Mobile Phone Customer Type Discrimination via Stochastic Gradient Boosting

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.