

# # #

Media Contact
Heather Hinman
Salford Systems
619-543-8880 ext. 130
hhinman@salford-systems.com

Tags:

workshop TreeNet predictive analytics Salford Systems RandomForests PAW predictive modeling gradient boosting model ensembles salford random forests treenst salford systems paw boston dean abbott

Quick facts

The course will cover the SPM™ software suite’s core components including, but not limited to, RandomForests® and TreeNet® stochastic gradient boosting to illustrate the importance of model ensembles when implementing advanced methods of modeling.
Tweet this
Salford Systems is sponsoring an advanced, hands-on workshop for predictive modeling, covering model ensembles, at Predictive Analytics World in Boston, MA, on October 3. Course instructor Dean Abbott, president of Abbott Analytics, will lead a class of active participants to a higher understanding of advanced model ensembles using the Salford Predictive Modeler® software suite.
Tweet this

Quotes

The most effective approach to win predictive analytics ‘data’ competitions and producing highly accurate predictive models is the use of model ensembles, a technique that combines predictions from multiple models into a single score.
Dean Abbott
Ensemble learning has revolutionized the theory and the practice of contemporary data mining and every practitioner should have a solid understanding of the approach.
Dan Steinberg