Mobile Phone Customer Type Discrimination via Stochastic Gradient Boosting
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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.
2007 ◽
Vol 3
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pp. 32-53
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2014 ◽
Vol 39
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pp. 1-6
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2019 ◽
Vol 15
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pp. 201-214
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2022 ◽
Vol 41
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pp. 849-859
2019 ◽
Vol 33
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pp. 04019024
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