Mobile Phone Customer Type Discrimination via Stochastic Gradient Boosting

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Author(s): Dan Steinberg (Salford Systems, USA), Mikhaylo Golovnya (Salford Systems, USA)and Nicholas Scott Cardell (Salford Systems, USA)
Copyright: 2008
Pages: 21
Source title: Electronic Commerce: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Annie Becker (Florida Institute of Technology, USA)
DOI: 10.4018/978-1-59904-943-4.ch149

Keywords: Business & Management / Business Science Reference / E-Commerce

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Abstract

Mobile phone customers face many choices regarding handset hardware, add-on services, and features to subscribe to from their service providers. Mobile phone companies are now increas-ingly interested in the drivers of migration to third generation (3G) hardware and services. Using real world data provided to the 10th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) 2006 Data Mining Competition we explore the effectiveness of Friedman’s stochastic gradient boosting (Multiple Additive Regression Trees [MART]) for the rapid development of a high performance predictive model.

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Mobile Phone Customer Type Discrimination via Stochastic Gradient Boosting

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