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Mobile Phone Customer Type Discrimination via Stochastic Gradient Boosting

Author

Listed:
  • Dan Steinberg

    (Salford Systems, USA)

  • Mikhaylo Golovnya

    (Salford Systems, USA)

  • Nicholas Scott Cardell

    (Salford Systems, USA)

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.

Suggested Citation

  • Dan Steinberg & Mikhaylo Golovnya & Nicholas Scott Cardell, 2007. "Mobile Phone Customer Type Discrimination via Stochastic Gradient Boosting," International Journal of Data Warehousing and Mining (IJDWM), IGI Global, vol. 3(2), pages 32-53, April.
    • Handle: RePEc:igg:jdwm00:v:3:y:2007:i:2:p:32-53
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