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A survival ensemble of extreme learning machine

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Unbiased recursive partitioning: A conditional inference framework 8 Challenge: modeling churn 9. Conditional inference survival tree partition with Kaplan-Meier estimates of the survival time which characterizes the players placed in every terminal node group 10 Linear rank statistics as splitting criterion Survival tree Random Forests. Conditional inference survival ensembles You just clipped your first slide!

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In mobile social games, decreasing churn is decisive to increase player retention and rise revenues. Churn prediction models allow to understand player loyalty and to anticipate when they will stop playing a game.

Thanks to these predictions, several initiatives can be taken to retain those players who are more likely to churn. Show related SlideShares at end. WordPress Shortcode.

Churn Prediction in Mobile Social Games: Towards a Complete Assessmen…

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Embeds 0 No embeds. No notes for slide. About us 2 Who are we? The model Survival Ensembles 5 6. Unbiased recursive partitioning: A conditional inference framework 8 Challenge: modeling churn 9. Conditional inference survival tree partition with Kaplan-Meier estimates of the survival time which characterizes the players placed in every terminal node group 10 Linear rank statistics as splitting criterion Survival tree Random Forests.

Conditional inference survival ensembles