Timeline for Interview Question at Gaming Company
Current License: CC BY-SA 4.0
5 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Feb 5, 2020 at 7:31 | comment | added | Adnan Tamimi | yes, along those lines. | |
| Feb 5, 2020 at 6:58 | comment | added | user2974951 | @AdnanTamimi Reading through your question again, I think I see where you were going with the Bayesian approach. Did you perhaps want to assign some sort of prior to each individual player, and then check how the posterior changes as his inactivity grows larger? | |
| Feb 5, 2020 at 6:53 | comment | added | user2974951 | @AdnanTamimi Yes, the assumption is that you have some labeled data or some sort of information. Otherwise I don't see a way to solve this. How could you determine whether someone is "churned" without any information about what churn is. It could happen that there are no churned players, that they just take really long pauses. Or it could be that they all churn immediately. So you either need some data to determine this, or you need some assumptions. | |
| Feb 5, 2020 at 6:43 | comment | added | Adnan Tamimi | Not sure if I get your approach. For logistic regression, how do you get labelled data? Assuming you take a threshold of days to label the users, how do you decide the optimal threshold? And again for fitting distribution, how do you decide the optimal quantile threshold. That's what the initial problem is in the first place. | |
| Feb 5, 2020 at 6:34 | history | answered | user2974951 | CC BY-SA 4.0 |