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Bagging and Boosting Classification Trees to Predict Churn
In this article, the authors explore the bagging and boosting classification techniques. They apply the two techniques to a customer database of an anonymous U.S. wireless telecommunications company, and both significantly improve accuracy in predicting churn. This higher predictive performance could ultimately lead to incremental profits for companies that use these methods. Furthermore, the results recommend the use of a balanced sampling scheme when predicting a rare event from large data sets, but this requires an appropriate bias correction.
Ketersediaan
| Call Number | Location | Available |
|---|---|---|
| JM4306 | PSB lt.dasar - Pascasarjana | 1 |
| Penerbit | Chicago: American Marketing Association 2006 |
|---|---|
| Edisi | Vol. 43, No. 2 (May, 2006), pp. 276-286 |
| Subjek | Consumer behavior Data mining Marketing analytics Journal of marketing research |
| ISBN/ISSN | 0022-2437 |
| Klasifikasi | NONE |
| Deskripsi Fisik | 11 p. |
| Info Detail Spesifik | Journal of Marketing |
| Other Version/Related | Tidak tersedia versi lain |
| Lampiran Berkas |