Mobile Phone Customer Type Discrimination via Stochastic Gradient Boosting

Dan Steinberg; Mikhaylo Golovnya; Nicholas Scott Cardell

doi:10.4018/jdwm.2007040104

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Mobile Phone Customer Type Discrimination via Stochastic Gradient Boosting

Dan Steinberg (Salford Systems, USA), Mikhaylo Golovnya (Salford Systems, USA) and Nicholas Scott Cardell (Salford Systems, USA)

Source Title: International Journal of Data Warehousing and Mining (IJDWM) 3(2)

DOI: 10.4018/jdwm.2007040104

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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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