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Activity Number: 86 - SPEED: Statistics and Econometrics
Type: Contributed
Date/Time: Sunday, July 29, 2018 : 5:05 PM to 5:50 PM
Sponsor: Section on Statistics in Marketing
Abstract #333036
Title: The Art of Ensemble Modeling with SPSS Modeler
Author(s): Zhen Zhang* and Lei Zhang and James Veillette and Timothy Tate
Companies: C Spire and Mississippi State Dept. of Health and C Spire and C Spire
Keywords: emsemble; algorithm; predictive modeling; accuracy; stability; SPSS Modeler

In the predictive analytics world, ensemble modeling strategy is often pursued to improve model accuracy and robustness. Ensemble modeling is the process of creating multiple models and incorporating them into a single scoring algorithm. The value of ensemble modeling for enhancing predictive accuracy and increasing model stability is widely recognized. However, it is an art that is not easily mastered. Through our predictive modeling practice within the telecommunication industry, we have found that in general, heterogenous ensemble modeling produces better results than homogeneous built-in ensembles. We have also found that depending on the size of the modeling target, homogeneous ensemble modeling may not always be the best choice, compared to a single model. In this paper, we present empirical ensemble strategies and suggest best practices pertaining to modeling techniques and target sizes.

Authors who are presenting talks have a * after their name.

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